Title :
Stochastic neural direct adaptive control
Author :
Ho, Tuan T. ; Ho, Hal T. ; Bialasiewicz, Jan T. ; Wall, Edward T.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Colorado Univ., Denver, CO, USA
Abstract :
A stochastic neural direct adaptive control algorithm for partially known state-space nonlinear time-varying plants is presented. A neural network is used to generate the control signal, which optimizes a quadratic (one-step-ahead prediction) performance index. In comparison to conventional stochastic state-space adaptive control, this neural control algorithm offers higher computation speed due to the parallel processing structure of the neural network. The algorithm is limited to known system matrices B(k) and C(k). For applications where B(k) and C(k) are unknown to the controller, an indirect neural adaptive control scheme may be used
Keywords :
adaptive control; neural nets; nonlinear control systems; optimal control; performance index; state-space methods; stochastic systems; one-step-ahead prediction performance index; partially known state-space nonlinear time-varying plants; quadratic performance index; stochastic neural direct adaptive control; Adaptive control; Equations; Joining processes; Neural networks; Nonlinear control systems; Predictive models; Size control; State-space methods; Stochastic processes; Stochastic systems;
Conference_Titel :
Intelligent Control, 1991., Proceedings of the 1991 IEEE International Symposium on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7803-0106-4
DOI :
10.1109/ISIC.1991.187353
