To develop an efficient variable speed compressor motor system

This research presents a proposed new method of improving the energy efficiency of a Variable Speed Drive (VSD) for induction motors. The principles of VSD are reviewed with emphasis on the efficiency and power losses associated with the operation of the variable speed compressor motor drive, particularly at low speed operation.The efficiency of induction motor when operated at rated speed and load torque is high. However at low load operation, application of the induction motor at rated flux will cause the iron losses to increase excessively, hence its efficiency will reduce dramatically. To improve this efficiency, it is essential to obtain the flux level that minimizes the total motor losses. This technique is known as an efficiency or energy optimization control method. In practice, typical of the compressor load does not require high dynamic response, therefore improvement of the efficiency optimization control that is proposed in this research is based on scalar control model.In this research, development of a new neural network controller for efficiency optimization control is proposed. The controller is designed to generate both voltage and frequency reference signals imultaneously. To achieve a robust controller from variation of motor parameters, a real-time or on-line learning algorithm based on a second order optimization Levenberg-Marquardt is employed. The simulation of the proposed controller for variable speed compressor is presented. The results obtained clearly show that the efficiency at low speed is significant increased. Besides that the speed of the motor can be maintained. Furthermore, the controller is also robust to the motor parameters variation. The simulation results are also verified by experiment

Adaptive Speed Control of Induction Motor for Driving Unbalanced Load

Vector control of an induction motor has advantage of a quick torque response, and has been applied in various industrial applications. In the design of a speed control system of induction motor, PI controller has been widely used because its structure is very simple. However, it is difficult to obtain robust and stable speed control characteristics because the gain of the controller can not be adjust automatically when the load disturbance or parameter changed. The motor used in a reciprocating unbalanced load experience abrupt load change by the movement of piston. So, its speed is fluctuated. This study proposes a new adaptive control system with conventional vector controller for a reciprocating unbalanced load. The proposed system consists of a load torque observer and a feed-forward compensation using neural network to obtain robust speed control characteristics. The observer designed based on the Gopinath' theory. And the neural estimator is consists of two layers, and is used to provide a real-time adaptive estimation of motor dynamics. The LMS(Least Mean Square) algorithm which has widely been used is applied as the learning algorithm for this network, to minimize the difference between the actual speed and that predicted value by the neural estimator. To verify the effectiveness of this algorithm, a computer simulation test was carried out on the basis of theoretical consideration.1. 서론 = 1 2. 불평형부하 구동 유도전동기 제어시스템 구성 = 3 2.1 벡터제어 = 3 2.2 공간전압 PWM 인버터 = 12 2.3 불평형 부하 = 17 3. 적응 제어기 설계 = 20 3.1 이산치형 부하토크 관측기 = 21 3.2 Neural estimator 설계 = 26 4. 시뮬레이션 및 시스템 구현 = 32 4.1 시뮬레이션 = 32 4.2 시스템 구현 = 36 4.3 실험 결과 = 42 5. 결론 = 4

An implementation of rotor speed observer for sensorless induction motor drive in case of machine parameter uncertainty

The paper describes observers using model reference adaptive system for sensorless induction motor drive with the pulse width modulator and the direct torque control under the circumstances of incorrect information of induction motor parameters. An approximation based on the definition of the Laplace transformation is used to obtain initial values of the parameters. These values are utilized to simulate sensorless control structures of the induction motor drive in Matlab-Simulink environment. Performance comparison of two typical observers is carried out at different speed areas and in presence of parameter uncertainty. A laboratory stand with the induction motor drive and load unit is set up to verify the properties of observers. Experimental results confirm the expected dynamic properties of selected observer

A Study on the Adaptive Control System of an Induction Motor for an Air Compressor

Vector control of an induction motor has advantage of a quick torque response, and has been applied to various industrial applications. In the design of a speed control system of induction motors, PI controller has been widely used because its structure is very simple. However, it is difficult to obtain robust and stable speed control characteristics because the gain of the controller can not be adjusted automatically when the load disturbance or system parameter change. The motor used in a reciprocating air compressor experience abrupt load change by the movement of piston. So, its speed is fluctuated. This study proposes a new adaptive control system with conventional vector controller for a reciprocating air compressor. The proposed system consists of a load torque observer and a feed-forward compensation using neural network to obtain a robust speed control characteristic. The observer is designed based on the Gopinath theory. And the neural estimator is consisted of two layers, and is used to provide a real-time adaptive estimation of motor dynamics. The LMS(Least Mean Square) algorithm which has widely been used is applied as the learning algorithm for this network to minimize the difference between the actual speed and the predicted value by the neural estimator. To verify the effectiveness of this algorithm, a computer simulation and a experimental test are carried out on the basis of theoretical consideration. From the experimental result, it is confirmed that the transient responses under the 1[atm] and 2[atm] condition are reduced 80[ms] and 50[ms] respectively compared with conventional method, and also steady state speed fluctuations are reduced 50 [rpm] and 80[rpm].목차 Abstract = ii 1. 서론 = 1 2. 유도전동기 벡터제어 = 3 2.1 벡터제어 = 3 2.2 좌표 변환 = 6 2.3 슬립주파수 계산 = 8 3. 공기압축기 = 12 3.1 공기압축기의 개요 = 12 3.2 피스톤 및 크랭크기구의 기기역학 = 13 4. 적응 제어기 설계 = 19 4.1 이산형 부하토크 관측기 = 20 4.2 신경망 추정기의 설계 = 25 5. 시뮬레이션 및 시스템 구현 = 31 5.1 시뮬레이션 = 31 5.2 시스템 구현 = 35 5.3 실험 및 결과검토 = 40 6. 결론 = 43 참고문헌 = 4

Sliding-mode neuro-controller for uncertain systems

In this paper, a method that allows for the merger of the good features of sliding-mode control and neural network (NN) design is presented. Design is performed by applying an NN to minimize the cost function that is selected to depend on the distance from the sliding-mode manifold, thus providing that the NN controller enforces sliding-mode motion in a closed-loop system. It has been proven that the selected cost function has no local minima in controller parameter space, so under certain conditions, selection of the NN weights guarantees that the global minimum is reached, and then the sliding-mode conditions are satisfied; thus, closed-loop motion is robust against parameter changes and disturbances. For controller design, the system states and the nominal value of the control input matrix are used. The design for both multiple-input-multiple-output and single-input-single-output systems is discussed. Due to the structure of the (M)ADALINE network used in control calculation, the proposed algorithm can also be interpreted as a sliding-mode-based control parameter adaptation scheme. The controller performance is verified by experimental results

Terminal sliding mode control strategy design for second-order nonlinear system

This study mainly focuses on the terminal sliding mode control (TSMC) strategy design, including an adaptive terminal sliding mode control (ATSMC) and an exact-estimator-based terminal sliding mode control (ETSMC) for second-order nonlinear dynamical systems. In the ATSMC system, an adaptive bound estimation for the lump uncertainty is proposed to ensure the system stability. On the other hand, an exact estimator is designed for exact estimating system uncertainties to solve the trouble of chattering phenomena caused by a sign function in ATSMC law in despite of the utilization of a fixed value or an adaptive tuning algorithm for the lumped uncertainty bound. The effectiveness of the proposed control schemes can be verified in numerical simulations.<br /

Development and Implementation of Some Controllers for Performance Enhancement and Effective Utilization of Induction Motor Drive

The technological development in the field of power electronics and DSP technology is rapidly changing the aspect of drive technology. Implementations of advanced control strategies like field oriented control, linearization control, etc. to AC drives with variable voltage, and variable frequency source is possible because of the advent of high modulating frequency PWM inverters. The modeling complexity in the drive system and the subsequent requirement for modern control algorithms are being easily taken care by high computational power, low-cost DSP controllers. The present work is directed to study, design, development, and implementation of various controllers and their comparative evaluations to identify the proper controller for high-performance induction motor (IM) drives. The dynamic modeling for decoupling control of IM is developed by making the flux and torque decoupled. The simulation is carried out in the stationary reference frame with linearized control based on state-space linearization technique. Further, comprehensive and systematic design procedures are derived to tune the PI controllers for both electrical and mechanical subsystems. However, the PI-controller performance is not satisfactory under various disturbances and system uncertainties. Also, precise mathematical model, gain values, and continuous tuning are required for the controller design to obtain high performance. Thus, to overcome these drawbacks, an adapted control strategy based on Adaptive Neuro-Fuzzy Inference System (ANFIS) based controller is developed and implemented in real-time to validate different control strategies. The superiority of the proposed controller is analyzed and is contrasted with the conventional PI controller-based linearized IM drive. The simplified neuro-fuzzy control (NFC) integrates the concept of fuzzy logic and neural network structure like conventional NFC, but it has the advantages of simplicity and improved computational efficiency over conventional NFC as the single input introduced here is an error instead of two inputs error and change in error as in conventional NFC. This structure makes the proposed NFC robust and simple as compared to conventional NFC and thus, can be easily applied to real-time industrial applications. The proposed system incorporated with different control methods is also validated with extensive experimental results using DSP2812. The effectiveness of the proposed method using feedback linearization of IM drive is investigated in simulation as well as in experiment with different working modes. It is evident from the comparative results that the system performance is not deteriorated using proposed simplified NFC as compared to the conventional NFC, rather it shows superior performance over PI-controller-based drive. A hybrid fuel cell (FC) supply system to deliver the power demanded by the feedback linearization (FBL) based IM drive is designed and implemented. The modified simple hybrid neuro-fuzzy sliding-mode control (NFSMC) incorporated with the intuitive FBL substantially reduces torque chattering and improves speed response, giving optimal drive performance under system uncertainties and disturbances. This novel technique also has the benefit of reduced computational burden over conventional NFSMC and thus, suitable for real-time industrial applications. The parameters of the modified NFC is tuned by an adaptive mechanism based on sliding-mode control (SMC). A FC stack with a dc/dc boost converter is considered here as a separate external source during interruption of main supply for maintaining the supply to the motor drive control through the inverter, thereby reducing the burden and average rating of the inverter. A rechargeable battery used as an energy storage supplements the FC during different operating conditions of the drive system. The effectiveness of the proposed method using FC-based linearized IM drive is investigated in simulation, and the efficacy of the proposed controller is validated in real-time. It is evident from the results that the system provides optimal dynamic performance in terms of ripples, overshoot, and settling time responses and is robust in terms of parameters variation and external load

Fuzzy second order sliding mode control of a unified power flow controller

Purpose. This paper presents an advanced control scheme based on fuzzy logic and second order sliding mode of a unified power flow controller. This controller offers advantages in terms of static and dynamic operation of the power system such as the control law is synthesized using three types of controllers: proportional integral, and sliding mode controller and Fuzzy logic second order sliding mode controller. Their respective performances are compared in terms of reference tracking, sensitivity to perturbations and robustness. We have to study the problem of controlling power in electric system by UPFC. The simulation results show the effectiveness of the proposed method especiallyin chattering-free behavior, response to sudden load variations and robustness. All the simulations for the above work have been carried out using MATLAB / Simulink. Various simulations have given very satisfactory results and we have successfully improved the real and reactive power flows on a transmission lineas well as to regulate voltage at the bus where it is connected, the studies and illustrate the effectiveness and capability of UPFC in improving power.В настоящей статье представлена усовершенствованная схема управления, основанная на нечеткой логике и режиме скольжения второго порядка унифицированного контроллера потока мощности. Данный контроллер обладает преимуществами с точки зрения статической и динамической работы энергосистемы, например, закон управления синтезируется с использованием трех типов контроллеров: пропорционально-интегрального, контроллера скользящего режима и контроллера скользящего режима нечеткой логики второго порядка. Их соответствующие характеристики сравниваются с точки зрения отслеживания эталонов, чувствительности к возмущениям и надежности. Необходимо изучить проблему управления мощностью в энергосистеме с помощью унифицированного контроллера потока мощности (UPFC). Результаты моделирования показывают эффективность предложенного метода, особенно в отношении отсутствия вибрации, реакции на внезапные изменения нагрузки и устойчивости. Все расчеты для вышеуказанной работы были выполнены с использованием MATLAB/Simulink. Различные расчетные исследования дали весьма удовлетворительные результаты, и мы успешно улучшили потоки реальной и реактивной мощности на линии электропередачи, а также регулирование напряжения на шине, к которой она подключена, что позволяет изучить и проиллюстрировать эффективность и возможности UPFC для увеличения мощности