Stable adaptive systems
Adaptive tuning of the fuzzy controller for robots
Fuzzy Sets and Systems
Dynamic Modeling and Tracking Control of a Nonholonomic Wheeled Mobile Manipulator with Dual Arms
Journal of Intelligent and Robotic Systems
Automatica (Journal of IFAC)
H∞ reinforcement learning control of robot manipulators using fuzzy wavelet networks
Fuzzy Sets and Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews - Special issue on information reuse and integration
A Sliding-Mode-Control Law for Mobile Robots Based on Epipolar Visual Servoing From Three Views
IEEE Transactions on Robotics
Adaptive Robust Motion/Force Control of Holonomic-Constrained Nonholonomic Mobile Manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Fractional Fuzzy Adaptive Sliding-Mode Control of a 2-DOF Direct-Drive Robot Arm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Nonsingular Terminal Sliding Mode Control of Robot Manipulators Using Fuzzy Wavelet Networks
IEEE Transactions on Fuzzy Systems
Neural-network control of mobile manipulators
IEEE Transactions on Neural Networks
Neural net robot controller with guaranteed tracking performance
IEEE Transactions on Neural Networks
Multidimensional wavelet frames
IEEE Transactions on Neural Networks
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This paper presents a robust adaptive sliding-mode control (RASMC) scheme for a class of condenser-cleaning mobile manipulator (CCMM) in the presence of parametric uncertainties and external disturbances. The development of control system is based on the fuzzy wavelet neural network (FWNN). First, a dynamic model is obtained in view of the practical CCMM system. Second, the FWNN is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using learning ability of neural networks, RASMC can coordinately control the condenser-cleaning mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the adaptive sliding-mode control. Finally, based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimately boundedness of the tracking error are all strictly guaranteed. Moreover, simulation results validate the superior control performance of the proposed adaptive control method.