Periodic Event-Triggered Control strategy for a (3,0) mobile robot network

Villarreal-Cervantes, Miguel Gabriel; Sánchez-Santana, J. Pedro; Guerrero-Castellanos, J.F.

doi:10.1016/j.isatra.2019.07.005

Cited by 23 publications

(8 citation statements)

References 36 publications

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“…Next, to validate the advantages of the proposed ET errorfeedback controller, a time-triggered controller which was adopted in Hossain et al (2021), Villarreal-Cervantes et al (2020 is further applied to the considered uncertain FO system. Here, the time-triggered sampling period is chosen as 0:05s, then the corresponding tracking error trajectory is given in Figure 7.…”

Section: Simulation Resultsmentioning

confidence: 99%

Event-triggered robust tracking control for fractional-order uncertain systems

Tian

Chen

2021

Transactions of the Institute of Measurement and Control

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In this paper, based on event-triggered (ET) mechanisms, the problem of output tracking for a class of fractional-order uncertain systems with order [Formula: see text] is investigated. Owing to the difficulties of measuring system full-state in practice, output tracking error is firstly used to construct an ET condition, which eventually decides whether the current signal should be transmitted. Then, by utilizing the designed error-based feedback controller, some sufficient conditions are presented to ensure that the controlled system output asymptotically tracks the reference signal. Finally, numerical simulations are performed to validate the effectiveness of the theoretical formulation.

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Section: Simulation Resultsmentioning

confidence: 99%

Event-triggered robust tracking control for fractional-order uncertain systems

Tian

Chen

2021

Transactions of the Institute of Measurement and Control

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“…A transmission strategy without a fixed period instead of the traditional fixed period transmission is proposed to balance control accuracy and network burden simultaneously, which is called event-triggered control. In event trigger control, whether the signal at a certain sampling moment is transmitted or not is determined by an event trigger condition, so the transmission cycle is not fixed [ 19 ].…”

Section: Theoretical Basismentioning

confidence: 99%

Kinematic and dynamic control model of wheeled mobile robot under internet of things and neural network

Liu

Cong

2022

J Supercomput

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This study aims to solve the issues of nonlinearity, non-integrity constraints, under-actuated systems in mobile robots. The wheeled robot is selected as the research object, and a kinematic and dynamic control model based on Internet of Things (IoT) and neural network is proposed. With the help of IoT sensors, the proposed model can realize effective control of the mobile robot under the premise of ensuring safety using the model tracking scheme and the radial basis function adaptive control algorithm. The results show that the robot can be controlled effectively to break the speed and acceleration constraints using the strategy based on the model predictive control, thus realizing smooth movement under the premise of safety. The self-adapting algorithm based on the IoT and neural network shows notable advantages in parameter uncertainty and roller skidding well. The proposed model algorithm shows a fast convergence rate of about 2 s, which has effectively improved performances in trajectory tracking and robustness of the wheeled mobile robot, and can solve the difficulties of wheeled mobile robots in practical applications, showing reliable reference value for algorithm research in this field.

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“…In contrast to some reported works where the tuning process is set at each specific time interval, in the present work, the control parameter update times are reduced by the incorporation of an event condition. The proposed event function relates the rate of change of the Lyapunov function between two instants [40,41]. Decreasing the control parameter updating, the computational cost for the overall tuning process can be reduced.…”

Section: A Contributionsmentioning

confidence: 99%

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

2021

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The fusion of bio-inspired algorithms into online controller tuning (adaptive controller tuning) is one of the main topics in Intelligent Control. One crucial issue is to reduce the times that the tuning process performs over time. In this work, a novel Asynchronous Adaptive Controller Tuning (AACT) approach is proposed to reduce the number of tuning process activations, and hence, this promotes resource savings in the overall computational cost for the tuning process. In this approach, an event function is designed to determine the control parameter update where the use of identification and predictive stages set the current control parameters. Furthermore, an elitist initialization in the differential evolution algorithm is also incorporated for solving the optimization problems at each stage. The speed regulation of the DC motor under disturbances is the study case in the ACCT approach. Comparative results with state-of-theart bio-inspired algorithms in control tuning reveal in the AACT approach that the elitist initialization in differential evolution notably benefits the controller performance. Moreover, the comparative results with the Synchronous Adaptive Controller Tuning (SACT) approach show that the proposal reduces 61% the tuning process computation frequency with a similar speed regulation performance when disturbances appear.INDEX TERMS Optimal controller tuning, DC motor, Event based tuning, Bio-inspired algorithm.

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Periodic Event-Triggered Control strategy for a (3,0) mobile robot network

Cited by 23 publications

References 36 publications

Event-triggered robust tracking control for fractional-order uncertain systems

Event-triggered robust tracking control for fractional-order uncertain systems

Kinematic and dynamic control model of wheeled mobile robot under internet of things and neural network

Novel Asynchronous Activation of the Bio-Inspired Adaptive Tuning in the Speed Controller: Study Case in DC Motors

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