Non-minimal state space model predictive control using Laguerre functions for reference tracking

This paper presents a new design of model predictive control which combines non-minimal state space (NMSS) and Laguerre functions. The utilization of NMSS omits the observer design process and brings convenience to multi-input, multi-output systems with time delay. But the dimensions of NMSS are high, which is harmful to online computation. The use of Laguerre functions helps reduce the data required in the optimization algorithm and makes up for the deficiency of NMSS. A modified solution for the optimal tracking problem using NMSS and Laguerre functions is proposed. Although the choice of the parameters in Laguerre functions needs experiments and experience, the method presented in this paper brings convenience to MPC design. Simulation results show that this approach not only reduces the computing effort without sacrificing the control performance significantly, but also has a good robustness to interference when tracking.