逆變器是微型電網系統中的重要電力電子介面，可將分佈式發電系統與當地負載連接構成微型電網系統，或者與公共大電網連接實現併網運行。隨著分佈式能源發電規模的擴大，考慮電力電子開關的應力以及系統冗餘性能，通常將多個小容量逆變器模組併聯以建立大容量的微電網系統。此外，介面逆變器也通過併聯運行方式將微型電網系統中不同的分佈式能源接至公共連接點。研究智慧型控制方法以提高微型電網系統中併聯逆變器模組的控制性能及優化微型電網輸出電力品質，對於提高分佈式能源接入微型電網的滲透率顯得相對重要。
為了提高微型電網孤島運行模式下併聯逆變器模組在不同負載及不同運行狀況下的動態性能及供電可靠性，本文設計基於主-從電流均衡控制策略下的併聯逆變器模组自適應模糊類神經網路模擬滑動模式控制(Adaptive Fuzzy-Neural-Network-Imitating Sliding-Mode Control, AFNNISMC)，將併聯逆變器模组視為主體，構建完整的數學模型以保證其系統級的穩定性，並在此基礎上，首先設計全域滑動模式控制(Total Sliding-Mode Control, TSMC)和具有自適應觀測器的全域滑動模式控制架構。為了提高系統的強健性、克服傳統全域滑動模式控制對系統詳細動力學模型的依賴，及消除由全域滑動模式控制引起的控制抖動現象，本文使用四層模糊類神經網路(Fuzzy Neural Network, FNN)來模擬全域滑動模式控制律，根據里亞普諾夫穩定理論(Lyapunov Stability Theorem)和投影算法(Projection Algorithm)，利用模糊神經網路與全域滑動模式控制律之間的近似誤差，設計網路參數的線上自適應調整律，以保證網路參數的收斂性和控制系統的穩定性。因此，即使系統存在不確定性的情況下，也可以保證併聯逆變器模組輸出高品質的電能，以及併聯逆變器模組之間高精度電流均衡性能。此外，當單一逆變器從併聯系統斷開或重新接入時，所提出的 AFNNISMC 可以保證併聯系統的不斷電運行，從而提高微型電網系統的冗餘度和操作靈活性。進一步，藉由數值模擬和實驗結果，驗證所提出自適應模糊神類經網路模擬滑動模式控制
的可行性和有效性。此外，亦與傳統的適應性全域滑動模式控制(Adaptive TSMC, ATSMC)和比例積分控制(Proportional-Integral Control, PIC)架構進行性能比較，驗證所提出的自適應模糊類神經網路模擬滑動模式控制的優越性。
考慮到固定結構的模糊神類經網路難以兼顧計算負擔及控制性能，本文進一步研究 一 種 自 組 織 結 構 模 糊 類 神 經 網 路 模 擬 滑 動 模 式 控 制 (Self-Constructing Fuzzy-Neural-Network-Imitating Sliding-Mode Control, SFNNISMC)，用於執行主-從電流均衡控制策略下的微型電網併聯逆變器模組的併網電流跟蹤控制，所設計的模糊類神經網絡同時具有結構和參數自學習能力。本文所提出自組織結構模糊類神經網路(Self-Constructing Fuzzy Neural Network, SFNN)中，輸入層的初始節點由併網逆變器模組的數目決定，而隸屬函數層的規則由動態規則生成機制依據當前的暫態輸入從無到有自動生成。同時，本結構還引入了動態派翠(Petri)網路實現規則刪減機制，派翠網路使用於重新激活與新接入的從逆變器相對應的規則，只有被派翠網路激活的規則相關的網路參數才會被線上更新，而不是所有的網路參數皆更新，從而減輕參數學習過程的計算負擔。此外，利用里亞普諾夫穩定理論和投影算法設計網路參數的線上學習律，保證網路參數及併網電流跟蹤誤差的收斂性。藉由數值模擬展示所提出的自組織結構模糊類神經網路模擬滑動模式控制在併聯逆變器模組不同運行狀況下規則演化的過程。本文亦利用兩個逆變器模組併聯的實驗平臺，亦與傳統的比例積分控制(PIC)、滑動模式控制(Sliding-Mode Control, SMC)及固定結構的自適應模糊神經網路模擬滑動模式控制(AFNNISMC)進行對比實驗，進一步驗證所提出的自組織結構模糊類神經網路模擬滑動模式控制方案的優越性。

Power inverters are important equipment that connect distributed generation (DG) systems with local loads to form a microgrid (MG) system, or connect a utility power grid to achieve grid-connected operation. With the expansion of DG system scale, and the consideration of power switches stress and system redundancy, several small-capacity inverters are operated in parallel to build a large-capacity MG system. Moreover, different DGs are integrated into the point of common coupling (PCC) by their interface inverters to be usually operated in parallel. Therefore, research on intelligent control strategies for the parallel-inverter system to improve the control performance and optimize the output power quality of the MG is of relative significance to enhance the penetration rate of the distributed sources into the MG.
In order to improve the dynamic performance and power supply reliability of a parallel-inverter system in an islanded MG under different loads and work conditions, an adaptive fuzzy-neural-network-imitating sliding-mode control (AFNNISMC) is developed for the parallel-inverter system in an islanded MG via a master-slave current sharing strategy in this dissertation. For ensuring the system-level stability, an entire dynamic model is constructed by viewing the parallel-inverter system as a whole. On this basis, a total sliding-mode control (TSMC) scheme, and the TSMC plus an adaptive observer to form an adaptive TSMC (ATSMC) framework are designed for the parallel-inverter system firstly. Then, a four-layer fuzzy neural network (FNN) is investigated to imitate the TSMC law to improve the system robustness, overcome the drawback of the dependence on detailed system dynamics, and deal with the chattering phenomena caused by the TSMC. According to the Lyapunov stability theorem and the projection algorithm, network parameters in the FNN are regulated online by employing the approximation error between the FNN and the TSMC law to ensure the convergence of the network and the stability of the control system. Thereby, the performance of high power quality and high-precision current sharing between inverters can be guaranteed even if system uncertainties exist. Moreover, the proposed AFNNISMC system can achieve the seamless disconnection and re-connection of slave inverters from and into an energized parallel-inverter system, which improves the redundancy and operation flexibility. In addition, numerical simulations and experimental results are given to demonstrate the feasibility and effectiveness of the proposed AFNNISMC scheme. Furthermore, performance comparisons with the ATSMC strategy and a conventional proportional-integral control (PIC) framework are provided to verify the superiority of the proposed scheme.
By considering the computational burden and the control performance of a fixed-structure fuzzy neural network, this dissertation further develops a self-constructing fuzzy neural network (SFNN) with the structure and parameter self-learning abilities to imitate a sliding-mode control (SMC), and implements the grid-connected current tracking control for a parallel-inverter system in a grid-connected MG with a master-slave current sharing strategy. In the proposed SFNN-imitating SMC (SFNNISMC) scheme, the initial nodes of the input layer are determined by the number of the grid-connected inverter units, and the rules of the membership layer are self-generated online from null online according to the instantaneous inputs based on the dynamic rule-generating scheme. Moreover, a dynamic Petri net is introduced to implement the pruning mechanism, and is utilized to recall the rules corresponding to the reconnected slave inverters. Only the parameters of favorable rules fired by the Petri net are updated online instead of all the parameters, which can significantly alleviate the computational burden of parameter learning. In addition, the projection algorithm and the Lyapunov stability theorem are adopted to ensure the convergence of the parameter adaptation and the grid-connected current-tracking errors. Furthermore, the rule evolutions of the proposed SFNNISMC in the structure self-learning process are illustrated in numerical simulations. The superiority of the proposed SFNNISMC framework is further validated by experimental comparisons with a proportional-integral control (PIC) strategy, an SMC scheme and an AFNNISMC framework with a fixed network structure from the previous research to be carried out on a parallel-inverter system with two single inverters.

References
[1] A. Muhtadi, D. Pandit, N. Nguyen and J. Mitra, “Distributed Energy Resources Based Microgrid: Review of Architecture, Control, and Reliability,” IEEE Trans. Ind. Appl., vol. 57, no. 3, pp. 2223-2235, Jun. 2021.
[2] S. P. Bihari et al., “A Comprehensive Review of Microgrid Control Mechanism and Impact Assessment for Hybrid Renewable Energy Integration,” IEEE Access, vol. 9, pp. 88942-88958, 2021.
[3] D. Wu, F. Tang, T. Dragicevic, J. C. Vasquez, and J. M. Guerrero, “A control architecture to coordinate renewable energy sources and energy storage systems in islanded microgrids,” IEEE Trans. Smart Grid, vol. 6, no. 3, pp. 1156-1166, May 2015.
[4] S. Kakran and S. Chanana, “Smart operations of smart grids integrated with distributed generation: A review,” Renew. Sust. Energ. Rev., vol. 81, pp. 524-535, Jan. 2018.
[5] N. Sockeel, J. Gafford, B. Papari and M. Mazzola, “Virtual Inertia Emulator-Based Model Predictive Control for Grid Frequency Regulation Considering High Penetration of Inverter-Based Energy Storage System,” IEEE Trans. Sustain. Energy, vol. 11, no. 4, pp. 2932-2939, Oct. 2020.
[6] M. M. Bijaieh, W. W. Weaver and R. D. Robinett, “Energy Storage Requirements for Inverter-Based Microgrids Under Droop Control in d-q Coordinates,” IEEE Trans. Energy Convers., vol. 35, no. 2, pp. 611-620, June 2020.
[7] Y. Li, R. Mai, L. Lu, and Z. He, “Active and reactive currents decomposition-based control of angle and magnitude of current for a parallel multi-inverter IPT system,” IEEE Trans. Power Electron., vol. 32, no. 2, pp. 1602-1614, Feb. 2017.
[8] S. Dasgupta, S. K. Sahoo and S. K. Panda, “Single-Phase Inverter Control Techniques for Interfacing Renewable Energy Sources With Microgrid—Part I: Parallel-Connected Inverter Topology With Active and Reactive Power Flow Control Along With Grid Current Shaping,” IEEE Trans. Power Electron., vol. 26, no. 3, pp. 717-731, Mar. 2011.
[9] V. Purba, B. B. Johnson, M. Rodriguez, S. Jafarpour, F. Bullo and S. V. Dhople, “Reduced-order Aggregate Model for Parallel-connected Single-phase Inverters,” IEEE Trans. Energy Convers., vol. 34, no. 2, pp. 824-837, June 2019.
[10] S. Yazdani, M. Ferdowsi, M. Davari, and P. Shamsi, “Advanced current-limiting and power-sharing control in a PV-based grid-forming inverter under unbalanced grid conditions,” IEEE Trans. Emerg. Sel. Topics Power Electron., vol. 8, no. 2, pp. 1084-1096, June 2020.
[11] P. Jain, V. Agarwal, and B. P. Muni, “Hybrid phase locked loop for controlling master-slave configured centralized inverters in large solar photovoltaic power plants,” IEEE Trans. Ind. Appl., vol. 54, no. 4, pp. 3566-3574, July-Aug. 2018.
[12] X. Meng, Z. Liu, H. Zheng, and J. Liu, “A universal controller under different operating states for parallel inverters with seamless transfer capability,” IEEE Trans. Power Electron., vol. 35, no. 9, pp. 9794-9812, Sept. 2020.
[13] S. Rahmani, A. Rezaei-Zare, M. Rezaei-Zare, and A. Hooshyar, “Voltage and frequency recovery in an islanded inverter-based microgrid considering load type and power factor,” IEEE Trans. Smart Grid, vol. 10, no. 6, pp. 6237-6247, Nov. 2019.
[14] A. Mortezaei, M. G. Simões, M. Savaghebi, J. M. Guerrero and A. Al-Durra, “Cooperative control of multi-master–slave islanded microgrid with power quality enhancement based on conservative power theory,” IEEE Trans. Smart Grid, vol. 9, no. 4, pp. 2964-2975, July 2018.
[15] K. Wang, X. Huang, B. Fan, Q. Yang, G. Li and M. L. Crow, “Decentralized Power Sharing Control for Parallel-Connected Inverters in Islanded Single-Phase Micro-Grids,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6721-6730, Nov. 2018.
[16] Q. Liu, T. Caldognetto and S. Buso, “Review and Comparison of Grid-Tied Inverter Controllers in Microgrids,” IEEE Trans. Power Electron., vol. 35, no. 7, pp. 7624-7639, July 2020.
[17] B. Zhao, C. Wang, and X. Zhang, “A survey of suitable energy storage for island stand-alone microgrid and commercial operation mode,” Automation of Electric Power Systems, vol. 37, no. 4, pp. 21-27, Apr. 2013.
[18] S. Kewat and B. Singh, “Multimode Robust Control for Reconfigurable Microgrid With Dynamic and EV Loads,” IEEE Trans. Ind. Appl., vol. 57, no. 6, pp. 6453-6464, Nov.-Dec. 2021.
[19] S. Tolani and P. Sensarma, “An instantaneous average current sharing scheme for parallel UPS modules,” IEEE Trans. Ind. Electron., vol. 64, no. 12, pp. 9210-9220, Dec. 2017.
[20] J. Chen, S. Hou, and J. Chen, “Seamless mode transfer control for master–slave microgrid,” IET Power Electronics, vol. 12, no. 12, pp. 3158-3165, Oct. 2019.
[21] M. B. Delghavi and A. Yazdani, “Sliding-mode control of AC voltages and currents of dispatchable distributed energy resources in master-slave-organized inverter-based microgrids,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 980-991, Jan. 2019.
[22] Q. C. Zhong, Y. Wang, and B. Ren, “UDE-based robust droop control of inverters in parallel operation,” IEEE Trans. Ind. Electron., vol. 64, no. 9, pp. 7552-7562, Mar. 2017.
[23] X. Huang, K. Wang, J. Qiu, L. Hang, and G. Li. “Decentralized control of multi-parallel grid-forming DGs in islanded microgrids for enhanced transient performance,” IEEE Access, vol. 7, no. 1, pp. 17958-17968, Feb. 2019.
[24] C. Zhang, J. M. Guerrero, J. C. Vasquez, and E. A. A. Coelho, “Control architecture for parallel-connected inverters in uninterruptible power systems,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 5176-5188, July 2016.
[25] Y. Wu, J. M. Guerrero, and Y. Wu, “Distributed coordination control for suppressing circulating current in parallel inverters of islanded microgrid,” IET Gener. Transm. Distrib., vol. 13, no. 7, pp. 968-975, Apr. 2019.
[26] M. A. Setiawan, F. Shahnia, S. Rajakaruna, and A. Ghosh, “ZigBeeBased communication system for data transfer within future microgrids,” IEEE Trans. Smart Grid, vol. 6, no. 5, pp. 2343-2355, Sept. 2015.
[27] H. J. Choi and J. H. Jung, “Enhanced power line communication strategy for DC microgrids using switching frequency modulation of power converters,” IEEE Trans. Power Electron., vol. 32, no. 6, pp. 4140-4144, June 2017.
[28] D. Li and C. N. M. Ho, “Master-slave control of parallel-operated interfacing inverters based on wireless digital communication,” in Proc. Int. Conf. Energy Conversion Congress and Exposition, Portland, American. pp. 1466-1472. Sept. 2018.
[29] D. Li and C. N. M. Ho, “A delay-tolerable master–slave current-sharing control scheme for parallel-operated interfacing inverters with low-bandwidth communication,” IEEE Trans. Ind. Appl., vol. 56, no. 2, pp. 1575-1586, Dec. 2019.
[30] D. Miller, G. Mirzaeva, C. D. Townsend and G. C. Goodwin, “The Use of Power Line Communication in Standalone Microgrids,” IEEE Trans. Ind. Appl., vol. 57, no. 3, pp. 3029-3037, May-June 2021.
[31] A. M. A. Haidar, A. Fakhar and K. M. Muttaqi, “An Effective Power Dispatch Strategy for Clustered Microgrids While Implementing Optimal Energy Management and Power Sharing Control Using Power Line Communication,” IEEE Trans. Ind. Appl., vol. 56, no. 4, pp. 4258-4271, July-Aug. 2020.
[32] D. Sharma, A. Dubey, S. Mishra and R. K. Mallik, “A Frequency Control Strategy Using Power Line Communication in a Smart Microgrid,” IEEE Access, vol. 7, pp. 21712-21721, Feb. 2019.
[33] D. Gutierrez-Rojas, P. H. J. Nardelli, G. Mendes and P. Popovski, “Review of the State of the Art on Adaptive Protection for Microgrids Based on Communications,” IEEE Trans. Ind. Informat., vol. 17, no. 3, pp. 1539-1552, Mar. 2021.
[34] S. A. Alavi, K. Mehran and Y. Hao, “Optimal Observer Synthesis for Microgrids With Adaptive Send-on-Delta Sampling Over IoT Communication Networks,” IEEE Trans. Ind. Electron., vol. 68, no. 11, pp. 11318-11327, Nov. 2021.
[35] L. Lei, Y. Tan, G. Dahlenburg, W. Xiang and K. Zheng, “Dynamic Energy Dispatch Based on Deep Reinforcement Learning in IoT-Driven Smart Isolated Microgrids,” IEEE Internet Things J., vol. 8, no. 10, pp. 7938-7953, 15 May15, 2021.
[36] J. Tan, F. Lin, C. Shih, and C. Kuo, “Intelligent control of microgrid with virtual inertia using recurrent probabilistic wavelet fuzzy neural network,” IEEE Trans. Power Electron., vol. 35, no. 7, pp. 7451-7464, July 2020.
[37] X. Li, L. Guo, and C. Wang, “Stability Analysis in a master-slave control based microgrid,” Trans. China Electrothchenical Society, vol. 29, no. 2, pp. 24-34, Feb. 2014.
[38] X. Sun, H. Ma, L. Jia, and X. Li, “A robust control strategy for eliminating the structure disturbance of islanding microgrid,” Trans. China Electrothchenical Society, vol. 35, no. 11, pp. 149-160, June 2020.
[39] Z. Liu, J. Liu, X. Hou, Q. Dou, D. Xue, and T. Liu, “Output impedance modeling and stability prediction of three-phase paralleled inverters with master–slave sharing scheme based on terminal characteristics of individual inverters,” IEEE Trans. Power Electron., vol. 31, no. 7, pp. 5306-5320, July 2016.
[40] H. Li, S. Peng, and D. Yao. “Adaptive sliding-mode control of Markov jump nonlinear systems with actuator faults,” IEEE Trans. Automat. Contr., vol. 62, no. 4, pp. 1933-1939, July 2017.
[41] G. Zhong, Z. Shao, H. Deng, and J. Ren, “Precise position synchronous control for multi-axis servo systems,” IEEE Trans. Ind. Electron., vol. 64, no. 5, pp. 3707-3717, May 2017.
[42] I. U. Haq, Q. Khan, I. Khan, R. Akmeliawati, K. S. Nisar, and I. Khan, “Maximum power extraction strategy for variable speed wind turbine system via neuro-adaptive generalized global sliding mode controller,” IEEE Access, vol. 8, pp. 128536-128547, Jan. 2020.
[43] Z. Zhou et al., “Game-Theoretical Energy Management for Energy Internet With Big Data-Based Renewable Power Forecasting,” IEEE Access, vol. 5, pp. 5731-5746, Feb. 2017.
[44] F. Jamil, N. Iqbal, Imran, S. Ahmad and D. Kim, “Peer-to-Peer Energy Trading Mechanism Based on Blockchain and Machine Learning for Sustainable Electrical Power Supply in Smart Grid,” IEEE Access, vol. 9, pp. 39193-39217, Feb. 2021.
[45] C. T. Lin and C. S. G. Lee, Neural Fuzzy Systems. Upper Saddle River, NJ, USA: Prentice-Hall, 1996.
[46] S. Hou, J. Fei, C. Chen, and Y. Chu, “Finite-time adaptive fuzzy-neural-network control of active power filter,” IEEE Trans. Power Electron., vol. 34, no. 10, pp. 10298-10313, Oct. 2019.
[47] Y. Chu, J. Fei, and S. Hou, “Adaptive global sliding-mode control for dynamic systems using double hidden layer recurrent neural network structure,” IEEE Trans Neural Netw. Learn. Syst., vol. 31, no. 4, pp. 1297-1309, Apr. 2020.
[48] S. Hou and J. Fei, “A self-organizing global sliding mode control and its application to active power filter,” IEEE Trans. Power Electron., vol. 35, no. 7, pp. 7640-7652, July 2020.
[49] S. Hou, Y. Chu, and J. Fei, “Intelligent global sliding mode control using recurrent feature selection neural network for active power filter,” IEEE Trans. Ind. Electron., vol. 68, no. 8, pp. 7320-7329, Aug. 2021.
[50] S. Hou, Y. Chu, and J. Fei, “Adaptive type-2 fuzzy neural network inherited terminal sliding mode control for power quality improvement,” IEEE Trans. Ind. Informat., vol. 17, no. 11, pp. 7564-7574, Nov. 2021.
[51] X. Chen, X. Ruan, D. Yang, W. Zhao, and L. Jia, “Injected grid current quality improvement for a voltage-controlled grid-connected inverter,” IEEE Trans. Power Electron., vol. 33, no. 2, pp. 1247–1258, 2017.
[52] Q. Liu, T. Caldognetto and S. Buso, “Review and Comparison of Grid-Tied Inverter Controllers in Microgrids,” IEEE Trans. Power Electron., vol. 35, no. 7, pp. 7624-7639, July 2020.
[53] M. A. Hannan, Z. A. Ghani, A. Mohamed, and M. N. Uddin, “Real-time testing of a fuzzy-logic-controller-based grid-connected photovoltaic inverter system,” IEEE Trans. Ind. Appl., vol. 51, no. 6, pp. 4775–4784, 2015.
[54] J. Selvaraj and N. A. Rahim, “Multilevel inverter for grid-connected PV system employing digital PI controller,” IEEE Trans. Ind. Electron., vol. 56, no, 1, pp. 149-158, Jan. 2009.
[55] G. Shen, X. Zhu, J. Zhang, and D. Xu, “A new feedback method for PR current control of LCL-filter-based grid-connected inverter,” IEEE Trans. Ind. Electron., vol. 57, no. 6, pp. 2033-2041, June 2010.
[56] F. Wu, X. Li, and J. Duan, “Improved elimination scheme of current zero-crossing distortion in unipolar hysteresis current controlled grid-connected inverter,” IEEE Trans. Ind. Informat., vol. 11, no. 5, pp. 1111-1118, Oct. 2015.
[57] R. J. Wai, C. Y. Lin, Y. C. Huang, and Y. R. Chang, “Design of high-performance stand-alone and grid-connected inverter for distributed generation applications,” IEEE Trans. Ind. Electron., vol. 60, no. 4, pp. 1542-1555, Apr. 2013.
[58] B. Guo, M. Su, Y. Sun, H, H. Wang, Z. Tang, and B. Cheng, “A robust second-order sliding mode control for single-phase photovoltaic grid-connected voltage source inverter,” IEEE Access, vol. 7, pp. 53202-53212, Apr. 2019.
[59] H. Wang, S. Wu, and Q.Wang, “Global Sliding Mode Control for Nonlinear Vehicle Antilock Braking System,” IEEE Access, vol. 9, pp. 40349-40359, Mar. 2021.
[60] Q. Hou, S. Ding, and X. Yu, “Composite super-twisting sliding mode control design for PMSM speed regulation problem based on a novel disturbance observer,” IEEE Trans. Energy Convers., vol. 36, no. 4, pp. 2591-2599, Dec. 2021.
[61] Q. Hou and S. Ding, “GPIO based super-twisting sliding mode control for PMSM,” IEEE Trans. Circuits Syst. II, Exp. Briefs, vol. 68, no. 2, pp. 747-751, Feb. 2021.
[62] X. Yu, Y. Fu, P. Li, and Y. Zhang, “Fault-tolerant aircraft control based on self-constructing fuzzy neural networks and multivariable SMC under actuator faults,” IEEE Trans. Fuzzy Syst., vol. 26, no. 4, pp. 2324-2335, Aug. 2018.
[63] Y. Zhu and J. Fei, “Adaptive global fast terminal sliding mode control of grid-connected photovoltaic system using fuzzy neural network approach,” IEEE Access, vol. 5, pp. 9476-9484, May 2017.
[64] K. Cheng, C. Hsu, C. Lin, T. Lee, and C. Li, “Fuzzy–neural sliding-mode control for DC–DC converters using asymmetric Gaussian membership functions,” IEEE Trans. Ind. Electron., vol. 54, no. 3, pp. 1528-1536, June 2007.
[65] R. J. Wai and L. C. Shih, “Adaptive fuzzy-neural-network design for voltage tracking control of a DC–DC boost converter,” IEEE Trans. Power Electron., vol. 27, no. 4, pp. 2104-2115, Apr. 2012.
[66] Y. Yang and R. J. Wai, “Design of adaptive fuzzy-neural-network-imitating sliding-mode control for parallel-inverter system in islanded microgrid,” IEEE Access, vol. 9, pp. 56376-56396, Apr. 2021.
[67] M. Ai, Y. Xie, S. Xie, F. Li, and W. Gui, “Data-driven-based adaptive fuzzy neural network control for the antimony flotation plant,” J. the Franklin Institute, vol. 356, no. 12, pp. 5944-5960, June 2019.
[68] N. Wang and M. J. Er, “Self-constructing adaptive robust fuzzy neural tracking control of surface vehicles with uncertainties and unknown disturbances,” IEEE Trans. Control Syst. Technol., vol. 23, no. 3, pp. 991–1002, May 2015.
[69] H. Han, X. Wu, Z. Liu, and J. Qiao, “Design of self-organizing intelligent controller using fuzzy neural network,” IEEE Trans. Fuzzy Syst., vol. 26, no. 5, pp. 3097-3111, Oct. 2018.
[70] C. M. Lin, R. Ramarao, and S. H. Gopalai, “Self-organizing adaptive fuzzy brain emotional learning control for nonlinear systems,” Int. J. Fuzzy Syst., vol. 21, no. 7, pp.1989-2007, Aug. 2019.
[71] K. Subramanian and S. Suresh, “A meta-cognitive sequential learning algorithm for neuro-fuzzy inference system,” Appl. Soft Comput., vol. 12, no. 11, pp. 3603–3614, Nov. 2012.
[72] H. J. Rong, Z. Yang, P. K. Wong, C. M. Vong, and G. S. Zhao, “A novel meta-cognitive fuzzy-neural model with backstepping strategy for adaptive control of uncertain nonlinear systems,” Neurocomputing, vol. 230, no. 22, pp. 332-344, Mar. 2017.
[73] H. Zhou, Y. Zhang, W. Duan, and H. Zhao, “Nonlinear systems modelling based on self-organizing fuzzy neural network with hierarchical pruning scheme,” Appl. Soft Comput., vol. 95, no. 7, pp. 1-17, Oct. 2020.
[74] A. J. Al-Mahasneh, S. G. Anavatti and M. A. Garratt, “Self-evolving neural control for a class of nonlinear discrete-time dynamic systems with unknown dynamics and unknown disturbances,” IEEE Trans. Ind. Informat., vol. 16, no. 10, pp. 6518-6529, Oct. 2020.
[75] R. J. Wai and Y. W. Lin, “Adaptive moving-target tracking control of a vision-based mobile robot via a dynamic Petri recurrent fuzzy neural network,” IEEE Trans. Fuzzy Syst., vol. 21, no. 4, pp. 688-701, Aug. 2013.
[76] F. J. Lin, C. I. Chen, G. D. Xiao, and P. R. Chen, “Voltage stabilization control for microgrid with asymmetric membership function-based wavelet Petri fuzzy neural network,” IEEE Trans. Smart Grid, vol. 12, no. 5, pp. 3731-3741, Sept. 2021.
[77] F. J. Lin, S. G. Chen and C. W. Hsu, “Intelligent backstepping control using recurrent feature selection fuzzy neural network for synchronous reluctance motor position servo drive system,” IEEE Trans. Fuzzy Syst., vol. 27, no. 3, pp. 413-427, Mar. 2019.
[78] Y. Yang and R. J. Wai, “Self-constructing fuzzy-neural-network-imitating sliding-mode control for parallel-inverter system in grid-connected microgrid,” IEEE Access, vol. 9, pp. 167389-167411, Dec. 2021.
[79] R. Pérez-Ibacache, C. A. Silva and A. Yazdani, “Linear State-Feedback Primary Control for Enhanced Dynamic Response of AC Microgrids,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3149-3161, May 2019.
[80] M. Raeispour, H. Atrianfar, H. R. Baghaee and G. B. Gharehpetian, “Robust Sliding Mode and Mixed H2/H∞ Output Feedback Primary Control of AC Microgrids,” IEEE Syst. J., vol. 15, no. 2, pp. 2420-2431, June 2021.
[81] K. J. Astrom and B. Wittenmark, Adaptive Control. New York, NY, USA: Addison-Wesley, 1995.
[82] L. X. Wang, A Course in Fuzzy Systems and Control. Englewood Cliffs, NJ, USA: Prentice-Hall, 1997.
[83] R. J. Wai and Y. Yang, “Design of backstepping direct power control for three-phase PWM rectifier,” IEEE Trans. Ind. Appl., vol. 55, no. 3, pp. 3160-3173, Jan. 2019.
[84] J. Lai, X. Lu, X. Yu, W. Yao, J. Wen and S. Cheng, “Distributed Multi-DER Cooperative Control for Master-Slave-Organized Microgrid Networks With Limited Communication Bandwidth,” IEEE Trans. Ind. Informat., vol. 15, no. 6, pp. 3443-3456, June 2019.
[85] R. Zhang and B. Hredzak, “Nonlinear Sliding Mode and Distributed Control of Battery Energy Storage and Photovoltaic Systems in AC Microgrids With Communication Delays,” IEEE Trans. Ind. Informat., vol. 15, no. 9, pp. 5149-5160, Sept. 2019.
[86] H. Yan, X. Zhou, H. Zhang, F. Yang and Z. -G. Wu, “A Novel Sliding Mode Estimation for Microgrid Control With Communication Time Delays,” IEEE Trans. Smart Grid, vol. 10, no. 2, pp. 1509-1520, Mar. 2019.
[87] D. Sharma and S. Mishra, “Disturbance-Observer-Based Frequency Regulation Scheme for Low-Inertia Microgrid Systems,” IEEE Syst. J., vol. 14, no. 1, pp. 782-792, Mar. 2020.
[88] L. Xia and L. Hai, "A Seamless Transfer Strategy Based on Multi-master and Multi-slave Microgrid," 2018 9th IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), 2018, pp. 1-5.
[89] W. Huang, Z. Shuai, X. Shen, Y. Li and Z. J. Shen, “Dynamical Reconfigurable Master-Slave Control Architecture (DRMSCA) for Voltage Regulation in Islanded Microgrids,” IEEE Trans. Power Electron., vol. 37, no. 1, pp. 249-263, Jan. 2022.
[90] M. Gao, M. Chen, B. Zhao, B. Li and Z. Qian, “Design of Control System for Smooth Mode-Transfer of Grid-Tied Mode and Islanding Mode in Microgrid,” IEEE Trans. Power Electron., vol. 35, no. 6, pp. 6419-6435, June 2020.
[91] K. H. Tan and T. Y. Tseng, “Seamless Switching and Grid Reconnection of Microgrid Using Petri Recurrent Wavelet Fuzzy Neural Network,” IEEE Trans. Power Electron., vol. 36, no. 10, pp. 11847-11861, Oct. 2021.
[92] K. -Y. Lo and Y. -M. Chen, “Design of a Seamless Grid-Connected Inverter for Microgrid Applications,” IEEE Trans. Smart Grid, vol. 11, no. 1, pp. 194-202, Jan. 2020.
[93] P. Buduma, M. K. Das, R. T. Naayagi, S. Mishra and G. Panda, “Seamless Operation of Master-Slave Organized AC Microgrid with Robust Control and Islanding Detection,” 2020 3rd International Conference on Energy, Power and Environment: Towards Clean Energy Technologies, 2021, pp. 1-6.
[94] S. Silwal, S. Taghizadeh, M. Karimi-Ghartemani, M. J. Hossain and M. Davari, “An Enhanced Control System for Single-Phase Inverters Interfaced With Weak and Distorted Grids,” IEEE Trans. Power Electron., vol. 34, no. 12, pp. 12538-12551, Dec. 2019.
[95] M. Davari and Y. A.-R. I. Mohamed, “Robust vector control of a very weak-grid-connected voltage-source converter considering the phase-locked loop dynamics,”IEEE Trans. Power Electron., vol. 32, no. 2, pp. 977–994, 2017.
[96] A. Aghazadeh, M. Davari, H. Nafisi and F. Blaabjerg, “Grid Integration of a Dual Two-Level Voltage-Source Inverter Considering Grid Impedance and Phase-Locked Loop,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 9, no. 1, pp. 401-422, Feb. 2021.
[97] Z. Shuai, Y. Peng, J. M. Guerrero, Y. Li and Z. J. Shen, “Transient Response Analysis of Inverter-Based Microgrids Under Unbalanced Conditions Using a Dynamic Phasor Model,” IEEE Trans. Ind. Electron., vol. 66, no. 4, pp. 2868-2879, Apr. 2019.
[98] Y. Peng, Z. Shuai, J. M. Guerrero, Y. Li, A. Luo and Z. J. Shen, “Performance Improvement of the Unbalanced Voltage Compensation in Islanded Microgrid Based on Small-Signal Analysis,” IEEE Trans. Ind. Electron., vol. 67, no. 7, pp. 5531-5542, July 2020.