Prediction control method and system for magnetic levitation system

Abstract

The invention discloses a prediction control method and system for a magnetic levitation system. By historical data of the magnetic levitation ball system and through adopting a system identificationmethod, an autoregressive model which takes a Gaussian radial basis function network as a coefficient and has an exogenous variable is established to describe nonlinear dynamic characteristics betweenan input voltage of an electromagnetic winding and a position of a steel ball. An ARX model coefficient is fit by an RBF neural network, so that an RBF-ARX model can well depict the nonlinear dynamiccharacteristics of the magnetic levitation ball system. Then, on the basis of a principle of a predictive functional control algorithm, a structure of a control input is determined so as to completerolling optimization and error correction. The predictive functional control algorithm has higher tracking capacity and higher robustness than PID control, and has a smaller online calculation amountand a higher control speed than conventional model prediction control; and when a system set value is a step signal, the control method can implement unbiased tracking.

Description

technical field [0001] The invention relates to the technical field of automatic control, in particular to a control method based on an RBF-ARX model and using a predictive function to control a magnetic levitation system. Background technique [0002] Magnetic levitation technology is a typical mechatronics technology integrating electromagnetics, electronic technology, control engineering, signal processing, mechanics and dynamics. Magnetic levitation has the characteristics of low noise, small friction and high speed, which makes maglev have broad application and development potential in aerospace, railway transportation and motors. In order to improve the control performance of magnetic levitation technology, the magnetic levitation ball system is often used as a main research platform. The magnetic levitation ball system mainly applies Newton's second law, passing a certain current to the electromagnetic winding to generate electromagnetic force, and giving the magneti...

AI Technical Summary

Problems solved by technology

The biggest problem with the predictive model control algorithm is the large amount of online calculations and the difficulty in meeting real-time requirements. Therefore, the application of model predictive control is generally limited to slow motion, and there are certain limitations in the fast follow-up application of the magnetic levitation ball system.

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