Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance

Vamvoudakis, Kyriakos G.; Ferraz, Henrique

doi:10.1016/j.automatica.2017.03.013

Cited by 114 publications

(62 citation statements)

References 14 publications

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“…Event-triggered controllers can also be learned from data without learning a model. Such approaches are proposed, for example, in [12]- [15]. In contrast to those approaches, we use a specific control design and use learning to obtain accurate dynamic models.…”

Section: Related Workmentioning

confidence: 99%

Event-triggered Pulse Control with Model Learning (if Necessary)

Baurnann

Solowjow

Johansson

et al. 2019

2019 American Control Conference (ACC)

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In networked control systems, communication is a shared and therefore scarce resource. Event-triggered control (ETC) can achieve high performance control with a significantly reduced amount of samples compared to classical, periodic control schemes. However, ETC methods usually rely on the availability of an accurate dynamics model, which is oftentimes not readily available. In this paper, we propose a novel eventtriggered pulse control strategy that learns dynamics models if necessary. In addition to adapting to changing dynamics, the method also represents a suitable replacement for the integral part typically used in periodic control. Accepted final version.

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Section: Related Workmentioning

confidence: 99%

Event-triggered Pulse Control with Model Learning (if Necessary)

Baurnann

Solowjow

Johansson

et al. 2019

2019 American Control Conference (ACC)

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“…To end this section, we want to mention some other recent results in the field of learning‐based adaptive control, namely, the (model‐based) reinforcement‐learning (RL) controllers, also known as, depending on the specific research community, (model‐based) approximate/ adaptive dynamic programming algorithms (ADP), or (model‐based) neuro‐dynamic programming (NDP), see references …”

Section: Model‐based Adaptive Controlmentioning

confidence: 99%

Model‐based vs data‐driven adaptive control: An overview

Benosman

2018

Adaptive Control & Signal

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In this paper, we present an overview of adaptive control by contrasting model-based approaches with data-driven approaches. Indeed, we propose to classify adaptive controllers into two main subfields, namely, model-based adaptive control and data-driven adaptive control. In each subfield, we cite monographs, survey papers, and recent research papers published in the last few years. We also include a few simple examples to illustrate some general concepts in each subfield.

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“…Next, we analyze the dynamics of

\frac{false‖ e false(t false) false‖}{false‖ x false(t false) false‖}

. When applying the intermittent feedback and the worst‐case disturbance , the closed‐loop system dynamics yields

false‖ \overset{x}{true} false‖ = false‖ A x + B K false(x + e false) false‖ \leq false(false‖ A false‖ + false‖ B false‖ false‖ K false‖ false) false(false‖ x false‖ + false‖ e false‖ false) .

Then, the dynamics of

\frac{false‖ e false‖}{false‖ x false‖}

satisfies

\frac{d}{d t} ((), \frac{false‖ e false‖}{false‖ x false‖}) \leq ((), false‖ A false‖ + false‖ B false‖ false‖ K false‖) {((), 1 + \frac{false‖ e false‖}{false‖ x false‖})}^{2}, \forall t \in ((], t_{normalk}, t_{normalk + 1}), k \in {double-struckZ}^{+} .

Then, based on the comparison lemma, one has

\frac{false‖ e false‖}{false‖ x false‖} \leq \frac{((), t - t_{normalk}) ((), false‖ A false‖ + false‖ B false‖ false‖ K false‖)}{1 - ((), t - t_{normalk}) ((), false‖ A false‖ + false‖ B false‖ false‖ K false‖)}, \forall t \in ((], t_{normalk}, t_{normalk + 1}), k \in {double-struckZ}^{+ <...>}

…”

Section: Model‐based Intermittent Feedback Designmentioning

confidence: 99%

“…See the work of Vamvoudakis and Ferraz. 31 Remark 3. Note that (13) is a general form of the GARE-based intermittent feedback control policy.…”

Section: The Hamiltonian Of the Intermittent Feedback Control Umentioning

confidence: 99%

Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of ‐stabilization

Yang

Vamvoudakis

Ferraz

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper proposes an intermittent model‐free learning algorithm for linear time‐invariant systems, where the control policy and transmission decisions are co‐designed simultaneously while also being subjected to worst‐case disturbances. The control policy is designed by introducing an internal dynamical system to further reduce the transmission rate and provide bandwidth flexibility in cyber‐physical systems. Moreover, a Q‐learning algorithm with two actors and a single critic structure is developed to learn the optimal parameters of a Q‐function. It is shown by using an impulsive system approach that the closed‐loop system has an asymptotically stable equilibrium and that no Zeno behavior occurs. Furthermore, a qualitative performance analysis of the model‐free dynamic intermittent framework is given and shows the degree of suboptimality concerning the optimal continuous updated controller. Finally, a numerical simulation of an unknown system is carried out to highlight the efficacy of the proposed framework.

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Model-free event-triggered control algorithm for continuous-time linear systems with optimal performance

Cited by 114 publications

References 14 publications

Event-triggered Pulse Control with Model Learning (if Necessary)

Event-triggered Pulse Control with Model Learning (if Necessary)

Model‐based vs data‐driven adaptive control: An overview

Dynamic intermittent Q‐learning–based model‐free suboptimal co‐design of ‐stabilization

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