Application of decoupling fuzzy sliding mode control with active disturbance rejection for MIMO magnetic levitation system

Abstract

Magnetic levitation techniques have been used to eliminate friction due to mechanical contact, decrease the maintaining cost and achieve high-precision positioning. Although there are many studies on the single degree-of-freedom magnetic levitation control algorithms, it is difficult to achieve excellent control performance using classical control methods for the multiple-in-multiple-out magnetic levitation system because of the coupling in the dynamic system and the nonlinearity of the electromagnetic force. This article presents a 3 degrees-of-freedom magnetic suspension stage. At first the dynamic model of the stage is derived; then a nonlinear intelligent decoupling controller is developed to stabilize the levitation system. The control architecture consists of three components: (1) fuzzy sliding mode technique for the uncertainty in the system parameter; (2) force distribution for decoupling; (3) extended state observer for compensating the system disturbance. Finally experiments are designed to verify the effectiveness of the proposed controller. Experimental results show that compared with the classical proportional–integral–derivative controller, the proposed controller provides excellent transient response performance and the system is robust against the parameter uncertainty and external disturbance.

Fuzzy sliding mode control based on PSO for magnetic levitation positioning stage

“…9. v 1 at t = 1 s In the Simulink model, α 11 and α 12 are treated as two variables, which are assigned in the MATLAB function of calculating fitness, and the output of the model is the fitness value defined by (16). The fitness function calls and runs the Simulink model to calculate the fitness value of each set of variables (α 11 , α 12 ).…”

“…Some scholars have already applied fuzzy sliding mode control to the magnetic levitation system. Jiayi designed a fuzzy sliding mode controller to offset the parameter uncertainty of the magnetic suspension [16]. Benomair suggested a fuzzy sliding mode control method with a nonlinear observer to achieve good control performance for a magnetic levitation system [17].…”

Neural network–based sliding-mode control of a tendon sheath–actuated compliant rescue manipulator

“…Sliding-mode control (SMC) is a control strategy capable of guaranteeing the control accuracy of nonlinear systems with parametric uncertainties and external disturbance. 15,16 It has been widely used in many mechatronics applications, such as electric circuit, 17 exoskeleton robot, 18,19 magnetic levitation system, 20 and so on. However, the inherent chattering problem of conventional SMC scheme may lead to unpredictable instabilities.…”

Modeling and position control of a therapeutic exoskeleton targeting upper extremity rehabilitation

“…14 This strategy has been applied in many robotic applications, such as robotic manipulators, 15 mobile robots, 16 induction motor drive systems, 17 prosthetic hand, 18 and magnetic levitation system. 19 However, the SMC algorithm introduces some drawbacks in practical implementations, such as undesirable control chattering, vulnerability to measurement noise, and large control signals associated with model uncertainties. 20 The chattering phenomenon may excite high-frequency system dynamics and, moreover, lead to unpredictable instabilities.…”