Browsing by Author "Yu, Wen"

Now showing 1 - 5 of 5
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    Human-behavior learning for infinite-horizon optimal tracking problems of robot manipulators
    (IEEE, 2022-02-01) Perrusquía, Adolfo; Yu, Wen
    In this paper, a human-behavior learning approach for optimal tracking control of robot manipulators is proposed. The approach is a generalization of the reinforcement learning control problem which merges the capabilities of different intelligent and control techniques in order to solve the tracking task. Three cognitive models are used: robot and reference dynamics and neural networks. The convergence of the algorithm is achieved under a persistent exciting and experience replay fulfillment. The algorithm learns online the optimal decision making controller according to the proposed cognitive models. Simulations were carry out to verify the approach using a 2-DOF planar robot.
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    An input error method for parameter identification of a class of Euler-Lagrange systems
    (IEEE, 2021-12-14) Perrusquía, Adolfo; Garrido, Ruben; Yu, Wen
    In this paper, an input error identification algorithm for a class of Euler-Lagrange systems is proposed. The algorithm has a state-observer structure which uses the input error between the real system and an estimated model instead of the output error. Both systems are controlled by two Proportional-Derivative (PD) controllers with the same gain values. An excitation signal is added to the PD controllers to guarantee parameter estimates convergence. Stability of the complete identification method and parameter estimates convergence are assessed via Lyapunov stability theory. Simulation studies are carried out to verify the approach.
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    Model-free reinforcement learning from expert demonstrations: a survey
    (Springer, 2021-10-18) Ramírez, Jorge; Yu, Wen; Perrusquía, Adolfo
    Reinforcement learning from expert demonstrations (RLED) is the intersection of imitation learning with reinforcement learning that seeks to take advantage of these two learning approaches. RLED uses demonstration trajectories to improve sample efficiency in high-dimensional spaces. RLED is a new promising approach to behavioral learning through demonstrations from an expert teacher. RLED considers two possible knowledge sources to guide the reinforcement learning process: prior knowledge and online knowledge. This survey focuses on novel methods for model-free reinforcement learning guided through demonstrations, commonly but not necessarily provided by humans. The methods are analyzed and classified according to the impact of the demonstrations. Challenges, applications, and promising approaches to improve the discussed methods are also discussed.
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    Optimal sliding mode control for cutting tasks of quick-return mechanisms
    (Elsevier, 2021-04-28) Perrusquía, Adolfo; Flores-Campos, Juan Alejandro; Yu, Wen
    A solution of the constant cutting velocity problem of quick-return mechanisms is the main concern of this paper. An optimal sliding mode control in the task space is used to achieve uniform and accurate cuts throughout the workpiece. The switching hyperplane is designed to minimize the position error of the slider-dynamics in an infinite horizon. A Jacobian compensator is used to exploit the mechanical advantage and ensure controllability. The velocity profile is constructed in terms of the mechanism and workpiece geometric properties. Stability of the closed-loop dynamics is verified with the Lyapunov stability theory. Experiments are carried out in a quick-return mechanism prototype to validate the proposal.
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    Stable robot manipulator parameter identification: a closed-loop input error approach
    (Elsevier, 2022-04-12) Perrusquía, Adolfo; Garrido, Ruben; Yu, Wen
    This paper presents an on-line parametric estimation method for robot manipulators. The identification algorithm estimates the parameters by using the input error between the robot and a parallel estimated model. Both, the robot and the estimated model are controlled by two Proportional–Derivative (PD) controller tuned with the same gain values, and a persistent excitation (PE) signal for ensuring parameters convergence is included. The exact model matching and the estimation error cases are analysed. Noisy state measurements and filters are avoided in the model parameterization by using only the states of the estimated model. A second parameter identification algorithm, which is based on a composite update law, is also proposed. It improves parameters convergence and robustness of the update rule in presence of estimation errors. The stability of the closed-loop dynamics related to the estimated model is assessed via Lyapunov stability theory. Simulations are carried out to validate the proposed approaches.