Fuzzy filters applied to predictive control of MIMO processes

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 addressed by discrete search techniques such as the branch-and-bound method, which has been successfully applied to MPC. The discretization, however, introduces a tradeoff between the number of discrete actions (computation time) and the performance. This paper proposes a solution to these problems for multivariable processes by using fuzzy predictive filters, which are represented as an adaptive set of control actions multiplied by gain factors. This keeps the number of necessary alternatives low and increases the performance. Herewith, the problems introduced by the discretization of the control actions are diminished. 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.