Abstract
Several challenges are encountered while integrating microgrids (MGs) with an existing utility grid. These are low inertia, intermittent nature of renewable energy resources (RESs), sensor and actuator faults, unbalanced and nonlinear loads, supply-demand mismatch, and uncertain switching functions of the power electronic converter. MG control is fully dependent on the communication network, which is also susceptible to different types of failures such as noise in communication links, communication delay, limited bandwidth, packet dropout, and various malicious cyber attacks. Very few papers are available in the literature that focus on the review of control schemes for MG. Consequently, this paper presents a comprehensive review of different robust and adaptive control schemes that address the challenges encountered due to communication constraints, uncertainties, and disturbances for different MG topologies such as AC, DC, and hybrid AC/DC MGs, as well as the current research trends in the field of robust and adaptive control. It can be concluded that to achieve control objectives of MG and overcome the existing challenges, robust and adaptive controllers show significantly improved performance in terms of transient and steady-state behavior and robustness as compared to traditional controllers.
THE microgrid (MG) is a small-scale power system network suitable for power supply to remote areas and provides flexibility to the existing network. An MG consists of several distributed energy resources (DERs), power electronic converters, energy storage elements, loads, and control units based on communication topologies. It has several advantages over a traditional grid, such as reduction in power losses, voltage drop during transmission, reduction in carbon dioxide emission, low installation cost, reliable and uninterrupted power supply, a two-way controlled power flow, and improvement of power quality [
The traditional grid consists of a number of synchronous generators which provide inertia to the power system network to control voltage and frequency. However, in MG, the renewable energy resource (RES) based distributed generator (DG) replaces the synchronous generator, which does not provide inertia to the system. So, the implementation of conventional droop control in power system is easier as compared to that in MG. To control voltage and frequency in MG, active power-frequency and reactive power-voltage based droop control mimics the behavior of synchronous generator in the traditional grid. In the traditional grid, there exists centralized generation, whereas in case of MG, DG exists. Therefore, the centralized supervisory control scheme cannot handle the overall burden of DG because of its complex network and single-point failure. The traditional grid adopts manual restoration during fault in the system with unidirectional communication, whereas MG has self-healing and self-monitoring facilities provided by advanced control with bidirectional communication network. The conventional proportional-integral (PI) controller adopted by the traditional grid cannot provide enough robustness during MG operations, i.e., plug and play (PnP), and the presence of uncertainties arises from system modelling or external environment.
However, integration of MG with a utility grid poses several challenges such as communication constraints, input and load uncertainties, unmodeled dynamics, disturbances, and bidirectional power flow [
Other factors such as PnP operation, data volume, size of the MG network also affect the effective communication. To resolve these issues, a suitable controller needs to be designed, which not only provides stability but also facilitates restoration of the normal operation of the MG system by regulating voltage and frequency at nominal values, sharing accurate active and reactive power, maintaining optimum power quality, and grid synchronization.
Several control schemes have been reported in the recent literature [
Characteristic | Conventional controller | Robust and adaptive controller |
---|---|---|
Complexity | Simple | Complex |
Robustness | Low | High |
Scalability | Moderate | High |
Adaptability | Low | High |
Gain | Fixed | Dynamic |
Reliability | Low | High |
Optimal operation | Not guaranteed | Guaranteed |
Dependency over system dynamics | More | Less |
Combining robust and adaptive control schemes with traditional controllers adds to the merits of traditional controllers to achieve robustness. It includes robust hierarchical control, robust sliding mode control, robust control, robust backstepping control, robust adaptive control, adaptive droop control, adaptive sliding mode control, adaptive backstepping control, adaptive event-triggered control, and adaptive passivity-based control. There is a significant increase in dynamic and transient responses of the system by employing one of these control schemes. Since the last two decades, there has been a significant progress in the development of MG controllers to deal with different challenges. Therefore, this paper presents the various challenges and a comprehensive review of the robust and adaptive control schemes for different MG topologies (AC, DC, hybrid AC/DC) and different operation modes (grid-connected and islanded).
The rest of this paper is organized as follows. In Section II, a brief discussion on challenges in MG operation and control is provided. Section III discusses control issues associated with MG in term of robustness. An overview of robust and adaptive control techniques is presented in Section IV. Section V presents the future trends in the robust and adaptive control. Section VI provides the conclusion.
MG is equipped with numerous power electronic converters, as well as communication and control protection devices to achieve optimum control performances as compared to traditional grid. Integration of MG into the existing power system brings several functional edges: reduction in carbon emission, continuous and independent supply of power to local and remote areas, improved overall power system quality, provision of backup power source during blackout, provision of PnP operations, and efficient bidirectional power flow. Although this integration brings several benefits, some challenges such as control, power quality, supply dependability, and outage resynchronization time are encountered.

Fig. 1 A generic model of MG.
One of the advantages of MG is that it facilities RES integration on the generation. However, several factors affect renewable energy generation.
In case of photovoltaic (PV) array, the availability of sunlight, duration of the day time, partial shedding, solar insolation over PV array, and rain hinders the optimal generation of electricity [
In an MG, several DGs are added to meet the increasing load demand. An MG is encountered with various uncertainties and nonlinearities such as pulse width modulation (PWM) switching function of VSI [
A fault occurs in MGs in many ways. It can be either through any hardware damage such as sensor and actuator faults or by means of software owing to the fault in communication links and malicious attacks. These faults adversely affect the performance of MG operation, and lead to unstable operation of MGs.
MG consists of various electrical and electronic components that are more susceptible to various types of faults. It includes changes in physical parameters due to adverse environmental conditions, electromagnetic compatibility problems, loose connection, and contact problems.

Fig. 2 Topology of AC MG.
The fault in power modules can be broadly classified as catastrophic fault (triggered by a single over-stress event such as over-current, over-voltage, or over-temperature rise) and wear-out fault (gradual degradation over time). The error that arises due to fault in sensing devices can be represented as additive faults. The system output can be represented as [
(1) |
where is the MG output under sensor fault; is the output matrix with the same dimension as the MG output; is the system parameter matrix; and is the sensor fault result of the DG. This sensor fault may be the combination of the abrupt fault caused by periodic fault and the incipient fault caused by precision degradation. Sometimes the sensors show null values due to the failure of supply. In [
(2) |
where and are the filter output voltages along the direct axis; is the unmodeled dynamics of the DG, which includes unknown disturbances and uncertainties; is the virtual control input to DG; and and are the biased fault severity and the partial loss of effectiveness fault severity of the DG, respectively.
The performance of the distributed controller is based on data exchange among the neighbors. While in the cyber layer, there are possibilities of cyber attacks, which include feeding wrong data to the system, viruses, or breaking the communication links among the DGs. This makes the system vulnerable to operating abnormally. The effect of the unbounded attack on MGs is shown in [
Generally, the loads in MGs are unpredictable. With an increase in the penetration of inverter-interfaced DG, presence of nonlinear loads, uneven distribution of single-phase load, asymmetric line impedance, and increasing load demand in MGs, voltage unbalance generally occurs, which affects the power quality issues [
In [
Noise, packet loss, delay, communication failure, communication uncertainties, switching or unreliable communication networks, time-varying delays, limited communication bandwidth, and additive noise in a network are the primary reasons for the communication degradation. The effect of noise on the DG output can be expressed as [
(3) |
where is the voltage perturbed with noises of the DG; is the actual voltage output of the DG; and is the zero-mean Gaussian noise of the DG.
In [
Communication delay is one of the most critical issues in the MG control. However, in most research, the delay is not considered. Some delay exists between transmitted and received signals, which is affected by the sampling rate. The delay can be classified as continuous time delay and random time delay [

Fig. 3 Signal flow in communication network with different types of communication delay.
Besides these factors, the computation time for the generation of control inputs, limited communication speed, extra time for the measurement message reception, and execution time for the inputs contribute to the communication delay. In [
MG is a complex system with a sustainably large number of components. Hence, several control issues involved in the MG are presented in the following.
Communication constraints such as communication delay and packet loss are often encountered in the communication network [
The DGs are connected to the common bus via various power electronic converters. The system inertia decreases significantly. Therefore, the importance of system stability and dynamics with regard to disturbances is a major concern. The presence of disturbances in the form of component mismatch, numerical errors, and transient power oscillation results in obtaining erroneous state information. Arbitrary switching [
The switching operation adds flexibility to the MG. The effective utilization of switching operations can significantly improve the system performance of MG. It can be accomplished by reducing network losses, isolating fault area, optimizing bus voltage profile, and enhancing resilience [
The cyber system should guarantee that the data are timely accessible. Attackers block or delay the data communication. The low inertia of MG affects the transient- and steady-state stability of MG. Furthermore, in the hybrid AC/DC MG, any cyber attack in either AC or DC MG will affect the other side through AC/DC MG interlinking power converters. It deteriorates the MG functionalities, i.e., current sharing and state estimation [
The state information of MG is required in order to establish the operating condition of the system. As a result, MG can be properly monitored and managed. Cyber assaults tamper with sensor data, leading to errors in the calculation of state variables such as voltage and frequency [
The stability issues that commonly occur in MGs involve frequency and voltage stability. It indicates the ability of MG system to maintain its operation parameters within an acceptable range. The voltage and frequency stability impacts a short-term and/or long-term stability problem. The high penetration of RES reduces system inertia, leads to significant rise in the rate of change of frequency, and increases the value of nadir frequency and the potential of unstable frequency in a power system. A sudden change in loads, presence of constant power load (CPL), penetration of DG units, and supply/demand imbalance are the major causes of voltage and frequency instability.
The grid and the connected loads are designed to operate at specified frequencies with a rated voltage and current level [
The presence of nonlinear load and switching resonance in the MG distorts the voltage and current harmonics. Switching operation also often introduces harmonics in the system parameters. The voltage and current include high-frequency harmonic components [
Uncertainties arise owing to the inaccuracy when modeling a system. A suitable robust controller needs to be designed to achieve robustness. Traditional controllers cannot handle the nonlinearities and uncertainties of the MG system to achieve MG control objectives, i.e., voltage and frequency restoration and proportional active and reactive power sharing.
Robust control refers to the control of plants with inaccuracy in dynamics subject to unknown disturbances.

Fig. 4 Structure of robust controller.
The uncertainties arise from unmodelled dynamics of the plant, external disturbances, and unknown noisy sensor data input. The presence of such uncertainties in the MG involves uncertain PWM switching function, unknown load dynamics, intermittent RES input, and noisy sensor and actuator data that can affect the observability, controllability, and stability of the system using limited information. It does not necessitate prior knowledge of uncertainties and disturbances. Although it is advantageous in disturbance rejection and setpoint tracking, it is not a suitable option for a system that encounters rapid change of uncertainties. The review of robust control schemes is discussed in [
Adaptive control is beneficial for the system that encounters parametric uncertainties and unpredictable parameter variations of system dynamics. Hence, adaptive control modifies its behavior in response to the changes in the dynamics of the system through a parameter adjustment mechanism, which is represented through dotted line in the

Fig. 5 Structure of typical adaptive controller.
To accomplish global voltage restoration, robust sliding-mode controllers are proposed against multiple communication delays and modeled uncertainties in [
Similarly, to achieve robustness against parametric uncertainties, exogenous disturbances, and fixed time delay in the MG, adaptive control is used to achieve voltage, frequency, and proportional active and reactive power sharing of the MG in [
Both robust control and adaptive control provide robustness in the face of uncertainties and disturbances. They are also integrated with other control schemes to achieve better performance of the system.

Fig. 6 Evolution of robust and adaptive control strategies.
The control objectives of MG vary with regard to significances and time scales. The hierarchical control handles MG operations at different control hierarchies. The communication network mainly incorporates in these hierarchies for optimized power sharing with system parameter restoration and resynchronization. Depending upon the sharing of information over communication links, the secondary control structure is classified as centralized, decentralized, and distributed. The centralized controller has the disadvantages such as single-point failure, computationally expensive, time-consuming, extensive use of communication network, and complex in nature. While decentralized and distributed controllers eliminate the disadvantages caused by centralized controller and add plug-and-play functionality, flexibility, and reduction in communication requirements. Each DG has a local controller in the distributed controller (consensus-based control), but the decentralized controller (master-slave control) employs a local controller to control all DGs. The communication links are established across each DG, which results in tracking of reference values of system parameters even in the presence of disturbances. These controllers are connected among themselves through two-way communication networks and exchange local information among themselves. In some cases, the decentralized controller is also impossible to implement because the operation of DGs in the MG is highly dependent on each other and can cause the system to become unstable or operate non-optimally. This makes the distributed controller more dedicated and reliable than the decentralized controller.
It is necessary to control the voltage (which is a local parameter), frequency (global parameter), and power quality (involves proportional sharing of active and reactive power) in the MG. The active power-frequency control and reactive power-voltage droop-based control are used in the primary layer of a hierarchical control scheme. The nominal frequency of the system is achieved by controlling the active power of the system and the nominal voltage is achieved by controlling the reactive power of the system. The robust controller in the primary layer is incorporated with conventional droop controller to improve the voltage regulation and eliminate the disadvantages of traditional controllers, i.e., line impedance dependency, load-dependent deviations, trade-off between the voltage/frequency deviations, accurate power sharing, and poor voltage regulation [
It is a robust design method that can survive external disturbances and parametric uncertainties in both linearized and nonlinearized systems or constrained norms for both deterministic and nondeterministic uncertainties. The implementation of sliding mode control involves selecting a hypersurface or a manifold so that the system trajectory exhibits desirable behavior. The sliding mode control has been intensively investigated for robust nonlinear control to ensure stability subject to parameter constraints. In [

Fig. 7 Block diagram of robust sliding mode controller for restoring voltage and frequency of MG.
The aim of control is to minimize the impact of uncertainties and the disturbance in the system transient- and steady-state performance. controllers are based on the synthesis approach for achieving robust performance or stabilization of a multi-variable linear system. In a nonlinear system, the robustness can be obtained in the state space by solving the Riccati equation of LMI technique. In [
The backstepping control is based on the recursive Lyapunov method, which allows the system to work in undesirable conditions with flexibility. It is used to extend the kinematic-level controller to dynamic case and to deal with the parameter uncertainties. A robust backstepping controller is proposed in [
The adaptation mechanism relies on prior information about the system, whereas the robust adaptive control involves modifying the control law with regard to the change in parameters of the system, unlike conventional one that uses the dynamic gain controller. Though it is less dependent on the mathematical models of the system, it is the most suitable for the MG system encountered with various challenges with unknown state parameters. In [
In [

Fig. 8 Block diagram of ESO-based adaptive super-twisting controller [
The conventional droop control has several disadvantages that are discussed in Section II-B-1). The conventional droop control is modified to the adaptive droop control to improve the voltage and frequency regulation, which is not affected by the system parameter under dynamic perturbation of the MG.
In [
A type of distributed adaptive voltage controller for the MG is proposed in [

Fig. 9 Block diagram of adaptive droop controller.
A type of improved mode-adaptive droop controller is proposed in [
The adaptive hierarchical control in the secondary layer can provide automated tuning of control gains in real time with regard to the uncertainties/nonlinearities and the external disturbances. In [
The robust control design will benefit from the use of adaptive control in terms of performance improvements and extension of the operation range. In [
In [
Though the event-triggered control improves the communication and control efficiencies, in case of disturbances and uncertainties in the system, the triggers run whenever the database fields are updated, which makes the system run slower. However, hybrid adaptive and event-triggered control makes the execution of database update faster by auto-updating and better use of the communication bandwidth. An event-triggered super-twisting sliding mode control is proposed in [
The passivity-based control is favored because of its low complexity. But it cannot eliminate the steady-state error of the output voltage caused by the system disturbances such as load and line variations. The conventional passivity-based control strategies, which are combined with the integral gain control, attenuate the system disturbance through the feedback regulation, which is relatively slow. In [
The robust and adaptive controllers exhibit robustness in the presence of uncertainties and disturbances. The robust droop controller eliminates the disadvantages of conventional droop controllers, mitigates the lower frequency oscillations [
The robustness of MG can also be improved by employing artificial intelligence techniques like neural network, fuzzy logic, and machine learning. In [
A brief summary of future trends of robust and adaptive controllers in the field of MG control is discussed below.
1) Non-uniform communication delay and mixed communication constraints. The time delay is unknown and encountered in different approaches, so the control should provide enough robustness towards the non-uniform communication delay and random delays, where the upper and lower bounds are uncertain [
2) Fast switching topologies and limited communication bandwidth. Various types of switching operations are needed for successful operations of the MG. The PnP functionality of DERs can ensure the stability of the MG system [
3) Automation control against cyber attacks. There are various types of cyber attacks in the cyber-enabled MG. Among them, the denial-of-service and false data injection attacks are more common [
4) Scalability. The scalability of a controller, i.e., how the system behaves when the complexity and data burden increase, has not been addressed. Thus, it is important to consider the multi-MG. With the shifting of research to model-free control, data-driven and machine learning approaches play a significant role in the control and management of the MG [
Challenge | Key feature | MG objective | Control scheme | Reference |
---|---|---|---|---|
Uncertain communication links | AC, islanded MG | Frequency | Adaptive control |
[ |
Aperiodic sampled time delay | AC, islanded MG | Voltage and frequency | Robust cooperative control |
[ |
Multiple communication delays and uncertainties | AC, islanded MG | Voltage | Robust integral sliding mode control |
[ |
Communication delay | AC, islanded MG | Frequency and load sharing | Distributed cooperative control |
[ |
Time-varying delays, data packet losses, and link failures | AC, islanded MG | Voltage, frequency, and active power sharing | Model predictive control |
[ |
Sensor fault, data attack | AC, islanded MG | Voltage and frequency | Adaptive resilient control |
[ |
Data disturbance and time delay | AC, islanded MG | Voltage and frequency | Robust control |
[ |
Cyber attacks | AC, islanded MG | Frequency and load sharing | Consensus based robust control |
[ |
Parametric uncertainty and exogenous disturbances | DC, islanded MG | Voltage | Robust centralized control |
[ |
Communication and system noise | AC, islanded MG | Voltage and frequency | Robust cooperative control |
[ |
External disturbances | AC, islanded MG | Voltage and frequency | Robust fractional sliding control |
[ |
External disturbance, uncertainty, and fixed time delays | AC, islanded MG | Voltage, frequency, and SOC | Robust control |
[ |
Parameter uncertainties and external disturbances | DC, islanded MG | Voltage | Robust voltage stabilization control |
[ |
Uncertainties and disturbances | AC, islanded MG | Voltage, frequency, and active power sharing | Distributed adaptive secondary control |
[ |
Topological, operational uncertainties, and latency | AC, grid-connected MG | Load current | Decentralized adaptive control |
[ |
Malicious time delays and packet loss | AC, islanded MG | Voltage and frequency | Robust data prediction control |
[ |
PnP | AC, islanded MG | Voltage | Extended proportional-derivative control |
[ |
Disturbances, uncertainties, noises, delays, and attacks | AC, islanded MG | Voltage, frequency, active and reactive power | Resilient adaptive consensus control |
[ |
This paper presents a comprehensive review of different challenges encountered during the integration of RESs to the existing grid with regard to the communication constraints during the MG operation and control. Various robust and adaptive control schemes to handle the control issues are presented, as well as the current research trends in the field of robust and adaptive control. It can be concluded that to achieve control objectives of the MG and overcome the above challenges, robust and adaptive controllers show significantly improved performance in terms of transient and steady-state behavior and robustness as compared to traditional controllers.
References
V. A. Suryad, S. Doolla, and M. Chandorkar, “Microgrids in India: possibilities and challenges,” IEEE Electrification Magazine, vol. 5, no. 2, pp. 47-55, Jun. 2017. [Baidu Scholar]
R. Badal, P. Das, S. K. Sarker et al., “A survey on control issues in renewable energy integration and microgrid,” Protection and Control of Modern Power Systems, vol. 4, no. 1, pp. 87-113, Dec. 2019. [Baidu Scholar]
X. Lu and J. Lai, “Communication constraints for distributed secondary control of heterogenous microgrids: a survey,” IEEE Transactions on Industry Applications, vol. 57, no. 6, pp. 5636-5648, Aug. 2021. [Baidu Scholar]
F. Gao, R. Kang, J. Cao et al., “Primary and secondary control in DC microgrids: a review,” Journal of Modern Power Systems and Clean Energy, vol. 7, no. 2, pp. 227-242, Mar. 2019. [Baidu Scholar]
S. Shrivastava and B. Subudhi, “Comprehensive review on hierarchical control of cyber-physical microgrid system,” IET Generation, Transmission & Distribution, vol. 14, no. 26, pp. 6397-6416, Nov. 2020. [Baidu Scholar]
T. K. Roy, M. A. Mahmud, A. M. Oo et al., “Nonlinear adaptive backstepping controller design for controlling bidirectional power flow of BESSs in DC microgrids,” in Proceedings of IEEE Industry Applications Society Annual Meeting, Portland, USA, Oct. 2016, pp. 1-8. [Baidu Scholar]
M. Huang and L. Ding, “Distributed observer-based fault-tolerant control for DC microgrids with sensor fault,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 4, pp. 1659-1670, Jan. 2021. [Baidu Scholar]
A. Afshari, M. Karrari, H. R. Baghaee et al., “Distributed fault-tolerant voltage/frequency synchronization in autonomous AC microgrids,” IEEE Transactions on Power Systems, vol. 35, no. 5, pp. 3774-3789, Sept. 2020. [Baidu Scholar]
M. A. Shahab, B. Mozafari, S. Soleymani et al., “Distributed consensus-based fault tolerant control of islanded microgrids,” IEEE Transactions on Smart Grid, vol. 11, no. 1, pp. 37-47, Jan. 2020 [Baidu Scholar]
S. Zuo, T. Altun, F. L. Lewis et al., “Distributed resilient secondary control of DC microgrids against unbounded attacks,” IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 3850-3859, May 2020. [Baidu Scholar]
H. F. Habib, C. R. Lashway, and O. A. Mohammed, “A review of communication failure impacts on adaptive microgrid protection schemes and the use of energy storage as a contingency,” IEEE Transactions on Industry Applications, vol. 54, no. 2, pp. 1194-1207, Nov. 2017. [Baidu Scholar]
N. M. Dehkordi, H. R. Baghaee, N. Sadati et al., “Distributed noise-resilient secondary voltage and frequency control for islanded microgrids,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3780- 3790, Jul. 2019. [Baidu Scholar]
J. Lai and X. Lu, “Communication constraints for distributed secondary control of heterogenous microgrids: a brief survey,” in Proceedings of IEEE IAS 57th Industrial and Commercial Power Systems Technical Conference, Las Vegas, USA, Apr. 2021, pp. 1-9. [Baidu Scholar]
A. Afshari, M. Karrari, H. R. Baghaee et al., “Resilient cooperative control of AC microgrids considering relative statedependent noises and communication time-delays,” IET Renewable Power Generation, vol. 14, no. 8, pp. 1321-1331, Jun. 2020. [Baidu Scholar]
W. Yao, Y. Wang, Y. Xu et al., “Distributed weight-average-prediction control and stability analysis for an islanded microgrid with communication time delay,” IEEE Transactions on Power Systems, vol. 37, no. 1, pp. 330-342, Jun. 2021. [Baidu Scholar]
A. B. Shyam, S. Anand, and S. R. Sahoo, “Effect of communication delay on consensus-based secondary controllers in DC microgrid,” IEEE Transactions on Industrial Electronics, vol. 68, no.4, pp. 3202-3212, Mar. 2020. [Baidu Scholar]
J. Lai, X. Lu, X. Yu et al., “Distributed voltage regulation for cyber-physical microgrids with coupling delays and slow switching topologies,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 1, pp. 100-110, Jan. 2020. [Baidu Scholar]
Y. Cheng, R. Azizipanah-Abarghooee, S. Azizi et al., “Smart frequency control in low inertia energy systems based on frequency response techniques: a review,” Applied Energy, vol. 279, p. 115798, Dec. 2020. [Baidu Scholar]
Q. Zhou, M. Shahidehpour, A. Paaso et al., “Distributed control and communication strategies in networked microgrids,” IEEE Communications Surveys and Tutorials, vol. 22, no. 4, pp. 2586-2633, Sept. 2020. [Baidu Scholar]
Y. A.-R. I. Mohamed and A. A. Radwan, “Hierarchical control system for robust microgrid operation and seamless mode transfer in active distribution systems,” IEEE Transactions on Smart Grid, vol. 2, no. 2, pp. 352-362, May 2011. [Baidu Scholar]
F. Nejabatkhah, Y. Li, H. Liang et al., “Cyber-security of smart microgrids: a survey,” Energies, vol. 14, no. 1, pp. 1-27, Dec. 2021. [Baidu Scholar]
X. Liu, M. Shahidehpour, Y. Cao et al., “Microgrid risk analysis considering the impact of cyber attacks on solar PV and ESS control systems,” IEEE Transactions on Smart Grid, vol. 8, no. 3, pp. 1330-1339, Oct. 2017. [Baidu Scholar]
C. Natesan, S. Ajithan, and P. Palani, “Survey on microgrid: power quality improvement techniques” ISRN Renewable Energy, vol. 2014, pp. 1-7, Jan. 2014. [Baidu Scholar]
B. Adineh, R. Keypour, P. Davari et al., “Review of harmonic mitigation methods in microgrid: from a hierarchical control perspective,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 3, pp. 3044-3060, Jun. 2020. [Baidu Scholar]
F. Mohammadi, B. Mohammadi-Ivatloo, G. B. Gharehpetian et al., “Robust control strategies for microgrids: a review,” IEEE Systems Journal, vol. 16, no. 2, pp. 2401-2412, Jun. 2021. [Baidu Scholar]
M. Gholami, A. Pilloni, A. Pisano et al., “Robust distributed secondary voltage restoration control of AC microgrids under multiple communication delays,” Energies, vol. 14, p. 1165, Jan. 2021. [Baidu Scholar]
G. Cao, G. Lou, W. Gu et al., “H∞ robustness for distributed control in autonomous microgrids considering cyber disturbances,” CSEE Journal of Power and Energy Systems. doi: 10.17775/CSEEJPES.2020.04110 [Baidu Scholar]
M. Raeispour, H. Atrianfar, H. R. Baghaee et al., “Resilient-consensus-based control of autonomous AC microgrids with uncertain time-delayed communications,” IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 3871-3884, Mar. 2020. [Baidu Scholar]
X. Li, Q. Xu, and F. Blaabjerg, “Adaptive resilient secondary control for islanded AC microgrids with sensor faults,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 5, pp. 5239-5248, Apr. 2020. [Baidu Scholar]
S. A. Hosseini, S. H. H. Sadeghi, and A. Nasiri, “Decentralized adaptive protection coordination based on agents social activities for microgrids with topological and operational uncertainties,” IEEE Transactions on Industry Applications, vol. 57, no. 1, pp. 702-713, Oct. 2020. [Baidu Scholar]
N. M. Dehkordi and S. Z. Moussavi, “Distributed resilient adaptive control of islanded microgrids under sensor/actuator faults,” IEEE Transactions on Smart Grid, vol. 11, no. 3, pp. 2699-2708, Dec. 2019. [Baidu Scholar]
P. Dorato, “A historical review of robust control,” IEEE Control Systems, vol. 7, no. 2, pp. 44-47, Apr. 1987. [Baidu Scholar]
S. G. Anavatti, F. Santoso, and M. A. Garratt, “Progress in adaptive control systems: past, present, and future,” in Proceedings of IEEE International Conference on Advanced Mechatronics, Intelligent Manufacture, and Industrial Automation (ICAMIMIA), Surabaya, Indonesia, Oct. 2015, pp. 1-8. [Baidu Scholar]
Q. Xu, C. Zhang, and P. Wang, “A robust droop-based autonomous controller for decentralized power sharing in DC microgrid considering large-signal stability,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1483-1494, Oct. 2019. [Baidu Scholar]
F. Mohammadi, G. A. Nazri, and M. Saif, “An improved droop-based control strategy for MTHVDC systems,” Electronics, vol. 9, no. 1, p. 87, Jan. 2020. [Baidu Scholar]
N. M. Dehkordi, N. Sadati, and M. Hamzeh, “Robust tuning of transient droop gains based on Kharitonov’s stability theorem in droop-controlled microgrids,” IET Generation, Transmission & Distribution, vol. 12, no. 14, pp. 3495-3501, Aug. 2018. [Baidu Scholar]
J. Hu and A. Lanzon, “Distributed finite-time consensus control for heterogeneous battery energy storage systems in droop-controlled microgrids,” IEEE Transactions on Smart Grid, vol. 10, no. 5, pp. 4751-4761, Sept. 2019. [Baidu Scholar]
Q. Xu, Y. Xu, C. Zhang et al., “A robust droop-based autonomous controller for decentralized power sharing in DC microgrid considering large-signal stability,” IEEE Transactions on Industrial Informatics, vol. 16, no. 3, pp. 1483-1494, Mar. 2020. [Baidu Scholar]
H. Cai and G. Hu, “Distributed robust hierarchical power sharing control of grid-connected spatially concentrated AC microgrid,” IEEE Transactions on Control Systems Technology, vol. 27, no. 3, pp. 1012-1022, May 2019. [Baidu Scholar]
Y. Xu, Q. Guo, H. Sun et al., “Distributed discrete robust secondary cooperative control for islanded microgrids,” IEEE Transactions on Smart Grid, vol. 10, no. 4, pp. 3620-3629, Jul. 2019. [Baidu Scholar]
J. Lai, X. Lu, X. Yu et al., “Distributed voltage regulation for cyber-physical microgrids with coupling delays and slow switching topologies,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 50, no. 1, pp. 100-110, Jan. 2020. [Baidu Scholar]
M. Raeispour, H. Atrianfar, H. R. Baghaee et al., “Robust hierarchical control of VSC-based off-grid AC microgrids to enhancing stability and FRT capability considering time-varying delays,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 6, pp. 7159-7172, Aug. 2020. [Baidu Scholar]
M. Raeispour, H. Atrianfar, H. R. Baghaee et al., “Robust distributed disturbance-resilient-based control of off-grid microgrids with uncertain communications,” IEEE Systems Journal, vol. 5, no. 2, pp. 2895-905, Mar. 2021. [Baidu Scholar]
M. Cucuzzella, G. P. Incremona, and A. Ferrara, “Decentralized sliding mode control of islanded AC microgrids with arbitrary topology,” IEEE Transactions on Industrial Electronics, vol. 64, no. 8, pp. 6706-6713, Aug. 2017. [Baidu Scholar]
H. R. Baghaee, M. Mirsalim, G. B. Gharehpetian et al., “Decentralized sliding mode control of WG/PV/FC microgrids under unbalanced and nonlinear load conditions for on- and off-grid modes,” IEEE Systems Journal, vol. 12, no. 4, pp. 3108-3119, Dec. 2018. [Baidu Scholar]
R. Zhang and B. Hredzak, “Nonlinear sliding mode and distributed control of battery energy storage and photovoltaic systems in AC microgrids with time delays,” IEEE Transactions on Industrial Informatics, vol. 15, no. 9, pp. 5149-5160, Sept. 2019. [Baidu Scholar]
A. E. M. Bouzid, P. Sicard, H. Chaoui et al., “Robust three degrees of freedom based on controller of voltage/current loops for DG unit in micro grids,” IET Power Electronics, vol. 12, no. 6, pp. 1413-1424, May 2019. [Baidu Scholar]
B. E. Sedhom, M. M. El-Saadawi, A. Y. Hatata et al., “Robust control technique in an autonomous microgrid: a multi-stage controller based on harmony search algorithm,” Iranian Journal of Science and Technology, Transactions of Electrical Engineering, vol. 44, no. 1, pp. 377-402, Mar. 2020. [Baidu Scholar]
Q. L. Lam, A. I. Bratcu, D. Riu et al., “Primary frequency control in stand-alone micro grids with storage units: a robustness analysis confirmed by real-time experiments,” International Journal of Electrical Power & Energy Systems, vol. 115, p. 105507, Feb. 2020. [Baidu Scholar]
M. Raeispour, H. Atrianfar, H. R. Baghaee et al., “Robust sliding mode and mixed output feedback primary control of AC microgrids,” IEEE Systems Journal, vol. 15, no. 2, pp. 1-12, Jun. 2020. [Baidu Scholar]
N. M. Dehkordi, N. Sadati, and M. Hamzeh, “Robust backstepping control of an interlink converter in a hybrid AC/DC microgrid based on feedback linearisation method,” International Journal of Control, vol. 90, no. 9, pp. 1990-2004, Sept. 2017. [Baidu Scholar]
H. Armghan, M. Yang, M. Wang et al., “Nonlinear integral backstepping based control of a DC microgrid with renewable generation and energy storage systems,” International Journal of Electrical Power & Energy Systems, vol. 117, p. 105613, May 2020. [Baidu Scholar]
N. M. Dehkordi and V. Nekoukar, “Robust reliable fault tolerant control of islanded microgrids using augmented backstepping control,” IET Generation, Transmission & Distribution, vol. 14, no. 3, pp. 432-440, Feb. 2020. [Baidu Scholar]
H. Amiri, G. A. Markadeh, N. M. Dehkordi et al., “Fully decentralized robust backstepping voltage control of photovoltaic systems for DC islanded microgrids based on disturbance observer method,” ISA Transaction, vol. 101, pp. 471-481, Jun. 2020. [Baidu Scholar]
T. V. Vu, D. Perkins, F. Diaz et al., “Robust adaptive droop control for DC microgrids,” Electric Power Systems Research, vol. 146, pp. 95-106, May 2017. [Baidu Scholar]
B. E. Sedhom, A. Y. Hatata, M. M. El‐Saadawi et al., “Robust adaptive H-infinity based controller for islanded microgrid supplying non-linear and unbalanced loads,” IET Smart Grid, vol. 2, no.3, pp. 420-435, Oct. 2019. [Baidu Scholar]
M. H. Khooban, T. Niknam, F. Blaabjerg et al., “A robust adaptive load frequency control for micro-grids,” ISA Transaction, vol. 65, pp. 220-229, Nov. 2016. [Baidu Scholar]
M. Hashemi and G. Shahgholian, “Distributed robust adaptive control of high order nonlinear multi agent systems,” ISA Transaction, vol. 74, pp. 14-27, Mar. 2018. [Baidu Scholar]
Z. A. Alrowaili, M. M. Ali, A. Youssef et al., “Robust adaptive HCS MPPT algorithm-based wind generation system using model reference adaptive control,” Sensors, vol. 21, no. 15, p. 5187, Jan. 2021. [Baidu Scholar]
P. Ge, X. Dou, X. Quan et al., “Extended-state-observer-based distributed robust secondary voltage and frequency control for an autonomous microgrid,” IEEE Transactions on Sustainable Energy, vol. 11, no. 1, pp. 195-205, Dec. 2020. [Baidu Scholar]
Y. A. R. I. Mohamed and E. F. El-Saadany, “Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids,” IEEE Transactions on Power Electronics, vol. 23, no. 6, pp. 2806-2816, Dec. 2008. [Baidu Scholar]
J. Kim, J. M. Guerrero, P. Rodriguez et al., “Mode adaptive droop control with virtual output impedances for an inverter-based flexible AC microgrid,” IEEE Transactions on Power Electronics, vol. 26, no. 3, pp. 689-701, Nov. 2010. [Baidu Scholar]
A. Bidram, A. Davoudi, F. L. Lewis et al., “Distributed adaptive voltage control of inverter-based microgrids,” IEEE Transactions on Energy Conversion, vol. 29, no. 4, pp. 862-872, Oct. 2014. [Baidu Scholar]
V. Nasirian, A. Davoudi, F. L. Lewis et al., “Distributed adaptive droop control for DC distribution systems,” IEEE Transactions on Energy Conversion, vol. 29, no. 4, pp. 944-956, Sept. 2014. [Baidu Scholar]
S. Augustine, M. K. Mishra, and N. Lakshminarasamma, “Adaptive droop control strategy for load sharing and circulating current minimization in low-voltage standalone DC microgrid,” IEEE Transactions on Sustainable Energy, vol. 6, no. 1, pp. 132-141, Nov. 2014. [Baidu Scholar]
M. Mokhtar, M. I. Marei, and A. A. El-Sattar, “An adaptive droop control scheme for DC microgrids integrating sliding mode voltage and current controlled boost converters,” IEEE Transactions on Smart Grid, vol. 10, no. 2, pp. 1685-1693, Nov. 2017. [Baidu Scholar]
J. Mohammadi and F. B. Ajaei, “Improved mode-adaptive droop control strategy for the DC microgrid,” IEEE Access, vol. 7, pp. 86421-86435, Jun. 2019. [Baidu Scholar]
J. C. Vasquez, J. M. Guerrero, A. Luna et al., “Adaptive droop control applied to voltage-source inverters operating in grid-connected and islanded modes,” IEEE Transactions on Industrial Electronics, vol. 56, no. 10, pp. 4088-4096, Jul. 2009. [Baidu Scholar]
Y. A.-R. I. Mohamed and E. F. El-Saadany, “Adaptive decentralized droop controller to preserve power sharing stability of paralleled inverters in distributed generation microgrids,” IEEE Transactions on Power Electronics, vol. 23, no. 6, pp. 2806-2816, Dec. 2008. [Baidu Scholar]
L. Ma and J. Zhang, “An adaptive hierarchical control method for microgrid considering generation cost,” IEEE Access, vol. 8, pp. 164187-164199, Sept. 2020. [Baidu Scholar]
T. Liu, X. Tang, H. Wang et al., “Adaptive hierarchical energy management design for a plug-in hybrid electric vehicle,” IEEE Transactions on Vehicular Technology, vol. 68, no. 12, pp. 11513-11522, Jul. 2019. [Baidu Scholar]
M. Raeispour, H. Atrianfar, H. R. Baghaee et al., “Robust hierarchical control of VSC-based off-grid AC microgrids to enhancing stability and FRT capability considering time-varying delays,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 9, no. 6, pp. 7159-7172, Aug. 2020. [Baidu Scholar]
M. Mu, Y. Guan, Y. Terriche et al., “Adaptive power management of hierarchical controlled hybrid shipboard microgrids,” IEEE Access, vol. 10, pp. 21397-21411, Feb. 2022. [Baidu Scholar]
A. H. Eshghi, J. Soltani, M. M. Rezaei et al., “A robust control strategy for a single-phase grid-connected multibus microgrid based on adaptive sliding mode control and dynamic phasor concept,” International Transactions on Electrical Energy Systems, vol. 31, no. 8, p. 12936, Aug. 2021. [Baidu Scholar]
Y. Han, R. Ma, and J. Cui, “Adaptive higher-order sliding mode control for islanding and grid-connected operation of a microgrid,” Energies, vol. 11, no. 6, p. 1459, Jun. 2018. [Baidu Scholar]
D. Zhang and J. Wang, “Adaptive sliding-mode control in bus voltage for an islanded DC microgrid,” Mathematical Problems in Engineering, vol. 2017, no. 8962086, pp. 1-9, Dec. 2017. [Baidu Scholar]
A. Bag, B. Subudhi, and P. K. Ray, “An adaptive sliding mode control scheme for grid integration of a PV system,” CPSS Transactions on Power Electronics and Applications, vol. 3, no. 4, pp. 362-371, Dec. 2018. [Baidu Scholar]
Z. F. Shavakhi, E. Rokrok, J. Soltani et al., “Adaptive sliding mode control of multi-DG, multi-bus grid-connected microgrid,” Journal of Operation and Automation in Power Engineering, vol. 7, pp. 65-77, May 2019. [Baidu Scholar]
Z. Chen, A. Luo, H. Wang et al., “Adaptive sliding-mode voltage control for inverter operating in islanded mode in microgrid,” International Journal of Electrical Power & Energy Systems, vol. 66, pp. 133-143, Mar. 2015. [Baidu Scholar]
U. K. Kalla, B. Singh B, S. S. Murthy et al., “Adaptive sliding mode control of standalone single-phase microgrid using hydro, wind, and solar PV array-based generation,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6806-6814, Jul. 2017. [Baidu Scholar]
M. Cucuzzella, G. P. Incremona, and A. Ferrara, “Decentralized sliding mode control of islanded AC microgrids with arbitrary topology,” IEEE Transactions on Industrial Electronics, vol. 64, no. 8, pp. 6706-6713, Aug. 2017. [Baidu Scholar]
J. Wu and Y. Lu, “Adaptive backstepping sliding mode control for boost converter with constant power load,” IEEE Access, vol. 7, pp. 50797-50807, Apr. 2019. [Baidu Scholar]
T. K. Roy, M. A. Mahmud, A. M. Oo et al., “Nonlinear adaptive backstepping controller design for islanded DC microgrids,” IEEE Transactions on Industry Applications, vol. 54, no. 3, pp. 2857-2873, Feb. 2018. [Baidu Scholar]
C. Guo, A. Zhang, H. Zhang et al., “Adaptive backstepping control with online parameter estimator for a plug-and-play parallel converter system in a power switcher,” Energies, vol. 11, no. 12, p. 3528, Dec. 2018. [Baidu Scholar]
J. Mao, X. Zhang, T. Dai et al., “An adaptive backstepping sliding mode cascade-control method for a DC microgrid based on nonlinear virtual inertia,” Electronics, vol. 10, no. 24, p. 3100, Jan. 2021. [Baidu Scholar]
V. Kumar, S. R. Mohanty, and S. Kumar, “Event trigger super twisting sliding mode control for DC micro grid with matched/unmatched disturbance observer,” IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 3837-3849, Apr. 2020. [Baidu Scholar]
C. Zhu, C. Li, X. Chen et al., “Event-triggered adaptive fault tolerant control for a class of uncertain nonlinear systems,” Entropy, vol. 22, no. 6, p. 598, May 2020. [Baidu Scholar]
J. Zhou, Y. Xu, H. Sun et al., “Distributed event-triggered consensus based current sharing control of DC microgrids considering uncertainties,” IEEE Transactions on Industrial Informatics, vol. 16, no. 12, pp. 7413-7425, Dec. 2019. [Baidu Scholar]
S. Sahoo and S. Mishra, “An adaptive event-triggered communication-based distributed secondary control for DC microgrids,” IEEE Transactions on Smart Grid, vol. 9, no. 6, pp. 6674-6683, Jun. 2017. [Baidu Scholar]
Y. Wan, C. Long, R. Deng et al., “Adaptive event-triggered strategy for economic dispatch in uncertain communication networks,” IEEE Transactions on Control of Network Systems, vol. 8, no. 4, pp. 1881-1891, Jun. 2021. [Baidu Scholar]
H. Ma, H. Li, R. Lu et al., “Adaptive event-triggered control for a class of nonlinear systems with periodic disturbances,” Science China Information Sciences, vol. 63, no. 5, pp. 1-15, May 2020. [Baidu Scholar]
C. Yang, W. Yao, J. Fang et al., “Dynamic event-triggered robust secondary frequency control for islanded AC microgrid,” Applied Energy, vol. 242, pp. 821-836, May 2019. [Baidu Scholar]
H. Li, X. Wang, and J. Xiao, “Adaptive event-triggered load frequency control for interconnected microgrids by observer-based sliding mode control,” IEEE Access, vol. 7, pp. 68271-68280, Jun. 2019. [Baidu Scholar]
M. A. Hassan, E. Li, X. Li et al., “Adaptive passivity-based control of DC-DC buck power converter with constant power load in DC microgrid systems,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 7, no. 3, pp. 2029-2040, Oct. 2018. [Baidu Scholar]
M. A. Hassan, C. Su, F. Chen et al., “Adaptive passivity-based control of DC-DC boost power converter supplying constant power and constant voltage loads,” IEEE Transactions on Industrial Electronics, vol. 69, no. 6, pp. 6204-6214, Jun. 2021. [Baidu Scholar]
C. A. Soriano-Rangel, W. He, F. Mancilla-David et al., “Voltage regulation in buck-boost converters feeding an unknown constant power load: an adaptive passivity-based control,” IEEE Transactions on Control Systems Technology, vol. 29, no. 1, pp. 395-402, Jan. 2020. [Baidu Scholar]
S. M. Azimi and M. Hamzeh, “Adaptive interconnection and damping assignment passivity-based control of interlinking converter in hybrid AC/DC grids,” IEEE Systems Journal, vol. 14, no. 4, pp. 4718-4725, Jan. 2020. [Baidu Scholar]
Z. Li and G. Chen, “Distributed adaptive control scheme for islanded AC microgrids with tolerance to uncertain communication links,” IEEE Systems Journal, vol. 16, no. 2, pp. 2741-2752, Dec. 2021. [Baidu Scholar]
R. Zhang and B. Hredzak, “Distributed control system with aperiodic time-delayed sampled data for batteries and renewable energy sources in microgrids,” IEEE Transactions on Sustainable Energy, vol. 11, no. 2, pp. 1013-1022, Apr. 2020. [Baidu Scholar]
J. Lai, X. Lu, and X. Yu, “Stochastic distributed frequency and load sharing control for microgrids with communication delays,” IEEE Systems Journal, vol. 13, no. 4, pp. 4269-4280, Mar. 2019. [Baidu Scholar]
M. Mottaghizadeh, F. Aminifar, T. Amraee et al., “Distributed robust secondary control of islanded microgrids: voltage, frequency, and power sharing,” IEEE Transactions on Power Delivery, vol. 36, no. 4, pp. 2501-2509, Apr. 2021. [Baidu Scholar]
Y. Liu, Y. Li, Y. Wang et al., “Robust and resilient distributed optimal frequency control for microgrids against cyber attacks,” IEEE Transactions on Industrial Informatics, vol. 18, no. 1, pp. 375-386, Apr. 2021. [Baidu Scholar]
M. Mehdi, S. Z. Jamali, M. O. Khan et al., “Robust control of a DC microgrid under parametric uncertainty and disturbances,” Electric Power Systems Research, vol. 179, p. 106074, Feb. 2020. [Baidu Scholar]
N. M. Dehkordi and V. Nekoukar, “Robust distributed stochastic secondary control of microgrids with system and communication noises,” IET Generation, Transmission & Distribution, vol. 14, no. 6, pp. 1148-1158, Mar. 2020. [Baidu Scholar]
H. Delavari and S. Naderian, “Design and HIL implementation of a new robust fractional sliding mode control of microgrids,” IET Generation, Transmission & Distribution, vol. 14, no. 26, pp. 6690-6702, Jan. 2021. [Baidu Scholar]
J. Lee, H.-S. Ahn, and J. Back, “Robust voltage stabilization controller for uncertain DC microgrids,” IEEE Access, vol. 9, pp. 99606-99616, Jul. 2021. [Baidu Scholar]
M. Keshavarz, A. Doroudi, M. H. Kazemi et al., “A novel adaptive distributed secondary voltage controller with high convergence rate for islanded microgrids,” IEEE Systems Journal, vol. 15, no. 3, pp. 4157-67, Oct. 2020. [Baidu Scholar]
R. Heydari, Y. Khayat, A. Amiri et al., “Robust high-rate secondary control of microgrids with mitigation of communication impairments,” IEEE Transactions on Power Electronics, vol. 35, no. 11, pp. 12486-12496, Nov. 2020. [Baidu Scholar]
A. Zarei, Y. Mousavi, R. Mosalanezhad et al., “Robust voltage control in inverter-interfaced microgrids under plug-and-play functionalities,” IEEE Systems Journal, vol. 14, no. 2, pp. 2813-2824, Dec. 2019. [Baidu Scholar]
C. Mu, Y. Zhang, H. Jia et al., “Energy-storage-based intelligent frequency control of microgrid with stochastic model uncertainties,” IEEE Transactions on Smart Grid, vol. 11, no. 2, pp. 1748-1758, Sept. 2019. [Baidu Scholar]
H. Karimi, M. T. H. Beheshti, A. Ramezani et al., “Intelligent control of islanded AC microgrids based on adaptive neuro-fuzzy inference system,” International Journal of Electrical Power & Energy Systems, vol. 133, p. 107161, Dec. 2021. [Baidu Scholar]
D. Xu, Y. Dai, C. Yang et al., “Adaptive fuzzy sliding mode command-filtered backstepping control for islanded PV microgrid with energy storage system,” Journal of the Franklin Institute, vol. 356, no. 4, pp. 1880-1898, Mar. 2019. [Baidu Scholar]
Y. Yin, J. Liu, J. A. Sanchez et al., “Observer-based adaptive sliding mode control of NPC converters: an RBF neural network approach,” IEEE Transactions on Power Electronics, vol. 34, no. 4, pp. 3831-3841, Jul. 2018. [Baidu Scholar]