Artificial Error Tuning Based on Design a Novel SISO Fuzzy Backstepping Adaptive Variable Structure Control

Abstract

This paper examines single input single output (SISO) chattering free variable structure control (VSC) which controller coefficient is on-line tuned by fuzzy backstepping algorithm to control of continuum robot manipulator. Variable structure methodology is selected as a framework to construct the control law and address the stability and robustness of the close loop system based on Lyapunove formulation. The main goal is to guarantee acceptable error result and adjust the trajectory following. The proposed approach effectively combines the design technique from variable structure controller is based on Lyapunov and modified Proportional plus Derivative (P+D) fuzzy estimator to estimate the nonlinearity of undefined system dynamic in backstepping controller. The input represents the function between variable structure function, error and the modified rate of error. The outputs represent joint torque, respectively. The fuzzy backstepping methodology is on-line tune the variable structure function based on adaptive methodology. The performance of the SISO VSC based on-line tuned by fuzzy backstepping algorithm (FBSAVSC) is validated through comparison with VSC. Simulation results signify good performance of trajectory in presence of uncertainty joint torque load

Design Intelligent System Compensator to Computed Torque Control of Spherical Motor

“…There are several methods for controlling a spherical motor, wh ich all of them fo llo w two co mmon goals, namely, hardware/software imp lementation and acceptable performance. Ho wever, the mechanical design of spherical motor is very important to select the best controller but in general two types schemes can be presented, namely, a joint space control schemes and an operation space control schemes [41][42][43][44][45][46][47][48]. Joint space and operational space control are closed loop controllers which they have been used to provide robustness and rejection of disturbance effect.…”

Design New Online Tuning Intelligent Chattering Free Fuzzy Compensator

“…The applications of artificial intelligence such as neural networks and fuzzy logic in modelling and control are significantly growing especially in recent years. For instance, the applications of artificial intelligence, neural networks and fuzzy logic, on robot arm control have reported in [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55].…”

“…Automatic control has played an important role in advance science and engineering and its extreme importance in many industrial applications, i.e., aerospace, mechanical engineering and robotic systems. The first significant wo rk in automat ic control was James Watt"s centrifugal governor for the speed control in motor engine in eighteenth century [41][42][43][44][45][46][47][48]. There are several methods for controlling a robot manipulator, which all of them follow t wo co mmon goals, namely, hardware/software imp lementation and acceptable performance.…”

“…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.…”

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

“…At present, in some applications robot arms are used in unknown and unstructured environment, therefore strong mathematical tools used in new control methodologies to design nonlinear robust controller with an acceptable safety performance (e.g., minimum error, good trajectory, disturbance rejection). According to the control theory, Although the fuzzy-logic control is not a new technique, its application in this current research is considered to be novel since it aimed for an automated dynamic-less response rather than for the traditional objective of uncertainties compensation [38][39][40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57]. The intelligent tracking control using the fuzzy-logic technique provides a cost-and-time efficient control implementation due to the automated dynamic-less input.…”