Title :
Learning from neural control of general Brunovsky systems
Author :
Liu, Tengfei ; Wang, Cong
Author_Institution :
Coll. of Autom. Sci. & Eng., South China Univ. of Technol., Guangzhou
Abstract :
In this paper, we investigate deterministic learning from adaptive neural control of general Brunovsky systems, in which the affine terms are unknown functions of system states. We firstly present an extension of a recent result on stability analysis of linear time varying (LTV) systems. We then analyze the difficulties caused by the unknown affine term in deterministic learning for general Brunovsky systems. By taking a state transformation, the closed-loop control system is transformed into a LTV form for which exponential stability can be guaranteed when the PE condition is satisfied. Consequently, locally-accurate approximation of the closed-loop control system dynamics can be achieved along a periodic orbit of closed-loop signals. Simulation studies are included to demonstrate the effectiveness of the approach
Keywords :
adaptive control; asymptotic stability; closed loop systems; learning (artificial intelligence); linear systems; neurocontrollers; nonlinear control systems; time-varying systems; adaptive neural control; closed-loop control system; deterministic learning; exponential stability; general Brunovsky systems; linear time varying systems; stability analysis; state transformation; Adaptive control; Control systems; Intelligent control; Learning systems; Nonlinear control systems; Nonlinear systems; Programmable control; Radial basis function networks; Stability analysis; Time varying systems;
Conference_Titel :
Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control, 2006 IEEE
Conference_Location :
Munich
Print_ISBN :
0-7803-9797-5
Electronic_ISBN :
0-7803-9797-5
DOI :
10.1109/CACSD-CCA-ISIC.2006.4777010
