Model-Free Adaptive Fuzzy Sliding Mode Controller Optimized by Particle Swarm for Robot Manipulator

Abstract

The main purpose of this paper is to design a suitable control scheme that confronts the uncertainties in a robot. Sliding mode controller (SMC) is one of the most important and powerful nonlinear robust controllers which has been applied to many non-linear systems. However, this controller has some intrinsic drawbacks, namely, the chattering phenomenon, equivalent dynamic formulation, and sensitivity to the noise. This paper focuses on applying artificial intelligence integrated with the sliding mode control theory. Proposed adaptive fuzzy sliding mode controller optimized by Particle swarm algorithm (AFSMC-PSO) is a Mamdani’s error based fuzzy logic controller (FLS) with 7 rules integrated with sliding mode framework to provide the adaptation in order to eliminate the high frequency oscillation (chattering) and adjust the linear sliding surface slope in presence of many different disturbances and the best coefficients for the sliding surface were found by offline tuning Particle Swarm Optimization (PSO). Utilizing another fuzzy logic controller as an impressive manner to replace it with the equivalent dynamic part is the main goal to make the model free controller which compensate the unknown system dynamics parameters and obtain the desired control performance without exact information about the mathematical formulation of model

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

“…This method is free of so me model-based techniques as in classical controllers. As mentioned that fuzzy logic application is not only limited to the modelling of nonlinear systems [31][32][33][34][35][36]but also this method can help engineers to design easier controller. Control robot arm manipulators using classical controllers are based on manipulator dynamic model.…”

“…Th is control problem is classified into two main groups. Firstly, transformation the desired motion ( ) to joint variable ( ) by inverse kinemat ics of robot manipulators [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50]. This control include simple PD control, PID control, inverse dynamic control, Lyapunov-based control, and passivity based control that explained them in the following section.…”

“…According to Ogata, to do the first significant wo rk in three -term or PID controllers wh ich Nicholas Minorsky worked on it by automatic controllers in 1922. In 1934, Stefen Black was invention of the [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40]. Negative feedback invited communicat ions engineer Harold Black in 1928 and it occurs when the output is subtracted from the input.…”

“…It should be mentioned that application of fu zzy logic is not limited to a system that"s difficult for modeling, but it can be used in clear systems that have complicated mathemat ics models because most of the time it can be shortened in design but there is no high quality design just sometimes we can find design with high quality. Besides using fuzzy logic in the main controller of a control loop, it can be used to design adaptive control, tuning parameters, working in a parallel with the classical and non classical control method [32][33][34][35][36][37][38][39]. The applications of artificial intelligence such as neural networks and fuzzy logic in modelling and control are significantly growing especially in recent years.…”

Design Sliding Mode Modified Fuzzy Linear Controller with Application to Flexible Robot Manipulator

“…Design a controller with the same formulation and different coefficient, low cost hardware and simple structure controller are some of most important independent-joint space controller advantages. In the absence of robot knowledge, proportional-integralderivative (PID) may be the best controller, because it is model-free, and its parameters can be adjusted easily and separately [24][25][26][27][28][29][30][31][32][33][34][35][36]. And it is the most used in flexible robot manipulators.…”