An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

Le, Viet-Anh; Le, Hai Xuan; Nguyen, Linh; Phan, Minh-Xuan

doi:10.1007/s11633-019-1174-y

Cited by 49 publications

(39 citation statements)

References 50 publications

(35 reference statements)

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“…5. It can be clearly seen that for the purposes of comparisons, in this experimental example we implemented three algorithms including the classical sliding mode control (SMC) (Wu (2012); Le et al (2017Le et al ( , 2019), the BSSMC as discussed in Section 3.1 and the proposed method RBFN-BSSMC introduced in Section 3.2. The results obtained by the three implemented approaches are expected to reach the references, which are early obtained from equations (39-44), all the time.…”

Section: Simulation Discussionmentioning

confidence: 99%

Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Nguyen

Thai

Pham³

et al. 2019

J Control Autom Electr Syst

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The paper introduces an adaptive strategy to effectively control a nonlinear dual arm robot under external disturbances and uncertainties. By the use of the backstepping sliding mode control (BSSMC) method, the proposed algorithm first allows the manipulators to be able to robustly track the desired trajectories. Furthermore, due to the nonlinear, uncertain and unmodelled dynamics of the dual arm robot, it is proposed to employ the radial basis function network (RBFN) to adaptively estimate the robot's dynamic model. Though the estimation of the dynamics is approximate, the adaptation law is derived from the Lyapunov theory, which provides the controller with ability to guarantee stability of the whole system in spite of its nonlinearities, parameter uncertainties and external load variations. The effectiveness of the proposed RBFN-BSSMC approach is demonstrated by implementation in a simulation environment with realistic parameters, where the obtained results are highly promising.

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Section: Simulation Discussionmentioning

confidence: 99%

Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Nguyen

Thai

Pham³

et al. 2019

J Control Autom Electr Syst

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“…q , g q . To deal with a system with the high uncertainties of a 3D gantry crane, the SMC approach would provide remarkable robustness in the control performance [30]. If we divide the matrices and vectors in (3) into the submatrice R 2×2 and the subvector R 2×1 then (3) can be represented by…”

Section: A Sliding Mode Controller For a 3d Overhead Cranementioning

confidence: 99%

“…τ max and τ min are the upper and lower bounds of τ. For more information about the saturation function in designing a SMC scheme, interested readers are referred to our previous works [30].…”

Section: A Sliding Mode Controller For a 3d Overhead Cranementioning

confidence: 99%

“…In addition, an adaptive SMC law based on the fuzzy wavelet neural networks was proposed for a 3D gantry crane as discussed by other authors [28]. In our previous works [29,30], we introduced the adaptive HSMC schemes for the 2D and 3D overhead cranes, respectively, where we employed the fuzzy observer and radial basis function network to adaptively estimate the unknown and uncertain crane parameters. Nevertheless, to the best of our knowledge, none of the previous adaptive control schemes for the 3D overhead crane considered the consistency of the control performance of the closed-loop control system when the payload mass is uncertain.As a result, in this work, in order to effectively and robustly control a 3D overhead crane even under uncertainty of the payload mass, we first propose to employ the radial basis function neural network to adaptively estimate the unknown and uncertain system kinetic parameters of the controller, which is designed based on the SMC technique.…”

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confidence: 99%

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Consistency of Control Performance in 3D Overhead Cranes under Payload Mass Uncertainty

Hoang

Thai

et al. 2020

Electronics

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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.

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“…The latest studies on HSMC focus on the strategies to improve the robustness facing unknown disturbances. [11]- [16] adopt quite complicated methods, such as radial basis function networks, self-recurrent wavelet neural networks, and the method of combining fuzzy control with adaptive control, to cope with the impact of unknown disturbances on the control accuracy. In practical applications, these methods are mainly used in the situation of low speed and low real-time requirements, such as the control of overhead crane, the attitude regulation control of satellite, and the control of low-speed mobile robot.…”

Section: Introductionmentioning

confidence: 99%

Fractional-Order Adaptive Integral Hierarchical Sliding Mode Control Method for High-Speed Linear Motion of Spherical Robot

Song

2020

IEEE Access

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The development of the spherical robot to meet the requirements of high-speed and high-precision tasks is of great importance. In this study, a fractional-order adaptive integral hierarchical sliding mode controller (F-AIHSMC) is proposed. F-AIHSMC enables the spherical robot to have better controlled performance when facing unknown disturbances and system chattering, which can seriously affect the high-speed and high-precision motion of the spherical robot. We establish the standard dynamic model of the spherical robot for high-speed linear motion first, and then use the feedforward compensation method to compensate the controllable influencing factors in the motion process. According to the standard dynamic model, the integral term and fractional calculus methods are integrated into the hierarchical sliding mode controller, and the adaptive method is used to evaluate and compensate unknown disturbances in the high-speed motion process. In order to verify the efficiency of the proposed F-AIHSMC, we test its control effect using the BYQ-GS spherical robot. The experimental results demonstrate that, compared with the classical hierarchical sliding mode controller and the adaptive hierarchical sliding mode controller, the F-AIHSMC has obvious advantages in response speed, convergence speed, stability and robustness when being applied to the control of high-speed linear motion of spherical robot. Moreover, the advantages of its control performance are more highlighted with the increase of the speed of the spherical robot. INDEX TERMS Adaptive control, fractional calculus, hierarchical sliding mode control, high-speed motion control, spherical robot.

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An Efficient Adaptive Hierarchical Sliding Mode Control Strategy Using Neural Networks for 3D Overhead Cranes

Cited by 49 publications

References 50 publications

Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Adaptive Neural Network-Based Backstepping Sliding Mode Control Approach for Dual-Arm Robots

Consistency of Control Performance in 3D Overhead Cranes under Payload Mass Uncertainty

Fractional-Order Adaptive Integral Hierarchical Sliding Mode Control Method for High-Speed Linear Motion of Spherical Robot

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