Control of magnetic levitation system using recurrent neural network-based adaptive optimal backstepping strategy

Abstract

In this paper, a novel approach is proposed for adjusting the position of a magnetic levitation system using projection recurrent neural network-based adaptive backstepping control (PRNN-ABC). The principles of designing magnetic levitation systems have widespread applications in the industry, including in the production of magnetic bearings and in maglev trains. Levitating a ball in space is carried out via the surrounding attracting or repelling magnetic forces. In such systems, the permissible range of the actuator is significant, especially in practical applications. In the proposed scheme, the procedure of designing the backstepping control laws based on the nonlinear state-space model is carried out first. Then, a constrained optimization problem is formed by defining a performance index and taking into account the control limits. To formulate the recurrent neural network (RNN), the optimization problem is first converted into a constrained quadratic programming (QP). Then, the dynamic model of the RNN is derived based on the Karush-Kuhn-Tucker (KKT) optimization conditions and the variational inequality theory. The convergence analysis of the neural network and the stability analysis of the closed-loop system are performed using the Lyapunov stability theory. The performance of the closed-loop system is assessed with respect to tracking error and control feasibility.

Based on Sliding Mode and Adaptive Linear Active Disturbance Rejection Control for a Magnetic Levitation System

“…In different cases, the adaptability of this method and its application in a maglev train must be further studied. In literature [25], a projection recursion method based on adaptive backstepping control was proposed to adjust the position of the maglev ball in real-time by using a neural network. The proposed method exhibits precise and rapid tracking of the desired position of the maglev ball.…”

Neural network compensation control of magnetic levitation ball position based on fuzzy inference

“…In response to the problems in neural network training, Patan et al proposed an adaptive iterative learning control method, which greatly improved the convergence speed and stability of neural network controller in maglev control system 19 . Fatemimoghadam et al designed a control structure based on a recurrent neural network, which effectively improved the tracking performance of the maglev closed-loop system by introducing a time sequence relationship to predict the control quantity 20 . Jafari et al proposed a more effective recurrent neural network training method for the identification of unknown system, which effectively reduced the tracking error of maglev system 21 .…”

Recurrent neural network based high-precision position compensation control of magnetic levitation system

“…Lin et al designed a hybrid computed force control method with an RNN uncertainty observer, which improved the tracking accuracy in position control of the magnetic levitation system 30 . Fatemimoghadam et al proposed an adaptive backstepping control scheme based on a projection RNN for the magnetic levitation system, which achieved a better control performance than the sliding mode control method 31 . Jafari and Hagan applied RNN to the model reference control of the magnetic levitation system and obtained better performance than linear PID controllers 32 .…”