Robust adaptive command filtered control of a robotic manipulator with uncertain dynamic and joint space constraints

Ahanda, Joseph Jean-Baptiste Mvogo; Mbede, Jean Bosco; Melingui, Achille; Zobo, Bernard Essimbi

doi:10.1017/s0263574718000036

Cited by 15 publications

(7 citation statements)

References 46 publications

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“…Ahanda et al [ 14 ] addressed the robust adaptive control of a robotic manipulator under uncertain dynamics and joint space constraints and adopted command filters to overcome the time derivatives of virtual control, eliminating the need for differentiating the desired trajectory. In addition, a barrier Lyapunov function was introduced to handle joint space constraints, and a robust adaptive support vector regression architecture was employed to suppress filtering errors, approximation errors, and dynamic uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of Flexible Articulated Manipulator Based on Robust Observer

Gao

Lü

2022

Computational Intelligence and Neuroscience

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In this paper, a robust observer-based sliding mode control algorithm is proposed to address the modelling and measurement inaccuracies, load variations, and external disturbances of flexible articulated manipulators. Firstly, a sliding mode observer was designed with exponential convergence to observe system state accurately and to overcome the measuring difficulty of the state variables, unmeasurable quantities, and external disturbances. Next, a robust sliding mode controller was developed based on the observer, such that the output error of the system converges to zero in finite time. In this way, the whole system achieves asymptotic stability. Finally, the convergence conditions of the observer were theoretically analyzed to verify the convergence of the proposed algorithm, and simulation was carried out to confirm the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Sliding Mode Control of Flexible Articulated Manipulator Based on Robust Observer

Gao

Lü

2022

Computational Intelligence and Neuroscience

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“…Consider (2) satisfying Assumption 1. Virtual inputs are given by ( 5) and ( 17) under the filters ( 8), ( 9), ( 15), (16), and (25). The adaptation laws are (10), ( 18), ( 26), ( 11), (19), and (27).…”

Section: Backstepping Control Signal Designmentioning

confidence: 99%

“…To drive the tracking error toward a sufficiently small value, one can use large W. However, too large a W value usually means that too much control energy is used. Thus, some other control methods based on CFC have been developed, for example, in references [13][14][15][16][17][18], noting that the dimensions of the virtual signal should be enlarged to involve the desired signal and its derivative. Yet, the above-mentioned literature only studied the estimation of some command derivatives, i.e., the results do not correspond to the ABC design.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Backstepping Controller Design for Uncertain Permanent Magnet Synchronous Motor Drive Chaotic Systems via Command Filter

Luo

Yan-ping

Xie³

2020

Front. Phys.

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In this study, an adaptive neural network (NN) command filtered control (CFC) method is proposed for a permanent magnet synchronous motor (PMSM) system with system uncertainties and external disturbance by means of a backstepping technique. At every backstepping step, a novel command filter is proposed, and the complicated virtual input and its derivative together can be approximated by this filter. The "explosion of complexity" problem in conventional backstepping design can be avoided because we do not need to calculate the derivative of the virtual input repeatedly. NNs are used to model system uncertainties and disturbances. Finally, an adaptive NN CFC is designed, and the convergence of the tracking error and the boundedness of all signals involved can be guaranteed. Finally, a simulation study is presented to verify the theoretical results.

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“…Besides, the multiplicative factor of the actuator faults is estimated by an adaptive estimation method, and the additive part is compensated by the upper bound of the faults. Compared with existing flexible manipulator control strategies, the main contributions of this paper are summarized as follows: Different from most of performance constraint for flexible manipulators [45, 50], this paper starts from the transient performance index, and the purpose of controlling overshoot, transient error, and steady-state error can be achieved by introducing two auxiliary functions. Then, a logarithmic BLF is utilized to constrain the transformed state variable. Compared with the traditional PPC shown in refs.…”

Section: Introductionmentioning

confidence: 99%

Broad learning control of a two-link flexible manipulator with prescribed performance and actuator faults

Niu

Kong

et al. 2023

Robotica

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In this paper, we present a broad learning control method for a two-link flexible manipulator with prescribed performance (PP) and actuator faults. The trajectory tracking errors are processed through two consecutive error transformations to achieve the constraints in terms of the overshoot, transient error, and steady-state error. And the barrier Lyapunov function is employed to implement constraints on the transition state variable. Then, the improved radial basis function neural networks combined with broad learning theory are constructed to approximate the unknown model dynamics of flexible robotic manipulator. The proposed fault-tolerant PP control cannot only ensure tracking errors converge into a small region near zero within the preset finite time but also address the problem caused by actuator faults. All the closed-loop error signals are uniformly ultimately bounded via the Lyapunov stability theory. Finally, the feasibility of the proposed control is verified by the simulation results.

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Robust adaptive command filtered control of a robotic manipulator with uncertain dynamic and joint space constraints

Cited by 15 publications

References 46 publications

Sliding Mode Control of Flexible Articulated Manipulator Based on Robust Observer

Sliding Mode Control of Flexible Articulated Manipulator Based on Robust Observer

Neural Network Backstepping Controller Design for Uncertain Permanent Magnet Synchronous Motor Drive Chaotic Systems via Command Filter

Broad learning control of a two-link flexible manipulator with prescribed performance and actuator faults

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