Title :
Generalized stochastic state space neural adaptive control
Author_Institution :
Adv. Syst. Res., Aurora, CO, USA
Abstract :
A generalized stochastic neural adaptive control algorithm is presented, where the system identification is based on the state space innovations model and a neural network architecture. This identification algorithm is derived from three different optimization approaches, i.e., the gradient, Newton, and minimum variance. The control law, also based on neural networks structure, is derived from a quadratic (one-step-ahead prediction) performance index, which in combination with neural identification constitutes a unique neural adaptive control algorithm with excellent performance
Keywords :
adaptive control; identification; neurocontrollers; optimisation; performance index; state-space methods; stochastic systems; Newton method; generalized stochastic neural adaptive control algorithm; gradient method; minimum variance; neural identification; neural network architecture; optimization; quadratic performance index; state space innovations model; system identification; Adaptive control; Neural networks; Performance analysis; Predictive models; State estimation; State-space methods; Stochastic processes; Stochastic systems; System identification; Technological innovation;
Conference_Titel :
Intelligent Control, 1993., Proceedings of the 1993 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
0-7803-1206-6
DOI :
10.1109/ISIC.1993.397644
