Showing items 1 - 5 of 5

A dynamic surface controller based on adaptive neural network for dual arm robots

- Le, Hai, Nguyen, Linh, Thiyagarajan, Karthick

  • Authors: Le, Hai , Nguyen, Linh , Thiyagarajan, Karthick
  • Date: 2020
  • Type: Text , Conference paper
  • Relation: 15th IEEE Conference on Industrial Electronics and Applications, ICIEA 2020 p. 555-560
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  • Description: The paper introduces an adaptive controller to efficiently manipulate the dual arms of a robot (DAR) under uncertainties including actuator nonlinearities, system parameter variations and external disturbances. It is proposed that by the use of the dynamic surface control (DSC) method, the control strategy is first established, which enables the robot arms to robustly operate on the desired trajectories. Nevertheless, the dynamic model parameters of the DAR system are unknown and impractically estimated due to its uncertain nonlinearities and unexpected external factors. Hence, it is then proposed to employ the radial basis function network (RBFN) to adaptively estimate the uncertain system parameters. The Lyapunov theory is theoretically utilized to derive the adaptation mechanism so that the stability of the closed-loop control system is guaranteed. The proposed RBFN-DSC approach was validated in a synthetic environment with the promising results. © 2020 IEEE.

Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots

- Van Nguyen, Thien, Le, Hai, Tran, Hoang, Nguyen, Duc, Nguyen, Minh, Nguyen, Linh

An efficient adaptive fuzzy hierarchical sliding mode control strategy for 6 degrees of freedom overhead crane

- Pham, Hung, Hoang, Quoc-Dong, Pham, Minh, Do, Dung, Phi, Nha-Hoang, Hoang, Duy, Le, Hai, Kim, Thai, Nguyen, Linh

  • Authors: Pham, Hung , Hoang, Quoc-Dong , Pham, Minh , Do, Dung , Phi, Nha-Hoang , Hoang, Duy , Le, Hai , Kim, Thai , Nguyen, Linh
  • Date: 2022
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 11, no. 5 (2022), p.
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  • Description: The paper proposes a new approach to efficiently control a three-dimensional overhead crane with 6 degrees of freedom (DoF). Most of the works proposing a control law for a gantry crane assume that it has five output variables, including three positions of the trolley, bridge, and pulley and two swing angles of the hoisting cable. In fact, the elasticity of the hoisting cable, which causes oscillation in the cable direction, is not fully incorporated into the model yet. Therefore, our work considers that six under-actuated outputs exist in a crane system. To design an efficient controller for the 6 DoF crane, it first employs the hierarchical sliding mode control approach, which not only guarantees stability but also minimizes the sway and oscillation of the overhead crane when it transports a payload to a desired location. Moreover, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by utilizing the fuzzy inference rule mechanism, which results in efficient operations of the crane in real time. More importantly, stabilization of the crane controlled by the proposed algorithm is theoretically proved by the use of the Lyapunov function. The proposed control approach was implemented in a synthetic environment for the extensive evaluation, where the obtained results demonstrate its effectiveness. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.

An efficient approach for SIMO systems using adaptive fuzzy hierarchical sliding mode control

- Van Nguyen, Thien, Le, Hai, Tran, Hoang, Nguyen, Duc, Nguyen, Minh, Nguyen, Linh

  • Authors: Van Nguyen, Thien , Le, Hai , Tran, Hoang , Nguyen, Duc , Nguyen, Minh , Nguyen, Linh
  • Date: 2021
  • Type: Text , Conference paper
  • Relation: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2021 p. 85-90
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  • Description: The paper addresses the problem of efficiently controlling a class of single input multiple output (SIMO) under-Actuated robotic systems such as a two dimensional inverted pendulum cart or a two dimensional overhead crane. It is first proposed to employ the hierarchical sliding mode control approach to design a control law, which guarantees stability and anti-swing of the vehicle when it is driven on a predefined trajectory. More importantly, the unknown and uncertain parameters of the system caused by its actuator nonlinearity and external disturbances are adaptively estimated and inferred by the proposed fuzzy logic mechanism, which results in the efficient operation of the SIMO under-Actuated system in real time. The proposed algorithm was then implemented in the synthetic environment, where the obtained results demonstrate its effectiveness. © 2021 IEEE.

Consistency of control performance in 3d overhead cranes under payload mass uncertainty

- Hoang, Uyen, Le, Hai, Thai, Nguyen, Pham, Hung, Nguyen, Linh

  • Authors: Hoang, Uyen , Le, Hai , Thai, Nguyen , Pham, Hung , Nguyen, Linh
  • Date: 2020
  • Type: Text , Journal article
  • Relation: Electronics (Switzerland) Vol. 9, no. 4 (2020), p.
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  • Description: The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
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