Computational considerations

Abstract

Motivated by issues arising in adaptive dynamic programming for optimal control, a function approximation method is developed that aims to approximate a function in a small neighborhood of a state that travels within a compact set. The development is based on the theory of universal reproducing kernel Hilbert spaces over the n-dimensional Euclidean space. Several theorems are introduced that support the development of this State Following (StaF) method. In particular, it is shown that there is a bound on the number of kernel functions required for the maintenance of an accurate function approximation as a state moves through a compact set. Additionally, a weight update law, based on gradient descent, is introduced where good accuracy can be achieved provided the weight update law is iterated at a high enough frequency, as detailed in Theorem 7.5. Simulation results are presented that demonstrate the utility of the StaF methodology for the maintenance of accurate function approximation as well as solving an infinite horizon optimal regulation problem. The results of the simulation indicate that fewer basis functions are required to guarantee stability and approximate optimality than are required when a global approximation approach is used.