Endec-decoder-based N-step model predictive control: Detectability, stability and optimization - Song, Yan; Wang, Zidong; Zou, Lei; Liu, Shuai (Automatica (Oxford), 2022)
In this paper, the N-step model predictive control problem is investigated for a class of networked control systems with limited communication capacity. By resorting to the dynamic uniform quantization method, a novel observer-based endec-decoder is put forward in order to accommodate the digital transmission requirement. In this sense, the state reconstructed by the observer is coded into certain codewords and then transmitted to the controller via a bandwidth-limited network. The aim of the problem addressed is to co-design the observer-based endec-decoder and the N-step model predictive controller such that the underlying system is detectable and asymptotically stable. By solving certain offline “min-max” optimization problems with matrix inequality constraints, a series of one step sets and the terminal constraint set are derived as well as the desired offline controller parameters. Then, in order to improve the convergence speed of the closed-loop system, a recursive algorithm is developed to design the online controller based on the results obtained from the offline optimization problems. Finally, a numerical example is given to demonstrate the validity of the proposed control scheme.