This paper studies the iterative learning control with prescribed performance (namely, prescribed iterative learning control, P-ILC) for switched systems, aiming to plan the dynamic and steady-state characteristics of the convergence process of iterative learning control. First, a performance function that converges exponentially with iteration is proposed to progressively constrain the tracking error. Then, a nonlinear system construction method is proposed that is applicable to any initial iteration error within the performance boundary. And the boundedness of the nonlinear system is equivalent to the error constraint of the prescribed performance. Furthermore, an iterative asymptotically stable controller is designed, which can ensure that the nonlinear system is always asymptotically stable under any sign of initial iteration error within the performance boundary. The designed iterative controller can achieve ondemand planning of tracking error accuracy and convergence speed in the iterative domain. Finally, the effectiveness of the proposed method is verified through simulations.

Disturbances and uncertainties affect the performance of control systems and must be addressed. In addition, due to the potential privacy leakage caused by the informatization and networking of control systems, ensuring system privacy has become an important technical challenge. The topic of this paper is active disturbance rejection control (ADRC) with differential privacy preserving for switched multiple systems. By adding privacy noise with Laplace and uniform distribution to the system state, the novel privacy-preserving ADRC (PP-ADRC) framework ensures the required ADRC performance and privacy of system information in a non-ideal network environment. The innovation of this paper is embodied in three aspects: (i) The designed performance-dependent differential privacy preserving mechanism (PD-DPPM) under Laplace noise can adaptively balance system privacy and control performance. (ii) The presented privacy budget lower bound of uniform noise can determine the size of the uniform noise, making the PD-DPPM method more general in the field of privacy preservation. (iii) The newly constructed dual design of switching law and controller for PP-ADRC can ensure boundedness andH∞performance. Lastly, illustrative examples are provided to explain effectiveness.

Differential privacy is an effective method to solve data privacy leakage. The common differential privacy method is achieved by adding privacy noises to the transmitted data, which may affect data accuracy. For the control system, data accuracy greatly affects the system performance. To circumvent this difficulty, we propose a novel privacy-preserved rolling optimization strategy (PP-ROS) for switched systems. The main contributions are reflected in three aspects: 1) The proposed PP-ROS is used to calculate the private control input by adding Laplace noise to the prediction and control horizons, instead of the transmitted data. 2) Privacy definitions of the prediction and control horizons are presented, and a private model predictive control (P-MPC) controller design is provided based on the PP-ROS. The P-MPC controller achieves the privacy of its parameters. 3) Under PP-ROS and P-MPC, the proof and calculation methods for the privacy levels of control input and system output are given. The results indicate that when noise is added to the horizons, both control input and system output are private. Finally, the availability and benefits of PP-ROS and P-MPC are demonstrated using two simulation examples and comparison results.

Differential privacy preserving ensures the privacy of the system data by adding certain regular noises to the data to cover up the real information. The main challenges of strong nonlinearities, uncertainty, and data privacy are considered together for switched systems, and a novel privacy-preserving robust model-free adaptive predictive control (PPR-MFAPC) method is proposed that guarantees both$H_{\infty }$performance and system privacy. At first, a performance-dependent differential privacy noise conforming the Laplace distribution is designed, which can adaptively adjust the noise size to balance the system performance and privacy. Then, a novel privacy level analysis with evaluation method is presented. Subsequently, the strong uncertainties of switched systems is solved through a dynamic linearization method. On this basis, a novel cost function is designed by considering both the$H_{\infty }$performance and system privacy to balance the system performance and privacy from the perspective of control design. Further, by incorporating a parameter estimator and a prediction algorithm, the private MFAPC anti-noise controller is obtained. Finally, the feasibility of the PPR-MFAPC is explained with illustrative example.

There is a tricky problem in iterative learning control (ILC), where network delays during data transmission may lead to mismatches between the updated values of controller and required parameters, thereby disrupting the experience inheritance mechanism. Multimode switched systems can describe a wider range of complex processes, but most of the existing switching laws are fixed and lack flexibility. To address these issues, on the one hand, a transitive ILC (T-ILC) method is proposed to mitigate the effect of data network delay; on the other hand, a novel tracking performance-driven switching law (TPD-SL) is proposed. T-ILC can ensure the correct order of data through timestamps and memory identification. TPD-SL can ensure that the system switches to the subsystem with better tracking performance according to the change rate of the tracking error. Furthermore, we adopt an event-triggered communication method to save network computing resources. The stability of the system is demonstrated under the T-ILC and TPD-SL, and the relationship among network delay, event-triggering, and synchronous switching (controller switching aligned with system mode transition) versus asynchronous switching (controller switching with delayed mode transition) is analyzed. Numerical simulation results confirm that the method is effective.

In this article, the full differential privacy-preserving problem for switched LPV systems is concerned. The security of a control system is the ability to avoid intentional sabotage or accidental interference affecting its normal operation. For switched LPV systems, it contains both internal (scheduling variable and switching signal) and external (system output) information that needs to be protected, we adopt differential privacy to achieve its full privacy preserving. The resulting challenges are: 1) privacy design for three types of system signals and 2) dual asynchronization analysis under triple privacy noise. Accordingly, three differential privacy definitions and Laplace (resp. uniform) noise design methods are presented. Sufficient conditions for$H_{\infty}$control design with dual asynchronization analysis are given. Finally, simulation results verify the effectiveness of the proposed method.

Uncertainty and disturbance are always the first issues to deal with in any networked system. Considering these factors, the active disturbance rejection control (ADRC) for networked switched systems (NSSs) with decentralized event-triggered communication was investigated in this study. This is the first time a comprehensive analysis and synthesis method for the ADRC of NSSs has been proposed. The technical contribution is mainly divided into two categories. On the one hand, a novel parameter-dependent switching constraint is proposed, which solves the problem of ADRC-based switching law construction and settles the complex coupling between switching, event-triggering, and ADRC components. On the other hand, triggering the output of linear extended state observer (LESO) rather than its input can result in a more accurate estimation, while reasonably configuring network resources. Accordingly, a new tight bound of the high gain adjustment parameter of the observer was obtained. In addition, the stability analysis and state convergence region calculations for switched ADRC systems are given. Finally, the effectiveness of the proposal is demonstrated by simulation.

This paper investigates event-triggered control for saturated switched systems with switching signals subject to dual-terminal denial-of-service (DoS) attacks. The original switching signals are sampled and transmitted to sub-system and sub-controller through networks, respectively. However, the openness of network may cause switching signal transmission to be affected by possible DoS attacks. Moreover, the existing dual-terminal mismatched network delays and attacks could lead to asynchronous phenomenon. Considering the impacts of the attacked switching signals and delays, a time-delay closed-loop switched system is developed. Subsequently, by utilizing piecewise Lyapunov functional technique, various cases caused by DoS attacks and different delays are analyzed. Then, novel conditions are derived to ensure the stability of closed-loop switched system, and sufficient conditions for the desired controller gains and event-triggering parameters are presented. Finally, simulation examples are given to verify the effectiveness of the proposed method. The simulation results show that in the case of single-terminal and dual-terminal attacks, switched systems both have good anti-attack characteristics.

This article addresses the asynchronous event-triggered control problem for switched Takagi–Sugeno (T–S) fuzzy systems under data injection attacks. In reducing communication data update frequency, event-triggered control is an effective control method. The periodic sampling-based adaptive event-triggering schemes with switching structure are first proposed, which can dynamically adjust triggered conditions based on system performance. It is assumed that only the event-triggered and quantized data of the system state and the event-triggered switching signal are available for controllers. However, since the switching signal is sampled and triggered, asynchronous switching may occur between subsystems and subcontrollers. Furthermore, the data injection attacks are considered in the communication network of the system. A time-delay closed-loop switched T–S fuzzy system model is obtained by using a model transformation technique. The resulting criterion provides sufficient conditions to ensure that the closed-loop system subject to data injection attacks is exponential stability with an$H_{\infty }$performance. Moreover, a set of conditions for the co-design of controllers and adaptive event-triggering schemes is presented. Finally, simulations are given to verify the effectiveness of the asynchronous event-triggered control design.

This article investigates stabilization problems of networked event-triggered switched systems under multiasynchronous switching. Different from the existing asynchronous literature, a novel problem is considered in this article, i.e., the event-triggering scheme and controller both have independent switching delays relative to the system, which is called multiasynchronous switching. An adaptive event-triggering scheme with switching structure is utilized to achieve better adjustment in triggering frequency. A systematic framework for analyzing multiasynchronous switching stability and solving controller gains is established. Different Lyapunov functionals are employed, and new tight bound conditions on average dwell time are constructed. Besides, an active packet loss approach for handling packets disorder issues caused by large transmission delays is presented. Since the activation instants of the system, triggering scheme, and controller are mutually staggered with the data updating instants of the actuator, a novel analytical method aiming at the coupling effect is proposed. Finally, the validity of the adopted method in this article is demonstrated.

This paper deals with event-triggered H∞filtering problem for switched continuous-time systems with quantization. A multiple periodic sampling-based event-triggering strategy is adopted to select those “necessary” sampled signals to be transmitted. As a result, the amount of communication and the frequency of signal updates can be reduced significantly. Regarding the nonideal communication network, we consider network-induced delays and packet disorders, which are common during packet transmission through networks. In order to cope with packet disorders, an active packet loss approach is introduced. As such, a new time-delay system model is established, by which the filtering error system is modeled as a switched system with an interval time-varying delay. Then, by employing piecewise Lyapunov functional method and average dwell time technique, some criteria are derived to ensure that the filtering error system can achieve a prescribed level of H∞performance. Moreover, the relationship between switching instants and the data updating instants in filter is discussed in detail, and the filter gains and event-triggering parameters can be jointly designed in terms of solutions to some linear matrix inequalities. Finally, numerical simulation is provided to verify the effectiveness of the proposed method.

This paper studies the problem of event-triggered and self-triggered H∞controls for uncertain switched linear systems with exogenous disturbances whose magnitude is bounded by the system state's norm. With the proposed schemes, the control task is carried out only when the triggering condition is met. That leads to changing and adaptive interexecution intervals and can further reduce the system resource costs to a certain extent. Moreover, to ensure the H∞control performance of the event-triggered switched system, a proof which makes certain that the occurrence of Zeno problem can be prevented is first provided. Then, by adopting the average dwell time method, a set of sufficient conditions for H∞performance analysis is developed. Subsequently, the codesign of controller gains and event-triggering schemes is provided. On the basis of the eventtriggered control, a solution to the self-triggered control problem is then presented. It can only utilize the current sampled data to predict the next triggered instant. Finally, in order to test the effectiveness of the proposed methods, numerical simulations are performed.