A LabVIEW-based online DC servomechanism control experiments incorporating PID controller for students’ laboratory

This paper presents low-cost experiments for a control systems laboratory module that is worth one and a third credits. The experiments are organized around the microcontroller-based control of a permanent magnet dc motor. The experimental setups were built in-house. Except for the operating system, the software used is primarily freeware or free for academic use. The objective of this module is that students-fresh from a first course in control systems theory, which introduces them to proportional-integral-derivative control and to design using Bode plots and root locus-learn how a simple modern control system is built. Student feedback and a 90-min end-of-semester practical exam show that, although the students find the experiments challenging, this module helps meet this objective. After having taken the first course and this laboratory module, the students feel that they can build control systems in addition to discussing them theoretically. This module is easy to set up and administer using the supporting material provided by the authors.

Adaptive control of DC motor using bacterial foraging algorithm

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 article considers the problem of the DC motors control that are often used in many electric drive systems. Due to the progress of industrial electronics and technology it has become possible to develop more efficient motor control circuits. The conventional speed control methods commit power losses in the system, which can be minimized by using the power electronics strategy. There is considered the thyristor control of DC motors of the ship electric drive. The DC motor control systems are described and simulation models in the MATLAB Simulink program are presented. The thyristor methods for controlling a DC motor speed are listed: single-phase semi-controlled converter (for motors with power up to 15 kW); single-phase drive with a controlled converter (available to operate in two quadrants); three-phase semi-controlled converter; three-phase controlled converter; single-phase reverse converter realized by connecting two single-phase converters (ensuring multi-mode operation); three-phase reverse converter realized as a single-phase converter. The mechanical characteristic of a DC motor was illustrated when the voltage supplied to the armature winding changed. It has been stated that control of the armature voltage is more favorable for speeds below the rated speed; flow control is preferable for speeds above the nominal speed. It has been inferred that speed control by means of power electronics devices provides large energy savings, in contrast to the traditional speed control methods, since the traditional methods experience significant energy losses.

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.

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.

The impulse in designing local agricultural machinery for curbing post-harvest losses in most African countries particularly Tanzania is unmatched. Locally made agricultural machines have proven to elevate the life of many small-scale farmers, which has increased the need to incorporate machine drives and controls to ease the process and operations. With potentials in Solar Energy, powering machine drive systems that operate in off-grid areas has been the best solution. Using the principles of Internet of Things (IoT) together with advancement in motor designs and readily available off the shelf microcontrollers such as the Raspberry Pi and Arduino UNO in the market, we achieve machinery that caters for our needs and the local content. Mobile apps play a huge role in industrialization where monitoring and even controls of machines can be performed by the mobile phones. This project incorporated Agile-Scrum methods to develop a control and monitoring system for a locally made avocado oil extraction machine that is powered by a solar system with 1600W panel arrays and 800Ah battery pack, and uses a Brushless Direct Current Motor coupled with electric solenoid valve, relay modules and a controller unit assisting on the control process and collecting crucial motor operation data such as voltage and current. The designed Mobile app ‘Blue’ acquire motor operation data from the Raspberry Pi via Bluetooth technology, delivering data to cloud server for later analysis. Easing data acquisition in off grid areas when engineers, technicians or operators have a physical access to the stations. It was concluded that this novel design would provide an effective control and monitoring mechanism with an acceptance on reliability, usability and effectiveness of up to 85.65% for a plethora of locally-made machinery that available in the market which still uses the manual means of operation emphasizing ease of use and productivity, thence joining hands with the global world on attaining some of the Sustainable Development Goals.

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.

In this work, a comparative study of three control strategies for the position control of a permanent magnet DC motor with dead zone is presented. The strategies analyzed are the classical PI controller, a new approach based on a linear controller with double integral effect, and the Inverse Dead Zone approach. Through the results here exposed it is shown that the new approach based on a controller with double integral effect results in a control system capable of achieving smaller position error, reducing the undesirable stick/slip effect without inducing high frequency oscillations or chattering in the control variable. In addition, and thanks to its linear nature, it is possible to determine stability and robustness of the resulting control system by means of the classical margins of gain and phase making this approach suitable for an engineering context

A sensorless drive scheme with adaptive gain speed controller for brushless DC motors is proposed in this paper. There are three observers in the control system, they are a disturbance voltage observer, a speed observer, and a disturbance torque observer. These observers are used not only for a position and speed estimator in the sensorless vector control, but also for a current controller. The rotor position and speed estimator are robust for parameter variations, such as the e.m.f. constant. The validity of the sensorless vector control of brushless DC motors is confirmed with computer simulations.

Main purpose of this study is to control of a brushless dc motor by Self-Tuning PID method without using any sensor. In this context, the motor phase current measurements and back EMF estimation are done. The phase currents are used in the closed-loop control system as feedback currents for comutation of the motor. Because of noisy current data the filtering is carried out on computer environment. The self tuning PID control system is preferred instead of conventional PID control system to reduce the noises of currents measured.

This paper studies the tracking control of dc permanent magnet motor (DC–PMM) in networked control systems (NCSs) with random delay and packet dropouts in feedback channel (sensor to controller) and the feedforward channel (controller to actuator). A sliding mode predictive tracking control algorithm based on the theory of pseudo partial derivative (PPD) is designed to compensate random delay and packet dropouts in process of motor control. The stability of the proposed method and the convergence of the tracking error are analyzed and proved. Finally, an example about dc motor in NCS is given to verify the effectiveness of the proposed method. Simulation results show that the outputs of dc motor can better track the desired trajectory after compensating by the presented method, and are quite stable.

This paper introduces a novel approach of designing a controller using a multi-layer feed-forward neural network (FFNN) for the speed control of a permanent magnet (PM) DC motor. The artificial neural network (ANN) controller with its massive parallel properties and learning capabilities offers a promising way to solving the problem of system nonlinearity, parameter variations and unexpected load excursions associated with a PM DC motor drive system. The self-tuning technique of the controller in real time is achieved through an improved on-line back-propagation training algorithm based on an output error propagation. The proposed ANN controller is implemented with a PM DC motor drive system in the laboratory. The laboratory test results validate the efficacy of the based controller for a high performance PM DC motor drive.

The main focus of this paper is to do stability analysis of a DC Motor (Field controlled and armature controlled). The stability analysis includes time domain analysis, frequency domain analysis and pole-zero domain analysis using state space averaging technique. The simulation and study is done using Matlab from its respective graphs obtained from above mentioned domains. Later the control of this motor is done using advanced controllers such as Fuzzy and Genetic algorithm optimization techniques.