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Robust economic model predictive control of nonlinear networked control systems with communication delays
Author(s) -
Mao Yawen,
Liu Su,
Liu Jinfeng
Publication year - 2020
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.3103
Subject(s) - control theory (sociology) , robustness (evolution) , model predictive control , controller (irrigation) , nonlinear system , actuator , lyapunov function , computer science , compensation (psychology) , control engineering , networked control system , estimator , engineering , control (management) , mathematics , artificial intelligence , psychology , biochemistry , chemistry , physics , statistics , quantum mechanics , biology , psychoanalysis , agronomy , gene
Summary In this work, we consider economic model predictive control of nonlinear networked control systems subject to external disturbances and communication delays in both sensor‐to‐controller and controller‐to‐actuator channels. The problem is addressed in the framework of the min‐max model predictive control. First, a delay compensation strategy is proposed to minimize the impact of communication delays on the control performance. In the compensation strategy, once the receiver at the controller node receives a new state measurement, the controller generates a control sequence and sends the sequence to the actuator to compensate for delayed control inputs. Subsequently, the presence of disturbance is explicitly considered for robustness and the semi‐feedback min‐max optimization algorithm is used to design the control law based on the estimate of the current state reconstructed by the estimator. Furthermore, the input‐to‐state practical stability of the proposed approach is established by constructing a modified Lyapunov function. Simulation results of a numerical example and a chemical process example demonstrate the applicability and effectiveness of our approach.