Parallel distributed processing: explorations in the microstructure of cognition, vol. 2: psychological and biological models
Adaptive control of mechanical manipulators
Adaptive control of mechanical manipulators
Adaptive control of mechanical manipulators
International Journal of Robotics Research
Application of a general learning algorithm to the control of robotic manipulators
International Journal of Robotics Research
On the adaptive control of robot manipulators
International Journal of Robotics Research
Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks for control
Artificial neural network based control on nonlinear systems with application to robotic manipulators
Fundamentals for Control of Robotic Manipulators
Fundamentals for Control of Robotic Manipulators
Feedback Systems: Input-Output Properties
Feedback Systems: Input-Output Properties
Motor Control and Learning by the State Space Model
Motor Control and Learning by the State Space Model
Friction modelling and compensation for motion control using hybrid neural network models
Engineering Applications of Artificial Intelligence
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This paper addresses the tracking control problem of robotic manipulators with unknown and changing dynamics. In this study, nonlinear dynamics of the robotic manipulator is assumed to be unknown and a control scheme is developed to adaptively estimate the unknown manipulator dynamics utilizing generic artificial neural network models to approximate the underlying dynamics. Based on the error dynamics of the controller, a parameter update equation is derived for the adaptive ANN models and local stability properties of the controller are discussed. The proposed scheme is simulated and successfully tested for trajectory following tasks. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics.