STATE ESTIMATION AND PREDICTIVE CONTROL USING FINITE MARKOV CHAINS

State estimation and model predictive control using finite Markov chains are considered. A Bayesian state estimate of the probability distribution of the systems current state is constructed, based on measured data and prior estimate. A control action is then determined under the predictive control paradigm, starting from the uncertain state estimate. A simulation illustrates the feasibility of the approach using a standard office PC.