H/sub ∞/ tracking-based sliding mode control for uncertain nonlinear systems via an adaptive fuzzy-neural approach

Abstract

A novel adaptive fuzzy-neural sliding-mode controller with H(infinity) tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H(infinity) tracking design technique and the sliding-mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H(infinity) tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach.

Approximation-Free Output-Feedback Non-Backstepping Controller for Uncertain SISO Nonautonomous Nonlinear Pure-Feedback Systems

“…Designing a controller for nonlinear systems containing unstructured uncertainties has been considerably advanced and performed in recent years. Conventionally, many adaptive controllers using universal approximators (UAs) such as neural networks (NNs) or fuzzy logic systems (FLSs) have been proposed (refer to [1][2][3][4][5][6][7][8][9][10][11][12] and references therein). More recently, controllers for nonlinear pure-feedback systems that contain unstructured uncertainties and unmatched disturbances have been actively proposed.…”

“…Consider the system(1) under Assumptions 1 and 2. The control input (29) using the HOSM differentiator (3) and input filter (13) makes the tracking error vector e to be exponentially stable in finite time.Proof.…”

Design of a new adaptive fuzzy controller and its application to vibration control of a vehicle seat installed with an MR damper

“…In this combination, a classical control plays the main control function and the others are used as main components to improve the control function simultaneously. These advantages are clearly pointed out in previous works [11][12][13][14][15][16][17][18][19]. In these works, fuzzy or fuzzy neural network models are frequently used for evaluating uncertain variables, while sliding mode control and the H infinity technique are used for controlling dynamic parameters of the system.…”

“…A direct adaptive fuzzy control for an MR damper was also studied in which the H infinity technique and the interval type 2 fuzzy model were adopted [17]. An adaptive dynamic surface control with the fuzzy neural model was presented by integrating the interval type 2 fuzzy model in order to reduce uncertainty errors [18] and a combination control technique of the H infinity with the sliding mode control was investigated via the adaptive fuzzyneural model [19]. It is noted here that there are many types of fuzzy models such as the Takagi-Sugeno and interval type 2 models.…”

“…In order to demonstrate some benefits of the proposed hybrid controller, both simulation and experiment are undertaken by applying the proposed controller to vibration control of a vehicle seat featuring an MR damper. Control performances such as displacement and acceleration at the driver's seat are presented in a time domain and compared with two existing adaptive fuzzy controllers [19,26].…”