AbstractsEngineering

Linear quadratic optimal control For trajectory tracking Applications of under actuated System;

by Vinodh kumar E




Institution: Anna University
Department: Linear quadratic optimal control For trajectory tracking Applications of under actuated System
Year: 2015
Keywords: Adaptive inertia weight factor; Adaptive Particle Swarm Optimization; Linear quadratic regulator
Record ID: 1207955
Full text PDF: http://shodhganga.inflibnet.ac.in/handle/10603/33165


Abstract

The objective of the thesis is to investigate the performance of newlineconstrained linear quadratic optimal control for stabilization and tracking newlineapplications of under actuated system Under actuated systems are mechanical newlinecontrol systems which have fewer independent actuators than the degrees of newlinefreedom to be controlled Due to their broad applications in robotics newlinelocomotive systems and aircraft under actuated systems constitute a good newlineframework of nonlinear control problems of both theoretical and practical newlineinterests newlineIn this thesis we evaluate the performance of the constrained linear newlinequadratic optimal controller on three bench mark under actuated systems newlinenamely inverted pendulum magnetic levitation and torsional system One of newlinethe major challenges in the design of linear quadratic regulator LQR for newlineunder actuated systems is the choice of weighting matrices which will result newlinein optimal response The weighting matrices Q and R regulate the penalties on newlinethe excursion in the trajectories of the state variables and control input newlinerespectively With random choice of Q and R matrices the optimal regulators newlinedo not provide good set point tracking response because of the absence of newlineintegral term The weighting matrices in the cost function are normally chosen newlinebased on trial and error approach to determine the state feedback gain but this newlineapproach is not only tedious but also time consuming Moreover manual newlineselection of weighting matrices may not result in optimal response Hence to newlineaddress the weight selection problem of LQR we propose an Adaptive newlineParticle Swarm Optimization APSO algorithm for selecting the Q and R newlinematrices of LQR An adaptive PSO to improve the convergence rate of newlineconventional PSO is employed for tuning the gains of LQR One of the newlinenotable changes of the proposed APSO is that the weights in the velocity newlineupdate equation of conventional PSO are adjusted adaptively in accordance newlinewith the success rate of the particles towards the best value An adaptive newlineinertia weight factor AIWF is adjusted adapt%%%reference p163-171.