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

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Nov i jednostavan hibridni neizraziti/PI regulator za istosmjerne motore bez četkica

A novel speed controller for the trapezoidal three--phase Brushless DC (BLDC) Motor Drive is proposed using a hybrid fuzzy logic and proportional plus integral (PI) control. The fuzzy logic control structure is different from conventional fuzzy logic implementations such that it only uses three simple rules based on speed error being either in the positive, negative or zero regions. The controller outputs a reference current, that is enforced through the motor phases by pulsewidth modulation (PWM) control. The proposed fuzzy logic controller can be used individually in applications requiring lower computation load and accepting small steady state offset. For high performance applications requiring offset free tracking, a PI controller is augmented with the fuzzy logic controller and a simple switching scheme is devised based on error variance to switch the active controller based on operating conditions. The response of the drive system under the proposed composite control structure is compared with the conventional PI based and the sliding mode controllers to demonstrate its improved performance. Simulations studies using detailed models in MATLAB/Simulink\u27s Simpowersystems toolbox are carried out to show the validity of proposed control.U ovome radu predlaže se nov regulator brzine za trapezoidalne trofazne istosmjerne motore bez četkica zasnovan na hibridnom regulatoru. Hibridni regulator sastoji se od dijela s neizrazitom logikom i proporcionano-integracijskog regulatora. Struktura neizrazitog regulatora razlikuje se od konvencionalnih implementacija neizrazitih regulatora po tome što koristi samo tri jednostavna pravila zasnovana na pogrešci brzine u pozitivnom, negativnom ili nultom području. Izlaz regulatora čini referentna struja, koja se šalje na faze motora pomoću širinsko-impulsne modulacije. Predloženi neizraziti regulator može se koristiti i zasebno u primjenama koje zahtijevaju manju računsku složenost i toleriraju malu pogrešku u stacionarnom stanju. Za slučajeve kada je potrebna visoka učinkovitost bez pogreške u stacionarnom stanju, s neizrazitim dijelom proširuje se PI regulator te je razvijen jednostavan postupak promijene regulatora zasnovan na varijanci pogreške. Odziv razmatranog sustava uspoređen je s konvencionalnim PI regulatorom i regulatorom u kliznom režimu rada kako bi se pokazala njegova učinkovitost. Izvršene su simulacije u Matlab/Simulinkovom SimPowerSystems alatu kako bi se pokazala ispravnost predloženog postupka

Estimation of rotor flux of an induction machine

The objective of this dissertation is to estimate rotor flux of an IM. Some of the material is focused on the functional block of the IM i.e. Torque estimator, Speed estimator etc. while a subsequent part deals with estimation of rotor flux. The dissertation is organized as follows:Chapter 1 describes background information of the machines then it focuses on the methodology how on to approach the task on a particular time with the help of Gantt chart.Chapter 2 presents the basic principals of rotating magnetic field of the IM and asserts brief overview of the AC machines. Later it talks about different kinds of IM rotors suggesting which one is good. It is crucial to start with good and appropriate reviews which were verified by numerous journals. Literature review is presented by analysing the previous work. (Busawan et al., 2001) summarises that a nonlinear observers for the estimation of the rotor flux and the load torque in an induction motor. The observers are designed on the basis of the standard alpha - beta Park's model. Finally, fuzzy logic is mentioned in more detailed way and Membership functions were also discussedChapter 3 explains the dynamic model of induction machine plant and the model was presented. Then the model is analysed, developed in MATLAB-SIMULINK which was discussed in Chapter 4. By considering following assumptions, dynamic model is implemented i.e. it should be symmetrical two-pole, three phase windings. Slotting effects are neglected, Permeability of the iron part is infinite, and iron losses are neglected. Dynamic d-q model and Axes transformation is implemented on stationary reference frame (a-b-c). Lastly torque equation is derived.Chapter 4 is the heart of this project by scrutinizing the model thoroughly and by introducing fuzzy controller logic using MATLAB-SIMULINK; simulations are performed to estimate the functional block such as torque, speed, flux, resistance with and without fuzzy logic. Results were obtained for different blocks and the m-file, DTC, Flux table were obtained and presented in the Appendixes.Chapter 5 concludes the simulation results and concentrates mainly on the future direction what more can be done to improve the platform in a more efficient manner

Induction Motor Performance Improvement using Super Twisting SMC and Twelve Sector DTC

Induction motor (IM) direct torque control (DTC) is prone to a number of weaknesses, including uncertainty, external disturbances, and non-linear dynamics. Hysteresis controllers are used in the inner loops of this control method, whereas traditional proportional-integral (PI) controllers are used in the outer loop. A high-performance torque and speed system is consequently needed to assure a stable and reliable command that can tolerate such unsettled effects. This paper treats the design of a robust sensorless twelve-sector DTC of a three-phase IM. The speed controller is conceived based on high-order super-twisting sliding mode control with integral action (iSTSMC). The goal is to decrease the flux, torque, the current ripples that constitute the major conventional DTC drawbacks. The phase current ripples have been effectively reduced from 76.92% to 45.30% with a difference of 31.62%. A robust adaptive flux and speed observer-based fuzzy logic mechanism are inserted to get rid of the mechanical sensor. Satisfactory results have been got through simulations in MATLAB/Simulink under load disturbance. In comparison to a conventional six-sector DTC, the suggested technique has a higher performance and lower distortion rate

Low Speed Estimation of Sensorless DTC Induction Motor Drive Using MRAS with Neuro Fuzzy Adaptive Controller

This paper presents a closed loop Model Reference Adaptive system (MRAS) observer with artificial intelligent Nuero fuzzy controller (NFC) as the adaptation technique to mitigate the low speed estimation issues and to improvise the performance of the Sensorless Direct Torque Controlled (DTC) Induction Motor Drives (IMD). Rotor flux MRAS and reactive power MRAS with NFC is explored and detailed analysis is carried out for low speed estimation. Comparative analysis between rotor flux MRAS and reactive power MRAS with PI as well as NFC as adaptive controller is performed and results are presented in this paper. The comparative analysis among these four speed estimation methods shows that reactive power MRAS with NFC as adaptation mechanism shows reduced speed estimation error and actual speed error at steady state operating conditions when the drive is subjected to low speed operation. Simulation carried out using MATLAB-Simulink software to validate the performance of the drive especially at low speeds with rated and variable load conditions

FPGA design methodology for industrial control systems—a review

This paper reviews the state of the art of fieldprogrammable gate array (FPGA) design methodologies with a focus on industrial control system applications. This paper starts with an overview of FPGA technology development, followed by a presentation of design methodologies, development tools and relevant CAD environments, including the use of portable hardware description languages and system level programming/design tools. They enable a holistic functional approach with the major advantage of setting up a unique modeling and evaluation environment for complete industrial electronics systems. Three main design rules are then presented. These are algorithm refinement, modularity, and systematic search for the best compromise between the control performance and the architectural constraints. An overview of contributions and limits of FPGAs is also given, followed by a short survey of FPGA-based intelligent controllers for modern industrial systems. Finally, two complete and timely case studies are presented to illustrate the benefits of an FPGA implementation when using the proposed system modeling and design methodology. These consist of the direct torque control for induction motor drives and the control of a diesel-driven synchronous stand-alone generator with the help of fuzzy logic

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Self-tuned neuro-fuzzy controller based induction motor drive / by Zhi Rui Huang.

Among various ac motors, induction motor (IM) occupies almost 90% of the industrial drives due to its simple, robust construction and generally satisfactory efficiency as compared to dc motor. However, the control of IM is complex due to its nonlinear nature and the parameters change with operating conditions. Since 1980s, field orientation principle (FOP) has been used for high performance control of IM. Due to the well-known drawbacks of the fixed-gain proportional-integral (PI), proportional-integral-derivative (PID) and various adaptive controllers, over the last two decades researchers have been working to apply artificial intelligent controller (AIC) for IM drives due to its advantages as compared to the conventional PI, PID and adaptive controllers. The main advantages are that these controllers can handle any nonlinearity of arbitrary complexity, and their performances are robust. Also fuzzy rules and neural network (NN) can be used to model a process for model reference or model predictive control. Meanwhile, the designs of these controllers do not depend on accurate system mathematical model. Neuro-fuzzy controller (NFC), as a kind of artificial intelligent controller (AIC), has attracted much attention by researchers as it takes advantages from both fuzzy logic controller (FTC) and NN by combining the expert human knowledge and the learning ability of the NN. Despite lots of research on AIC application for motor drives, industries are still reluctant to use AIC for real-life industrial drives. The main reason is that most of AIC require complex calculation and hence suffer from high computational burden. Therefore, attention needs to be paid to develop AIC which is suitable for practical applications. In order to achieve that, in this thesis, first, a novel, low computational and simplified self-tuned NFC is developed for the speed control of IM drive. For the proposed NFC only the speed error is used as the input, unlike conventional NFCs, which utilize both speed error and its derivative as inputs. Obviously, this simplification lowers down computational burden and makes the NFC easier to be implemented in practical applications. Next, a faulty IM with broken rotor bars (IMBRB) is considered and a NFC is developed to minimize the speed ripple of that motor. The speed error and rotor electrical angle are used as two inputs o f the NFC. A supervised self-tuning method is also developed for the developed NFCs. The system error, instead of controller error, has been utilized to tune the membership functions and weights because the desired controller output is not readily available. Also the convergences/divergences of the weights are analyzed and investigated. Simulation models for indirect field oriented control of IM incorporating both of the developed NFCs are developed in Matlab/Simulink. IM drives based on both of the developed NFCs are successfully implemented in real-time using DSP board DS1I04. For the first NFC, comparisons with conventional NFC and PI are done both in simulation and experiment at different operating conditions for a laboratory 1/3 hp IM. Also the effectiveness o f the second NFC is tested for a laboratory 0.5 hp IMBRB both in simulation and experiment, compared to a well-tuned PI controller. It is found from the experimental results that the proposed NFC reduces the fundamental and second harmonic components of speed ripple which are significant components as compared to high frequency components