Neural network aided fractional-order sliding mode controller for frequency regulation of nonlinear power systems

2021 ◽  
Vol 96 ◽  
pp. 107534
Author(s):  
Vivek Patel ◽  
Dipayan Guha ◽  
Shubhi Purwar
2020 ◽  
Vol 42 (9) ◽  
pp. 1594-1617
Author(s):  
Gomaa Haroun AH ◽  
Yin-Ya Li

In this article, a novel hybrid intelligent Proportional Integral Derivative (PID)-based sliding mode controller (IPID-SMC) is proposed to solve the LFC problem for realistic interconnected multi-area power systems. The optimization task for best-regulating parameters of the suggested controller structure is fulfilled by the ant lion optimizer (ALO) technique. To assess the usefulness and practicability of the suggested ALO optimized IPID-SMC controller, three test systems – that is, four-area hybrid power system, two-area reheat thermal-photovoltaic system and two-area multi-sources power system – are employed. Different nonlinearities such as generation rate constraint (GRC) and governor dead band (GDB) as a provenance of physical constraints are taken into account in the model of the two-area multi-sources power systems to examine the ability of the proposed strategy for handling the practical challenges. The acceptability and novelty of the ALO-based IPID-SMC controller to solve the systems mentioned above are appraised in comparison with some recently reported approaches. The specifications of time-domain simulation disclose that the designed proposed controller provides a desirable level of performance and stability compared with other existing strategies. Furthermore, to check the robustness of the suggested technique, sensitivity analysis is fulfilled by varying the operating loading conditions and plant parameters within a particular tolerable range.


Author(s):  
Sunhua Huang ◽  
Linyun Xiong ◽  
Jie Wang ◽  
Penghan Li ◽  
Ziqiang Wang ◽  
...  

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Juntao Fei ◽  
Xiao Liang

An adaptive fractional-order nonsingular terminal sliding mode controller for a microgyroscope is presented with uncertainties and external disturbances using a fuzzy neural network compensator based on a backstepping technique. First, the dynamic of the microgyroscope is transformed into an analogical cascade system to guarantee the application of a backstepping design. Then, a fractional-order nonsingular terminal sliding mode surface is designed which provides an additional degree of freedom, higher precision, and finite convergence without a singularity problem. The proposed control scheme requires no prior knowledge of the unknown dynamics of the microgyroscope system since the fuzzy neural network is utilized to approximate the upper bound of the lumped uncertainties and adaptive algorithms are derived to allow online adjustment of the unknown system parameters. The chattering phenomenon can be reduced simultaneously by the fuzzy neural network compensator. The stability and finite time convergence of the system can be established by the Lyapunov stability theorem. Finally, simulation results verify the effectiveness of the proposed controller and the comparison of root mean square error between different fractional orders and integer order is given to signify the high precision tracking performance of the proposed control scheme.


2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.


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