Abstract
To suppress the speed ripple of a permanent magnet synchronous motor in a seeker servo system, we propose an accelerated iterative learning control with an adjustable learning interval. First, according to the error of current iterative learning for the system, we determine the next iterative learning interval and conduct real-time correction on the learning gain. For the learning interval, as the number of iterations increases, the actual interval that needs correction constantly shortens, accelerating the convergence speed. Second, we analyze the specific structure of the controller while applying reasonable assumptions pertaining to its condition. Using the λ-norm, we analyze and apply our mathematical knowledge to obtain a strict mathematical proof on the P-type iterative learning control and obtain the condition of convergence for the controller. Finally, we apply the proposed method for periodic ripple inhibition of the torque rotation speed of the permanent magnet synchronous motor and establish the system model; we use the periodic load torque to simulate the ripple torque of the synchronous motor. The simulation and experimental results indicate the effectiveness of the method.
Highlights
The permanent magnet synchronous motor has the advantages of high efficiency, high power density, and a high torque inertia ratio [1] and has been widely applied to the fields of aerospace, robotics, and transportation [2,3,4,5]
To address the speed ripple of a permanent magnet synchronous motor, we propose an accelerated iterative learning control
The seeker servo system can be simplified as the following control system of a permanent magnet synchronous motor: ( dθ (t) dt = ω ( t )
Summary
The permanent magnet synchronous motor has the advantages of high efficiency, high power density, and a high torque inertia ratio [1] and has been widely applied to the fields of aerospace, robotics, and transportation [2,3,4,5]. Researchers worldwide have proposed multiple methods to suppress the torque ripple of the permanent magnet synchronous motor, which are mainly divided into two classes: Improving the main body design of the motor and optimizing the design of the software control algorithm. The characteristics of the proposed method are as follows: For the learning interval of this control, as the number of iterations increases, the actual interval that needs correction constantly shortens, which accelerates the convergence speed. In this time interval, we conduct real-time correction of the learning gain. We give a strict mathematical proof and verify the effectiveness and accuracy of the theory through simulation and experiment
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