Event-triggered intermittent sampling for nonlinear model predictive control

Hashimoto, Kazumune; Adachi, Shuichi; Dimarogonas, Dimos V.

doi:10.1016/j.automatica.2017.03.028

Cited by 108 publications

(35 citation statements)

References 30 publications

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“…A looped-functional approach is for robust analysis of sampled-data system either considering directly the sampled-data system formulation [186] or the impulsive system formulation [19,187]. Model predictive control plays an important role in dealing with constraints, such as actuator or physical limitations [?, 78,79]. Sliding model control, as an effective robust control strategy has also been applied to NCSs [76,196].…”

Section: General and Augmented Lyapunov Functional Methodsmentioning

confidence: 99%

Survey on time-delay approach to networked control

Liu

Selivanov

Fridman

2019

Annual Reviews in Control

127

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This paper provides a survey on time-delay approach to networked control systems (NCSs). The survey begins from a brief summary on fundamental network-induced issues in NCSs and the main approaches to the modelling of NCSs. In particular, a comprehensive introduction to time-delay approach to sampled-data and networked control is provided. Then, recent results on time-delay approach to event-triggered control are recalled. The survey highlights time-delay approach developed to modelling, analysis and synthesis of NCSs, under communication constraints, with a particular focus on Round-Robin, Try-once-discard and stochastic protocols. The time-delay approach allows communication delays to be larger than the sampling intervals in the presence of scheduling protocols. Moreover, some results on networked control of distributed parameter systems are surveyed. Finally, conclusions and some future research directions are briefly addressed.

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Section: General and Augmented Lyapunov Functional Methodsmentioning

confidence: 99%

Survey on time-delay approach to networked control

Liu

Selivanov

Fridman

2019

Annual Reviews in Control

127

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“…As we all know, nonlinear systems have advantages in modeling practical dynamic processes [44] , [45] with respect to linear systems. There have also been some results on nonlinear PMJSs [20] , [21] , the nonlinear MPC [28] , [46] , and the MPC of nonlinear MJSs [27] . For positive systems, few efforts are devoted to the topics mentioned above.…”

Section: Extensions To General Systemsmentioning

confidence: 99%

Distributed model predictive control of positive Markov jump systems

Zhang

Deng

Zhang

et al. 2020

Journal of the Franklin Institute

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This paper proposes a new distributed model predictive control (DMPC) for positive Markov jump systems subject to uncertainties and constraints. The uncertainties refer to interval and polytopic types, and the constraints are described in the form of 1-norm inequalities. A linear DMPC framework containing a linear performance index, linear robust stability conditions, a stochastic linear co-positive Lyapunov function, a cone invariant set, and a linear programming based DMPC algorithm is introduced. A global positive Markov jump system is decomposed into several subsystems. These subsystems can exchange information with each other and each subsystem has its own controller. Using a matrix decomposition technique, the DMPC controller gain matrix is divided into nonnegative and non-positive components and thus the corresponding stochastic stability conditions are transformed into linear programming. By virtue of a stochastic linear co-positive Lyapunov function, the positivity and stochastic stability of the systems are achieved under the DMPC controller. A lower computation burden DMPC algorithm is presented for solving the min-max optimization problem of performance index. The proposed DMPC design approach is extended for general systems. Finally, an example is given to verify the effectiveness of the DMPC design.

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“…Reference 17 took advantage of the tightened state constraint 20 and removed the requirement of the prediction horizon for guaranteeing the feasibility in Reference 10. Besides, a time‐constrained scheme 21 was designed by evaluating the time when the optimal state trajectory enters a local set. Another research line is the ETMPC with codesign 22‐25 .…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered model predictive control for disturbed linear systems under two‐channel transmissions

Hao

et al. 2020

Intl J Robust & Nonlinear

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This article studies an event-triggered model predictive control problem for constrained continuous-time linear systems subject to bounded disturbances. Two different event-triggered strategies are constructed in the sensor and the controller nodes for reducing the communication and computational loads, respectively. The continuous predicted control trajectory generated by the controller is applied to the plant under a sample-and-hold implementation. By constructing a feasible control sequence, the sufficient conditions are derived to guarantee the feasibility and stability of the closed-loop system. Furthermore, the case of multiple samples within an event-triggered control update interval is considered. It is shown that a larger number of samples will improve the triggering performance while increasing the amount of transmission information. Finally, a simulation example is provided to show the feasibility and the effectiveness of the proposed strategy.

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Event-triggered intermittent sampling for nonlinear model predictive control

Cited by 108 publications

References 30 publications

Survey on time-delay approach to networked control

Survey on time-delay approach to networked control

Distributed model predictive control of positive Markov jump systems

Event‐triggered model predictive control for disturbed linear systems under two‐channel transmissions

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