Optimization problems in multivariable fuzzy predictive control Original Research Article

The application of model predictive control (MPC) to complex, nonlinear processes results in a non-convex optimization problem for computing the optimal control actions. This optimization problem can be solved by discrete search techniques such as the branch-and-bound method (B&B), which has been successfully applied to MPC. However, the discretization induced by B&B introduces a tradeoff between the number of discrete actions and the performance. This paper proposes a solution for non-convex optimization problems in multiple-input multiple-output (MIMO) systems. Fuzzy predictive filters, which are represented as an adaptive set of control actions multiplied by gain factors, are extended for MIMO systems. This solution keeps the number of necessary alternatives low and increases the performance. The proposed MPC method using fuzzy predictive filters is applied to the control of a gantry crane. Simulation results show the advantages of the proposed method.