Leveraging Linear Quadratic Regulator Cost and Energy Consumption for Ultrareliable and Low-Latency IoT Control Systems

Yang, Haojun; Zheng, Kan; Qian, Yi

doi:10.1109/jiot.2020.2990449

Cited by 14 publications

(4 citation statements)

References 60 publications

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“…The LQR is one of the optimal control techniques that consider the states of the dynamic system and control input to make the optimal control decisions. This is both simple and robust [ 74 , 75 ]. The purpose of the LQR controlling method is to determine a state-feedback optimal control force, which can minimize the definite quadratic cost function (Equation ( 35 )).…”

Section: Theory and Modelingmentioning

confidence: 99%

Smart Active Vibration Control System of a Rotary Structure Using Piezoelectric Materials

Hashemi

Jang

Hosseini-Hashemi

2022

Sensors

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A smart active vibration control (AVC) system containing piezoelectric (PZT) actuators, jointly with a linear quadratic regulator (LQR) controller, is proposed in this article to control transverse deflections of a wind turbine (WT) blade. In order to apply controlling rules to the WT blade, a state-of-the-art semi-analytical solution is developed to obtain WT blade lateral displacement under external loadings. The proposed method maps the WT blade to a Euler–Bernoulli beam under the same conditions to find the blade’s vibration and dynamic responses by solving analytical vibration solutions of the Euler–Bernoulli beam. The governing equations of the beam with PZT patches are derived by integrating the PZT transducer vibration equations into the vibration equations of the Euler–Bernoulli beam structure. A finite element model of the WT blade with PZT patches is developed. Next, a unique transfer function matrix is derived by exciting the structures and achieving responses. The beam structure is projected to the blade using the transfer function matrix. The results obtained from the mapping method are compared with the counter of the blade’s finite element model. A satisfying agreement is observed between the results. The results showed that the method’s accuracy decreased as the sensors’ distance from the base of the wind turbine increased. In the designing process of the LQR controller, various weighting factors are used to tune control actions of the AVC system. LQR optimal control gain is obtained by using the state-feedback control law. The PZT actuators are located at the same distance from each other an this effort to prevent neutralizing their actuating effects. The LQR shows significant performance by diminishing the weights on the control input in the cost function. The obtained results indicate that the proposed smart control system efficiently suppresses the vibration peaks along the WT blade and the maximum flap-wise displacement belonging to the tip of the structure is successfully controlled.

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Section: Theory and Modelingmentioning

confidence: 99%

Smart Active Vibration Control System of a Rotary Structure Using Piezoelectric Materials

Hashemi

Jang

Hosseini-Hashemi

2022

Sensors

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“…In [26], an offline scheduling algorithm has been proposed for machine-to-machine communications; however, as mentioned earlier offline algorithms are less adaptable to real-time changes. A joint optimization method for Quadratic Linear Regulator (LQR) cost and energy consumption is analyzed in [67], providing an energyto-control efficiency framework for URLLC in IoT systems, but where factors such as channel capacity and number of users have not been taken into account.…”

Section: Previous Workmentioning

confidence: 99%

Joint Optimization Via Deep Reinforcement Learning in Wireless Networked Controlled Systems

et al. 2022

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This paper proposes a deep Reinforcement Learning (RL) based co-design approach for jointoptimization of wireless networked control systems (WNCS) where the co-design approach can help achieve optimal control performance under network uncertainties e.g. delay and variable throughput. Compared to traditional and modern control methods where the dynamics of the system are important for predicting a system's future response, a model-free approach can adapt to many applications of stochastic behaviour. Our work provides a comparison of how the control performance is affected by network uncertainties such as delays and bandwidth consumption under an unknown number of devices. The control data is transmitted under different network conditions where several applications transmit background traffic data using the same network. The problem contains several sub-optimization problems because the optimal number of devices is non-deterministic under network delay and channel capacity constraints. The proposed approach seeks to minimize control error in wireless network control systems in order to improve Quality of Service and Quality of Control. This proposed approach is used and compared using three model-free RL Q-learning algorithms for high-throughput flow control in a double emulsion droplets formation application. The results show that the allowable number of devices for reliable network communication under bounded network constraints is 10 when using binary search. The control performance of the system without considering network effect in the reward function (Scenario 1) was good with the C51 algorithm; when including OMNet++ based network effect in the reward function (Scenario 2), the best performance was achieved with all three algorithms (C51, DQN, DDQN) with an exponential reward function, and only with C51 in the case of a linear reward function. Finally, under random network conditions (Scenario 3), C51 and DDQN performed well, but DQN did not converge. Comparisons with other machine learning and nonmachine learning algorithms also highlight the superior performance of the utilized algorithms.INDEX TERMS Wireless Networked Control Systems; Co-Design strategies; Reinforcement Learning.

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“…However, communication and coordination among agents tremendously impact performance of systems controlled in the distributed manner. Therefore, researches have started exploring strategies that seek a balance between optimality and coordination efforts influenced by communication and computational demands [Dörfler et al, 2014;Yang et al, 2020]. The new line of works has led to system partitioning problem, in which the overall system is decomposed into several subsystems and control inputs are assigned to some agents within the subsystems.…”

Section: Introductionmentioning

confidence: 99%

A new method of adaptive mesoscale control in complex multiagent networked dynamical systems

Amelin

Granichin

Леонова

et al. 2022

CAP

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Centralized strategies applied to large-scale systems require a vast amount of computational and communication resources. In contrast to them, distributed strategies offer higher scalability and reliability. However, communication and coordination among agents tremendously impact performance of systems controlled in the distributed manner. The existing methods lead to clustering, where the coordination between agents is limited to groups of entities to be controlled. The size of these groups are usually known in advance. In turn, many systems exhibit self-organization and dynamically form clustering structure. In that sense, control methods should adapt to such dynamic structures offering the same balance between performance and communication/computational demands. In this paper, we propose a new approach to complex system control based on efficient cluster (mesoscopic) control paradigm. We demonstrate its efficacy in scenarios, where a group of agents should reach a certain goal.

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Leveraging Linear Quadratic Regulator Cost and Energy Consumption for Ultrareliable and Low-Latency IoT Control Systems

Cited by 14 publications

References 60 publications

Smart Active Vibration Control System of a Rotary Structure Using Piezoelectric Materials

Smart Active Vibration Control System of a Rotary Structure Using Piezoelectric Materials

Joint Optimization Via Deep Reinforcement Learning in Wireless Networked Controlled Systems

A new method of adaptive mesoscale control in complex multiagent networked dynamical systems

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