Compensatory Data-Driven Networked Iterative Learning Control With Communication Constraints and DoS Attacks

Abstract

Considering the three critical factors of data quantization, channel fading, and denial of service (DoS) attack introduced by the networked control systems (NCSs) simultaneously, we propose a novel compensatory data-driven networked iterative learning control (COMP-DDNILC) method for nonlinear repetitive NCSs under a model-free design and analysis framework. By reformulating the iterative input-and-output (I/O) dynamics of the nonlinear NCS as an iterative linear data model (iLDM), an iterative linear predictive data model (iLPDM) is developed to predict the missing data arisen from DoS attacks. Then, a relationship is built to describe the coupling effects of the three critical factors, based on which the COMP-DDNILC is designed by involving the compensatory mechanism of DoS attacks and the fading coefficient inversion to improve the control performance. The COMP-DDNILC also involves an iterative adaption mechanism to update the iLPDM to enhance the robustness against uncertainties. The data-driven nature of COMP-DDNILC makes it applicable to practical NCSs without model information available. The simulation study verifies the results. Note to Practitioners—Networked control systems (NCSs) have becoming an important topic in practical processes owing to the shareable communication among the sensors, actuators, and controllers. However, the communication constraint and the cyber attack are inevitably induced to NCSs where the former is mainly involved with finite channel capacity and channel fading. Among different cyber attacks, denial of service (DoS) attack is most common and reachable. Further, it is worth noting that data quantization is an effective method to deal with channel capacity, but may degrade control performance by compressing I/O data. Therefore, we are motivated to propose a compensatory data-driven networked iterative learning control method by considering the three factors of data quantization, channel fading and DoS attack simultaneously to compensate the negative effect of the communication constraints and cyber attacks. An iterative linear data model is developed to formulate the iteration-based I/O dynamics of nonlinear NCS, and is also used to predict the missing data. The proposed method is data-driven and includes an iterative adaptation mechanism to enhance the robustness, therefore becoming much applicable to realistic nonlinear NCSs.