Adaptive control of DC motor using bacterial foraging algorithm

Modified bacterial foraging algorithm based multilevel thresholding for image segmentation

Optimal multilevel thresholding using bacterial foraging algorithm

Based on the multi-resolution analysis and the wavelet transform, a wavelet network is presented for the control of DC motors. One of the basic advantages of wavelet network is that training is done using the recursive least square method which is suitable for online training usually required for adaptive control. The wavelet network is used to design adaptive speed controllers for a DC motor to achieve high performance speed control even if the motor model is unknown, the load characteristics are also unknown function of speed and the load torque changes online. Simulation and experimental results are presented to validate the proposed controllers.

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.

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 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.

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.

DC motor is the most common choice if the wide range of adjustable speed drive operation is desired. In many studies, the DC motor controllers have been implemented without considering the final control element (FCE), leading to a non-realistic performance. In this paper, the authors have suggested a new approach to obtain the adaptive and robust control of DC motor. Buck converter, driven by high frequency PWM signals obtained from PID controller has been chosen as FCE in this study. Ziegler-Nichols method is applied to the combined system of dc motor and buck converter, to get the initial settings of PID controller. Using fuzzy logic, these settings are online updated corresponding to the changes that may occur during system operating conditions. Also, this approach has a robust performance against disturbance/parameter variations. The buck converter has been simulated using sim-power system library of MATLAB. Comparative results have been shown for PID controller and adaptive fuzzy-PID controller for both tracking and regulatory type of control problems to obtain excellent performance with no steady state error.

To improve the control precision of the closed-loop speed regulation of the brushless DC motor (Brushless DC motor, BLDCM) used in hybrid vehicles, the paper established a fuzzy controller for closed loop control of speed, taking the speed difference and the conversion rate of the speed difference as the inputs, and the increment of the supply voltage as the output, and proposed a method of optimizing fuzzy control rules based on genetic algorithm. Established the mathematical model of BLDCM in MATLAB/Simulink, simulated the traditional PID controller, the fuzzy controller and the fuzzy controller optimized by genetic algorithm. Then the hardware-in-the-loop motor control system is constructed based on dSPACE. The simulation and experiment prove that the optimization of fuzzy rules by genetic algorithm is an effective method to improve the accuracy of speed control, and it has good tracking performance for frequently changing speed requirements in hybrid vehicle.

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

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 practical method for operation of a semi-converter-fed DC motor drive at the point of maximum efficiency while maintaining any particular torque-speed load point. Design and operation of an economical, microprocessor-based adaptive controller used to retrofit a commercially available SCR drive for minimum loss operation of a DC motor is described. Experimental and simulation results are presented in graphical forms, giving comparison of overall drive system efficiencies between loss minimization adaptive control and conventional constant field current operation for a standard-efficiency DC motor with a 7½ hp rating for both separately excited and shunt connected configurations. The greatest potential for energy savings is found to exist for the case of light load at low speeds.

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.

Digital world and modern technology makes sharing of real laboratories on the internet and creating a virtual on-line laboratory possible. Many educational institutes do not afford expensive Laboratory equipment's or they don't want to spend time in building experiments which are less useful for them. Using laboratory experiments virtually may be helpful for such situations. LabVIEW (Laboratory of Virtual Instrumentation Environment Workbench) is a software which provides virtual instrumentation environment very well. Integration with NI (National Instruments) Hardware and web services feature of this software makes monitoring and controlling of experiments through web very simple. DC motor control is one of the laboratory experiments which can be done virtually by using LABVIEW, NI Hardware's and custom built Hardware. By using thyristor based full wave convertor, speed control of the DC motor can be done by varying the firing angle of pulse given to the thyristor.

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.