Title :
Robust adaptive quadratic tracking control of continuous-time linear systems with unknown dynamics
Author :
Yue Fu ; Tianyou Chai ; Jialu Fan
Author_Institution :
State Key Lab. of Synthetical Autom. for Process Ind., Northeastern Univ., Shenyang, China
Abstract :
In this paper, an online robust adaptive quadratic tracking control algorithm is proposed for continuous-time linear systems with unknown dynamics and nonlinear dynamic uncertainties. The robust stability and suboptimality of the closed-loop system composed of the considered system and the quadratic tracking policy is proved. For finding the solution of the linear quadratic tracking (LQT) problem without knowing any know- ledge of the system matrices and uncertainties, a new policy iteration approach is presented. The presented method utilizes the approximate dynamic programming technique to iteratively solve the augmented algebraic Riccati equation on the LQT problem by using the collected information of state, reference trajectory, and input. An online algorithm is summarized.
Keywords :
Riccati equations; adaptive control; approximation theory; closed loop systems; continuous time systems; dynamic programming; iterative methods; linear quadratic control; linear systems; nonlinear dynamical systems; robust control; stability; tracking; uncertain systems; LQT problem; approximate dynamic programming technique; augmented algebraic Riccati equation; closed-loop system suboptimality; continuous-time linear systems; linear quadratic tracking problem; nonlinear dynamic uncertainties; online robust adaptive quadratic tracking control algorithm; policy iteration approach; quadratic tracking policy; reference trajectory; robust stability; unknown dynamics; Adaptive systems; Heuristic algorithms; Linear systems; Nonlinear dynamical systems; Robustness; Symmetric matrices; Uncertainty;
Conference_Titel :
American Control Conference (ACC), 2015
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4799-8685-9
DOI :
10.1109/ACC.2015.7171064
