Radial-basis-functions neural network sliding mode control for underactuated manipulators

Mahjoub, Sonia; Mnif, F.; Derbel, Nabil

doi:10.1109/ssd.2013.6564106

Cited by 6 publications

(2 citation statements)

References 18 publications

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“…Indeed, the stabilization requires the transformation of the system in the strict-feedback form to define the sliding variable with respect to the desired trajectory expressed in (21). The final control law term is expressed in (8). The proposed sliding variable required to implement the STW controller in our case is expressed as…”

Section: Proposed Control Approach a Design Of The Super-twisting Controllermentioning

confidence: 99%

Disturbance Observer-Based Super-Twisting Control for the Inertia Wheel Inverted Pendulum

Hfaiedh¹,

Chemori

Abdelkrim³

2020

2020 17th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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In this paper, the problems of stabilization and disturbance rejection in the control of class I of underactuated mechanical systems (UMS) are addressed. Based on a global change of coordinates, the original dynamic model is transformed into a strict-feedback form and a Super-Twisting (STW) controller is designed to resolve the stabilization problem. To deal with the problem of uncertainties and external disturbances in UMS, a disturbance observer (DO) is proposed for the estimation of the input disturbances in the aim of compensating them in the controller and improving the performance and robustness of the resulting closed-loop system. Indeed, the proposed observer-based scheme is compared with the standard STW controller. Both controllers have been implemented and validated through real-time experiments on the inertia wheel inverted pendulum. The obtained results show clearly the superiority of the proposed observer-based STW control scheme and its effectiveness in terms of external disturbance rejection.

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Section: Proposed Control Approach a Design Of The Super-twisting Controllermentioning

confidence: 99%

Disturbance Observer-Based Super-Twisting Control for the Inertia Wheel Inverted Pendulum

Hfaiedh¹,

Chemori

Abdelkrim³

2020

2020 17th International Multi-Conference on Systems, Signals &Amp; Devices (SSD)

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“…Therefore, in this work, we propose a new efficient adaptive sliding mode control scheme for a 3D overhead crane system that comprises of hierarchical sliding surfaces and radial basis function networks (RBFN). In literature, the RBFN based SMC method has been employed in industry robotic systems [40], manipulators [41], [42], underactuated mechanical systems [43] and static var compensators [44]. The controller proposed in this paper is first created by the HSMC approach, where a second-level sliding surface is linearly constructed by two first-level sliding surfaces of two corresponding actuated and un-actuated subsystems.…”

Section: Introductionmentioning

confidence: 99%

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

Nguyen

et al. 2019

Int. J. Autom. Comput.

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In this paper, a new adaptive hierarchical sliding mode control scheme for a 3D overhead crane system is proposed. A controller is first designed by the use of a hierarchical structure of two first-order sliding surfaces represented for two actuated and unactuated subsystems in the bridge crane. Parameters of the controller are then intelligently estimated, where uncertain parameters due to disturbances in the 3D overhead crane dynamic model are proposed to be represented by radial basis function networks whose weights are derived from a Lyapunov function. The proposed approach allows the crane system to be robust under uncertainty conditions in which some uncertain and unknown parameters are intractable to be determined. Moreover, stability of the sliding surfaces is proved to be guaranteed. Effectiveness of the proposed approach is then demonstrated by implementing the algorithm in both synthetic and real-life systems, where the results obtained by our method are highly promising.

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Radial-basis-functions neural network sliding mode control for underactuated mechanical systems

Mahjoub

Mnif

Derbel

et al. 2014

Int. J. Dynam. Control

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Radial-basis-functions neural network sliding mode control for underactuated manipulators

Cited by 6 publications

References 18 publications

Disturbance Observer-Based Super-Twisting Control for the Inertia Wheel Inverted Pendulum

Disturbance Observer-Based Super-Twisting Control for the Inertia Wheel Inverted Pendulum

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

Radial-basis-functions neural network sliding mode control for underactuated mechanical systems

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