Feedforward fuzzy trajectory compensator with robust adaptive observer at input trajectory level for uncertain multi-link robot manipulators

Chen, Yuan; Wang, Kangling; Zhai, Longying; Gao, Jun

doi:10.1016/j.jfranklin.2017.02.034

Cited by 10 publications

(6 citation statements)

References 24 publications

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“…Theorem 1. Consider the dynamics function of an n-link rigid robot manipulator (1), choose the control law (15) and the RBFNN adaptive law (24), then the tracking error and the convergence of the closed-loop system parameters can be achieved.…”

Section: Nn-based Adaptive Tracking Controller Designmentioning

confidence: 99%

“…Similar research can be referred to the literature. [14][15][16][17] However, there still exist many problems in practical applications when using adaptive control method, such as it is difficult to determine the initial value and estimate range of parameter estimation. In addition, it is necessary to linearize the dynamics equations in the controller design process, and a large number of calculations are needed to determine the regression matrix.…”

Section: Introductionmentioning

confidence: 99%

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Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

Wang

Zhang

2020

International Journal of Advanced Robotic Systems

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In this article, a robust adaptive tracking controller is developed for robot manipulators with uncertain dynamics using radial basis function neural network. The design of tracking control systems for robot manipulators is a highly challenging task due to external disturbance and the uncertainties in their dynamics. The improved radial basis function neural network is chosen to approximate the uncertain dynamics of robot manipulators and learn the upper bound of the uncertainty. The adaptive law based on the Lyapunov stability theory is used to solve the uniform final bounded problem of the radial basis function neural network weights, which guarantees the stability and the consistent bounded tracking error of the closed-loop system. Finally, the simulation results are provided to demonstrate the practicability and effectiveness of the proposed method.

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Section: Nn-based Adaptive Tracking Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

Wang

Zhang

2020

International Journal of Advanced Robotic Systems

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“…The adaptive control method is often combined with other control methods such as neural network, [30][31][32] genetic algorithm, 33 and fuzzy control. [34][35][36][37] Trying to ensure that the uncertain manipulator systems could work in optimal states. However, adaptive control often requires a large amount of calculation in identifying, learning, and adjusting parameters.…”

Section: Introductionmentioning

confidence: 99%

Precise torques and sliding mode compensation for trajectory tracking of manipulator with uncertainty

Lyu

Wang

Li³

et al. 2022

International Journal of Advanced Robotic Systems

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The control based on dynamic model could improve the dynamic performance of manipulators and obtain better control effects than the control based on kinematic model. As manipulators are complex online multivariable systems, there are various uncertainties in different environments and working conditions. Accurate dynamic parameters are difficult to obtain in practical engineering applications. In this article, a controller is designed by combining the precise control torques obtained by the analytical dynamics method with the compensation control torques obtained by sliding mode method for trajectory tracking of the manipulator with bounded uncertainty. Precise control torques obtained from the Udwadia–Kalaba modeling method could be applied for the control of the ideal manipulator tracking the desired trajectory. Compensation control torques obtained from the sliding mode concept and the Lyapunov stability theory could be applied to compensate the uncertainties of parameters and external disturbances, thus enhancing the robustness of the system. By combining precise control torques with compensation control torques, the end point of the manipulator with uncertainty could track the end of the ideal manipulator and then track the desired trajectory. The simulation results of the three-link manipulator with uncertainty show that the control method can make the controlled target approach the desired trajectory in relatively small torque ranges and obtain high stability accuracy, and the chattering is effectively reduced at the stage of approaching the sliding mode surface.

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“…Due to its applicability scope in a vast variety of knowledge fields, many control approaches have been proposed, investigated and applied to these systems. The main objective is to improve the system performance and to successfully achieve the required task, for instance: adaptive control [7], sliding mode control [8], [9], Lyapunov based control [10], nonlinear predictive control [11], fuzzy logic control [12], optimal control [13], among others. Besides, there exist some attractive robust control strategies that can be adapted to the control and synchronization of robotic manipulators and underactuated systems [9], [14], [15], and output feedback tracking control subject to time-varying input delay and additive bounded disturbances [16].…”

Section: Introductionmentioning

confidence: 99%

Periodic Event-Triggered Control for the Stabilization of Robotic Manipulators

et al. 2020

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This paper proposes a periodic event-triggered control (PETC) design applied to nonlinear robotic systems for joint space stabilization. The update of the control signal is based on a predefined triggering mechanism that periodically considers state measurements. So, continuous state measurements required in the continuous event-triggered control (CETC), are no longer needed in this proposal. The main advantages of the proposal are: 1) to assure the convergence of the closed-loop trajectories to a compact ball centered in the origin of the error space, and 2) the relaxation of continuous measurements for the evaluation of the event condition which implies a more suitable design for real-time implementation purposes. Experimental comparative results of the proposed design with a time-triggered control (TTC) scheme show that the PETC can present suitable performance with a significant reduction of 82.20% of the controller updates mainly given in the steady-state of the closed-loop system. This implies a decrement of the computation resources due to information transfer.

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Feedforward fuzzy trajectory compensator with robust adaptive observer at input trajectory level for uncertain multi-link robot manipulators

Cited by 10 publications

References 24 publications

Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

Improved neural network-based adaptive tracking control for manipulators with uncertain dynamics

Precise torques and sliding mode compensation for trajectory tracking of manipulator with uncertainty

Periodic Event-Triggered Control for the Stabilization of Robotic Manipulators

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