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Complexity Reduction of Region-Free Explicit Model Predictive Control via Separator Function

The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite many recent achievements on complexity reduction in terms of the computation time and resource usage, merging these results has been much less considered until. In this paper, by a separator function the saturated regions is eliminated and then the remained regions and associated control inputs are recovered by the method of free-region explicit MPC. The methods of removing the regions corresponding to the saturated optimal control inputs and free-region explicit MPC is unified that results in significantly decrement of requirement memory and CPU runtime in embedded devices. Analytical expressions show merits of the proposed approach.

The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite many recent achievements on complexity reduction in terms of the computation time and resource usage, merging these results has been much less considered until. In this paper, by a separator function the saturated regions is eliminated and then the remained regions and associated control inputs are recovered by the method of free-region explicit MPC. The methods of removing the regions corresponding to the saturated optimal control inputs and free-region explicit MPC is unified that results in significantly decrement of requirement memory and CPU runtime in embedded devices. Analytical expressions show merits of the proposed approach.