Real-Time NMPC With Convex–Concave Constraints and Application to Eco-Driving

Abstract

In this brief, we propose a novel real-time numerical algorithm for solving nonlinear model predictive control (NMPC) with convex-concave constraints, which arise in various practical applications. Instead of requiring full convergence for each problem at every sampling time, the proposed algorithm, called real-time iteration sequential convex programming (RTI-SCP), solves only one convex subproblem but iterates as the problem evolves. Compared with previous methods, the RTI-SCP adopts a more refined approach by linearizing only the concave components of the constraints. It retains and efficiently utilizes all the underlying convex structures, thereby transforming subproblems into structured forms that can be solved using the existing tools. In addition, to the best of our knowledge, the widely investigated eco-driving control strategy for autonomous vehicles is now formulated for the first time into a convex-concave programming problem with strong theoretical properties. Eventually, the experimental results demonstrate that the proposed strategy can improve computational efficiency and overall control performance, and it is suitable for real-time implementation.