Journal ArticleDOI
Adaptive Neural Control for Robotic Manipulators With Output Constraints and Uncertainties
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This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position, in which the barrier Lyapunov function is employed to guarantee that the joint constraints are not violated.Abstract:
This paper investigates adaptive neural control methods for robotic manipulators, subject to uncertain plant dynamics and constraints on the joint position. The barrier Lyapunov function is employed to guarantee that the joint constraints are not violated, in which the Moore–Penrose pseudo-inverse term is used in the control design. To handle the unmodeled dynamics, the neural network (NN) is adopted to approximate the uncertain dynamics. The NN control based on full-state feedback for robots is proposed when all states of the closed loop are known. Subsequently, only the robot joint is measurable in practice; output feedback control is designed with a high-gain observer to estimate unmeasurable states. Through the Lyapunov stability analysis, system stability is achieved with the proposed control, and the system output achieves convergence without violation of the joint constraints. Simulation is conducted to approve the feasibility and superiority of the proposed NN control.read more
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References
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Petros Ioannou,Jing Sun +1 more
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Barrier Lyapunov Functions for the control of output-constrained nonlinear systems
TL;DR: This paper presents control designs for single-input single-output (SISO) nonlinear systems in strict feedback form with an output constraint, and explores the use of an Asymmetric Barrier Lyapunov Function as a generalized approach that relaxes the requirements on the initial conditions.
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Neural Network Control of Robot Manipulators and Nonlinear Systems
TL;DR: This graduate text provides an authoritative account of neural network (NN) controllers for robotics and nonlinear systems and gives the first textbook treatment of a general and streamlined design procedure for NN controllers.
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Multilayer neural-net robot controller with guaranteed tracking performance
TL;DR: A multilayer neural-net (NN) controller for a general serial-link rigid robot arm is developed using a filtered error/passivity approach and novel online weight tuning algorithms guarantee bounded tracking errors as well as bounded NN weights.
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