Design Intelligent Robust Back stepping Controller

Abstract

The increasing demand for multi-degree-offreedom (DOF) continuum robot in presence of highly nonlinear dynamic parameters in a number of industries has motivated a flurry of research in the development of soft computing nonlinear methodology. The robust backstopping controller proposed in this research is used to further demonstrate the appealing features exhibited by the continuum robot. Robust feedback controller is used to position control of continuum robot in presence of uncertainties. Using Lyapunov type stability arguments, a robust backstopping controller is designed to achieve this objective. The controller developed in this research is designed into two steps. Firstly, a robust stabilizing torque is designed for the nominal continuum robot dynamics derived using the constrained Lagrangian formulation based on modified PD backstopping controller. Next, the fuzzy logic methodology applied to it to solution uncertainty problem. The fuzzy model free problem is formulated to estimate the nonlinear formulation of continuum robot. The eventual stability of the controller depends on the torque generating capabilities of the continuum robots.

Design New PID like Fuzzy CTC Controller: Applied to Spherical Motor

“…This is suitable for real-time control applications when powerful processors, which can execute complex algorithms rapidly, are not accessible. However, since joints coupling is neglected, control performance degrades as operating speed increases and a motor controlled in this way is only appropriate for relatively slow motion [44,46]. The fast motion requirement results in even higher dynamic coupling between the various spherical motor joints, which cannot be compensated for by a standard motor controller such as PID [50], and hence model-based control becomes the alternative.…”

“…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]. The intelligent tracking control using the fuzzy-logic technique provides a cost-and-time efficient control implementation due to the automated dynamic-less input.…”

“…Even though, this methodology is used in wide range areas but, pure computed torque method has an important drawbacks beside uncertain system and also in presence of external disturbance. Uncertainty in system can causes some problems about safety in industrial factory [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48][49][50].…”

Design Intelligent Model base Online Tuning Methodology for Nonlinear System

“…The remaining terms in the dynamic equations resulting from gravitational potential energies and spring energies are collected in the matrix . The coefficient matrices of the dynamic equations are given below, [58][59][60][61][62][63][64][65][66][67][68][69]. However the application area for fuzzy control is really wide, the basic form for all command types of controllers consists of;…”

“…The intelligent tracking control using the fuzzy-logic technique provides a cost-and-time efficient control implementation due to the automated dynamic-less input. This in turn would further inspire multi-uncertainties testing for continuum robot [58][59][60][61][62][63][64][65][66][67][68][69]. In project we can used fuzzy logic theory when a plant can be considered as a black box with outputs available for measurement and a possibility of changing inputs.…”