Local Model-Based Predictive Control for Spatially-Distributed Systems Based on Linear Programming

A local model-based predictive control strategy based on linear programming is proposed for partial differential equation descriptions unknown spatially distributed systems (SDSs). First, the interval type-2 T-S fuzzy based local modeling approach is developed to estimate the dynamics of the SDS based on the input-output data. On the basis of the local IT2 T-S fuzzy model, the local model-based predictive controller is designed to obtain local controlled outputs through minimizing the local optimization objective. Finally, the global controlled outputs are obtained by a linear programming method, where the deviations of the spatial temporal outputs from their spatial set points over the prediction horizon are considered as the optimal objective. The accuracy and efficiency of the proposed methodologies are tested in the simulation case.