Title :
Neural-based adaptive control design for general nonlinear systems and its application to process control
Author :
Ge, S.S. ; Hang, C.C. ; Zhang, T.
Author_Institution :
Dept. of Electr. Eng., Nat. Univ. of Singapore, Singapore
Abstract :
In this work, a neural-based adaptive controller is presented to solve the tracking control problem for a general class of unknown nonlinear systems. The proposed controller ensures that the output tracking error converges to a small neighborhood of the origin. The weight updating law of neural networks (NNs) is derived using Lyapunov theory and the stability of the closed-loop system is guaranteed. The proposed control scheme has been successfully applied to the composition control in a continuously stirred tank reactor (CSTR) in chemical processes
Keywords :
Lyapunov methods; adaptive control; closed loop systems; control system synthesis; neurocontrollers; nonlinear control systems; process control; stability; uncertain systems; CSTR; Lyapunov theory; chemical processes; closed-loop system stability; composition control; continuously stirred tank reactor; general nonlinear systems; neural-based adaptive control design; output tracking error convergence; process control; tracking control problem; weight updating law; Adaptive control; Continuous-stirred tank reactor; Control systems; Error correction; Inductors; Neural networks; Nonlinear control systems; Nonlinear systems; Programmable control; Stability;
Conference_Titel :
American Control Conference, 1998. Proceedings of the 1998
Conference_Location :
Philadelphia, PA
Print_ISBN :
0-7803-4530-4
DOI :
10.1109/ACC.1998.694631
