Position Control of a LBDCM Drive

The paper studies the performance of a new controller utilized for the position control of DC motors. The new controller is inspired by the human voluntary body action control (dubbed motor control) mechanism. The controller is called Experience Mapping based Prediction Controller (EMPC). EMPC is designed with the concepts of the human motor action prediction-control mechanism as part of its core. The controller has auto-learning features without the need for a plant model which is inspired by the experiential learning ability of humans. The ability to adapt to environmental changes has also been developed as part of EMPC. The simulation results for position control of DC motors for inertial and friction load changes are presented to show that accurate control is achieved using EMPC with good adaptation. The performance of EMPC is compared with MRAC based position controller. Position control of DC motors with load changes is practically implemented using EMPC and the results are presented.

The study proposes a new efficient method for a robust position control of DC motor, i.e., the so called non-linear sliding surface method, for class of higher order non-linear uncertain system to improve a transient response. It consist of a non-linear function which is used to tune the control law so as to improve the performance of the higher order system. The proposed scheme increases the damping ratio of third order system. The paper describes the use of a non-linear sliding surface for the position control of DC motor. Integration of this method offers an efficient and robust position control of DC motor. This paper also shows the comparison of the proposed technique with linear sliding mode technique. The merits of using a non-linear sliding surface are illustrated by examples and simulation results.

The paper describes the development and use of response sampling for prediction correction in Experience Mapping Based Prediction Controller (EMPC). This deviates from the quasi-open-loop approach employed in EMPC which is based on the Human Learning mechanism without any need for a mathematical plant model. The proposed method for Prediction Correction is employed to improve the adaptation of EMPC for the position control of DC Motors. The simulation results are provided for step changes of inertia, static friction torque, applied terminal voltage and applied external active torque. The proposed technique is implemented on a practical DC motor position control system and the results are provided for the same. Constantly changing loads are used to test the robustness of the controller and the obtained implementation results are provided.

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

In this study, it is aimed to model a dc motor which will be able to achieve panel movement in solar panels and realize position control using artificial neural networks method in LabView. Position control of DC motors is possible with position sensors integrated to motors or external position sensors. The most popular of these sensors are encoder and transducer. Within this study, by using the LabView program in the computer environment, DC motor model was created and simulations were done on this model. In simulations, the motor current, position and velocity were measured from motor model. The Current and position data collected from simulations was used as the closed-loop feedback control system. As the control system, artificial neural network (ANN) was used due to the nonlinear structure of the current data.

This paper deals with the application of the surface‐current method to the analysis of electromagnetic devices. The basics of the method are outlined and new developments involving the implementation of automated procedures for the definition of the geometry and sources, the contour subdivision according to suitable error indexes and the upgrading of the magnetic permeability to analyse saturated devices are described. As examples of application, the method is applied to the analysis of a single‐phase step motor and a linear brushless DC motor; the results are discussed and compared with the outputs of corresponding finite‐element method analyses.

Controlling DC motors is mainly done by controlling either voltage or field of their armature. Numerous methods have been proposed so far for this purpose. Some intelligent methods such as XCSR and machine learning systems are used to control position of a separately excited DC motor. Having set output position of the motor to its basic position, voltage of armature becomes zero and the motor stops working. Characteristic features of the methods in this paper are resistance against changing friction and moment of inertia. Meanwhile, time to reach stability in this type of controllers is considerably lower than that of PID controller with no oscillations being observed in the responses. DOI: http://dx.doi.org/10.11591/ijece.v4i3.5355

This paper presents position sensorless control for brushless DC motor based on a commercial application-specific integrated circuit (ASIC) ML4425. ML4425 is often used for sensorless control of brushless DC motor. The position detection scheme based on terminal voltage sensing and the controller function of ML4425 is analyzed. The control system based on ML4425 will be developed. The experimental results is received and discussed as well.

In this chapter, a brief explanation is given about fuzzy logic and neuro-fuzzy systems. DC motor position control is realized to explain usage of neuro-fuzzy controller. DC motor model is given in the form of mathematical equations and transfer function. First, a tuned by trial-error method PID controller is applied to DC motor system and responses are collected as a dataset to train neural networks of neuro-fuzzy system. Trained neuro-fuzzy system is used to design an adaptive neuro-fuzzy controller to control position of a DC Motor. Matlab Neuro-Fuzzy Designer Toolbox and Matlab/Simulink is used to design neuro-fuzzy and simulate the control system. As a results, neuro-fuzzy controller achieved successfully in the position control of DC motor. The results also shows that neuro-fuzzy controller can support energy consumption.

This paper compares the performance of Integer and Fractional order Algebraic estimator approaches for identifying on-line parameters and states of the motor. Identified parameters would be used to update an adaptive position control scheme. Adaptive position control is implemented using robust sliding mode control and fractional order sliding mode control methods. Effect of external disturbance (noise) on above techniques is also analyzed. Simulation is carried out which shows a superiority of fractional method. Results of both methods are compared and briefly discussed at the end.

This paper presents a recurrent fuzzy neural network (RFNN) controller to provide sensorless speed control for the two-phase linear brushless DC motor. It is not easy to arrange current and speed sensors along the linear motor's required railway. The RFNN controller has the capability of tracing the system dynamics due to the inherent learning function. With the help of RFNN controller, the conventional PID control can be made to provide better performance by tracing the system dynamics. The paper will demonstrate dynamic performance improvement scenarios. The recurrent structure also enhances the conventional fuzzy neural network (FNN) controller with embedded memory function. The results from experimental evaluation indicate that the PID with RFNN controller can provide better performance than the conventional PID controller.

An adaptive discrete-time tracking controller for a direct current (DC) motor with controlled excitation flux is presented. A high order neural network is used to identify the plant model; this network is trained with an extended Kalman filter. Then, the discrete-time block control and sliding mode techniques are used to develop the reference tracking control for the angular position of a DC motor with separate winding excitation. The scheme is illustrated via simulations.

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.

A reduced order time-delay control is derived and applied to the position control of a brushless DC motor with a highly simplified hardware configuration: use of six-step commutation without current control unit. In addition, the closed-loop stability has been analyzed by using the singular perturbation method. Throughout experimental studies, it is observed that reduced order time delay control effectively compensates for parameter variations and non-linearities, which a conventional PID control cannot handle with adequate performances. This result shows that reduced order time-delay control enables an economical design without compromising performance. More importantly, the example establishes a case that: a good control method can compensate for the hardware deficiency in a given plant, and as a result it even enables a simpler design of plants at the design stage.

A reduced-order time delay control (RTDC) has been derived and applied to the position control of a brushless DC motor (BLDCM) with a highly simplified hardware configuration: use of six-step commutation without current control unit. In addition, the closed-loop stability has been analyzed by using the singular perturbation method. Throughout simulation and experimental studies, RTDC has been observed to achieve a performance level approaching to that of a BLDCM adopting sinusoidal commutation with current control unit. More specifically, it has been observed that RTDC effectively compensates for parameter variations, nonlinearities, and the torque ripple caused by six-step commutation, which a conventional PID control cannot handle with adequate performances. This result shows that RTDC enables an economical design without compromising performance. More importantly, the example establishes a case that: a good control method can compensate for the hardware deficiency in a given plant; and as a result it even enables a simpler design of plants at the design stage.