Reinforcement learning control for coordinated manipulation of multi-robots

Li, Yanan; Chen, Long; Tee, Keng Peng; Li, Qingquan

doi:10.1016/j.neucom.2015.02.091

Cited by 25 publications

(6 citation statements)

References 27 publications

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“…e desirable of trolley's position is set as a constant so that x d � 0.1 m, while the sway angle is supposed to be minimized so that θ d � 0 rad. e length of the rope is the crucial element influencing the stability of the crane system [19], and we set an uncertainty of ± 20% to the rope length to show the ability of the applied control scheme to handle the uncertainty of rope length. e probability to explore potentially optimal fuzzy rules is set as ε � 0.5 during initial 60 s, ε � 0.3 from 60 s to 100 s, and ε � 0 after 100 s.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In recent years, there have been developments in the use of fuzzy reinforcement learning theory to solve control problems of nonlinear systems. In a recent approach [19], for the coordinated control problem of multiple manipulators, a reinforcement learning method was used to deal with the uncertainties of the dynamic models. is approach took into account minimizing both the errors of tracking trajectory and the control quantities for each robot, thereby solving the problem of the inconsistencies between different manipulators.…”

Section: Introductionmentioning

confidence: 99%

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Reinforcement Learning-Based Backstepping Control for Container Cranes

Sun

Xie

2020

Mathematical Problems in Engineering

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A novel backstepping control scheme based on reinforcement fuzzy Q-learning is proposed for the control of container cranes. In this control scheme, the modified backstepping controller can handle the underactuated system of a container crane. Moreover, the gain of the modified backstepping controller is tuned by the reinforcement fuzzy Q-learning mechanism that can automatically search the optimal fuzzy rules to achieve a decrease in the value of the Lyapunov function. The effectiveness of the applied control scheme was verified by a simulation in Matlab, and the performance was also compared with the conventional sliding mode controller aimed at container cranes. The simulation results indicated that the used control scheme could achieve satisfactory performance for step-signal tracking with an uncertain lope length.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reinforcement Learning-Based Backstepping Control for Container Cranes

Sun

Xie

2020

Mathematical Problems in Engineering

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“…In [27], an adaptive fuzzy neural network control method with impedance learning was presented for robots with constraints. In [28], the optimal coordination control which was applied to multi-robots to follow expected trajectories was presented by means of reinforcement learning. Tang et al [29] employed a reinforcement learning-based adaptive optimal control method to realize the optimal tracking of n-DOF manipulators.…”

Section: Related Workmentioning

confidence: 99%

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

Ren

2021

Complex Intell. Syst.

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This paper investigates a feature tracking control method for visual servoing (VS) manipulators adaptive dynamic programming (ADP)-based the unknown dynamics. The major superiority of ADP-based optimal control lies in that the visual tracking problem is converted to the feature tracking error control with optimal cost function. Moreover, an adaptive neural network observer is developed to approximate the entire uncertainties, which are utilized to construct an improved cost function. By establishing a critic neural network, the Hamilton–Jacobi–Bellman (HJB) equation is solved, and the approximate optimal error control policy is derived. The closed-loop VS manipulator system is verified to be ultimately uniformly bounded with the developed ADP-based feature tracking control strategy according to the Lyapunov theory. Finally, simulation results under various situations demonstrate that the proposed method achieves higher tracking accuracy than other methods, as well as satisfies energy optimal requirements.

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“…Consider two homogeneous manipulators with 2 revolute joints [50]. These manipulators with force sensor mounted on the end-effectors share same parameters as listed in TABLE I, where m i , l i and I i , i = 1, 2 are mass, length and inertia of the ith link, respectively.…”

Section: A Robot Manipulator Modelmentioning

confidence: 99%

Admittance-Based Adaptive Cooperative Control for Multiple Manipulators With Output Constraints

Yang

Yan

et al. 2019

IEEE Trans. Neural Netw. Learning Syst.

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This paper proposes a novel adaptive control methodology based on admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. Firstly, an admittance model is creatively applied to generate reference trajectory online for each manipulator according to the desired path of the rigid object, which is the reference input of the controller. Then, an innovative integral Barrier Lyapunov function (iBLF) is utilized to tackle the constraints due to the physical and environmental limits. Adaptive neural networks (NN) are also employed to approximate the uncertainties of the manipulator dynamics. Different from the conventional NN approximation method, which is usually semiglobally uniformly ultimately bounded (SGUUB), a switching function is presented to guarantee the global stability of the closed-loop. Finally, the simulation studies are conducted on planar 2-link robot manipulators to validate the efficacy of the proposed approach.

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Reinforcement learning control for coordinated manipulation of multi-robots

Cited by 25 publications

References 27 publications

Reinforcement Learning-Based Backstepping Control for Container Cranes

Reinforcement Learning-Based Backstepping Control for Container Cranes

Adaptive dynamic programming-based feature tracking control of visual servoing manipulators with unknown dynamics

Admittance-Based Adaptive Cooperative Control for Multiple Manipulators With Output Constraints

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