Data-Driven Resilient Predictive Control under Denial-of-Service

Liu, Wenjie; Sun, Jian; Wang, Gang; Bullo, Francesco; Chen, Jie

doi:10.48550/arxiv.2110.12766

Cited by 4 publications

(6 citation statements)

References 51 publications

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“…In addition, the Fundamental Lemma does not account for any noise, so it cannot be directly employed for control and trajectory prediction. Building on existing works [15], [19], we propose a new data-driven MPC that, at each triggered time t , computes a window of optimal control inputs and associated outputs, using the received noisy data ζ [t −η,t −1] . The control inputs are subsequently employed one by one at times…”

Section: A Data-driven Mpc Schemementioning

confidence: 99%

“…Condition (18) ensures that problem ( 10) is feasible at t +1 if it is feasible at t , which is also known as recursive feasibility. In addition, condition (19) guarantees the Lyapunov function of system (3) decreases along the self-triggered times. Therefore, the self-triggering law reduces the communication load while ensuring the recursive feasibility of (10).…”

Section: B Self-triggering Mechanismmentioning

confidence: 99%

“…Use the past η measurements, i.e., u [t−η,t−1] and ζ [t−η,t−1] , to solve problem (10). Determine the next self-triggered time t +1 such that the inter-triggering time obeys ( 17)- (19).…”

Section: B Self-triggering Mechanismmentioning

confidence: 99%

“…framework, including stabilization and optimization in [7]- [11], linear quadratic regulation in [12], robust control in [13], quantized control in [14], model predictive control (MPC) in [15]- [19], and control of complex networks in [20].…”

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confidence: 99%

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Data-driven Self-triggered Control via Trajectory Prediction

Liu¹,

Sun²,

Wang³

et al. 2022

Preprint

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Self-triggered control, a well-documented technique for reducing the communication overhead while ensuring desired system performance, is gaining increasing popularity. However, existing methods for self-triggered control require explicit system models that are assumed perfectly known a priori. An end-to-end control paradigm known as data-driven control learns control laws directly from data, and offers a competing alternative to the routine system identification-then-control method. In this context, the present paper puts forth data-driven self-triggered control schemes for unknown linear systems using data collected offline. Specifically, for output feedback control systems, a datadriven model predictive control (MPC) scheme is proposed, which computes a sequence of control inputs while generating a predicted system trajectory. A data-driven self-triggering law is designed using the predicted trajectory, to determine the next triggering time once a new measurement becomes available. For state feedback control systems, instead of capitalizing on MPC to predict the trajectory, a data-fitting problem using the precollected input-state data is solved, whose solution is employed to construct the self-triggering mechanism. Both feasibility and stability are established for the proposed self-triggered controllers, which are validated using numerical examples.Index terms-Data-driven control, data-driven MPC, selftriggered control, predicted control. I. INTRODUCTIONThanks to recent advances in data acquisition and computing technologies, data-driven control has attracted considerable attention in the past years. Designing control laws directly from data without resorting to any system identification step, offers an appealing alternative to the traditional model-based control paradigm [1], [2]. This is because in real-world applications, it is always difficult or even impossible to acquire an accurate system model [3]- [5]. Indeed, a number of publications are devoted to studying data-driven control. These were mainly inspired by the celebrated Fundamental Lemma proposed in [6], which lays the theoretical foundation for data-driven control. Several control problems have been addressed under the new

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Section: A Data-driven Mpc Schemementioning

confidence: 99%

Section: B Self-triggering Mechanismmentioning

confidence: 99%

Section: B Self-triggering Mechanismmentioning

confidence: 99%

mentioning

confidence: 99%

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Data-driven Self-triggered Control via Trajectory Prediction

Liu¹,

Sun²,

Wang³

et al. 2022

Preprint

Self Cite

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“…However, both schemes lack the aforementioned closed-loop guarantees, and moreover, rely on the knowledge of a priori measured disturbances. Recently, Liu et al [21] proposed a scheme guaranteeing closed-loop stability and recursive feasibility in the presence of process disturbances, which, however, lacks of guarantees for closed-loop constraint satisfaction.…”

Section: Introductionmentioning

confidence: 99%

A novel constraint tightening approach for robust data-driven predictive control

Klöppelt¹,

Berberich²,

Allgöwer³

et al. 2022

Preprint

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In this paper, we present a data-driven model predictive control (MPC) scheme that is capable of stabilizing unknown linear time-invariant systems under the influence of process disturbances. To this end, Willems' lemma is used to predict the future behavior of the system. This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order. First, we develop a state-feedback MPC scheme, based on input-state data, which guarantees closed-loop practical exponential stability and recursive feasibility as well as closed-loop constraint satisfaction. The scheme is extended by a suitable constraint tightening, which can also be constructed using only data. In order to control a priori unstable systems, the presented scheme contains a pre-stabilizing controller and an associated input constraint tightening. We first present the proposed data-driven MPC scheme for the case of full state measurements, and also provide extensions for obtaining similar closed-loop guarantees in case of output feedback. The presented scheme is applied to a numerical example.

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A novel constraint‐tightening approach for robust data‐driven predictive control

Klöppelt

Berberich

Allgöwer

et al. 2022

Intl J Robust & Nonlinear

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In this paper, we present a data-driven model predictive control (MPC) scheme that is capable of stabilizing unknown linear time-invariant systems under the influence of process disturbances. To this end, Willems' lemma is used to predict the future behavior of the system. This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order. First, we develop a state-feedback MPC scheme, based on input-state data, which guarantees closed-loop practical exponential stability and recursive feasibility as well as closed-loop constraint satisfaction. The scheme is extended by a suitable constraint tightening, which can also be constructed using only data. In order to control a priori unstable systems, the presented scheme contains a prestabilizing controller and an associated input constraint tightening.We first present the proposed data-driven MPC scheme for the case of full state measurements, and also provide extensions for obtaining similar closed-loop guarantees in case of output feedback. The presented scheme is applied to a numerical example.

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Data-Driven Resilient Predictive Control under Denial-of-Service

Cited by 4 publications

References 51 publications

Data-driven Self-triggered Control via Trajectory Prediction

Data-driven Self-triggered Control via Trajectory Prediction

A novel constraint tightening approach for robust data-driven predictive control

A novel constraint‐tightening approach for robust data‐driven predictive control

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