Title :
Adaptive control of partially known nonlinear multivariable systems using neural networks
Author :
Ge, S.S. ; Wang, C. ; Tan, Y.H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
Abstract :
An adaptive neural control scheme is proposed for a class of partially known interconnected MIMO nonlinear systems in block-triangular form, with both unknown nonlinearities and parametric uncertainties. The MIMO systems is composed of interconnected subsystems. The system state interconnections make it difficult to conclude the stability of the whole system by stability analysis of individual subsystem separately. By exploiting the block-triangular structure properties, we first design for each subsystem a full state feedback controller, and then conclude the stability of all the state variables in a nested iterative manner. Semi-global uniform ultimate boundedness of all the signals in the closed-loop of MIMO nonlinear systems is guaranteed. The outputs of the systems are proven to converge to small neighborhoods of the desired trajectories. The control performance of the closed-loop system is guaranteed by suitably choosing the design parameters
Keywords :
MIMO systems; adaptive control; closed loop systems; control system synthesis; multivariable control systems; neurocontrollers; nonlinear control systems; state feedback; uncertain systems; adaptive neural control; block-triangular form; interconnected MIMO nonlinear systems; neural networks; parametric uncertainties; partially known nonlinear multivariable systems; stability analysis; state interconnections; unknown nonlinearities; Adaptive control; Adaptive systems; Control nonlinearities; Control systems; MIMO; Nonlinear control systems; Nonlinear systems; Programmable control; Stability analysis; Uncertainty;
Conference_Titel :
Intelligent Control, 2001. (ISIC '01). Proceedings of the 2001 IEEE International Symposium on
Conference_Location :
Mexico City
Print_ISBN :
0-7803-6722-7
DOI :
10.1109/ISIC.2001.971524
