Improving fault tolerance in model predictive control through enlargement of the recursively feasible set

Feasibility of the optimization problem in a predictive controller may be compromised in the event of a fault. One alternative to recover feasibility is to relax the constraints. The terminal constraints seem like suitable candidates for relaxation, as they are often artificially introduced to ensure recursive feasibility. As an advantage, the physical constraints over the states and controls can be preserved. To solve this infeasibility issue, this work proposes the removal of terminal constraints, followed by the enlargement of the recursively feasible set through retuning the cost function. Simulation results are presented to illustrate the potential benefits of the proposed technique.