Nonlinear Model Predictive Control using sampling and goal-directed optimization

In this paper a novel method called Sampling-Based Model Predictive Control (SBMPC) is proposed as an efficient MPC algorithm to generate control inputs and system trajectories. The algorithm combines the benefits of sampling-based motion planning with MPC while avoiding some of the major pitfalls facing both traditional sampling-based planning algorithms and traditional MPC. The method is based on sampling (i.e., discretizing) the input space at each sample period and implementing a goal-directed optimization method (e.g., A*) in place of standard numerical optimization. This formulation of MPC readily applies to systems with nonlinear dynamics and avoids the local minima which can limit the performance of MPC algorithms implemented using traditional, derivative-based, nonlinear programming. The SBMPC algorithm is compared with a more standard online MPC algorithm using cluttered environment navigation for an Ackerman steered vehicle and a set point problem for a nonlinear, continuous stirred-tank reactor (CSTR).