Title :
Adaptive control of unknown plants using dynamical neural networks
Author :
Rovithakis, George A. ; Christodoulou, Manolis A.
Author_Institution :
Dept. of Electron. & Comput. Eng., Tech. Univ. of Crete, Chania, Greece
fDate :
3/1/1994 12:00:00 AM
Abstract :
In this paper, we are dealing with the problem of controlling an unknown nonlinear dynamical system. The algorithm is divided into two phases. First a dynamical neural network identifier is employed to perform “black box” identification and then a dynamic state feedback is developed to appropriately control the unknown system. We apply the algorithm to control the speed of a nonlinearized DC motor, giving in this way an application insight. In the algorithm, not all the plant states are assumed to be available for measurement
Keywords :
adaptive control; feedback; neural nets; nonlinear control systems; state-space methods; adaptive control; black box identification; dynamic state feedback; dynamical neural networks; nonlinearized DC motor; unknown nonlinear dynamical system; Adaptive control; Backpropagation; Control systems; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Robust stability; Senior members; Student members;
Journal_Title :
Systems, Man and Cybernetics, IEEE Transactions on
