Abstract
We propose a terminal sliding mode control (SMC) law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system. First, a novel nonaffine nonlinear approximation algorithm is proposed for observer and controller design. Then, an adaptive fuzzy-neural observer is introduced to identify the simplified model and resolve the problem of the unavailability of the state variables. Moreover, based on the information of the adaptive observer, the terminal SMC law is designed. The Lyapunov synthesis approach is used to guarantee a global uniform ultimate boundedness property of the state estimation error and the asymptotic output tracking of the closed-loop control systems in spite of unknown uncertainties/disturbances, as well as all the other signals in the closed-loop system. Finally, using the designed terminal sliding mode controller, the simulation results on the dynamic model demonstrate the effectiveness of the proposed new control techniques.
Highlights
Sliding mode control (SMC) is known to be a robust control scheme applicable for controlling uncertain systems
We propose a terminal sliding mode control (SMC) law based on adaptive fuzzy-neural observer for nonaffine nonlinear uncertain system
The dynamics performance of a SMC system is affected by the suggested sliding manifolds upon which the control structure is switched
Summary
Sliding mode control (SMC) is known to be a robust control scheme applicable for controlling uncertain systems. SMC techniques cannot provide satisfactory results when suffering poorly modeling unless the designers know the bounds of uncertainty. The class of nonlinear systems to which the adaptive control technique can be employed is improved by using universal function approximators. Another benefit is that we can use conventional and advanced adaptive techniques with a novel robust action which compensates the errors. A novel dynamic model approximation method is first proposed to approximate the nonaffine nonlinear dynamics, which is a solution that bridges the gap between affine and nonaffine control systems. Some conclusions are made at the end of this paper
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