This chapter presents an algorithm for robust optimal control of regular languages under specified uncertainty bounds for the event costs of a language measure that has been recently reported in literature and is presented in Chapter 1. The performance index for the proposed robust optimal policy is obtained by combining the measure of the supervised plant language with uncertainty. The performance of a controller is represented by the language measure of the supervised plant and is minimized over the given range of event cost uncertainties. Synthesis of the robust optimal control policy requires at most n iterations, where n is the number of states of the deterministic finite state automaton (DFSA) model generated from the regular language of the unsupervised plant behavior. The computational complexity of control synthesis is polynomial in n.
All Science Journal Classification (ASJC) codes
- Computer Science(all)