Adaptive Control
Intelligent bounds on modeling uncertainty: applications to sliding mode control
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators
IEEE Transactions on Fuzzy Systems
Brief Adaptive robust nonlinear control of a magnetic levitation system
Automatica (Journal of IFAC)
Electromagnetic design of a magnetic suspension system
IEEE Transactions on Education
Chaotifying linear Elman networks
IEEE Transactions on Neural Networks
Further results on adaptive control for a class of nonlinear systems using neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Adaptive dynamic CMAC neural control of nonlinear chaotic systems with L2 tracking performance
Engineering Applications of Artificial Intelligence
Hi-index | 0.00 |
In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.