Distributed model predictive control based on adaptive sampling mechanism

Abstract

Fig. 1 DMPC structure based on adaptive sampling mechanism. In the DMPC based on the adaptive sampling mechanism proposed in this paper, an anomaly detector and an adaptive regulator are added to the traditional feedback channel. The function of the anomaly detector is to detect the state of the output of the system. If a sampling indicates that the process has obvious fluctuations (not necessarily indicating an abnormality), then the corresponding subsystem is sent data on changes in the system’s dynamic behavior after the detection by the anomaly detector. Next, the adaptive regulator calculates the abnormal sampling state corresponding to the optimal sampling interval based on the data and modifies the sampling interval parameter of the model predictive controller’s next rolling optimization. Then, as long as the process does not display an exception, the process will sample at the sampling interval initially set. In this work, an adaptive sampling control strategy for distributed predictive control is proposed. According to the proposed method, the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function. Then, the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller, and the sampling interval of the controller is changed accordingly before the next sampling period begins. In the next control period, the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system, and this process is repeated. Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object. It can also accurately capture dynamic changes, meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment, significantly improving the performance of distributed model predictive control (DMPC). A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.