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Thang Nguyen Trong
Department of Electrical Engineering and Automation, Haiphong Private University, Haiphong 181810, Vietnam

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Journal article
Published: 01 March 2018 in Energies
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In any drive system, there are always couplings between the motor and the load. Since the hardness of these couplings is finite, they have elastic properties, causing unwanted vibration and negatively affecting system quality. When the couplings are springs with nonlinear characteristics, control is particularly difficult because it is very difficult or impossible to define the parameters of the controlled object. To solve these difficulties, this article proposes an adaptive controller of the major functions for controlling a drive system with nonlinear elastic couplings of unidentified parameters. For the proposed control system, we measure the response speed of the object, use a Luenberger observer to estimate the state variables of the system, and use an adaptive controller to control the system. The experimental results demonstrate that the control object can be controlled without knowing the parameters: the control quality of the system is very good, close to that of a system with a hard coupling, there is no vibration or overshoot, and the transition time is small.

ACS Style

Dung Tran Anh; Thang Nguyen Trong. Adaptive Controller of the Major Functions for Controlling a Drive System with Elastic Couplings. Energies 2018, 11, 531 .

AMA Style

Dung Tran Anh, Thang Nguyen Trong. Adaptive Controller of the Major Functions for Controlling a Drive System with Elastic Couplings. Energies. 2018; 11 (3):531.

Chicago/Turabian Style

Dung Tran Anh; Thang Nguyen Trong. 2018. "Adaptive Controller of the Major Functions for Controlling a Drive System with Elastic Couplings." Energies 11, no. 3: 531.

Journal article
Published: 14 December 2017 in Energies
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Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajectory approach to the same target is from each distinct orientation. In this research, we analyze the design of the sliding surface for this model and demonstrate which type of sliding surface is the best for the consensus problem. Lastly, simulation results are presented to certify the correctness and the effectiveness of the proposed control method.

ACS Style

Thang Nguyen Trong; Minh Nguyen Duc. Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller. Energies 2017, 10, 2127 .

AMA Style

Thang Nguyen Trong, Minh Nguyen Duc. Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller. Energies. 2017; 10 (12):2127.

Chicago/Turabian Style

Thang Nguyen Trong; Minh Nguyen Duc. 2017. "Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller." Energies 10, no. 12: 2127.

Journal article
Published: 09 December 2017 in Micromachines
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A swarm robot is a collection of large numbers of simple robots used to perform complex tasks that a single robot cannot perform or only perform ineffectively. The swarm robot works successfully only when the cooperation mechanism among individual robots is satisfied. The cooperation mechanism studied in this article ensures the formation and the distance between each pair of individual robots while moving to their destination while avoiding obstacles. The solved problems in this article include; controlling the suction/thrust force between each pair of individual robots in the swarm based on the fuzzy logic structure of the Singer-Input-Singer-Output under Mamdani law; demonstrating the stability of the system based on the Lyapunov theory; and applying control to the multitasking system of the swarm robot based on Null-Space-Behavioral control. Finally, the simulation results make certain that all the individual robots assemble after moving and avoid obstacles.

ACS Style

Nga Le Thi Thuy; Thang Nguyen Trong. The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic. Micromachines 2017, 8, 357 .

AMA Style

Nga Le Thi Thuy, Thang Nguyen Trong. The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic. Micromachines. 2017; 8 (12):357.

Chicago/Turabian Style

Nga Le Thi Thuy; Thang Nguyen Trong. 2017. "The Multitasking System of Swarm Robot based on Null-Space-Behavioral Control Combined with Fuzzy Logic." Micromachines 8, no. 12: 357.