Predictive control compensation for networked control system with time-delay

Abstract

This study proposes a novel predictive control compensation method to deal with random time-delay in networked control system. Different from other compensation strategies, this study adopts different compensation strategies for input time-delay and output time-delay to improve the control effect. First, for the input channel time-delay, the corresponding buffer is set by the time-stamp in the packet. Combining the historical output value and the control variable, the fast implicit generalized predictive control algorithm is adopted to design the predictive controller to compensate for the input channel time-delay. Second, for the output channel time-delay, the controller cannot measure it. A control compensator is designed by adding a feedback loop. The output control variable of the predictive controller is adjusted by predicting the error between the actual control signal and the controller output at the historical sampling time, so as to compensate for the time-delay of the output channel. In addition, the stability of the proposed time-delay compensation method is analyzed. Finally, simulation and experimental results show that the proposed method has good control and compensation effect, and improves the output performance of the system. The networked control system with random time-delay is stable. Furthermore, the simulation results also show that this time-delay compensation method needs less computing time and is more suitable for the practical applications.

Scheduling strategy for networked control system based on variable sampling period and deadband feedback

“…Due to the introduction of network, it can lead to network induced delay, packet confusion, packet loss, and other phenomena. These problems will degrade the overall performance of NCS 3,4 . In recent years, how to reduce data transmission delay, packet loss, and packet timing chaos through reasonable scheduling strategy is an important research content of the NCS.…”

Adaptive fuzzy Lyapunov-based model predictive control for parallel platform driving simulators

“…Model predictive control (MPC) is an advanced control method based on time-domain optimization techniques in presence of constraints (Han et al, 2018; Inel et al, 2021; Li et al, 2021; Tian and Wang, 2022). Recently, along with the development of computer technology and the availability of models of many engineering subjects, the construction and implementation of MPCs in hardware systems is not a challenging problem anymore.…”

“…Moreover, owing to the capability of explicitly handling hard constraints existing in the system, which are normally neglected in the traditional control methods mentioned above, the MPC method is considered as a potent tool to solve control engineering problems (Santos et al, 2019; Song and Huh, 2021; Wen et al, 2018). Tian and Wang (2022) introduced a novel predictive control compensation method to effectively handle the random time-delay in a networked control system using different compensation strategies for input time-delay and output time-delay. The fast implicit generalized predictive control algorithm is employed, which combines the historical output and control variable value to compensate for the time-delay in the input channel.…”

Overcoming non‐convexity of the robust nonlinear output feedback problem: An adaptive linear matrix inequality solution

“…Scope: Some other works exist which do not employ LMIs nor adaptive techniques for output feedback: via sliding modes, 30 optimal control, 31 growth conditions, 32,33 low-gain approach and dynamic compensator, 34 using high-gain observers, [35][36][37] predictive control compensation, 38,39 among others; they are out of the scope of this work which is concerned with LMI solutions for adaptive output feedback.…”