Model-Based Multivariable Control of a Secondary Air System Using Controlled Finite Markov Chains

Controlled finite Markov chains are used for solving a multivariable control problem. The controlled system was the secondary air flow system in a fluidized-bed combustion power plant. A four-input four-output system control problem was formulated as a CFMC, and solved using dynamic programming. A non-linear system model, developed in earlier studies, was used in this model-based design. Three filters were applied to smoothen the (finite) control actions, for feasible plant control. These were based on averaging over finite state-space, references, and averaging over time. Simulations were conducted to illustrate the approach, and compare it with respect to SISO PID design. The simulations indicated that system control can be succesfully designed based on CFMC techniques, even for a 4×4 MIMO system.