Fuzzy rule-based optimization in nonlinear predictive control

The application of predictive control to nonlinear systems results in a non-convex optimization problem for computing the optimal control actions. The optimization problem can be addressed by discrete search techniques such as the branch-and-bound method, which has been successfully applied to nonlinear predictive control. Such a discrete approach introduces a tradeoff between computation time and performance. Previously, a solution was proposed that uses adaptive decision alternatives as control actions. This paper proposes the use of fuzzy rules to adapt the decision alternatives (possible control actions), resulting in easier tuning and a smoother behavior of the controller. Control of a HVAC system is considered, and the results are compared with those obtained with similar control schemes.