Sampling-Based Explicit Nonlinear Model Predictive Control For Output Tracking

In many applications, it is necessary to design controllers that enable the system output to track a time-varying reference signal. In this paper, a low-complexity sampling-based output tracking explicit nonlinear model predictive controller (ENMPC) is proposed for a class of bounded, time-varying reference signals, where only the bounds on the family of admissible reference signals are known to the designer a priori. The basic idea is to sample the state and reference space using deterministic sampling and construct the ENMPC by using linear regression. Feasibility and stability guarantees are provided and the effectiveness of the proposed approach is demonstrated via a numerical example.