Fuzzy Neural Sliding Mode Control Research Articles

Self-Evolving Recurrent Chebyshev Fuzzy Neural Sliding Mode Control for Active Power Filter

A self-evolving recurrent Chebyshev fuzzy neural network (SERCFNN) approximator based on a fractional order sliding mode controller (FOSMC) is developed for an active power filter to suppress harmonic distortions. The self-evolving algorithm, which incorporates the structure learning with parameter learning, is able to dynamically adjust the number of fuzzy rules and the shape of fuzzy partitions. The consequent part of the proposed SERCFNN combines with Chebyshev polynomials to expand the dimensionality of the input. For relaxing the requirement of the parametric and functional certainty, a SERCFNN-based uncertainty approximator is utilized to dynamically approximate the compound unknown function, yielding an approximator-based FOSMC to tolerate extensive uncertainties. The approximator-based control law and parameter updating laws are obtained from the Lyapunov stability theory, which guarantee the designed control system is asymptotically stable. The control algorithm is implemented in a dSPACE-based experimental system to validate its feasibility, and the hardware experimental results confirm its superiority in harmonic compensation regardless of load disturbances.

Adaptive type-2 neural fuzzy sliding mode control of a class of nonlinear systems

The objective of this study is to design an optimal control scheme for the control of a class of nonlinear flexible multi-body systems with extremely coupled dynamics and infinite dimensions. The assumed mode method (AMM) acquires a finite-dimensional model, but there are uncertainties in the truncated model that make the system a difficult control problem. The proposed control scheme is a hybrid of the sliding mode control (SMC) and the type-2 neural fuzzy system (NFS). A new modified conjugate gradient (CG) algorithm is used to optimize the NFS parameters. The control law of the proposed control scheme requires the estimation of the uncertain system functions, which is provided by the NFS. Moreover, NFS plays an essential role in avoiding the chattering phenomenon commonly observed in conventional SMC. The control scheme stability is assured by the Lyapunov stability theorem. Various other intelligent control schemes have also been tested to make a comparison with the proposed control scheme. The simulation results evidently indicate that the proposed control scheme has enhanced tracking efficiency while managing the inherent deflections of the system and is therefore concluded as a reliable control technique for the nonlinear, flexible multi-body class of systems.

Governor design for hydropower plants by intelligent sliding mode variable structure control

This work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. Considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. By linearizing this mode, a sliding mode controller is designed. The linearized mode is subject to uncertainties. The uncertainties are generated in the process of linearization. A radial basis function (RBF) neural network is introduced to compensate for the uncertainties. The update formulas for the neural networks are derived from the Lyapunov direct method. For the chattering phenomenon of the sliding mode control, a fuzzy logic inference system is adopted. In the sense of Lyapunov, the asymptotical stability of the system can be guaranteed. Compared with the internal mode control and the conventional PID control method, some numerical simulations verify the feasibility and robustness of the proposed scheme.

Neural-fuzzy sliding mode control of non-linear systems

In this paper, neural-fuzzy sliding mode control is proposed for robust asymptotic stabilisation of a non-linear system. The sliding mode control has two terms as continuous mode and discontinuous mode are approximated using direct neural network control method based on neural sliding mode control and fuzzy sliding mode control method respectively. The combined control is termed as neural-fuzzy sliding mode control. The weights of the neural network are updated online to minimise the cost function. The cost function is the difference between reference value and plant output. The performance of the proposed controller is tested using an example of non-linear inverted pendulum and results are compared with conventional SMC and FSMC methods to demonstrate its superiority in terms of parameter variations and disturbance rejection.

Observer-based decentralized fuzzy neural sliding mode control for interconnected unknown chaotic systems via network structure adaptation

In this paper, an observer-based fuzzy neural sliding mode control (OFNSMC) scheme for interconnected unknown chaotic systems is developed. The OFNSMC system is composed of a computation controller and a robust controller. The computation controller containing a self-structuring fuzzy neural network (SFNN) identifier is the principle controller, and the robust controller is designed to achieve L 2 tracking performance. The SFNN identifier uses the structure and parameter learning phases to perform the estimation of the interconnected unknown chaotic system dynamics. The structure learning phase consists of the growing of membership functions, the splitting of fuzzy rules and the pruning of fuzzy rules, and thus the SFNN identifier can avoid the time-consuming trial-and-error tuning procedure for determining the network structure of fuzzy neural network. The total states of the interconnected chaotic systems are not assumed to be available for measurement. Also, the unknown nonlinearities of the interconnected chaotic systems are not restricted to the systems output only. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

On-line genetic algorithm-based fuzzy-neural sliding mode controller using improved adaptive bound reduced-form genetic algorithm

In this article, a novel on-line genetic algorithm-based fuzzy-neural sliding mode controller trained by an improved adaptive bound reduced-form genetic algorithm is developed to guarantee robust stability and good tracking performance for a robot manipulator with uncertainties and external disturbances. A general sliding manifold, which can be non-linear or time varying, is used to construct a sliding surface and reduce control law chattering. In this article, the sliding surface is used to derive a genetic algorithm-based fuzzy-neural sliding mode controller. To identify structured system dynamics, a B-spline membership function fuzzy-neural network, which is trained by the improved genetic algorithm, is used to approximate the regressor of the robot manipulator. The sliding mode control with a general sliding surface plays the role of a compensator when the fuzzy-neural network does not approximate the dynamics regressor of the robot manipulator well in the transient period. The adjustable parameters of the fuzzy-neural network are tuned by the improved genetic algorithm, which, with the use of the sequential-search-based crossover point method and the single gene crossover, converges quickly to near-optimal parameter values. Simulation results show that the proposed genetic algorithm-based fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.

Convergence Conditions for Ascent Methods

Convergence Conditions for Ascent Methods