Output Feedback Model Predictive Control for NCSs with Input Quantization

Hongchun Qu, Yu Li, Wei Liu and Lingzhong Guo

Complexity, 2022, vol. 2022, 1-20

Abstract: This paper addresses the robust output feedback model predictive control (MPC) schemes for networked control systems (NCSs) with input quantization. The logarithmic quantizer is considered in this paper, and the sector bound approach is applied, which appropriately treats the quantization error as a sector-bounded uncertainty. The presented method involves an offline designed state observer using linear matrix inequality (LMI) and online robust output feedback MPC algorithms which optimize one free control move followed by the output feedback using the estimated state. Moreover, due to the uncertainty of estimation error, a technique of refreshing the bound of estimation error which involves the quantization error is provided so as to guarantee the recursive feasibility of the optimization problem. The proposed MPC schemes inherit the characteristics of the synthesis approach of MPC, guaranteeing the recursive feasibility of the optimization problem and the stability of a closed-loop system, and explicitly account for quantization error. Two simulation examples are given to illustrate the effectiveness of the proposed methods.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6929902

DOI: 10.1155/2022/6929902

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