scholarly journals Position Control Based on Add-on-Type Iterative Learning Control with Nonlinear Controller for Permanent-Magnet Stepper Motors

2021 ◽  
Vol 11 (2) ◽  
pp. 587
Author(s):  
Sangmin Suh ◽  
Wonhee Kim
Keyword(s):  
Permanent Magnet ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Position Tracking ◽  
Nonlinear Controller ◽  
Integral Control ◽  
Stepper Motors ◽  
Current Error ◽  
A Current

In this paper, a current-error-based iterative learning controller (ILC) with a nonlinear controller is proposed to improve the position-tracking performance in permanent-magnet (PM) stepper motors. Our proposed method comprises a current-error-based ILC for mechanical dynamics and a nonlinear controller for current dynamics. A nonlinear controller using a variable structure is designed to obtain the field-oriented control. This nonlinear controller can cause the PM stepper motor become a single-input single-output linear system after finite time. The add-on-type ILC with proportional–integral control is designed to improve the position-tracking performance as the systems repeatedly perform the same operation. To increase the rate of error convergence, the current-error-based ILC is designed using the plant inversion method. The condition that the error converges to zero is mathematically derived. Thus, the proposed method can reduce the position-tracking error as the systems repeatedly perform the same operation. Furthermore, the proposed method can be easily plugged into the pre-designed controller. The performance of our proposed method was evaluated via simulations. In simulations, it is observed that the proposed method reduces the position-tracking error compared to the previous methods.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

Backstepping Adaptive Fuzzy Control for Permanent Magnet Synchronous Motor Servo Systems

2012 ◽  
Vol 591-593 ◽  
pp. 1483-1489 ◽  
Author(s):  
Ren Hui Du ◽  
Yi Fei Wu ◽  
Wei Chen ◽  
Qing Wei Chen
Keyword(s):  
Fuzzy Control ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Tracking Error ◽  
Adaptive Fuzzy Control ◽  
Synchronous Motor ◽  
Position Tracking ◽  
Servo Systems ◽  
Nonlinear Part ◽  
Adaptive Fuzzy

An adaptive fuzzy controller based on the backstepping method is developed for permanent magnet synchronous motor (PMSM) servo systems with unknown parameters, nonlinear friction and other load torque disturbances. The adaptive fuzzy logic system is used to approximate the nonlinear part of the system online, which can eliminate the influence of uncertainties and nonlinear factors effectively and realize the high-precision position tracking. By adopting the Lyapunov method, it is proved that the position tracking error converges exponentially. Compared with the traditional backstepping adaptive control (BAC), the simulation results show that the backstepping adaptive fuzzy control (BAFC) has better robustness and accuracy.

scholarly journals Position/Force Tracking Impedance Control for Robotic Systems with Uncertainties Based on Adaptive Jacobian and Neural Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Jinzhu Peng ◽  
Zeqi Yang ◽  
Tianlei Ma
Keyword(s):  
Neural Network ◽  
Impedance Control ◽  
Tracking Error ◽  
Tracking Performance ◽  
Robotic Systems ◽  
Position Tracking ◽  
External Disturbances ◽  
Outer Loop ◽  
Control Scheme ◽  
Force Tracking

In this paper, an adaptive Jacobian and neural network based position/force tracking impedance control scheme is proposed for controlling robotic systems with uncertainties and external disturbances. To achieve precise force control performance indirectly by using the position tracking, the control scheme is divided into two parts: the outer-loop force impedance control and the inner-loop position tracking control. In the outer-loop, an improved impedance controller, which combines the traditional impedance relationship with the PID-like scheme, is designed to eliminate the force tracking error quickly and to reduce the force overshoot effectively. In this way, the satisfied force tracking performance can be achieved when the manipulator contacts with environment. In the inner-loop, an adaptive Jacobian method is proposed to estimate the velocities and interaction torques of the end-effector due to the system kinematical uncertainties, and the system dynamical uncertainties and the uncertain term of adaptive Jacobian are compensated by an adaptive radial basis function neural network (RBFNN). Then, a robust term is designed to compensate the external disturbances and the approximation errors of RBFNN. In this way, the command position trajectories generated from the outer-loop force impedance controller can be then tracked so that the contact force tracking performance can be achieved indirectly in the forced direction. Based on the Lyapunov stability theorem, it is proved that all the signals in closed-loop system are bounded and the position and velocity errors are asymptotic convergence to zero. Finally, the validity of the control scheme is shown by computer simulation on a two-link robotic manipulator.

Nonlinear H2 control for position tracking of permanent magnet stepper motors

2013 ◽  
Author(s):  
Youngwoo Lee ◽  
Seung-Hi Lee ◽  
Wonhee Kim ◽  
Donghoon Shin ◽  
Chung Choo Chung
Keyword(s):  
Permanent Magnet ◽  
Position Tracking ◽  
Stepper Motors

Implementing Iterative Learning Control to Reduce Torque Ripple of Permanent Magnet Synchronous Machine

2011 ◽  
Vol 301-303 ◽  
pp. 1676-1681
Author(s):  
Zhao Huang ◽  
Han Xiang Cheng
Keyword(s):  
Permanent Magnet ◽  
Iterative Learning Control ◽  
High Performance ◽  
Learning Control ◽  
Torque Ripple ◽  
Synchronous Machine ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Permanent Magnet Synchronous Machine ◽  
Torque Ripples

Permanent magnet synchronous machines (PMSM) are appealing candidates for many high-performance applications such as robotics, machine tools and other electrical propulsion system because of their attractive characteristics. PMSM have advantages of high power density, high torque-to-inertia ratio, and high electrical efficiency. However, the main disadvantage of PMSM is the torque ripples. Therefore, the study of how to compensate the torque ripples so as to improve the tracking performance of PMSM is very meaningful and necessary. In this paper, we focus on the control of a non-sinusoidal permanent magnet synchronous machine. First, we compare the various methods which to reduce the torque ripple. Then, we built a basic PMSM model. After that the Iterative Learning Control (ILC) algorithm is applied to compensate the torque ripple, and improve the tracking performance of the PMSM. The effectiveness of using ILC method to reduce torque ripple is demonstrated by simulink results.

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