On the adaptive control of robot manipulators
International Journal of Robotics Research
Adaptive control: stability, convergence, and robustness
Adaptive control: stability, convergence, and robustness
Stable adaptive systems
Composite adaptive control of robot manipulators
Automatica (Journal of IFAC)
Direct adaptive control algorithms: theory and applications
Direct adaptive control algorithms: theory and applications
Artificial Intelligence Review - Special issue on lazy learning
Constructive incremental learning from only local information
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Nonlinear and Adaptive Control Design
Nonlinear and Adaptive Control Design
Locally Weighted Projection Regression: Incremental Real Time Learning in High Dimensional Space
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Output feedback control of nonlinear systems using RBF neural networks
IEEE Transactions on Neural Networks
Nonlinear adaptive control using networks of piecewise linear approximators
IEEE Transactions on Neural Networks
Letters: Adaptive biomimetic control of robot arm motions
Neurocomputing
Adaptive control with composite learning for tubular linear motors with micro-metric tolerances
ACC'09 Proceedings of the 2009 conference on American Control Conference
International Journal of Knowledge Engineering and Data Mining
Adaptive control for nonlinear systems with time-varying control gain
Journal of Control Science and Engineering - Special issue on Adaptive Control Theory and Applications
Composite adaptive posicast control for a class of LTI plants with known delay
Automatica (Journal of IFAC)
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This paper introduces a provably stable learning adaptive control framework with statistical learning. The proposed algorithm employs nonlinear function approximation with automatic growth of the learning network according to the nonlinearities and the working domain of the control system. The unknown function in the dynamical system is approximated by piecewise linear models using a nonparametric regression technique. Local models are allocated as necessary and their parameters are optimized on-line. Inspired by composite adaptive control methods, the proposed learning adaptive control algorithm uses both the tracking error and the estimation error to update the parameters.We first discuss statistical learning of nonlinear functions, and motivate our choice of the locally weighted learning framework. Second, we begin with a class of first order SISO systems for theoretical development of our learning adaptive control framework, and present a stability proof including a parameter projection method that is needed to avoid potential singularities during adaptation. Then, we generalize our adaptive controller to higher order SISO systems, and discuss further extension to MIMO problems. Finally, we evaluate our theoretical control framework in numerical simulations to illustrate the effectiveness of the proposed learning adaptive controller for rapid convergence and high accuracy of control.