Model-based process control via finite Markov chains
Predictive and optimal process control using finite Markov chains is considered. A basic procedure is outlined, consisting of space discretization; model conversion; specification of costs; computation of control policy; and,
analysis of the closed-loop system behavior. A simulation illustrates the fiability of the approach using a standard office PC. Discussion of nonlinear process control emphasizing in on-line learning from uncertain data ends the paper.