Communications of the ACM - Special issue on parallelism
On the adaptive control of robot manipulators
International Journal of Robotics Research
Connectionist learning for control: an overview
Neural networks for control
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Three-dimensional neural net for learning visuomotor coordination of a robot arm
IEEE Transactions on Neural Networks
Gaussian networks for direct adaptive control
IEEE Transactions on Neural Networks
Emergence and Categorization of Coordinated Visual Behavior ThroughEmbodied Interaction
Machine Learning - Special issue on learning in autonomous robots
Fuzzy-neural-genetic layered multi-agent reactive control of robotic soccer
Data mining for design and manufacturing
Feedback error learning and nonlinear adaptive control
Neural Networks
Adaptive hybrid control for noise rejection
NN'08 Proceedings of the 9th WSEAS International Conference on Neural Networks
Impedance control of a manipulator using a fuzzy model reference learning controller
ACMOS'08 Proceedings of the 10th WSEAS International Conference on Automatic Control, Modelling & Simulation
Parallel distributed fuzzy PID control of hydro turbine generator
FS'08 Proceedings of the 9th WSEAS International Conference on Fuzzy Systems
Design and analysis of GA based neural/fuzzy optimum adaptive control
WSEAS Transactions on Systems and Control
Modeling and fault tolerant controller design for PM spherical actuator
ICIRA'10 Proceedings of the Third international conference on Intelligent robotics and applications - Volume Part I
Advances in Artificial Neural Systems
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Neuro-controller design for nonlinear fighter aircraft maneuver using fully tuned RBF networks
Automatica (Journal of IFAC)
Misconceptions of PD control in animation
EUROSCA'12 Proceedings of the 11th ACM SIGGRAPH / Eurographics conference on Computer Animation
Misconceptions of PD control in animation
Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Novel method for using Q-learning in small microcontrollers
Proceedings of the 51st ACM Southeast Conference
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This paper presents new learning schemes using feedback-error-learning for a neural network model applied to adaptive nonlinear feedback control. Feedback-error-learning was proposed as a learning method for forming a feedforward controller that uses the output of a feedback controller as the error for training a neural network model. Using new schemes for nonlinear feedback control, the actual responses after learning correspond to the desired responses which are defined by an inverse reference model implemented as a conventional feedback controller. In this respect, these methods are similar to Model Reference Adaptive Control (MRAC) applied to linear or linearized systems. It is shown that learning impedance control is derived when one proposed scheme is used in Cartesian space. We show the results of applying these learning schemes to an inverted pendulum and a 2-link manipulator. We also discuss the convergence properties of the neural network models employed in these learning schemes by applying the Lyapunov method to the averaged equations associated with the stochastic differential equations which describe the system dynamics.