Multilayer feedforward networks are universal approximators
Neural Networks
Neural adaptive tracking control of a DC motor
Information Sciences: an International Journal
Decoupled fuzzy controller design with single-input fuzzy logic
Fuzzy Sets and Systems - Control and applications
Computers and Operations Research
Adaptive friction compensation using neural network approximations
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Intelligent bounds on modeling uncertainty: applications to sliding mode control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Decoupled fuzzy sliding-mode control
IEEE Transactions on Fuzzy Systems
Brief Robust neural control for robotic manipulators
Automatica (Journal of IFAC)
Universal approximation bounds for superpositions of a sigmoidal function
IEEE Transactions on Information Theory
Intelligent adaptive control for MIMO uncertain nonlinear systems
Expert Systems with Applications: An International Journal
GA-based modified adaptive fuzzy sliding mode controller for nonlinear systems
Expert Systems with Applications: An International Journal
Modeling and control for nonlinear structural systems via a NN-based approach
Expert Systems with Applications: An International Journal
Short Communication: Intelligent fuzzy accelerated method for the nonlinear 3-D crane control
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Decoupled sliding-mode controller based on time-varying sliding surfaces for fourth-order systems
Expert Systems with Applications: An International Journal
Indirect sliding mode neural-network control for holonomic constrained robot manipulators
International Journal of Intelligent Systems Technologies and Applications
Expert Systems with Applications: An International Journal
Nonlinear controller design of a ship autopilot
International Journal of Applied Mathematics and Computer Science
Information Sciences: an International Journal
Hi-index | 12.06 |
In this paper, an adaptive neural network sliding-mode controller design approach with decoupled method is proposed. The decoupled method provides a simple way to achieve asymptotic stability for a class of fourth-order nonlinear system. The adaptive neural sliding-mode control system is comprised of neural network (NN) and a compensation controller. The NN is the main regulator controller, which is used to approximate an ideal computational controller. The compensation controller is designed to compensate for the difference between the ideal computational controller and the neural controller. An adaptive methodology is derived to update weight parts of the NN. Using this approach, the response of system will converge faster than that of previous reports. The simulation results for the cart-pole systems and the ball-beam system are presented to demonstrate the effectiveness and robustness of the method. In addition, the experimental results for seesaw system are given to assure the robustness and stability of system.