Gain-Scheduling Control Solutions for Magnetic Levitation Systems 

Abstract

The paper presents three Gain-Scheduling Control (GS-C) design procedures starting with classical Proportional-Integral (PI) controllers, resulting in PIGS -C structures for positioning control of a Magnetic Levitation System (MLS) with two laboratory electromagnets. The nonlinear mathematical model of the MLS is first linearized at seven operating points and next stabilized by a state feedback control structure. Three PIGS -C structures, namely as Lagrange, Cauchy and Switching GS versions, are next designed in order to ensure zero steady-state control error and the switching between PI controllers. All control solutions are validated by real-time experiments.

Facilitating Autonomous Systems with AI-Based Fault Tolerance and Computational Resource Economy

“…The model is actually extensively discussed in [45]. It is the nature of the EMS system that makes it appealing as a test system [46], i.e., being inherently unstable, safety-critical system, under non-trivial control requirements. Figure A1a presents the EMS schematic diagram, the input excitation profile and Figure A1c lists the performance constraints.…”

Results on Tensor Product-based Model Transformation of Magnetic Levitation Systems

“…The topic at hand should be of interest to many engineers hoping to apply the TPmodel as a numerical modeling approach. The main contributions of this paper, which required restructuring in all sections including authors team, are pointed out as follows: the authors use the same main steps as the ones presented in [17] in order to obtain the TPmodel of the stabilized reduced order linearized model of a magnetic levitation system (referred to as stMaglev) and discussed in (Inteco, 2008) [18] and (Bojan-Dragos et al, 2018) [19]. However, the derived model is tested using four new testing scenarios.…”