In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.

Iterative Learning Control: Brief Survey and Categorization

Parasitic torque pulsations exist in permanent magnet synchronous motors (PMSMs) due to nonsinusoidal flux density distribution around the air-gap, errors in current measurements, and variable magnetic reluctance of the air-gap due to stator slots. These torque pulsations vary periodically with rotor position and are reflected as speed ripple, which degrades the PMSM drive performance, particularly at low speeds. Because of the periodic nature of torque ripple, iterative learning control (ILC) is intuitively an excellent choice for torque ripple minimization. In this paper, first we propose an ILC scheme implemented in time domain to reduce periodic torque pulsations. A forgetting factor is introduced in this scheme to increase the robustness of the algorithm against disturbance. However, this limits the extent to which torque pulsations can be suppressed. In order to eliminate this limitation, a modified ILC scheme implemented in frequency domain by means of Fourier series expansion is presented. Experimental evaluations of both proposed schemes are carried out on a DSP-controlled PMSM drive platform. Test results obtained demonstrate the effectiveness of the proposed control schemes in reducing torque ripple by a factor of approximately three under various operating conditions.

Torque ripple minimization in PM synchronous motors using iterative learning control

Two improved torque-sharing functions for implementing torque ripple minimization (TRM) control are presented in this paper. The proposed torque-sharing functions are dependent on the turn-on angle, overlap angle, and the expected torque. This study shows that for a given torque the turn-on angle and the overlap angle have significant effects upon speed range, maximum speed, copper loss, and efficiency. Hence, genetic algorithm is used to optimize the turn-on angle and the overlap angle at various expected torque demands operating under the proposed TRM control in order to maximize the speed range and minimize the copper loss. Furthermore, four torque-sharing functions are used to derive the optimized results. At the same time, a fast and accurate online approach to compute the optimal turn-on and overlap angles is proposed. Therefore, this paper provides a valuable method to improve the performances of switched reluctance motor drives operating under TRM control.

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Optimization and Evaluation of Torque-Sharing Functions for Torque Ripple Minimization in Switched Reluctance Motor Drives

In this paper, a modular control approach is applied to a permanent-magnet synchronous motor (PMSM) speed control. Based on the functioning of the individual module, the modular approach enables the powerfully intelligent and robust control modules to easily replace any existing module which does not perform well, meanwhile retaining other existing modules which are still effective. Property analysis is first conducted for the existing function modules in a conventional PMSM control system: proportional-integral (PI) speed control module, reference current-generating module, and PI current control module. Next, it is shown that the conventional PMSM controller is not able to reject the torque pulsation which is the main hurdle when PMSM is used as a high-performance servo. By virtue of the internal model, to nullify the torque pulsation it is imperative to incorporate an internal model in the feed-through path. This is achieved by replacing the reference current-generating module with an iterative learning control (ILC) module. The ILC module records the cyclic torque and reference current signals over one entire cycle, and then uses those signals to update the reference current for the next cycle. As a consequence, the torque pulsation can be reduced significantly. In order to estimate the torque ripples which may exceed certain bandwidth of a torque transducer, a novel torque estimation module using a gain-shaped sliding-mode observer is further developed to facilitate the implementation of torque learning control. The proposed control system is evaluated through real-time implementation and experimental results validate the effectiveness.

A modular control scheme for PMSM speed control with pulsating torque minimization

This paper presents a technical overview for low-noise switched reluctance motor (SRM) drives in electric vehicle (EV) applications. With ever-increasing concerns over environmental and cost issues associated with permanent magnet machines, there is a technical trend to utilize SRMs in some mass production markets. The SRM is gaining much interest for EVs due to its rare-earth-free characteristic and excellent performance. In spite of many advantages compared with conventional adjustable-speed drives, SRMs suffer from torque ripple and radial distortion (and thus noise and vibration) by their nature. Therefore, for high-performance vehicle applications, it is important and urgent to optimize the SRM system to overcome the drawbacks of the noise and vibration. In order to present clear solutions to the acoustic noise in SRMs, this paper starts by analyzing the mechanism of the radial vibration and torque ripples inherent in the motors, and then focuses on the state-of-the-art technologies to mitigate the radial force and torque ripples. It highlights two categories for low-noise SRMs, including the machine topology improvement and control strategy design for radial vibration mitigation and torque ripple reduction. Advanced technologies are reviewed, classified, and compared accordingly. In addition to these methodologies, the schemes that have been developed by authors are also presented and discussed. Finally, the research status on this topic is summarized and forecast research hotspots are presented. It is our intention that this paper provides the guidance on performance improvements for low-noise SRM drives in EV applications.

A Review on Machine Topologies and Control Techniques for Low-Noise Switched Reluctance Motors in Electric Vehicle Applications

Torque control of the switched reluctance motor is complicated by its highly nonlinear torque-current-position characteristics. The purpose of this paper is the development of simple and efficient control algorithms for the constant torque control of switched reluctance motors. The approach consists of two distinct steps, i.e., determination of appropriate phase current waveforms for some specified torque and the subsequent generation of suitable phase voltage profiles for faithful tracking of these waveforms by the respective stator windings of the motor. At both the stages of the control design, the principles -of iterative learning control have been exploited. Firstly, the desired current waveform is generated by repeated corrections from iteration to iteration starting from the conventional rectangular current profile as the initial waveform. This scheme requires much less a priori knowledge of the magnetic characteristics of the motor. In the second stage, the voltage profiles to be impressed upon the stator phases for the tracking of the desired current waveforms are leamed iteratively. Simulation results show impressive response characteristics for a four-phase switched reluctance motor.

Low Torque Ripple Control of Switched Reluctance Motors Using Iterative Learning

The paper deals with the investigations on an iterative learning approach to determine the desired current waveforms for switched reluctance motors, which give rise to ripple-free torque. The current waveforms are generated by repeated corrections from iteration to iteration starting from the conventional rectangular pulse profile as the initial waveform. The scheme requires much less a priori knowledge of the magnetic characteristics of the motor. The algorithms have been formulated for both one-phase-on and two-phase-on schemes, for a four-phase switched reluctance motor, in the light of the principles behind iterative learning. Based on the observations from the simulation results of these schemes, a modified scheme has been proposed by incorporating a suitable commutation process, often called torque sharing functions, in order to generate reasonably smooth current waveforms for the ease of tracking by the stator circuit of the motor. The performances of all the proposed schemes have been verified by computer simulation.

Determination of current waveforms for torque ripple minimisation in switched reluctance motors using iterative learning: an investigation

A current modulation scheme for direct torque control of switched reluctance motor using fuzzy logic

Sliding mode control of a static VAR controller for synchronous generator stabilization

This paper deals with an iterative learning approach for modulating the desired torque profile so as to obtain ripple-free torque in switched reluctance motors. Because of the highly nonlinear relation between torque, current, and rotor position for this motor, it is not possible to obtain a closed-form mathematical expression for current as a function of torque and rotor position. Thus, the current waveforms are conventionally computed by using the linear torque model of the motor, and it is well known that such a scheme results in high torque ripple. In this paper, a novel method is proposed to minimize the ripple. In this new scheme, the current is still computed using the linear torque model, but the value of the torque used for this is not the desired (specified) torque, but rather a modulated-desired torque that is obtained by repeated corrections to the desired torque from iteration to iteration. The conventional rectangular pulse profile is taken as the initial current waveform. The method require...

N.C. Sahoo

Papers

Low Torque Ripple Control of Switched Reluctance Motors Using Iterative Learning

Determination of current waveforms for torque ripple minimisation in switched reluctance motors using iterative learning: an investigation

A current modulation scheme for direct torque control of switched reluctance motor using fuzzy logic

Sliding mode control of a static VAR controller for synchronous generator stabilization

An Iterative Learning-Based Modulation Scheme for Torque Control in Switched Reluctance Motors