Application of a general learning algorithm to the control of robotic manipulators
International Journal of Robotics Research
A model reference control structure using a fuzzy neural network
Fuzzy Sets and Systems
CMAC with general basis functions
Neural Networks
Design of a single-input fuzzy logic controller and its properties
Fuzzy Sets and Systems
Single-input CMAC control system
Neurocomputing
Optimal design of CMAC neural-network controller for robotmanipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Smooth trajectory tracking of three-link robot: a self-organizingCMAC approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Design and stability analysis of single-input fuzzy logiccontroller
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive CMAC-based supervisory control for uncertain nonlinear systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A self-organizing CMAC network with gray credit assignment
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Learning PID structures in an introductory course of automaticcontrol
IEEE Transactions on Education
Generalizing CMAC architecture and training
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
High-order MS CMAC neural network
IEEE Transactions on Neural Networks
Diagonal recurrent neural networks for dynamic systems control
IEEE Transactions on Neural Networks
Learning automata based dynamic guard channel algorithms
Computers and Electrical Engineering
Indirect adaptive self-organizing RBF neural controller design with a dynamical training approach
Expert Systems with Applications: An International Journal
Chaos synchronization of nonlinear gyros using self-learning PID control approach
Applied Soft Computing
Adaptive PI Hermite neural control for MIMO uncertain nonlinear systems
Applied Soft Computing
The intelligent integrated speed controller of DTC for induction motor
Artificial Life and Robotics
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A cerebellar model articulation controller (CMAC) control system, which contains only one single-input controller implemented by a differentiable CMAC, is proposed in this paper. In the proposed scheme, the CMAC controller is solely used to control the plant, and no conventional controller is needed. Without a preliminary offline learning, the single-input CMAC controller can provide the control effort to the plant at each online learning step. To train the differentiable CMAC online, the gradient descent algorithm is employed to derive the learning rules. The sensitivity of the plant, with respect to the input, is approximated by a simple formula so that the learning rules can be applied to unknown plants. Moreover, based on a discrete-type Lyapunov function, conditions on the learning rates guaranteeing the convergence of the output error are derived in this paper. Finally, simulations on controlling three different plants are given to demonstrate the effectiveness of the proposed controller.