Neurocontroller design via supervised and unsupervised learning

In this paper we study the role of supervised and unsupervised neural learning schemes in the adaptive control of nonlinear dynamic systems. We suggest and demonstrate that the teacher's knowledge in the supervised learning mode includes a-priori plant sturctural knowledge which may be employed in the design of exploratory schedules during learning that results in an unsupervised learning scheme. We further demonstrate that neurocontrollers may realize both linear and nonlinear control laws that are given explicitly in an automated teacher or implicitly through a human operator and that their robustness may be superior to that of a model based controller. Examples of both learning schemes are provided in the adaptive control of robot manipulators and a cart-pole system.