Sliding mode controller based on fuzzy neural network optimization for direct torque controlled PMSM

Purpose – Micro aerial vehicle is nonlinear plant; it is difficult to obtain stable control for MAV attitude due to uncertainties. The purpose of this paper is to propose one robust stable control strategy for MAV to accommodate system uncertainties, variations, and external disturbances. Design/methodology/approach – First, by employing interval type-II fuzzy neural network (ITIIFNN) to approximate the nonlinearity function and uncertainty functions in the attitude angle dynamic model of micro aircraft vehicle (MAV). Then, the Lyapunov stability theorem is used to testify the asymptotic stability of the closed-loop system, the parameters of the ITIIFNN and gain of sliding mode control can be tuned on-line by adaptive laws based on Lyapunov synthesis approach, and the Lyapunov stability theorem has been used to testify the asymptotic stability of the closed-loop system. Findings – The validity of the proposed control method has been verified through real-time experiments. The experimental results show that the performance of interval type-II fuzzy neural network based gain adaptive sliding mode controller (GASMC-ITIIFNN) is significantly improved compared with conventional adaptive sliding mode controller (CASMC), type-I fuzzy neural network based sliding mode controller (GASMC-TIFNN). Practical implications – This approach has been used in one MAV, the controller works well, and which could guarantee the MAV control system with good performances under uncertainties, variations, and external disturbances. Originality/value – The main original contributions of this paper are: the proposed control scheme makes full use of the nominal model of the MAV attitude control model; the overall closed-loop control system is globally stable demonstrated by Lyapunov stable theory; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the MAV attitude control system based on GASMC-ITIIFNN controller can achieve favourable tracking performance than GASMC-TIFNN and CASMC.

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Fuzzy Neural Network Optimization by a Particle Swarm Optimization Algorithm

Advances in Neural Networks - ISNN 2006 - Lecture Notes in Computer Science ◽

10.1007/11759966_110 ◽

2006 ◽

pp. 752-761 ◽

Cited By ~ 1

Author(s):

Ming Ma ◽

Li-Biao Zhang ◽

Jie Ma ◽

Chun-Guang Zhou

Keyword(s):

Neural Network ◽

Particle Swarm Optimization ◽

Optimization Algorithm ◽

Network Optimization ◽

Fuzzy Neural Network ◽

Particle Swarm Optimization Algorithm ◽

Particle Swarm ◽

Swarm Optimization ◽

Neural Network Optimization ◽

Fuzzy Neural

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Adaptive Backstepping Fuzzy Neural Network Fractional-Order Control of Microgyroscope Using a Nonsingular Terminal Sliding Mode Controller

Complexity ◽

10.1155/2018/5246074 ◽

2018 ◽

Vol 2018 ◽

pp. 1-12 ◽

Cited By ~ 45

Author(s):

Juntao Fei ◽

Xiao Liang

Keyword(s):

Neural Network ◽

Fractional Order ◽

Fuzzy Neural Network ◽

Sliding Mode ◽

Sliding Mode Controller ◽

Terminal Sliding Mode ◽

Nonsingular Terminal Sliding Mode ◽

Control Scheme ◽

Fuzzy Neural ◽

Neural Network Compensator

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.

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