Set Membership identification for NMPC complexity reduction

A Set Membership (SM) approach is proposed to reduce the computational burden of Nonlinear Model Predictive Control (NMPC) algorithms. In particular, a SM identification method is applied to derive an approximation and tight bounds of the NMPC control law, using a set of its values computed offline. These quantities are used online to reduce the dimension and the volume of the search domain of the NMPC optimization algorithm, and to perform a warm start, allowing a significant shortening of the computational time. The developed NMPC methodology is tested in simulation, considering an obstacle avoidance application in a realistic autonomous vehicle scenario. The obtained results demonstrate the effectiveness of the proposed approach in terms of computation time, without affecting the solution quality.