A self-tuning fuzzy inference sliding mode control scheme for a class of nonlinear systems

Abstract

A self-tuning fuzzy inference sliding mode control method is presented for single inverted pendulum position tracking control. Sliding mode control is a special nonlinear control method which has a quick response, is insensitive to parameters’ variation and disturbance; and is very suitable for nonlinear system control. Neuro-fuzzy logic systems are used to directly generate the "equivalent control term". In this case, a neuro-fuzzy system was described as a self-tuning fuzzy inference system optimized online using Takagi-Sygeno type of rules and a back-propagation algorithm to minimize a cost function. The cost function is made up of a quadratic error term and a weight decay term that prevents an excessive growth of parameters. The definition of sliding mode control was presented, and on the basis of the inverted pendulum system the sliding mode controller was designed. Stability of the proposed control scheme is proved by the Lyapunov theorem and the control scheme is applied to an inverted pendulum system. Simulation studies show that the method is effective and can be applied to a nonlinear control system.

Disturbance observer and finite-time tracker design of disturbed third-order nonholonomic systems using terminal sliding mode

“…Sliding mode control (SMC) has been presented extensively as an effective robust method for the control of nonlinear systems in which the large uncertainties, nonlinearities and bounded external disturbances exist (Chaouch et al., 2012; Mobayen, 2014b; Ngo and Hong, 2012). SMC is a powerful control method which has the capability of the access to the desired performance in despite of the parametric uncertainties and external disturbances (Song and Sun, 2011; Jawaada et al., 2012).…”

A roll-motion control system for a mobile-wheeled platform: a preliminary test platform for roll-motion control of ships

“…Thus, an auxiliary control input must be added to eliminate the effect of the unpredictable uncertainties. Unfortunately, the value of the uncertainties is difficult to obtain precisely in advance for practical applications (Zborowski and Taylan, 1989;Youssef et al, 2003;Nasrallah et al, 2007;Surendran et al, 2007;Perez and Goodwin, 2008;Marzouk and Nayfeh, 2009;Fang et al, 2010;Hassan and Parviz, 2010;Huang et al, 2010;Khaled and Chalhoub, 2011;Zhigang et al, 2011;Chaouch et al, 2012). A fuzzy inference mechanism and a sliding surface are combined to cope with this problem.…”

“…Aloui (2011) and Faieghi et al (2012) proposed adaptive fuzzy sliding-mode-based tracking controller for a class of nonlinear multiple-input and multiple-output (MIMO) systems; but there still existed chattering control efforts under serious uncertainties. Additionally, neural network approaches, such as neural PID (Hsieh and Hwang, 2010), neural-fuzzy (Kim et al, 2010;Li and Wang, 2011;Yeh et al, 2012), recurrent fuzzy neural network (Juang et al, 2010), robust wavelet-neural-network SMC (El-Sousy, 2011), self-tuning neural-fuzzy SMC scheme (Chaouch et al, 2012) etc. have been proposed for the electro-hydrostatic actuator, electrical servo drive or other dynamic systems.…”

A new and robust control strategy for a class of nonlinear power systems: Adaptive general type-II fuzzy

“…It should be noted that adaptive control generally guarantees parameter convergence only if parameter changes are slow enough. 54 To address this problem, Shahnazi and Akbarzadeh in 2008 55 introduced a PI adaptive fuzzy controller which could efficiently reject fast and large disturbances. Lin et al, 39 in 2009, introduced a direct adaptive interval type-2 fuzzy logic controller (DAIT2FLC) in a way that H ∞ tracking performance could be satisfied for a general class of multi-input multi-output nonlinear systems.…”