Torque Control System Research Articles

DTC-SVM strategy of double star induction motor using sliding mode controller enhanced by fuzzy logic

Extensive research and numerous articles on Direct Torque Control (DTC) systems have consistently highlighted various deficiencies associated with the employment of hysteresis control units. Specifically, these issues include significant torque and stator flux ripples, as well as distortions in stator current. Additionally, the sensitivity to parametric variations stemming from Proportional-Integral (PI) controllers poses a challenge due to the complexities involved in their adjustment. To address these drawbacks, this article explores the implementation of Space Vector Modulation DTC (SVM-DTC) technology integrated with a Sliding Mode Controller (SMC). The proposed control scheme for DTC amalgamates the benefits derived from SMC control and the SVM algorithm. The SMC controller is utilized to oversee PI controllers, employing this nonlinear technique ensures superior dynamic performance and robust resistance against external disturbances. Notwithstanding its advantages, one notable issue with SMC is chattering. To mitigate this phenomenon, we adopt an approach that integrates Fuzzy Logic with SMC. By replacing the saturation function (Sat) with a fuzzy inference system, we effectively minimize chattering. The simulation is conducted using MATLAB SIMULINK. Comparative analysis between DTC-SVM strategy and traditional methods including standard SMC and Fuzzy-enhanced Sliding Mode Controller (FSMC), demonstrates that our hybrid nonlinear controller exhibits high efficiency and remarkable robustness.

Direct torque motor control technology based on NPC three-level SVPWM

This paper introduces a direct torque control technology based on NPC three-level SVPWM. It uses the Matlab platform to build a simulation model. It verifies the ideal waveform of the torque, magnetic chain, and current of the NPC-SVPWM direct torque control system under high speed and low speed with no load and load. This technique improves the waveform of the stator current and stator magnetic chain and solves the problem of the switching frequency not being fixed. Through this technique, it not only retains the robustness of the direct torque technique to the parameter change, but also effectively reduces the torque pulsation.

Automotive wheel torque control systems

The approaches of foreign specialists to improving electronic control systems for the dynamic state of vehicles (traction, directional stability, rollover resistance) to improve their safe operation are analyzed. The three main components of the Active Transmission Torque Management (ADTM) system are considered: the center clutch, the electronic limited slip differential (ELSD), and the torque vectoring system. The possibilities and limitations of using active torque control systems to ensure vehicle safety are shown. Keywords: electronic control, vehicle dynamics, wheel torque, torque vectoring, active safety. alexey.scherbin@nami.ru

Intelligent vector-frequency control system for asynchronous electric motors

This article analyzes the main directions and principles of developing a mathematical model for creating an intelligent system for vector-frequency control of asynchronous electric motors. Particular attention is paid to the development of a frequency converter structure that allows the implementation of an energy-saving algorithm in a direct torque control (DTC) system to reduce stator current consumption from the power source. This approach guarantees stable operation of the electric motor under various load conditions and voltage changes in the electrical network. The synthesis of a speed control system and current control loops are considered and generalized models of a vector control system are developed using the Matlab software package and the main Simulink library.

Dynamic Performance Enhancement of a Self-Excited Induction Generator Connected to the Grid Using an Effective Control Algorithm

The current study aims to present a comprehensive analysis of a control technique used for improving the dynamic performance of a grid connected self-excited induction generator (SEIG). All components in the control system are modelled and explained in details. The management of SEIG operation is achieved through controlling the machine side converter using a new formulated predictive voltage control scheme (PVC). The proposed (PVC) is compared to field oriented control (FOC), model predictive current control (MPCC) and model predictive direct torque control (MPDTC) systems. MPDTC and MPCC have several drawbacks like high ripple, high load commutation, and using a weighting factor in their cost function, While FOC systems depend on machine parameters for variable estimation factors, need to use PI regulators and co-ordinate transformations, which complicate the system and slow down the dynamic response. The results obtained indicate that the proposed PVC is effective, as the ripples are reduced by 43% when compared to the MPDTC-used one and by 30% when compared to MPCC. Additionally, PVC's time response is less than that of the FOC by 15%, MPCC by 9%, and MPDTC by 4%. Furthermore, PVC produces 38% fewer commutations than MPCC and 40% fewer commutations than MPDTC. Consequently, the generator's efficiency and dependability both increased as a result of its better performance.

Modeling of nonlinear characteristics of switched reluctance motor based on optimized RBF neuron network

The strong nonlinearity of switched reluctance motor (SRM) makes it difficult to establish an accurate mathematical model of its electromagnetic characteristics. In this paper, an artificial neural network modeling method is adopted to estimate the nonlinear flux linkage characteristics of a four-phase SRM. Based on the samples obtained by experimental measurement, a radial basis function neural network is trained and further optimized using the K-Means algorithm. The modeling accuracy of the optimized flux linkage model is analyzed in detail and also verified by dynamic simulation of a direct instantaneous torque control system of the motor.

Induction motor torque closed-loop vector control system based on flux observation and harmonic current suppression

As for the torque control system of induction motors, there are generally two technical problems. One is the torque feedback error caused by the low accuracy of the flux observer, and the other is the torque ripple caused by insufficient harmonic current suppression. In order to provide accurate electromagnetic torque feedback information, an improved voltage and current hybrid model-based flux observer is designed, which improves the effectiveness of flux observation and the accuracy of electromagnetic torque feedback. Furthermore, an improved quasi-resonant linear active disturbance rejection-based current loop control method is proposed to suppress the harmonic current and reduce the torque ripple. And the disturbance observation characteristics, parameter influence, and self-stability of the control system are analyzed based on the frequency domain method. Finally, to verify the effectiveness of the proposed method, simulation, and physical experiments are designed in this paper. The experimental results show that the proposed method can effectively reduce the flux observation error and the current harmonic component, and thus greatly improve the torque tracking control performance.

Analysis of fuzzy and neural controllers in direct torque controlled synchronous motors

<p><span>In this study, multiple intelligent control systems for direct torque-controlled Synchronous motors are implemented and compared. Using a lookup table to pick a vector through the inverter voltage space, the direct torque control (DTC) system can be obtained. To replicate the state selector in relation to the look-up table, intelligent controllers are deployed. Intelligent logic controllers like fuzzy and neural are used to regulate the performance of permanent magnet synchronous motors (PMSM). In steady-state applications, neural and fuzzy controllers reduce the torque ripple and stator current harmonic distortion. These outcomes are compared with those obtained when the synchronous motor was put under the basic direct torque control method using a proportional integral (PI) controller. The accuracy and effectiveness of the suggested control topologies have been verified using computer simulation software like MATLAB/Simulink.</span></p>

Enhancing the maximum power of wind turbine using artificial neural network

Wind energy conversion systems (WECS) now play a significant role in meeting the world's energy needs. Several different approaches are used to try to increase the reliability of these renewable energy systems. Smart systems are designed to be more proactive to improve the performance of renewable energy equipment. Artificial neural networks (ANNs) have a variety of applications, including controlling renewable energy systems. Using optimal torque control (OTC) system based prediction techniques, a controller to monitor a maximum point of wind turbine output power (MPPT) is designed and modeled in this paper. MATLAB/Simulink package tools for neural networks are also used to design the controller and simulate it to achieve the necessary results and to obtain an appropriate analysis for the controller. The results show that the ANN is more active and delivers better output than the traditional controller.

МОДЕЛИРОВАНИЕ СИСТЕМЫ «СИНХРОННЫЙ ГЕНЕРАТОР – ПРЕОБРАЗОВАТЕЛЬ ЧАСТОТЫ – СИНХРОННЫЙ ДВИГАТЕЛЬ»

The relevance of the use of AC-AC power transmission systems on vehicles, including autonomous locomotives, using synchronous generators and traction synchronous motors is showed. Typical block diagram of electric transmission of traction rolling stock and functional diagram of electric power transmission with synchronous generator and traction motors are given. Based on the theory of a generalized electrical motor, a mathematical description has been devel-oped in a two-phase coordinate system for a synchronous generator with excitation winding and starting winding and a synchronous motor with permanent magnets, its equivalent schemes are shown. The results of simulation in Matlab Simulink of electric system with two traction motors connected to frequency converters with two-level voltage source inverters are given. Direct torque control system with a table function of switching the voltage source inverter by output signals of relay regulators of torque and flux linkage is used for motor control. Based on the obtained re-sults, recommendations for the synthesis of energy-efficient control systems for synchronous motors with permanent magnets are given

Variable Flux Direct Torque Control System of Switched Reluctance Motor

Direct Torque Control (DTC) strategy is very suitable for controlling Switched Reluctance Motors (SRM) due to its advantages of the simple control structure and fast system response, etc. This paper first analyzes the principle of DTC and establishes the DTC model of three-phase 6/4 SRM by Matlab/Simulink. In this paper, a three-closed-loop variable flux DTC system with velocity as the given quantity is constructed to solve the problem that the stator current of constant flux DTC is too large when the motor is in a steady state. The simulation results show that the variable flux DTC system has a better inhibition effect on the stator current and torque ripple of SRM than the conventional DTC system.

Formation of a Task of the Stator Flux Linkage of a Synchronous Motor with Permanent Magnets in a Direct Torque-Control System

Formation of a Task of the Stator Flux Linkage of a Synchronous Motor with Permanent Magnets in a Direct Torque-Control System

Direct torque control and dynamic performance of induction motor using fractional order fuzzy logic controller

<p><span lang="EN-US">Conventional direct torque control (DTC) is one of the best control systems for regulating the torque of an induction motor (IM). However, the DTC’s enormous waves in flux and torque cause acoustic noise that degrades control performance, especially at low speeds due to the DTC’s low switching frequency. Direct torque control systems, which focus just on torque and flux, have been proposed as a solution to these problems. In order to improve DTC control performance, this work introduces a fractional-order fuzzy logic controller method. The objective is to analyze this technique critically with regard to its efficacy in reducing ripple, its tracking speed, its switching loss, its algorithm complexity, and its sensitivity to its parameters. Simulation in MATLAB/Simulink verifies the anticipated control approach’s performance.</span></p>

OPTIMIZATION OF THE DC MOTOR MODEL COMPLEXITY LEVEL SELECTION FOR THE SYSTEM USING A NEURAL NETWORK

A mathematical and computer model of the torque control system of a DC motor with independent excitation was built using the NARMA 2 neurocontroller. NARMA - a nonlinear moving average autoregressive model is one of the basic structures of a discrete and nonlinear model. The task was to conduct a comparative analysis of the operation of the neurocontroller-based control system using plant model of varying complexity. For the experiment, three models of the object were built with different degrees of detail, and therefore the reliability of the object. The full model includes an independently excited DC motor model that takes into account all the main parameters of the DC machine, internal friction parameters, excitation circuits, etc. The simplified model is a linear approximation of the DC motor by a second-order system that takes into account both mechanical and electrical time constants. The simplest model is a linear approximation of a DC motor by a first-order aperiodic link that takes into account only the mechanical time constant. The neurocontroller was trained using the above object models and an experiment was conducted to process the reference torque signal by the system. For each of the three training cases, the neurocontroller was set to 1000 epochs, since further training performance improvement is unnecessary due to the huge loss of computation time. The NARMA-L2 neurocontroller, which is also called feedback linearization control, was used. This controller can be implemented using a pre-identified NARMA-L2 object model. Neural networks that are trained on simplified object models do not take into account most of the electrical processes in a DC motor, especially the excitation winding is not modeled at all. The obtained results were compared with the use of a neurocontroller as a torque regulator. Further research in this direction involves the study of the necessary computing power for the microprocessor implementation of the neurocontroller.

High-precision dynamic torque control of high stiffness actuator for humanoids

The high stiffness actuator (HSA), applied to each joint of an electrical driven humanoid robot, can directly affect the motion performance of the torque-controlled humanoid robots. For high control performance of HSA, a high-precision dynamic torque control (HDTC) is proposed. The HDTC consists of two phases: (1) A novel dynamic current control is used to linearize high stiffness actuator torque control system, which can estimate and compensate the nonlinear coupling parts; (2) An enhanced internal model control is designed to ensure high tracking accuracy in the system containing noisy torque signal and even numerical differentiation signals. Benefitting from dynamic current control and the enhanced internal model control, the proposed HDTC is accurate and adaptable. Finally, the superiority of the HDTC is verified with comparative experiments.

Simulation of a direct torque control system in the presence of winding asymmetry in induction motor

The operation of a direct torque control (DTC) system for induction motors with the possible presence of unbalanced modes in the motor windings is studied. It is shown that in the presence of asymmetric modes in the motor windings, the classical DTC algorithm becomes unstable. It is proposed to improve the DTC by taking into account the unbalance of the active resistances of the motor stator windings when determining the DTC parameters. This will make it possible to study electrodynamic processes in DTC with the possible presence of asymmetric modes in the windings of induction motor.

Performance analysis on parallel operation of multimode droop controller doubly fed induction generator based wind energy conversion system with Other wind unit, solar units, loads and facts device

Due to their numerous advantages, wind turbines with changeable speed power production systems are increasingly being used. This study proposes the architecture and specifications of an induction turbine-generating wind system, as well as a PI controller-based DC voltage link regulator and optimal torque control system. The suggested control technique as stated in this paper allows the system to function in grid-linked mode as well as the islanded mode and also in parallel with other power generation units, loads, FACTS devices, etc. This paper involves the analysis of different test cases in grid-connected mode as well as an islanded mode with a PV generation unit, passive loads, induction motor loads, and other wind energy generation systems. The paper also includes an analysis of the response of the system with the switching on and off of the other units. MATLAB 2013a thereby validates the system findings and analysis.

Implementation of a new flux rotor based on model reference adaptive system for sensorless direct torque control modified for induction motor

Introduction. In order to realize an efficient speed control of induction motor, speed sensors, such as encoder, resolver or tachometer may be utilized. However, some problems appear such as, need of shaft extension, which decreases the mechanical robustness of the drive, reduce the reliability, and increase in cost. Purpose. In order to eliminate of speed sensors without losing. Several solutions to solve this problem have been suggested. Based on the motor fundamental excitation model, high frequency signal injection methods. The necessity of external hardware for signal injection and the adverse influence of injecting signal on the motor performance do not constitute an advantage for this technique. Fundamental model-based strategies method using instantaneous values of stator voltages and currents to estimate the rotor speed has been investigate. Several other methods have been proposed, such as model reference adaptive system, sliding mode observers, Luenberger observer and Kalman filter. The novelty of the proposed work consists in presenting a model reference adaptive system based speed estimator for sensorless direct torque control modified for induction motor drive. The model reference adaptive system is formed with flux rotor and the estimated stator current vector. Methods. The reference model utilizes measured current vector. On the other hand, the adjustable model uses the estimated stator current vector. The current is estimated through the solution of machine state equations. Practical value. The merits of the proposed estimator are demonstrated experimentally through a test-rig realized via the dSPACE DS1104 card in various operating conditions. The experimental results show the efficiency of the proposed speed estimation technique. Experimental results show the effectiveness of the proposed speed estimation method at nominal speed regions and speed reversal, and good results with respect to measurement speed estimation errors obtained.

TORQUE RESPONSE PERFORMANCE ANALYSIS OF PMSM DTC

In this paper, the basic principle of permanent magnet synchronous motor direct torque control, and the torque response performance of permanent magnet synchronous motor direct torque control are investigated. PMSM model is built and simulated on MATLAB / SIMULINK platform. The simulation results show that the permanent magnet synchronous motor direct torque control system have fast torque response, good dynamic performance and can quickly follow a variety changes of given torque. But in the full range of run-time, permanent magnet synchronous motor direct torque control system has relatively large torque ripple and rich harmonics, which is the traditional permanent magnet synchronous motor direct torque control’s major defects

Intelligent Computed Torque Control With Recurrent Legendre Fuzzy Neural Network for Permanent-Magnet Assisted Synchronous Reluctance Motor

The goal of this research is to develop an intelligent controlled permanent-magnet assisted synchronous reluctance motor (PMASynRM) drive system by utilizing an intelligent computed torque control with recurrent Legendre fuzzy neural network (ICTCRLFNN), in order to adjust the nonlinear and time-varying control specifications of the motor. The team first proposes an ANSYS Maxwell-2D dynamic model that contains a maximum torque per ampere (MTPA) control PMASynRM drive. A lookup table (LUT) is composed of the finite element analysis (FEA) results, which bring about the current angle of command within the MTPA. Subsequently, the team designs a computed torque control (CTC) system to control the speed reference command. Creating a working CTC for practical applications is quite complex because the detailed system dynamics, which includes the unpredictability of the PMASynRM drive system, is not available beforehand. Thus, this study suggests that a recurrent Legendre fuzzy neural network (RLFNN) can act as a close substitute for the CTC to resolve its existing complications. Furthermore, the team modifies an adaptive compensator to proactively adjust for the potential calculated deviance of the RLFNN. Asymptotical stability is assured by using the Lyapunov stability method, which generates the RLFNN’s online learning algorithms. This study concludes that certain experimental results verify the effective and robust qualities of the suggested ICTCRLFNN controlled PMASynRM drive.