Adaptive Dynamic Programming based Control Scheme for Uncertain Two-Wheel Robots
- 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, 28 April 2021 through 29 April 2021 p. 111-116
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- Description: The paper addresses the problem of effectively controlling a two-wheel robot given its inherent non-linearity and parameter uncertainties. In order to deal with the unknown and uncertain dynamics of the robot, it is proposed to employ the adaptive dynamic programming, a reinforcement learning based technique, to develop an optimal control law. It is interesting that the proposed algorithm does not require kinematic parameters while finding the optimal state controller is guaranteed. Moreover, convergence of the optimal control scheme is theoretically proved. The proposed approach was implemented in a synthetic two-wheel robot where the obtained results demonstrate its effectiveness. © 2021 IEEE.
An adaptive hierarchical sliding mode controller for autonomous underwater vehicles
- Authors: Van Vu, Quang , Dinh, Tuan , Van Nguyen, Thien , Tran, Hoang , Nguyen, Linh
- Date: 2021
- Type: Text , Journal article
- Relation: Electronics (Switzerland) Vol. 10, no. 18 (2021), p.
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- Description: The paper addresses a problem of efficiently controlling an autonomous underwater vehicle (AUV), where its typical underactuated model is considered. Due to critical uncertainties and nonlinearities in the system caused by unavoidable external disturbances such as ocean currents when it operates, it is paramount to robustly maintain motions of the vehicle over time as expected. Therefore, it is proposed to employ the hierarchical sliding mode control technique to design the closed-loop control scheme for the device. However, exactly determining parameters of the AUV control system is impractical since its nonlinearities and external disturbances can vary those parameters over time. Thus, it is proposed to exploit neural networks to develop an adaptive learning mechanism that allows the system to learn its parameters adaptively. More importantly, stability of the AUV system controlled by the proposed approach is theoretically proved to be guaranteed by the use of the Lyapunov theory. Effectiveness of the proposed control scheme was verified by the experiments implemented in a synthetic environment, where the obtained results are highly promising. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Linh Nguyen" is provided in this record**
An efficient approach for SIMO systems using adaptive fuzzy hierarchical sliding mode control
- 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
- Full Text: false
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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.