Modelling and control of a complex system using a new approach
SMO'06 Proceedings of the 6th WSEAS International Conference on Simulation, Modelling and Optimization
Intelligent adaptive control for MIMO uncertain nonlinear systems
Expert Systems with Applications: An International Journal
Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC
Neural Processing Letters
Recurrent Fuzzy CMAC for Nonlinear System Modeling
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
A New Method for Accelerometer Dynamic Compensation Based on CMAC
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Advances in Neural Networks
Preventing bursting in approximate-adaptive control when using local basis functions
Fuzzy Sets and Systems
H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Optimal control of uncertain nonlinear systems using a neural network and RISE feedback
ACC'09 Proceedings of the 2009 conference on American Control Conference
Standalone CMAC control system with online learning ability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
International Journal of Robotics and Automation
System identification using hierarchical fuzzy CMAC neural networks
ICIC'06 Proceedings of the 2006 international conference on Intelligent computing: Part II
Brief paper: Asymptotic optimal control of uncertain nonlinear Euler-Lagrange systems
Automatica (Journal of IFAC)
Tracking control based on neural network for robot manipulator
TAINN'05 Proceedings of the 14th Turkish conference on Artificial Intelligence and Neural Networks
Gait Pattern Based on CMAC Neural Network for Robotic Applications
Neural Processing Letters
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This paper is concerned with the application of quadratic optimization for motion control to feedback control of robotic systems using cerebellar model arithmetic computer (CMAC) neural networks. Explicit solutions to the Hamilton-Jacobi-Bellman (H-J-B) equation for optimal control of robotic systems are found by solving an algebraic Riccati equation. It is shown how the CMAC can cope with nonlinearities through optimization with no preliminary off-line learning phase required. The adaptive-learning algorithm is derived from Lyapunov stability analysis, so that both system-tracking stability and error convergence can be guaranteed in the closed-loop system. The filtered-tracking error or critic gain and the Lyapunov function for the nonlinear analysis are derived from the user input in terms of a specified quadratic-performance index. Simulation results from a two-link robot manipulator show the satisfactory performance of the proposed control schemes even in the presence of large modeling uncertainties and external disturbances