Performance Comparison between Classic and Intelligent Methods for Position Control of DC Motor

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 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 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 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.

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 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.

On robust position control of DC motors by ∈-PID controller and its application to humanoid robot arms

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.

In this paper, the dynamic model estimation and quantitative position control of a linear brushless DC motor (LBDCM) drive are studied. First, the inverter-fed LBDCM drive is established and its dynamic model is estimated from measurements. Then, according to the estimated model at nominal case, a two-degree-of-freedom controller (2DOFC) is designed to meet the prescribed tracking and load regulation position control responses. As the system parameter and operating condition changes occur, an output feedback linear model following controller (MFC) is added to reduce the effects of perturbations, and hence the performance degradation can be reduced. Finally, a ramp position command with suitable changing rate is generated to avoid the occurrence of velocity limitation, and thus the overshoot of position response due to large-step command change can be eliminated.

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.

This work presents the use of technological tools and remote laboratories developed in order to teaching process control in engineering courses. The proposal is to demonstrate tools that can be used to allow teaching mediated by technologies, with their benefits and limitations, considering the need to carry out practical activities to complement the theoretical classes aiming at better assimilation of the contents for an effective learning. Furthermore, an overview of projects using remote laboratories developed are presented, such as: “WebLab for Speed ??and Position Control of DC Motor”, “WebLab for Mobile Robot Motion Control”, “WebLab for Temperature Control of a Didactic Oven" and finally a "Level Control System with Remote Web Access". This last remote laboratory will be detailed showing the didactic resources used, the available functionalities, the experiments available and the first steps for those who want to implement a remote laboratory for process control.

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.

Sometimes conventional feedback controllers may not perform well due to changes in environmental conditions, changes in process dynamics that occur over time and changes in characteristics of disturbances. To overcome these problems, adaptive control methods have been developed. In this regard, a Model Reference Adaptive PID Controller (MRAPIDC) is designed using the MIT rule for position control of the DC motor in this study. At the same time, this control method is implemented using a low cost STM32F4 application development kit with Matlab-Simulink supported Waijung block set and compared with PID and fuzzy logic control methods. The results obtained with/without measurement noise disturbance under unit step and sinusoidal inputs are presented.

Adaptive control of DC motor using bacterial foraging algorithm