A Synthesis Approach to Output Feedback MPC for LPV Model With Bounded Disturbance

Jianchen Hu,Baocang Ding,Jun Wang,Xiaoming Tang

doi:10.1109/access.2020.3042654

Abstract

The model with polytopic parametric uncertainty and bounded disturbance is controlled by the approach named dynamic OFRMPC (Output Feedback Robust Model Predictive Control). A key knob for control performance and region of attraction for this approach is the selection of Lyapunov matrix. A Lyapunov matrix, which does not have structural restriction, is proposed. In the ICCA (Iterative Cone Complementary Approach), which is invoked in optimizing the control law parameters, the starting up steps are designed as a variant CCA (Cone Complementary Approach). ICCA designs an outer loop, over CCA, for searching the minimum cost bound, while the variant CCA omits the outer loop by adding the cost bound in CCA objective function. This starting up can reduce the computational burden. The suboptimal dynamic OFRMPC (where CCA is avoided) is discussed, and a previous approach is re-formulated. A numerical example is given to show the advantages of the proposed approach.

Highlights

I N the industrial circle, MPC (Model Predictive Control) has been proven as the most successful among advanced control techniques
As usual, LPV model refers to the model with polytopic parametric uncertainty, which takes a linear form but can represent a wide variety of nonlinear and/or uncertain systems
The work in [22] has provided an LMI (Linear Matrix Inequality) based minmax MPC approach to handle system with model parametric uncertainty, which is extended in several works with improvements on computational efficiency or control performance

Summary

INTRODUCTION

I N the industrial circle, MPC (Model Predictive Control) has been proven as the most successful among advanced control techniques (see e.g., [1]–[6]). In order to remove these limitations and improve the control performance, in the present paper we develop a dynamic OFRMPC approach without restrictions on the structure of Lyapunov matrix, for LPV model with bounded disturbance. The work [36] uses CCA (Cone Complementarity Approach) to handle the mutual inverse positive-definite matrices in the optimization problem, and invoke an outer loop over CCA in order to decrease the cost bound. We call this CCA with outer loop as ICCA. The timedependence of the MPC decision variables is often omitted for brevity

PROBLEM STATEMENT

NEAR-OPTIMAL DYNAMIC OFRMPC

SUBOPTIMAL DYNAMIC OFRMPC

THE FUEL CELL EXAMPLE

CONCLUSION