Adaptive RBF network control for robot manipulators
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TLDR
Simulations and comparisons with a robust neural network control approach show the efficiency of the proposed control approach applied on the articulated robot manipulator driven by permanent magnet DC motors.Abstract:
The uncertainty estimation and compensation are challenging problems for the robust control of robot manipulators which are complex systems. This paper presents a novel decentralized model-free robust controller for electrically driven robot manipulators. As a novelty, the proposed controller employs a simple Gaussian Radial-Basis-Function network (RBF network) as an uncertainty estimator. The proposed network includes a hidden layer with one node, two inputs and a single output. In comparison with other model-free estimators such as multilayer neural networks and fuzzy systems, the proposed estimator is simpler, less computational and more effective. The weights of the RBF network are tuned online using an adaptation law derived by stability analysis. Despite the majority of previous control approaches which are the torque-based control, the proposed control design is the voltage-based control. Simulations and comparisons with a robust neural network control approach show the efficiency of the proposed control approach applied on the articulated robot manipulator driven by permanent magnet DC motors.read more
Citations
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Journal ArticleDOI
Adaptive fuzzy tracking control of robot manipulators actuated by permanent magnet synchronous motors
TL;DR: A model-free controller for robot manipulators driven by permanent magnet synchronous motors (PMSM) that combines the electrical and mechanical equations of the robotic system to simplify the controller design procedure.
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Chaos synchronization using the Fourier series expansion with application to secure communications
TL;DR: A new method for secure communication based on chaos synchronization is proposed, consisted of a state feedback controller and a robust control term using the Fourier series expansion for compensation of uncertainties.
Journal ArticleDOI
Robust control of electrically driven robots using adaptive uncertainty estimation
TL;DR: This paper presents a novel robust control for electrically driven robot manipulators by designing an adaptive uncertainty estimator based on the first order Taylor series that is simpler, less computational, and more efficient.
Journal ArticleDOI
Discrete-time optimal adaptive RBFNN control for robot manipulators with uncertain dynamics
TL;DR: A novel optimal adaptive radial basis function neural network control has been investigated for a class of multiple-input-multiple-output (MIMO) nonlinear robot manipulators with uncertain dynamics in discrete time.
Journal ArticleDOI
Task-space control of robots using an adaptive Taylor series uncertainty estimator
TL;DR: A robust task-space control approach using an adaptive Taylor series uncertainty estimator for electrically driven robot manipulators is presented and the effectiveness of the proposed controller is shown through simulation and comparison with two valuable control schemes applied on the Selective Compliance Assembly Robot Arm.
References
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Jooyoung Park,Irwin W. Sandberg +1 more
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