Abstract
Fuzzy sliding mode control as a robust and intelligent nonlinear control technique is proposed to control processes with severe nonlinearity and unknown models. This paper proposes a new adaptive tracking fuzzy sliding mode controller for nonlinear systems in the presence of fuzzy compensation. The main contribution of the proposed method is that the fuzzy system is used to realize the adaptive approximation of the unknown part of the model, and the fuzzy gain can be reduced effectively. The fuzzy self-adaptive rate is derived through the Lyapunov method, and the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value. The performance of the proposed approach is evaluated for double joint rigid manipulator problems. The simulation results illustrate the effectiveness of our proposed controller.
Highlights
Fuzzy control method is widely used in control theory and control engineering systems due to its great theoretical value and successful applications in complex practical systems
This paper proposes a new adaptive tracking fuzzy sliding mode controller for nonlinear systems in the presence of fuzzy compensation
Since the adaptive fuzzy approaches can approximate the complex nonlinear functions through fuzzy rules, the application of the fuzzy logic to deal with the chattering problem is proven as an effective way; it has been used in many engineering applications, such as robot systems and active suspension system [2, 3]
Summary
Fuzzy control method is widely used in control theory and control engineering systems due to its great theoretical value and successful applications in complex practical systems. Traditional methods that replace the relay control by a saturating approximation, integral sliding control, and boundary layer technique have been proposed to eliminate this chattering problem [20,21,22]. We developed an adaptive fuzzy compensator SMC to control double joint rigid manipulator system It has the advantage of representing system’s nonlinear functions without the dynamic model requirement. In this paper, during the control of the nonlinear system, the fuzzy system is used to realize the adaptive approximation of the unknown part of the model, and the fuzzy gain can be reduced effectively. Combining the SMC with adaptive fuzzy compensation method, the stability and convergence of the whole closed-loop system are guaranteed by adjusting the adaptive weight value.
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