Multiple-Loop Self-Triggered Model Predictive Control for Network Scheduling and Control

Henriksson, Erik; Quevedo, Daniel E.; Peters, Edwin G. W.; Sandberg, Henrik; Johansson, Karl Henrik

doi:10.1109/tcst.2015.2404308

Cited by 99 publications

(45 citation statements)

References 30 publications

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“…Compared to the PID and LQR control, the MPC framework efficiently handles constraints. Moreover, MPC can handle missing measurements or control commands [62], [63], which can appear in a NCS setting. 5) Estimator Design: Due to network uncertainties, plant state estimation is a crucial and significant research field of NCSs [48], [22].…”

Section: ) Controller Designmentioning

confidence: 99%

Wireless Network Design for Control Systems: A Survey

Park

Coleri

Fischione

et al. 2018

IEEE Commun. Surv. Tutorials

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399

280

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Abstract-Wireless networked control systems (WNCS) are composed of spatially distributed sensors, actuators, and controllers communicating through wireless networks instead of conventional point-to-point wired connections. Due to their main benefits in the reduction of deployment and maintenance costs, large flexibility and possible enhancement of safety, WNCS are becoming a fundamental infrastructure technology for critical control systems in automotive electrical systems, avionics control systems, building management systems, and industrial automation systems. The main challenge in WNCS is to jointly design the communication and control systems considering their tight interaction to improve the control performance and the network lifetime. In this survey, we make an exhaustive review of the literature on wireless network design and optimization for WNCS. First, we discuss what we call the critical interactive variables including sampling period, message delay, message dropout, and network energy consumption. The mutual effects of these communication and control variables motivate their joint tuning. We discuss the effect of controllable wireless network parameters at all layers of the communication protocols on the probability distribution of these interactive variables. We also review the current wireless network standardization for WNCS and their corresponding methodology for adapting the network parameters. Moreover, we discuss the analysis and design of control systems taking into account the effect of the interactive variables on the control system performance. Finally, we present the state-of-the-art wireless network design and optimization for WNCS, while highlighting the tradeoff between the achievable performance and complexity of various approaches. We conclude the survey by highlighting major research issues and identifying future research directions.

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Section: ) Controller Designmentioning

confidence: 99%

Wireless Network Design for Control Systems: A Survey

Park

Coleri

Fischione

et al. 2018

IEEE Commun. Surv. Tutorials

Self Cite

399

280

View full text Add to dashboard Cite

show abstract

“…On the other hand, a smaller ρ tends to give a sparser output from the definition of the soft thresholding operator S 1/ρ ; see (14) or Fig. 1.…”

Section: Selection Of Penalty Parameter ρmentioning

confidence: 99%

“…MPC is a very attractive research topic to which sparsity methods are applied; in [11,12] Gallieri and Maciejowski have proposed asso -MPC to reduce actuator activity, and in [13] Aguilera et al have discussed minimization of the number of active actuators subject to closed-loop stability by using the 0 norm. Sparse MPC is further investigated based on self-triggered control in [14]. Motivated by these researches, the maximum hands-off control has been proposed in [15,16] for continuous-time systems.…”

Section: Introductionmentioning

confidence: 99%

Discrete-time hands-off control by sparse optimization

Nagahara

Østergaard

Quevedo

2016

EURASIP J. Adv. Signal Process.

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Maximum hands-off control is a control mechanism that maximizes the length of the time duration on which the control is exactly zero. Such a control is important for energy-aware control applications, since it can stop actuators for a long duration and hence the control system needs much less fuel or electric power. In this article, we formulate the maximum hands-off control for linear discrete-time plants by sparse optimization based on the 1 norm. For this optimization problem, we derive an efficient algorithm based on the alternating direction method of multipliers (ADMM). We also give a model predictive control formulation, which leads to a robust control system based on a state feedback mechanism. Simulation results are included to illustrate the effectiveness of the proposed control method.

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“…Many of the aforementioned results only consider single-loop control systems. There exists limited literature that study multi-loop control systems [9]- [11]. One limitation is that many of these results only investigate linear scalar systems.…”

Section: Introductionmentioning

confidence: 99%

DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

Demirel

Ramaswamy

Quevedo

et al. 2018

IEEE Control Syst. Lett.

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We consider networked control systems consisting of multiple independent controlled subsystems, operating over a shared communication network. Such systems are ubiquitous in cyber-physical systems, Internet of Things, and large-scale industrial systems. In many large-scale settings, the size of the communication network is smaller than the size of the system. In consequence, scheduling issues arise. The main contribution of this paper is to develop a deep reinforcement learning-based control-aware scheduling (DEEPCAS) algorithm to tackle these issues. We use the following (optimal) design strategy: First, we synthesize an optimal controller for each subsystem; next, we design a learning algorithm that adapts to the chosen subsystems (plants) and controllers. As a consequence of this adaptation, our algorithm finds a schedule that minimizes the control loss. We present empirical results to show that DEEPCAS finds schedules with better performance than periodic ones.

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Multiple-Loop Self-Triggered Model Predictive Control for Network Scheduling and Control

Cited by 99 publications

References 30 publications

Wireless Network Design for Control Systems: A Survey

Wireless Network Design for Control Systems: A Survey

Discrete-time hands-off control by sparse optimization

DeepCAS: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling

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