Abstract
In this article, an adaptive neural terminal sliding mode is implemented for tracking control of magnetic levitation systems with the presence of dynamical uncertainty and exterior perturbation. By proposing a novel fast terminal sliding manifold function with the dynamic coefficients, the system state variables quickly converge the equilibrium point on the manifold function. Besides, an adaptive, robust reaching control law combined with radial basis function neural network compensator drives the system fast approaching the sliding manifold function regardless of whether the initial value is near or far from the sliding manifold and reduces the chattering of the conventional terminal sliding mode control. With a design approach based on the combination of the proposed sliding manifold and the combined control law, the implemented control method provides a control performance with significant improvement in the terms of chattering reduction, high tracking accuracy, fast convergence along with simple design for real applications. The experimental work is implemented for a real magnetic levitation system to demonstrate the superior efficiency of the proposed terminal sliding mode control. The stable evidence of the proposed method is also completely verified by Lyapunov-based method.
Highlights
Magnetic levitation Systems (MLSs) have been widely applied for many real applications in the industrial field, such as rocket-guiding projects, gyroscopes, contactless melting, frictionless bearings, magnetic bearings, maglev trains, wafer distribution systems, vibration isolation systems, microrobotics, and so on
A development followed by a combination of nonsingular terminal sliding mode controls (NTSMCs) and FTSMC to produce the non-singular fast terminal sliding mode control (NFTSMC) [24]–[28] or finite-time control [49]
Remark 1: For the convenience of naming in the analysis of experimental results, we call the controller in Eqs. (14) - (16) named NFTSMC2 and the controller in Eqs. (26) - (31) named radial basis function neural network (RBFNN)-NFTSMC2
Summary
Magnetic levitation Systems (MLSs) have been widely applied for many real applications in the industrial field, such as rocket-guiding projects, gyroscopes, contactless melting, frictionless bearings, magnetic bearings, maglev trains, wafer distribution systems, vibration isolation systems, microrobotics, and so on. A development followed by a combination of NTSMC and FTSMC to produce the non-singular fast terminal sliding mode control (NFTSMC) [24]–[28] or finite-time control [49] Those methods avoid the singularity and reject glitch in the reaching stage with the arbitrary initial states. This article proposes a completely different approach from the pre-existing methods (such as [29]–[31]) for MLSs. The implemented control method is expected to provide a control performance with significant improvement in the terms of chattering reduction, high tracking accuracy, fast convergence along with simple design for real applications. The target of this article is to develop a new, robust control method for MLSs in presence of dynamical uncertainties and external disturbances to further improve control performance such as chattering reduction in the control signal, high tracking accuracy, and fast convergence along with simple design method
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