Sliding Mode Control with Adaptive Fuzzy Compensation for Uncertain Nonlinear System

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.

ANFIS-based optimisation for achieving the maximum torque per ampere in induction motor drive with conventional PI

“…The membership functions have been shown in Table 1 for easy understanding of the minimization of error. The fuzzy inference system (FIS) is essential to the MAX-MIN design process [11]. The inference process is crucial in real-time applications because it converts imprecise control results into precise actions.…”

A multi‐strategy fuzzy control method based on the Takagi‐Sugeno model

“…Then, the LQR was applied for rules S (2,2) , S (2,3) , S (2,4) , S (3,2) , S (3,4) , S (4,2) , S (4,3) , and S (4,4) F…”

“…Based on the method in Section 2.4, external rules (S (1,1) , S (1,2) , S (1,3) , S (1,4) , S (1.5) , S (2,1) , S (2,5) , S (3,1) , S (3,5) , S (4,1) , S (4,5) , S (5,1) , S (5,2) , S (5,3) , S (5,4) , and S (5,5) ) for control actions are shown in Figure 8. The system was assumed to be constrained between −500 ≤ u ≤ 500, due to its physical restrictions.…”

“…Research on nonlinear systems and related control methods has produced many results over the years: 3‐5 control methods based on linearization, global feedback linearization, nonlinear $$$ {H}_{\infty } $$$ control, sliding mode control, variable structure control, state dependent Riccati equation control and so forth.…”

Diseño de un Controlador Difuso en Modo Deslizante para un Motor de Inducción Trifásico

“…El diseño de esta estrategia de control se basa en una perspectiva de ensayo y error con un alto grado heurístico [12]. Por su lado el Control en Modo deslizante (Sliding Mode Control) divide el espacio de estado en al menos dos semisuperficies y define la distancia de la superficie de deslizamientos en término de una función deseada de Lyapunov [2] [13] [14]. Esta distancia disminuye a lo largo de todas las trayectorias del sistema, que a su vez hace que las trayectorias del sistema converjan hacia la superficie de deslizamiento y una vez en ella no puede salir de él, llevando el error al valor lo más próximo a cero [15], [2], [1].…”