Explicit nonlinear predictive control for a magnetic levitation system

Abstract

International audienc

Nonlinear modeling and control approach to magnetic levitation ball system using functional weight RBF network-based state-dependent ARX model

“…the maglev system and to reduce computation burden online. Ulbig et al [12] built a piecewise affine (PWA) model of a maglev system and constructed a explicit nonlinear predictive control law in order to reduce the computation time and to improve the performance of real-time control. Baechle et al [1] utilized a tailored gradient optimization method to enhance stability and computation efficiency.…”

“…For instance, the feedback linearization [3] or feedforward linearization [4] technology was applied to design a trajectory tracking controller for a nonlinear maglev system. Fuzzy-PID control [5] and adaptive control [3,6,7], sliding-model control [8], fuzzy control [9,10], neural network control [11] and predictive control [1,[12][13][14] were also used to achieve the trajectory tracking in order to improve the robustness and to expand the range of effective control. However, these approaches are mostly on the basis of the physical model of a maglev system.…”

Fuzzy logic-based gain scheduling of Exact Feedforward Linearization controller for magnetic ball levitation system

“…In [2], an adaptive homogeneous controller for the nonlinear magnetic levitation system with some assumptions in design was proposed. An algorithmic construction of an explicit polynomial state feedback function approximating the constrained finite time control was presented in [3]. The main advantage of this scheme is the continuity of the state feedback and the adaption of the approximation with respect to the chosen partition of the state space.…”

Offset-Free Model Predictive Control for Active Magnetic Bearing Systems

“…In this scenario, Model Predictive Control (MPC) has gained increasing attention in the last few years and has been implemented in a variety of applications and in different forms to tackle issues of nonlinearities and variability of the operating conditions. A rich literature describes the potentialities of this approach in magnetic levitation [19][20][21][22][23]. In particular, MPC has shown an improved performance in compensating for gyroscopic effects [24] and revealed properties of good stability and robustness when compared with standard control architectures for the levitation of flywheels [25].…”