This paper provides an overview and makes a deep investigation on sampled-data-based event-triggered control and filtering for networked systems. Compared with some existing event-triggered and self-triggered schemes, a sampled-data-based event-triggered scheme can ensure a positive minimum inter-event time and make it possible to jointly design suitable feedback controllers and event-triggered threshold parameters. Thus, more attention has been paid to the sampled-data-based event-triggered scheme. A deep investigation is first made on the sampled-data-based event-triggered scheme. Then, recent results on sampled-data-based event-triggered state feedback control, dynamic output feedback control, $H_\infty$ filtering for networked systems are surveyed and analyzed. An overview on sampled-data-based event-triggered consensus for distributed multiagent systems is given. Finally, some challenging issues are addressed to direct the future research.

An Overview and Deep Investigation on Sampled-Data-Based Event-Triggered Control and Filtering for Networked Systems

Computer Controlled Systems: Theory and Design

Networked control systems are spatially distributed systems in which the communication between sensors, actuators, and controllers occurs through a shared band-limited digital communication network. Several advantages of the network architectures include reduced system wiring, plug and play devices, increased system agility, and ease of system diagnosis and maintenance. Consequently, networked control is the current trend for industrial automation and has ever-increasing applications in a wide range of areas, such as smart grids, manufacturing systems, process control, automobiles, automated highway systems, and unmanned aerial vehicles. The modelling, analysis, and control of networked control systems have received considerable attention in the last two decades. The &#x2018 control over networks &#x2019 is one of the key research directions for networked control systems. This paper aims at presenting a survey of trends and techniques in networked control systems from the perspective of &#x2018 control over networks &#x2019 , providing a snapshot of five control issues: sampled-data control, quantization control, networked control, event-triggered control, and security control. Some challenging issues are suggested to direct the future research.

Networked control systems: a survey of trends and techniques

Networked control systems are often subject to limited communication resources. By only communicating output measurements when needed, event-triggered control is an adequate method to reduce the usage of communication resources while retaining desired closed-loop performance. In this work, a novel event-triggered control (ETC) strategy for a class of nonlinear feedback systems is proposed that can simultaneously guarantee a finite $\mathcal{L}_{p}$ - gain and a strictly positive lower bound on the inter-event times. The new ETC scheme can be synthesized in an output-based and/or decentralized form, takes the specific medium access protocols into account, and is robust to (variable) transmission delays by design. Interestingly, in contrast with the majority of existing event-generators that only use static conditions, the newly proposed event-triggering conditions are based on dynamic elements, which has several advantages including larger average inter-event times. The developed theory leads to families of event-triggered controllers that correspond to different tradeoffs between (minimum and average) inter-event times, maximum allowable delays and $\mathcal{L}_{p}$ - gains. A linear and a nonlinear numerical example will illustrate all the benefits of this new dynamic ETC scheme.

Output-Based and Decentralized Dynamic Event-Triggered Control With Guaranteed $\mathcal{L}_{p}$- Gain Performance and Zeno-Freeness

https://hal.archives-ouvertes.fr/hal-01363448/document

Recent developments on the stability of systems with aperiodic sampling: An overview

https://heemels.tue.nl/content/papers/GomAnt_AUT14a.pdf

Self-triggered linear quadratic control ☆

Brief paper: Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis

In this paper, we study MIMO Iterative Learning Control (ILC) and its robustness against model uncertainty. Although it is argued that, so-called, norm optimal ILC controllers have some inherent robustness, not many results are available that can make quantitative statements about the allowable model uncertainty. In this paper, we derive sufficient conditions for robust convergence of the ILC algorithm in presence of an uncertain system with an additive uncertainty bound. These conditions are applied to norm optimal ILC, resulting in guidelines for robust controller design. Theoretical results are illustrated by simulations.

/pdf/robustness-against-model-uncertainties-of-norm-optimal-2bnfcyzr1s.pdf

Robustness against model uncertainties of norm optimal iterative learning control

Recent developments in computer and communication technologies are leading to an increasingly networked and wireless world. This raises new challenging questions in the context of networked control systems, especially when the computation, communication and energy resources
of the system are limited. To efficiently use the available resources it is desirable to limit the control actions to instances when the system really needs attention. Unfortunately, the classical time-triggered control paradigm is based on performing sensing and actuation actions periodically in time (irrespective of the state of the system) rather than when the system needs attention. Therefore, it is of interest to consider event-triggered control as
an alternative paradigm as it is more natural to trigger control actions based on the system state, output, or other available information. Event-triggered control can thus be seen as the introduction of feedback in the sensing, communication, and actuation processes. To facilitate an easy implementation of event-triggered control, we propose to combine the principles and particularly the benefits of event-triggered control and classical periodic time-triggered control. The idea is to periodically evaluate the triggering condition and to decide, at every sampling instant, whether the feedback loop needs to be closed. This leads to the so-called periodic event-triggered control (PETC) systems. In this chapter, we discuss PETC strategies, their benefits and two analysis and design frameworks for linear and nonlinear plants, respectively.

/pdf/periodic-event-triggered-control-4ib4fbpub2.pdf

Periodic Event-Triggered Control

This paper examines a new event-triggered control design approach for discrete-time linear parameter-varying (LPV) systems to reduce the data transmission of the scheduling variables and states to the controller. A parameter dependent state-feedback controller and an event-triggering condition are designed jointly to stabilize the closed-loop system. Then, a procedure is proposed to obtain an event-based reference tracking controller for the LPV systems such that the steady-state response of the system output tends to a desired reference signal and the required tracking performance specifications are satisfied. Sufficient conditions for the design of the event-triggered controller are obtained in the form of linear matrix inequalities (LMI). Simulation results are presented to demonstrate the effectiveness of the proposed control design approach.

Tijs Donkers

Papers

Self-triggered linear quadratic control ☆

Brief paper: Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis

Robustness against model uncertainties of norm optimal iterative learning control

Periodic Event-Triggered Control

Event-triggered control for discrete-time linear parameter-varying systems