Learning position controls for hybrid step motors: from current-fed to full-order models

The experimental comparison of two different global learning position controls (namely, ‘adaptive learning’ and ‘repetitive learning’ controls) for hybrid step motors performing repetitive tasks has been recently presented in the literature. Related benefits and drawbacks have been successfully analyzed on the same robotic application. However, the design of the two aforementioned learning controls - though relying on a rigorous stability analysis - are based on a simplified current-fed model of the motor. They cannot achieve precise current tracking due to the mere presence of PI control actions in the outer current control loops. The aim of this paper is to enrich and update the results of the above comparison in the light of the latest contributions that generalize the theoretical design to the fullorder voltage-fed motor models of hybrid step motors. Learning actions are now included in the outer current control loops: they generalize the corresponding PI actions to the periodic scenario and allow to solve a control problem whose solution was seeming very difficult to be obtained

A fractional delay variable frequency repetitive control for torque ripple reduction in PMSMs

Based on the internal model principle, repetitive controller (RC) is capable to reduce periodic torque ripple by generating a compensating action that consequently need to be synchronized with the original ripple. However, the synchronization is difficult to achieve using the conventional RC when the sampling frequency is not integer multiple of the speed (known as fractional delay issue), or when the speed varies widely. To solve this problem, this paper presents a fractional delay variable frequency torque ripple reduction method for PMSM drives using the combination of angle-based RC and deadbeat current control (DBCC). Four aspects of innovations are included in the proposed control to improve the synchronization. The experimental results show that the proposed control can effectively reduce torque ripple even during speed and load transient

Sensorless Adaptive Fuzzy Logic Control Of Permanent Magnet Synchronous Motor

Permanent Magnet Synchronous Motors (PMSM) require an electromechanical rotor position sensor to operate. The rotor position sensor has disadvantages, such as reliability, size, higher cost, and increased electrical connections. PMSM is used in many speed and position control industrial applications. Proportional integral (PI) and proportional integral derivative (PID) controllers have been widely utilised as speed controllers in PMSM drives. However, these controllers are very sensitive to step change of command speed, parameter variations and load disturbance. In this work, an adaptive fuzzy logic speed controller is proposed. The main features of the proposed controller are; quick recovery of motor’s speed from load disturbances and insensitivity to parameter variation over a wide speed range. The proposed controller is a hybrid model reference adaptive speed controller (HMRASC) which mainly consists of two functional blocks. The first block is a direct FLC that has the error and the change of error as inputs. The error signal is measured between the actual motor speed and the desired speed and the output is the change in the torque command. The second block implements a model reference adaptive controller. In the proposed system, the output speed of the reference model is compared with the actual speed of the motor and the resulted speed error is applied to a PI controller. The output signal of the PI controller is added to the direct FLC output to compensate any deviations in the motor speed from the reference speed due to parameters variation and disturbances in the load. The design and optimisation of the FLC are carried out using an adaptive fuzzy inference system network that uses the backpropagation, least square and gradient algorithms. The fuzzy inference system is trained and designed using an adaptive network. The rules and the implication method used are also optimised and minimised in order to shorten the computation time. In addition, the effect of different types and distributions of the membership functions were investigated and presented. This work also presents the estimation of the rotor position, which works effectively with nearly zero estimation error over wide speed range, to replace the electrometrical rotor position sensor. An estimation method based on the back EMF and flux estimation is presented to calculate the rotor position for medium to high speed. At low speed, the rotor position is calculated using signal injection where a high frequency low voltage signal is injected on the stator winding. In the proposed method, the measured motor’s current and the estimated motor’s voltage are processed through a signal processing block and a PI regulator to calculate the angle of the rotor position.Finally the performance of the HMRASC and the rotor position angle estimation algorithms are evaluated by simulation and verified experimentally for two motors using MCK2407 kit and IMDM15 board which are based on the TMS320LF2407 fixed point Digital Signal Processor (DSP) for different operating conditions. The first motor is rated at 50W and the second is rated at 380W. Both experimental and simulation results obtained from the HMRASC and the position angle estimation algorithms showed superior results compared to other methods presented in the literature

The design of a position-based repetitive control for speed ripple reduction in PMLSMs

Periodic speed errors can occur in permanent magnet linear synchronous machines for two reasons: 1) a periodic reference signal; 2) cogging force and friction. For reducing such periodic errors, iterative learning control or repetitive control approaches, used in conjunction with more common control actions, can be strongly effective. However, the design of the stability filter, robustness filter and other parameters for a traditional repetitive controller can be a complex task and may need to be adjusted when the frequency of such periodic error varies. Existing solutions tend to develop more adaptive tuning methods for repetitive controller to enhance the whole control system. This paper shows that the performance of a traditional speed loop can be enhanced with a repetitive controller without complicating the tuning of the repetitive controller. Consequently, a position-based repetitive control combined with deadbeat current control method is proposed. Simulation results show that the proposed method is effective for reducing speed ripple at difference frequencies without necessarily adjusting its parameters

Application of Optimal Switching Using Adaptive Dynamic Programming in Power Electronics

In this dissertation, optimal switching in switched systems using adaptive dynamic programming (ADP) is presented. Two applications in power electronics, namely single-phase inverter control and permanent magnet synchronous motor (PMSM) control are studied using ADP. In both applications, the objective of the control problem is to design an optimal switching controller, which is also relatively robust to parameter uncertainties and disturbances in the system. An inverter is used to convert the direct current (DC) voltage to an alternating current (AC) voltage. The control scheme of the single-phase inverter uses a single function approximator, called critic, to evaluate the optimal cost and determine the optimal switching. After offline training of the critic, which is a function of system states and elapsed time, the resulting optimal weights are used in online control, to get a smooth output AC voltage in a feedback form. Simulations show the desirable performance of this controller with linear and nonlinear load and its relative robustness to parameter uncertainty and disturbances. Furthermore, the proposed controller is upgraded so that the inverter is suitable for single-phase variable frequency drives. Finally, as one of the few studies in the field of adaptive dynamic programming (ADP), the proposed controllers are implemented on a physical prototype to show the performance in practice. The torque control of PMSMs has become an interesting topic recently. A new approach based on ADP is proposed to control the torque, and consequently the speed of a PMSM when an unknown load torque is applied on it. The proposed controller achieves a fast transient response, low ripples and small steady-state error. The control algorithm uses two neural networks, called critic and actor. The former is utilized to evaluate the cost and the latter is used to generate control signals. The training is done once offline and the calculated optimal weights of actor network are used in online control to achieve fast and accurate torque control of PMSMs. This algorithm is compared with field-oriented control (FOC) and direct torque control based on space vector modulation (DTC-SVM). Simulations and experimental results show that the proposed algorithm provides desirable results under both accurate and uncertain modeled dynamics

Recent Advances in Robust Control

Robust control has been a topic of active research in the last three decades culminating in H_2/H_\infty and \mu design methods followed by research on parametric robustness, initially motivated by Kharitonov's theorem, the extension to non-linear time delay systems, and other more recent methods. The two volumes of Recent Advances in Robust Control give a selective overview of recent theoretical developments and present selected application examples. The volumes comprise 39 contributions covering various theoretical aspects as well as different application areas. The first volume covers selected problems in the theory of robust control and its application to robotic and electromechanical systems. The second volume is dedicated to special topics in robust control and problem specific solutions. Recent Advances in Robust Control will be a valuable reference for those interested in the recent theoretical advances and for researchers working in the broad field of robotics and mechatronics

DesigN of a robotic device for automated nucleic acid extraction from biological samples

Thesis (Master)--Izmir Institute of Technology, Mechanical Engineering, Izmir, 2009Includes bibliographical references (leaves: 81-82)Text in English; Abstract: Turkish and Englishxii, 115 leavesNucleic Acids (DNA or RNA) present the genetic structure of the cell or the organism and so are the essential components to make genetic testing. Molecular genetic testing allows one to analyze the genetic structure of an organism to have an idea about the present temporary or hereditary characteristics of the tissue or the whole organism, or specifically define its species. In order to analyze the genetic structure, one must extract and isolate the nucleic acids (NA), which are most of the time inside the cell. The aim of this thesis study is to design and manufacture an automated device with low throughput DNA extraction. Currently, the automated devices used for extraction of genetic material are being manufactured only by the foreign companies. Automated commercial devices used for this purpose were investigated in detail as well as the manual NA extraction hand tools for use in NA extraction. Commercially available components (pipette tip, reagent cartridges, tubes, etc.) to isolate NA were reviewed. Mechanism design process for a low cost and high precision system that requires minimal human operator intervention is carried out. The conceptual designs were developed and the final design of the device was made to comply with the selected components. Electronic equipments (motors, drivers, interface card, etc.) and a suitable graphical user interface compatible with the electronic components was selected and adapted to the system. Finally, a device which is competitive with the commercial ones has been designed and its prototype has been manufactured as a result of this thesis study

On the Influence of Eccentricities on Flux Linkages of Permanent Magnet Synchronous Machines

The noise behavior of electrical machines is influenced by tolerances. Eccentricities in particular lead to poorer noise behavior. However, the measurement of NVH quantities is usually very complex. Therefore, it is of interest to be able to detect such tolerances also by other measurands. In this paper, the influence of eccentricities on the flux linkages is investigated. For this purpose, detailed investigations were carried out using FEA. In a further step, these are compared with the results obtained from a test rig measurement. Prior to this, a methodology is presented with which the angle-dependent flux linkages can be determined. It is shown that eccentricities cause only slight changes in the harmonic components of the flux linkages. Due to the symmetry properties of the investigated machine, the changes in the flux linkage caused by the different air gap lengths cancel each other out. This could also be confirmed in the experiment

Development of intelligent learning motion control systems

Ph.DDOCTOR OF PHILOSOPH