Design of Model-Free Speed Regulation System for Permanent Magnet Synchronous Linear Motor Based on Adaptive Observer

Abstract

To simplify the system structure of permanent magnet synchronous linear motor (PMSLM), optimize the kinematic performance of the control system, and further improve the speed tracking accuracy of PMSLM, a model-free speed regulation system design of PMSLM based on the adaptive observer is proposed. Aiming at the control instability problem caused by the parameter uncertainty of PI control in PMSLM, a model-free speed controller (MFC) based on the ultra-local model is proposed, which has strong robustness to the uncertainty of motor parameters. Meanwhile, the actual model of PMSLM is used to build the reference model of the adaptive observer, and the adjustable model is designed with the current equation of PMSLM, to identify the speed of PMSLM. The design not only reduces the dependence of the speed controller on the motor parameters, but also simplifies the complexity and cost of the control system, and improves the control performance and anti-interference ability of the control system. The MFC and the observer based on the model reference adaptive system (MRAS) are built by simulation software and applied to the PMSLM system to verify the superiority of the designed control system. Compared with PI controller, sliding mode controller (SMC), and sliding mode observer (SMO), this control method not only simplifies the control system in structure, but also improves the accuracy of PMSLM tracking speed, improves the dynamic response performance of PMSM control system, and optimizes the control ability of the control system.

Design of Lumped Disturbance Observer in Current Loop of IPMSM Based on Recursive Integral Sliding Mode Surface

“…e e e f u (22) where ef = f − 𝒇 = [efd efq] T , and usmoT is the sliding mode control function and is also the observed value of 𝒇 . The sliding mode surface is selected as (23) according to study [29], and the recursive sliding mode surfaces is selected as (24): While using the traditional terminal sliding mode method to observe the system states, the system state equation is usually in the form of an integral chain [28].…”

“…e f = . f − u smoT (22) where e f = f − f= [e fd e fq ] T , and u smoT is the sliding mode control function and is also the observed value of f. The sliding mode surface is selected as (23) according to study [29], and the recursive sliding mode surfaces is selected as (24):…”

“…The other methods obtained system information through sensors or observers and improve the controller [18][19][20][21][22][23][24][25][26]. Among them, disturbance observer (DO) gains the favor of many scholars and becomes a research hotspot because of its simple structure, lack of dependence on system model accuracy and lack of an additional hardware cost.…”

“…In [21], a multiple-models adaptive disturbance observer is proposed to solve the steady-state current error by estimating the lumped disturbances, which can improve the robustness to parameter perturbations and model mismatch. Study [22] proposes an adaptive observer based on the model reference adaptive system (MRAS), which improves the control performance, anti-interference ability and robustness of the control system. Study [23] presents a novel super-twisting algorithm associated with extended state observer (ESO) for FOC, which can estimate system disturbances, eliminate chattering, improve the system anti-interference ability, and obtain a fast dynamic response.…”

Speed Estimation Method of Linear Motor Extended Kalman Filter Based on Attenuation Memory

“…In this paper, the mathematical model of a synchronous rotating coordinate axis is selected for controller analysis. It is assumed that in the motor air gap, the excitation magnetic field and armature reaction magnetic field have a sinusoidal distribution, ignoring the magnetic saturation effect, motor eddy current, and hysteresis loss, and ignoring nonlinear friction and other factors [18][19][20]. The voltage equation of PMLSM can be obtained as:…”