Energy-aware wireless networked control using radio-mode management

Castro, Nicolas Cardoso de; Wit, Carlos Canudas de; Garin, Federica

doi:10.1109/acc.2012.6315044

Cited by 9 publications

(4 citation statements)

References 19 publications

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“…Since only nodes in S δ (m 0 ) are expanded, we have n ≤ |S δ (m 0 )|, and by using (12) if κ * (m 0 ) = 1…”

Section: Proofmentioning

confidence: 99%

“…The connection between stability and optimality would be interesting, but is not yet understood for the discounted type of cost required by OPD and OP-MDP. For instance, when discounting is present stability cannot generally be guaranteed even in the linear case, as illustrated by [31], while [12] emphasized similar difficulties in the NCS setting. Many works in optimal control use discounted costs without guaranteeing stability, such as [1], [29] which consider network effects, as well as works on classic optimal control of linear systems [27], nonlinear systems [34], singularly perturbed systems [21], or systems with discrete-valued variables as in our case [5], [15], [20].…”

Section: Introductionmentioning

confidence: 99%

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Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems

Buşoniu

Daafouz

2016

IEEE Trans. Automat. Contr.

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We consider problems where a controller communicates with a general nonlinear plant via a network, and must optimize a performance index. The system is modeled in discrete time and may be affected by a class of stochastic uncertainties that can take finitely many values. Admissible inputs are constrained to belong to a finite set. Exploiting some optimistic planning algorithms from the artificial intelligence field, we propose two control strategies that take into account the communication constraints induced by the use of the network. Both strategies send in a single packet long-horizon solutions, such as sequences of inputs. Our analysis characterizes the relationship between computation, near-optimality, and transmission intervals. In particular, the first strategy imposes at each transmission a desired near-optimality, which we show is related to an imposed transmission period; for this setting, we analyze the required computation. The second strategy has a fixed computation budget, and within this constraint it adapts the next transmission instant to the last state measurement, leading to a self-triggered policy. For this case, we guarantee long transmission intervals. Examples and simulation experiments are provided throughout the paper.

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“…Since only nodes in S δ (m 0 ) are expanded, we have n ≤ |S δ (m 0 )|, and by using (12) if κ * (m 0 ) = 1…”

Section: Proofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems

Buşoniu

Daafouz

2016

IEEE Trans. Automat. Contr.

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“…In most cases, the communication subsystem is the main power consumer, even when idle [ 16 ]. In this sense, some energy conservation strategies consist in control operation modes of the wireless devices (active, idle and sleep), in order to obtain an adequate trade-off between control performance and energy cost, taking in consideration that transition between operation modes involves energy expenditure and latency [ 22 ].…”

Section: Energy Saving Strategies For Wncsmentioning

confidence: 99%

The Benefits of Soft Sensor and Multi-Rate Control for the Implementation of Wireless Networked Control Systems

Mansano

Godoy

Porto

2014

Sensors

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Recent advances in wireless networking technology and the proliferation of industrial wireless sensors have led to an increasing interest in using wireless networks for closed loop control. The main advantages of Wireless Networked Control Systems (WNCSs) are the reconfigurability, easy commissioning and the possibility of installation in places where cabling is impossible. Despite these advantages, there are two main problems which must be considered for practical implementations of WNCSs. One problem is the sampling period constraint of industrial wireless sensors. This problem is related to the energy cost of the wireless transmission, since the power supply is limited, which precludes the use of these sensors in several closed-loop controls. The other technological concern in WNCS is the energy efficiency of the devices. As the sensors are powered by batteries, the lowest possible consumption is required to extend battery lifetime. As a result, there is a compromise between the sensor sampling period, the sensor battery lifetime and the required control performance for the WNCS. This paper develops a model-based soft sensor to overcome these problems and enable practical implementations of WNCSs. The goal of the soft sensor is generating virtual data allowing an actuation on the process faster than the maximum sampling period available for the wireless sensor. Experimental results have shown the soft sensor is a solution to the sampling period constraint problem of wireless sensors in control applications, enabling the application of industrial wireless sensors in WNCSs. Additionally, our results demonstrated the soft sensor potential for implementing energy efficient WNCS through the battery saving of industrial wireless sensors.

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“…An important remark is that much of the literature focuses on stability [38,31], whereas our aim is to provide near-optimality guarantees. Stability is a separate, difficult problem for discounted costs [26,11,34]. Nevertheless, in some cases our approach can exploit existing stability conditions: e.g.…”

Section: Introductionmentioning

confidence: 99%

Planning for optimal control and performance certification in nonlinear systems with controlled or uncontrolled switches

et al. 2017

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We consider three problems for discrete-time switched systems with autonomous, general nonlinear modes. The first is optimal control of the switching rule so as to optimize the infinite-horizon discounted cost. The second and third problems occur when the switching rule is uncontrolled, and we seek either the worst-case cost when the rule is unknown, or respectively the expected cost when the rule is stochastic. We use optimistic planning (OP) algorithms that can solve general optimal control with discrete inputs such as switches. We extend the analysis of OP to provide certification (upper and lower) bounds on the optimal, worst-case, or expected costs, as well as to design switching sequences that achieve these bounds in the deterministic case. In this case, since a minimum dwell time between switching instants must often be ensured, we introduce a new OP variant to handle this constraint, and analyze its convergence rate. We provide consistency and closed-loop performance guarantees for the sequences designed, and illustrate that the approach works well in simulations.

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Energy-aware wireless networked control using radio-mode management

Cited by 9 publications

References 19 publications

Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems

Near-Optimal Strategies for Nonlinear and Uncertain Networked Control Systems

The Benefits of Soft Sensor and Multi-Rate Control for the Implementation of Wireless Networked Control Systems

Planning for optimal control and performance certification in nonlinear systems with controlled or uncontrolled switches

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