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

Wang, Donghui; Zhang, Shijie

doi:10.1177/1729881420947562

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…To realize the online learning of weight, the adaptive algorithm and the gradient descent are combined. According to Equation ( 5) and the Lyapunov stability principle [36], the adaptive learning rate of the output layer of the PCR network is designed as shown in Equation (20).…”

Section: Weight Update Of Pcr Networkmentioning

confidence: 99%

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Parallel Network-Based Sliding Mode Tracking Control for Robotic Manipulators with Uncertain Dynamics

Zhang

Song

et al. 2023

Actuators

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Robot dynamics model uncertainty and unpredictable external perturbations are important factors that influence control accuracy and stability. To accurately compensate for the dynamics model in sliding mode control (SMC), a new parallel network (PCR) is proposed in this paper. The network parallelizes the radial basis function and convolutional neural network, which gives it the advantage of making full use of one-dimensional data fitting results and two-dimensional data feature information, realizing the deep learning of multidimensional data and improving the model’s compensation accuracy and anti-interference ability. Meanwhile, based on the integration of adaptive control techniques and gradient descent, a new weight update algorithm is designed to realize the online learning of PCR networks under loss-free functions. Then, a new sliding mode controller (PCR-SMC) is established. The model-free intelligent control of the robot is accomplished without knowledge of the predetermined upper bounds. Additionally, the stability analysis of the control system is proved by the Lyapunov theorem. Lastly, robot tracking control simulations are performed on two trajectories. The results demonstrate the high-precision tracking performance of this controller in comparison with the RBF-SMC controller.

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Section: Weight Update Of Pcr Networkmentioning

confidence: 99%

“…This method effectively improved the tracking control accuracy by independently compensating for joint uncertainty. Wang et al [20] improved the input of an RBFNN using a nearestneighbor clustering algorithm, and then applied it to the uncertainty compensation of robotic systems.…”

Section: Introductionmentioning

confidence: 99%

Parallel Network-Based Sliding Mode Tracking Control for Robotic Manipulators with Uncertain Dynamics

Zhang

Song

et al. 2023

Actuators

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“…In some applications of coordinated manipulators, some adaptive control methods are also attractive that behave with perfect performance [7][8][9][10]. In order to achieve higher performance, adaptive control schemes are usually combined with other control methods, such as robust control methods [11,12], and neural networks [13,14].…”

Section: Introductionmentioning

confidence: 99%

A New Recursive Composite Adaptive Controller for Robot Manipulators

Wang

Xin

et al. 2021

Space Sci Technol

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In this paper, a new recursive implementation of composite adaptive control for robot manipulators is proposed. We investigate the recursive composite adaptive algorithm and prove the stability directly based on the Newton-Euler equations in matrix form, which, to our knowledge, is the first result on this point in the literature. The proposed algorithm has an amount of computation On, which is less than any existing similar algorithms and can satisfy the computation need of the complicated multidegree manipulators. The manipulator of the Chinese Space Station is employed as a simulation example, and the results verify the effectiveness of this proposed recursive algorithm.

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“…In recent years, many control techniques have been proposed for manipulators. Wang and Zhang proposed an adaptive neural network tracking control scheme for robotic manipulator, which achieved good performance in dealing with model uncertainty [7]. Aydin proposed a robust sliding mode control scheme to suppress model uncertainty [8].…”

Section: Introductionmentioning

confidence: 99%

Repetitive Control Scheme of Robotic Manipulators Based on Improved B‐Spline Function

Wang

et al. 2021

Complexity

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In this paper, a repetitive control scheme of a 2-DOF robotic manipulator based on the improved cubic B-spline curve is proposed. Firstly, a repetitive controller for robotic manipulator is designed, which is composed of an iterative controller and disturbance observer. Then, an improved B-spline optimization scheme is introduced to divide the task of the robotic manipulator into three intervals. A correction function is added to each interval of cubic spline interpolation. Finally, a variety of cases are designed and simulated by MATLAB. The experimental results show that, compared with the conventional B-spline, the improved B-spline has better performance in tracking accuracy and smoothness of motion trajectory. By changing the mechanism of the manipulator, the cases of different weights and lengths are designed. The experimental results in these cases show that the proposed scheme can be applied to most of the 2-DOF robotic manipulator control systems.

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

Cited by 6 publications

References 20 publications

Parallel Network-Based Sliding Mode Tracking Control for Robotic Manipulators with Uncertain Dynamics

Parallel Network-Based Sliding Mode Tracking Control for Robotic Manipulators with Uncertain Dynamics

A New Recursive Composite Adaptive Controller for Robot Manipulators

Repetitive Control Scheme of Robotic Manipulators Based on Improved B‐Spline Function

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