Laguerre function-based quasi-infinite horizon nonlinear model predictive control

This paper proposes Laguerre function-based quasi-infinite horizon nonlinear model predictive control (QIH-NMPC) in order to solve the trio control problems of strong nonlinearities, very noisy control signals and highly compute-intensive control laws. In the proposed method, a nonlinear dynamic model is used for process state prediction, while Laguerre function is used for modeling future control signal sequence. The proposed method therefore combines the closed-loop stability guaranteed attribute of QIH-NMPC with the noise-filtering ability of Laguerre function. An unstable nonlinear two-state, single input system and a non-minimum phase four tank system are used to illustrate the effectiveness of the proposed method. The simulation results obtained show that the proposed Laguerre function-based QIH-NMPC compared favorably with Kautz function-based QIH-NMPC. Both the Laguerre and Kautz function-based QIH-NMPC result in smoother and more cautious control signals in the face of very noisy process measurements compared with the traditional QIH-NMPC. The proposed method also requires fewer parameters to achieve the same performance as the traditional QIH-NMPC and is less computationally intensive.