Self tuned NFC and adaptive hysteresis based DTC scheme for IM drive

The concept of field oriented control scheme brought the revolutionary change in industrial drives. Due to the high initial and running costs of the DC machines, the trend shifted from DC to AC motor drives to obtain high performance variable speed drives. The major achievement with field oriented control was the decoupled and independent control of stator and rotor quantities like DC machines. It is agreed that the control scheme for ac machines is complicated as compared to DC machines. The inherited problem with the ac machine control is the nonlinear relation between process variables e.g. speed and manipulated variables e.g. current, torque etc. Moreover, magnetic saturation of its rotor core causes developed torque relation of nonlinear nature

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

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 logic based online adaptation of current and speed controllers for improved performance of IPMSM drive

Precise torque and speed control of electric motors is a key issue in industries for variable speed drives (VSD). Over the years the induction motors have been widely utilized in industries for VSD applications. However, induction motor has some significant drawbacks like low efficiency, lagging power factor, asynchronous speed, low torque density etc. Nowadays the interior permanent magnet synchronous motor (IPMSM) is becoming popular for high performance variable speed drive (HPVSD) due to its high torque-current ratio, large power-weight ratio, high efficiency, high power factor, low noise and robustness as compared to conventional induction and other ac motors. Smooth torque response, fast and precise speed response, quick recovery of torque and speed from any disturbance and parameter insensitivity, robustness in variable speed domain and maintenance free operations are the main concerns for HPVSD. This work proposes a closed loop vector control of an IPMSM drive incorporating two separate fuzzy logic controllers (FLCs). Among them one FLC is designed. to minimize the developed torque ripple by varying online the hysteresis band of the PWM current controller. Another Sugeno type FLC is used to tune the gains of a proportional-integral (PI) controller where the PI controller actually serves as the primary speed controller. Thus, the limitations of traditional PI controllers will be avoided and the performance of the drive system can be improved. A flux controller is also incorporated in such a way that both torque and flux of the motor can be controlled while maintaining current and voltage constraints. The flux controller is designed based on maximum-torque- per-ampere (MTPA) operation below the rated speed and flux weakening operation above the rated speed. Thus, the proposed drive extends the operating speed limits for the motor and enables the effective use of the reluctance torque. In order to verify the performance of the proposed IPMSM drive, first a simulation model is developed using Matlab/Simulink. Then the complete IPMSM drive has been implemented in real-time using digital signal processor (DSP) controller board DS1104 for a laboratory 5 HP motor. The effectiveness of the proposed drive is verified both in simulation and experiment at different operating conditions. In this regard, a performance comparison of the proposed FLC based tuned PI and adapted hysteresis controllers based drive with the conventional PI and fixed bandwidth hysteresis controllers based drive is provided. These comparison results demonstrate the better dynamic response in torque and speed for the proposed IPMSM drive over a wide speed range

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

Online loss minimization based direct torque and flux control of IPMSM drive

With the advent of high energy rare earth magnetic material such as, third generation neodymium-iron-boron (NdFeB), permanent magnet synchronous motor (PMSM) is becoming more and more popular in high power industrial applications (e.g., high-speed railway) due to its advantageous features such as high energy density, stable parameters, high power factor, low noise and high efficiency as compared to the conventional ac motors. Over the years, vector control and direct torque and flux control (DTFC) techniques have been used for high performance motor drives. But, the DTFC is faster than that of conventional vector control as the DTFC scheme doesn't need any coordinate transformation, pulse width modulation (PWM) and current regulators. The DTFC utilizes hysteresis band comparators for both flux and torque controls. Most of the past researches on DTFC based motor drives mainly concentrated on the development of the inverter control algorithm with less torque ripple as it is the major drawback of DTFC. The torque reference value is obtained online based on motor speed error between actual and reference values through a speed controller. Traditionally, researchers chose a constant value of air-gap flux reference based on trial and error method which may not be acceptable for high performance drives as the air-gap flux changes with operating conditions and system disturbance. Efficient high performance drives require fast and accurate speed response to cope with disturbances and algorithm to minimize motor losses. However, if the reference air-gap flux is maintained constant it is not possible to control the motor losses. Therefore, this thesis presents a novel loss minimization based DTFC scheme for interior type PMSM drive so that the drive system can maintain both high efficiency and high dynamic performance. An online model based loss minimization algorithm (LMA) is developed to estimate the air-gap flux so that the motor operates at minimum loss condition while taking the general advantages of DTFC over conventional vector control. The performance the proposed LMA based DTFC for PMSM drive is tested in both simulation and real-time implementation at different operating conditions. The results verify the effectiveness of the proposed flux observer based DTFC scheme for PMSM drive

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

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

Volume 1 – Symposium: Tuesday, March 8

Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Components:Group A: Digital Hydraulics Group B: Intelligent Control Group C: Valves Group D | G | K: Fundamentals Group E | H | L: Mobile Hydraulics Group F | I: Pumps Group M: Hydraulic Component

Modelling, Monitoring, Control and Optimization for Complex Industrial Processes

This reprint includes 22 research papers and an editorial, collected from the Special Issue "Modelling, Monitoring, Control and Optimization for Complex Industrial Processes", highlighting recent research advances and emerging research directions in complex industrial processes. This reprint aims to promote the research field and benefit the readers from both academic communities and industrial sectors