Sensorless speed control of DC motor using EKF estimator and TSK fuzzy logic controller

In this article, sensorless speed control of DC motor has been proposed using the extended Kalman filter (EKF) estimator and Takagi–Sugeno-Kang (TSK) fuzzy logic controller (FLC). In the industry, high-cost measurement systems/sensors are necessary for better controlling and monitoring, which can be replaced by a sensorless control technique to reduce the cost, size and increase system reliability and robustness. EKF has been used to perform the sensorless speed control by estimating the speed of the DC motor using the armature current only and TSK-FLC is used to reduce the effect of motor parameter variation and load torque nonlinearity in close loop speed control for various speed references. The performance of EKF-based TSK-FLC is compared with EKF-based PID controller. The time-domain specification and absolute error performance indices indicate that EKF-based TSK-FLC is superior to the EKF-based PID under similar conditions. The proposed system is executed in the MATLAB/Simulink environment, and sensorless speed control of DC motor prototype model has been developed for validating the proposed technique with the help of a micro-controller

A Review of Control Techniques for Wind Energy Conversion System

Wind energy is the most efficient and advanced form of renewable energy (RE) in recent decades, and an effective controller is required to regulate the power generated by wind energy. This study provides an overview of state-of-the-art control strategies for wind energy conversion systems (WECS). Studies on the pitch angle controller, the maximum power point tracking (MPPT) controller, the machine side controller (MSC), and the grid side controller (GSC) are reviewed and discussed. Related works are analyzed, including evolution, software used, input and output parameters, specifications, merits, and limitations of different control techniques. The analysis shows that better performance can be obtained by the adaptive and soft-computing based pitch angle controller and MPPT controller, the field-oriented control for MSC, and the voltage-oriented control for GSC. This study provides an appropriate benchmark for further wind energy research

Development of Fuzzy Applications for High Performance Induction Motor Drive

This chapter develops a sliding mode and fuzzy logic-based speed controller, which is named adaptive fuzzy sliding-mode controller (AFSMC) for an indirect field-oriented control (IFOC) of an induction motor (IM) drive. Essentially, the boundary layer approach is the most popular method to reduce the chattering phenomena, which leads to trade-off between control performances, and chattering elimination for uncertain nonlinear systems. For the proposed AFSMC, a fuzzy system is assigned as the reaching control part of the fuzzy sliding-mode controller so that it improves the control performances and eliminates the chattering completely despite large and small uncertainties in the system. A nonlinear adaptive law is also implemented to adjust the control gain with uncertainties of the system. The adaptive law is developed in the sense of Lyapunov stability theorem to minimize the control effort. The applied adaptive fuzzy controller acts like a saturation function in the thin boundary layer near the sliding surface to guarantee the stability of the system. The proposed AFSMC-based IM drive is implemented in real-time using digital signal processor (DSP) board TI TMS320F28335. The experimental and simulation results show the effectiveness of the proposed AFSMC-based IM drive at different operating conditions such as load disturbance, parameter variations, etc

Development and implementation of various speed controllers for wide speed range operation of IPMSM drive / by Md Muminul Islam Chy.

Despite many advantageous features of interior permanent magnet synchronous motor (IPMSM), the precise speed control of an IPMSM drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinearity present in the electromagnetic developed torque due to magnetic saturation of the rotor core particularly, at high speeds (above rated speed). Fast and accurate response, quick recovery of speed from any disturbances and insensitivity to parameter variations are some of the important characteristics of high performance drive system used in robotics, rolling mills, traction and spindle drives. The conventional controllers such as PI, PID are sensitive to plant parameter variations and load disturbance. For the purpose of obtaining high dynamic performance, recently researchers developed several non-linear as well as intelligent controllers. Most of the reported works on controller design of IPMSM took an assumption of d-axis stator current (i[subscript d]) equal to zero in order to simplify the development of the controller. However, with this assumption it is not possible to control the motor above the rated speed and the reluctance torque of IPMSM can not be utilized efficiently. Furthermore, this assumption leads to an erroneous result for motor at all operating conditions. In this thesis, some controllers are developed for the IPMSM drive system incorporating the flux-weakening technique in order to control the motor above the rated speed. A detailed analysis of the flux control based on various operating regions is also provided in this thesis. In order to get the optimum efficiency, an adaptive backstepping based nonlinear control scheme incorporating flux control for an IPM synchronous motor drive is taken into account at the design stage of the controller. Thus, the proposed adaptive nonlinear backstepping controller is capable of conserving the system robustness and stability against all mechanical parameters variation and external load torque disturbance. To ensure stability the controller is designed based on Lyapunov's stability theory. A novel fuzzy logic controller (FLC) including both torque and flux control is also developed in this work. The proposed FLC overcomes the unknown and nonlinear uncertainties of the drive and controls the motor over a wide speed range. For further improvement of the FLC structure, the membership function of the controller is tuned online. An integral part of this work is directed to develop an adaptive-network based fuzzy interference system (ANFIS) based neuro fuzzy logic controller. In this work, an adaptive tuning algorithm is also developed to adjust the membership function and consequent parameters. In order to verify the effectiveness of the proposed IPMSM drive, at first simulation model is developed using Matlab/Simulink. Then the complete IPMSM drive incorporating various control algorithms have been successfully implemented using digital signal processor (DSP) controller board-DSI104 for a laboratory 5 hp motor. The effectiveness of the proposed drive is verified both in simulation and experiment at different operating conditions. The results show the robustness of the drive and it's potentiality to apply for real-time industrial drive application. This thesis also provides through knowledge about development and various speed real-time applications of controllers for IPMSM drive, which will be useful for researchers and practicing engineers

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Intelligent control of induction motors

This thesis presents the development and implementation of an integral field oriented intelligent control for an induction motor (IM) drive using Fuzzy Logic Controller (FLC), and an Artificial Neural Network (ANN), employing a finite element controller and making use of a Proportional Integral (PI) adaptive controller as well. An analytical model of an induction motor drive has been developed. In order to prove the superiority of the proposed controller, the performance of this controller is compared with conventional PI-based IM drives. The performance of the proposed IM drive is investigated extensively at different operating conditions in simulation. The proposed adaptive PI-based speed controller’s performance is found to be robust and it is a potential candidate for high performance industrial drive applications. The novel work focuses on using a Finite Element Controller map (FECM) to manipulate adaptive controllers for motor control drives. A digital signal processing (DSP) board DS1104 and laboratory induction motor were used to implement the complete vector control scheme. The test results have been compared with simulated results at different dynamic operating conditions. The effectiveness of this control scheme has been evaluated, and it has been found to be more efficient than the conventional PI controller

A Novel DTFC Based Efficiency and Dynamic Performance Improvement of IPMSM Drive

With the advancements in magnetic materials and semiconductor technology, permanent magnet synchronous motor (PMSM) is becoming more and more popular in high power industrial applications due to its high energy density, high power factor, low noise and high efficiency as compared to conventional AC motors. Field oriented vector control (VC) and direct torque and flux control (DTFC) are used for high performance drives. Among these two techniques DTFC is faster and simpler than that of conventional VC scheme as DTFC scheme doesn’t need any coordinate transformation, pulse width modulation and current regulators. The DTFC based motor drives utilizes hysteresis band comparators for both torque and flux controls. Both torque and flux are controlled simultaneously by the selection of appropriate voltage vectors from the inverter. However, DTFC suffers from high torque ripples due to discrete nature of control system and limited voltage vectors from the inverter. Torque ripples can be minimized by increasing the sector numbers of the DTFC scheme which increases the switching frequency of the inverter. Traditionally, researchers chose a constant value of reference air-gap flux to make the control task easy but it is not acceptable for high performance drives as the air-gap flux changes with the operating conditions and system disturbances. Furthermore, if the reference air-gap flux is maintained constant, it is not possible to control the motor over the wide speed range operation. Moreover, conventional six-sector based DTFC scheme suffers from high torque ripples, which is the major drawbacks to achieve high dynamic performance. Therefore, this thesis presents a novel eighteen-sector based DTFC scheme to achieve high dynamic performance with minimum torque ripples. In addition, the loss minimization algorithm (LMA) is incorporated with proposed DTFC scheme in order to improve the efficiency while maintaining high dynamic performance. This thesis further presents modified eighteen-sector based DTFC scheme to overcome the unbalanced voltage effects in any sector of conventional six-sector based system to improve the dynamic performance of the proposed system. This thesis also presents a novel sector determination algorithm to determine the sector number of the stator flux linkage vector which reduces the computational burden to the microprocessor. A five level torque hysteresis comparator based DTFC scheme is also proposed to reduce the torque ripple. Further, a backstepping based nonlinear controller is developed for IPMSM drive that achieves the lowest possible torque ripples in steady state. In this controller development, the control variable is motor electromagnetic developed torque and stator air-gap flux linkages similar to classical DTFC but the errors are forced to zero using backstepping process to get better dynamic performance. The effectiveness of the proposed systems is verified through the development of a simulation model using Matlab/Simulink. Performance of the proposed nonlinear controller is investigated extensively at different operating conditions such as sudden speed and load changes. Then the complete IPMSM drives, incorporating the proposed LMA and eighteen-sector based DTFC scheme and nonlinear controller with torque and flux as virtual control variables are successfully implemented in real-time using digital signal processor (DSP) board-DS1104 board for laboratory 5-hp motor. The effectiveness of the proposed control techniques are verified in both simulation and experiment at different operating conditions. It is found that, the nonlinear controller based IPMSM drive provides the best performance in terms of torque ripple among all the DTFC scheme developed in the thesis. The results show the robustness of the drive and it’s potentiality to apply for real-time industrial drive applications

Investigations on Direct Torque and Flux Control of Speed Sensorless Induction Motor Drive

The Induction motors (IM) are used worldwide as the workhorse in most of the industrial applications due to their simplicity, high performance, robustness and capability of operating in hazardous as well as extreme environmental conditions. However, the speed control of IM is complex as compared to the DC motor due to the presence of coupling between torque and flux producing components. The speed of the IM can be controlled using scalar control and vector control techniques. The most commonly used technique for speed control of IM is scalar control method. In this method, only the magnitude and frequency of the stator voltage or current is regulated. This method is easy to implement, but suffers from the poor dynamic response. Therefore, the vector control or field oriented control (FOC) is used for IM drives to achieve improved dynamic performance. In this method, the IM is operated like a fully compensated and separately excited DC motor. However, it requires more coordinate transformations, current controllers and modulation schemes. In order to get quick dynamic performance, direct torque and flux controlled (DTFC) IM drive is used. The DTFC is achieved by direct and independent control of flux linkages and electromagnetic torque through the selection of optimal inverter switching which gives fast torque and flux response without the use of current controllers, more coordinate transformations and modulation schemes. Many industries have marked various forms of IM drives using DTFC since 1980. The linear fixed-gain proportional-integral (PI) based speed controller is used in DTFC of an IM drive (IMD) under various operating modes. However, The PI controller (PIC) requires proper and accurate gain values to get high performance. The PIC gain values are tuned for a specific operating point and which may not be able to perform satisfactorily when the load torque and operating point changes. Therefore, the PIC is replaced by Type-1 fuzzy logic controller (T1FLC) to improve the dynamic performance over a wide speed range and also load torque disturbance rejections. The T1FLC is simple, easy to implement and effectively deals with the nonlinear control system without requiring complex mathematical equations using simple logical rules, which are decided by the expert. In order to further improve the controller performance, the T1FLC is replaced by Type-2 fuzzy logic controller (T2FLC). The T2FLC effectively handles the large footprint of uncertainties compared to the T1FLC due to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, whereas the T1FLC consists only a Type-1 fuzzy sets and single membership function. The training data for T1FLC and T2FLC is selected based on the PIC scheme

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science