Сравнительный анализ тиристорных схем управления двигателями постоянного тока, применяемых на судах

Due to the limitation of traditional controller circuit integration, the traditional DC motor speed control method has the problem of low control accuracy, which greatly affects the stability of DC motor operation. Therefore, an adaptive control method of DC motor speed based on STC MCU is proposed. Combined with the basic structure and working principle of DC motor, the mathematical model of DC motor is established. Under this model, the running law of motor speed is analyzed, and STC single chip microcomputer is used to optimize and refit the traditional controller. The speed value of DC motor is measured in real time, and the adaptive control value of speed is determined according to the control target of speed, which is used as the input of controller to realize the adaptive control of DC motor speed. Through the control test experiment, the conclusion is drawn: compared with the traditional control method, the speed control error of the design method is reduced by 8.75r/min, and the design method has more advantages in the control accuracy.

The DC motors an outstanding portion of apparatus in automotive and automation industrial applications requiring variable speed and load characteristics due to its ease of controllability. Creating an interface control system for multi DC motor drive operations with centralized speed control, from small-scale models to large industrial applications much demand. By using Lab VIEW (laboratory virtual instrument engineering workbench) as the motor controller, can control a DC motor for multiple purposes using single software environment. The aim of this paper is to propose the centralized speed control of DC motor using Lab VIEW. Here, the Lab VIEW is used for simulating the motor, whereas the input armature voltage of the DC motor is controlled using a virtual Knob in Lab VIEW software. The hardware part of the system (DC motor) and the software (in personal computer) are interfaced using a data acquisition card (DAQ) -Model PCI- 6024E. The voltage and Speed response is obtained using LABVIEW software. Using this software, group of motors’ speed can be controlled from different location using remote telemetry. The propose work also focuses on controlling the speed of the individual DC motor using PWM scheme (Duty cycle based Square wave generation) and DAQ. Help of the DAQ along with Lab VIEW front panel window, the DC motor speed and directions can be change easily in remote way. In order to test the proposed system the laboratory model for an 80W DC motor group (multi drive) is developed for different angular displacements and directions of the motor. The simulation model and experimental results conforms the advantages and robustness of the proposed centralized speed control.

The control of DC motor system is difficult and mathematically tedious due to their high non linearity property.To overcome this difficulty, a new approach has been required.This paper presented a systematic procedure to develop PI based speed controller for DC motor.The designing of speed controller through state estimation approaches.The optimal parameter of PI controller is finding out by using PSO algorithms.The performances and characteristics of DC motor are observed.The simulation results verify that steady state error is reduced, the rising time is improved and the disturbances affect is reduced hence the better performances of DC motor.

The main problem experienced by persons with disabilities and disabilities is the limitation of doing all activities, in general they need the help of others to perform activities in everyday life. It causes dependence on others which impact on the lack of productivity and hamper their potential in their lives. Based on these problems it is necessary to contribute technological innovation as a solution to help facilitate the activity of disabled and disability patient.The use of fuzzy logic method on DC motor control system integrated with Bluetooth communication media system to Android Smartphone using Rasberry phi microcomputer system integrated with arduino microcontroller can be used on the result of technology research of automatic wheelchair control system that can be driven using, voice recognition based control Android smartphone. The test results are performed on several parameters such as DC motor movement, DC motor load capacity, integration of voice recognition communication to DC motor control system and sound intensity to Android Smartphone. The results of this research are expected to help facilitate the activities of the disabled or disability so as to increase their productivity.

This paper presents an advanced model of a brushless dc motor(BLDCM) control system for the electric power steering(EPS) applications considering the suppression of torque ripples. All modules of the EPS system with a BLDCM are modeled under the Matlab/Simulink environment. Among them, the DSP module and the back-EMF module are achieved by the C MEX S-function. The DSP module can output six PWM signals and commutation logic is just considered as a real DSP chip. Furthermore, all six PWM signal ports can be controlled independently, which is convenient in the suppression of commutation torque ripples. The back-EMF module can realize any waveform of back-EMF and the ADC module performs a sample-and-hold function. The model with all the modules considered highlights the consistency with the actual system, which can be used to study the suppression of the torque ripple of the BLDCM, as well as to provide an effective platform for the development of an EPS system with a BLDCM.

This paper presents a robust fuzzy proportional-integral-derivative (PID) controller for brushless DC motor (BLDCM) control system. The hardware circuit of the BLDCM control system is designed and implemented using a digital signal processor (DSP) TMS320LF2407A and a monolithic BLDCM controller MC33035 as the core. Furthermore, a fuzzy PID controller, which combines the advantages of good robustness of fuzzy controller and high precision of conventional PID controller, is employed in the hardware system, thereby yielding a digital, intelligent BLDCM control system. Experimental results have shown that the control system can run steadily and control accurately, and have convincingly demonstrated the usefulness of the proposed fuzzy PID controller in BLDCM control system.

This paper presents an online self-tuning artificial-neural-network (ANN)-based speed control scheme of a permanent magnet (PM) DC motor. For precise speed control, an online training algorithm with an adaptive learning rate is introduced, rather than using fixed weights and biases of the ANN. The complete system is implemented in real time using a digital signal processor controller board (DS1102) on a laboratory PM DC motor. To validate its efficacy, the performances of the proposed ANN-based scheme are compared with a proportional-integral controller-based PM DC motor drive system under different operating conditions. The comparative results show that the ANN-based speed control scheme is robust, accurate, and insensitive to parameter variations and load disturbances.

Recently, many studies on mechatronics experiment system are conducted. In this paper, unique educational experiment systems are proposed for undergraduate students to be able to efficiently learn basic mechatronics techniques. The first system is used to learn input/output port operations, periodically LED lights ON/OFF and a stepping motor control. The second system is effective to learn A/D conversion for several sensor information, D/A conversion for DC motor control and a PID control method. Also, the third system is designed by using a robot arm with 4-DOFs to learn PWM (Pulse Width Modulation) control of a DC motor and force control of an end-effector. Further, the fourth system is designed using multiple mobile robots in which a subsumption control architecture is incorporated. Each of the systems individually has an important factor which students should acquire, so that it is expected that the combination will yield promising results. The effectiveness of the proposed systems is evaluated through experimental lectures in the department of mechanical engineering, Tokyo University of Science, Yamaguchi.

Aiming at the problems of nonlinearity, time-variant parameters, and uncertainty of system mathematical model in the PID regulator of DC motor control, a fuzzy controller of DC motor speed control based on TMS320LF2407A is designed in this paper, using the fuzzy control strategy to control, and its design procedure is described. The experimental results show that the speed control system of DC motor by using DSP controller and fuzzy control algorithms has a fast response, slight overshot and good stability, and improves the control effect.

This study addresses the sensorless speed control of a permanent magnet DC motor using non-ideal voltage and current sensors. A Proportional-Integral-Derivative (PID) type speed controller with Kalman Filter (KF) estimator was used and Integral of Absolute Error (IAE), peak overshoot and settling time were chosen as performance indices. Though KF helped reduce the noise, the PID controller gains tuned via MATLAB resulted in large peak overshoot and IAE with a relatively long settling time. In comparison, a Fuzzy Logic Controller (FLC) based PID (FLC-PID), tuned using genetic algorithms (GA), reduced the settling times by 75.98%, and the IAE and the maximum overshoot by 56.2% and 97.89% respectively. Compared to the conventional PID without KF, the FLC-PID radically improved the reference command speed tracking and sudden load changes disturbance rejection for the dc motor model.

Nowadays, the New energy vehicles(NEVs) have been developed in a key way as an industry adjusted by China’s national strategy. Aiming at the problems of poor parameter adjustment and low response speed existing in traditional PID control of energy vehicles in the complex driving environment, a speed control method of DC motor based on BP neural network and traditional PID was proposed, which greatly improved the speed control efficiency and accuracy of new energy vehicles. According to the nonlinear and multivariable characteristics of brushless DC motor control system, the motor and control principle are modeled and analyzed by Simulink. Then based on Proteus, STC89C51 single-chip microcomputer combined with the drive control module is used to simulate motor speed control. Meanwhile, in order to enhance the adaptability and rapidity of the parameter adjustment in speed regulation system, BP neural network and traditional PID control are applied in the designation of controller, which can implement the self-adaptation control of system and self-tuning of PID parameters. At last, the simulation is verified by MATLAB. The experimental results show that the BP-PID neural network controller has the advantages of good robustness, strong stability and fast response. At the same time, the BP-PID neural network controller has stronger anti-disturbance performance under the disturbed conditions, which can prove the correctness and feasibility of the designed model above. This approach provides a new solution to improve the performance of NEVs.

Summary form only given, as follows. The presentation will go over motor drive fundamentals including dc motor control and ac induction motor control. In general, the motor drive includes two control loops: (1) speed and (2) current. The speed loop normally deals with field and torque commands, and the current loop is to control the instantaneous armature current. Such a general motor drive control concept is applicable to both dc and ac motors. With understanding of dc motor speed control, the rationale for the need of ac motor vector control will be derived. Different vector control methods will be explained using the decoupled equivalent circuits. In terms of current control, both dc and ac motors need the so-called pulse-width-modulated (PWM) converters or inverters to regulate the current. Different PWM techniques will be explained in this presentation. Finally the PWM inverter related motor drive issues will be addressed.

Surface mobility control is a fundamental challenge in robotics, and its applications range from autonomous vehicles to industrial automation. The main aim of the paper is to analyze the PID controller parameters for the speed control of a surface mobility platform mobile robot. MATLAB simulation toolbox and mathematical model of the DC motor are used to find the optimal PID controller parameters to complete the DC motor speed control system. For the SMP motion control system, the mobile robot kinematic model is supported as a differential drive system. In this paper, the PID controller parameters for the DC motor control system are analyzed with simulation results using various KP, KI, KD gains. The experimental results of the SMP mobile robot with optimal PID controller parameters are analyzed by driving on the rough, slope and even terrains. According to simulation results and experimental tests, the proposed system effectively controls the speed of the SMP mobile robot, demonstrating satisfactory performance in settling time, rise time, and system overshoot. Among PI and PID controllers, the designed PID controller can quickly control to reach the desired DC motor speed. Using the optimal PID coefficient parameters, all motors reached their desired speed in 0.12 seconds during simulations. In the experimental tests on various terrain (even, slope and rough), all motors achieved their desired speed simultaneously within 25 seconds.Keywords: Differential drive, PID controller, motion trajectory, real-time speed control. skid steering, SMP mobile robot, surface mobility control

This research designed a DC motor control system experiments and developed a platform for on-line implementation of these experiments. This was with a view to contributing to the number of online laboratory experiments while providing a common platform for other on-line users. In this research, three experiments were designed using LabVIEW software. The developed platform is implemented online using the Massachusetts Institute of Technology interactive laboratory architecture. The control system performance was then evaluated by administering questionnaires to users with the aim of eliciting information on the ease of use of the operating system. The successful control of the DC motor and integration of the experiment engines into the iLab Interactive architecture were achieved. In conclusion, the online DC motor control laboratory was deployed and provided ready access to a DC motor control over the Internet with beginner and intermediate-level experiments in the field of control system and engineering

Adaptive control of DC motor using bacterial foraging algorithm