A Decentralized Event-Based Approach for Robust Model Predictive Control

Kolarijani, Arman Sharifi; Bregman, Sander; Esfahani, Peyman Mohajerin; Keviczky, Tamás

doi:10.1109/tac.2019.2945285

Cited by 17 publications

(3 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In network-based control, there exists a contradiction between limited resources and multiple control tasks. To save the computing and communication resources, the event-triggered control (ETC) method comes into being, [19][20][21][22] in which the control signals are updated and transmitted only when certain conditions are satisfied rather than periodically, while ensuring the required system performance. In recent years, many event-triggered PPC methods have been proposed for nonlinear systems, especially for lower-triangular nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

Sampled‐data‐based event‐triggered adaptive prescribed performance control of uncertain strict‐feedback nonlinear systems

Hou

2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This article studies the problem of global adaptive prescribed performance control (PPC) for a class of uncertain strict-feedback nonlinear systems based on the sampled-data-based event-triggering mechanism. A novel time-varying scaling function, whose reciprocal plays the role of a prescribed performance function, is constructed and utilized to remove the range restriction on the initial tracking error and achieve the global PPC. Meanwhile, to reduce the communication frequency and avoid the Zeno behavior in mechanism, a sampled-data-based event-triggering mechanism is introduced, and an event-triggered global adaptive prescribed performance controller is designed by employing the error transformation technique and the adaptive backstepping method. It is proved that all signals of the closed-loop system are bounded and the tracking error is confined within the prescribed performance boundary for arbitrary initial values.Since there is no need to continuously monitor whether the triggering condition is satisfied, the obtained result is conducive to physical implementation. Finally, a network-based robotic manipulator system is employed to verify the effectiveness of the proposed method.

show abstract

Section: Introductionmentioning

confidence: 99%

Sampled‐data‐based event‐triggered adaptive prescribed performance control of uncertain strict‐feedback nonlinear systems

Hou

2023

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Alternatively, the aperiodically distributed control method is an attractive solution to alleviate the communication burden among the MAS while guaranteeing the comparable control performance, in which the system states are only updated and transmitted to neighbors at triggering instants [15]. Based on different triggering mechanisms, the aperiodic DMPC methods in existing literature can be mainly classified into two categories: event-triggered [32,53,73,85,183], and selftriggered DMPC [100,156,174]. Compared with the event-triggered algorithms, the requirement of the continuous check of the controlled system and predesigned trigger-ing conditions at each sampling instant are removed in the self-triggered DMPC algorithms.…”

Section: Introductionmentioning

confidence: 99%

Self-Triggered Min–Max DMPC for Asynchronous Multiagent Systems With Communication Delays

Wei

Zhang

Shi

2022

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Distributed coordination of multi-agent systems (MASs) has been widely studied in various emerging engineering applications, including connected vehicles, wireless networks, smart grids, and cyber-physical systems. In these contexts, agents make the decision locally, relying on the interaction with their immediate neighbors over the connected communication networks. The study of distributed coordination for the multi-agent system (MAS) with constraints is significant yet challenging, especially in terms of ubiquitous uncertainties, the heavy communication burden, and communication delays, to name a few. Hence, it is desirable to develop distributed algorithms for the constrained MAS with these practical issues. In this dissertation, we develop the theoretical results on robust distributed model predictive control (DMPC) algorithms for two types of control problems (i.e., formation stabilization problem and consensus problem) of the constrained and uncertain MAS and apply robust DMPC algorithms in applications of cooperative marine vehicles.More precisely, Chapter 1 provides a systematic literature review, where the state-of-the-art DMPC for formation stabilization and consensus, robust MPC, and MPC for motion control of marine vehicles are introduced. Chapter 2 introduces some notations, necessary definitions, and some preliminaries. In Chapter 3, we study the formation stabilization problem of the nonlinear constrained MAS with uncertainties and bounded time-varying communication delays. We develop a min-max DMPC algorithm with the self-triggered mechanism, which significantly reduces the communication burden while ensuring closed-loop stability and robustness. Chapter 4 investigates the consensus problem of the general linear MAS with input constraints and bounded time-varying delays. We design a robust DMPC-based consensus protocol that integrates a predesigned consensus protocol with online DMPC optimization techniques. Under mild technical assumptions, the estimation errors propagated over prediction due to delay-induced inaccurate neighboring information are proved bounded, based on which a robust DMPC strategy is deliberately designed to achieve robust consensus while satisfying control input constraints. Chapter 5 proposes a Lyapunov-based DMPC approach for the formation tracking control problem of cooperative autonomous underwater vehicles (AUVs) subject to environmental disturbances. A stability constraint leveraging the extended state observer-based auxiliary control law and the associated Lyapunov function is incorporated into the optimization problem to enforce the stability and enhance formation tracking performance. A iv collision-avoidance cost is designed and employed in the DMPC optimization problem to further guarantee the safety of AUVs. Chapter 6 presents a tube-based DMPC approach for the platoon control problem of a group of heterogeneous autonomous surface vehicles (ASVs) with input constraints and disturbances. In particular, a coupled inter-vehicle safety constraint is added to the DMPC ...

show abstract

“…This setting may still require continuous measurements, a shortcoming that is overcome by introducing a "sampling-based event-triggered" method in which the measurements are only available at specific time instances whereas the control law is updated based on the triggering mechanism [8,16,12]. Event-triggering techniques also prove to be an effective tool to implement optimization-based control techniques [20].…”

Section: Introductionmentioning

confidence: 99%

Robust Output Regulation: Optimization-Based Synthesis and Event-Triggered Implementation

Sarafraz,

Proskurnikov,

Tavazoei

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

We investigate the problem of practical robust output regulation: keeping the state of an uncertain dynamical system uniformly bounded while the system output eventually resides within a prescribed ball centered at a desired target. We consider uncertain systems that are possibly nonlinear and the uncertainty of the linear part is modeled element-wise through a parametric family of matrix boxes. Justifying that a static proportional controller may not be able to accomplish our desired objective, we develop an optimization program characterizing the coefficients of a dynamic output control whose optimal value determines the precision of the output regulation task. We further propose a sampled-time event-triggered redesign of this controller ensuring that the output regulation is still certified and its precision is computationally available. The objective of this study is motivated by an application on behavioral control of a network of selfish agents in a non-cooperative environment.

show abstract

A Decentralized Event-Based Approach for Robust Model Predictive Control

Cited by 17 publications

References 37 publications

Sampled‐data‐based event‐triggered adaptive prescribed performance control of uncertain strict‐feedback nonlinear systems

Sampled‐data‐based event‐triggered adaptive prescribed performance control of uncertain strict‐feedback nonlinear systems

Self-Triggered Min–Max DMPC for Asynchronous Multiagent Systems With Communication Delays

Robust Output Regulation: Optimization-Based Synthesis and Event-Triggered Implementation

Contact Info

Product

Resources

About