Use of Remote Access Technologies and WebLabs as Teaching-Learning Strategy in Engineering

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

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

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.

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

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 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.

The integration of Artificial Intelligence (AI) in virtual practical labs is revolutionizing the education sector by providing immersive, adaptive, and cost-effective learning experiences. Virtual labs offer significant advantages, such as flexibility, scalability, and accessibility. This paper explores the current state of AI-driven virtual labs, their benefits, challenges, and future prospect, especially in the context of STEM education and research.We analyse AI applications in laboratory simulations, personalized learning, automated assessment, and real-time feedback. Virtual practical lab powered by AI – enhance Virtual Practical labs provide impressive, interactive ,and personalized learning experience, enabling students to conduct experiments in simulated environment that conduct mimic real- world scenarios .Furthermore, we discuss ethical considerations, technological advancements, and case studies of AI-based virtual labs in science, engineering, and medical education. It’s impact on modern education, and the challenges associated with the implementation. The research concludes with recommendations for optimizing AI in virtual practical labs for enhanced pedagogical outcomes.

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.

Ever since the COVID-19 pandemic, higher education witnessed a paradigm shift in the teaching-learning strategies everywhere from the original way where everything was largely conducted face-to-face. Teachers, educators, and students had to suddenly move to the virtual mode to continue their classes in schools, institutes, and universities. Recently, many published studies have revealed the utmost need to improve teaching strategies, particularly in an online lecture setting. However, the major impediment was observed in higher educational institutions (HEIs) where teachers had to conduct laboratory experiments in hard sciences, particularly in physics, chemistry and, biochemistry. Of these subjects taught in graduate classes, chemistry is a complex subject. A chemistry learner needs to acquire hands-on experience, especially when performing organic preparations and instrumental analyses. Chemistry teachers believed that nothing could ever replace a traditional laboratory setting, but now the situation has changed to a certain extent. It has always been argued that students do not learn by merely clicking on simulated glassware and instruments. In a typical organic chemistry laboratory, students prepare organic compounds, purify them, and determine various properties of the pure compounds. Students gain experience by trial-error while adding reagents in organic preparation, devising reaction mechanisms, observing the physical properties of purified compounds, and even separating complex mixtures. Virtual laboratories cannot replace all these with a mere simulator, but when one deals with corrosive chemicals and advanced instrumental methods, it seems a plausible solution to augment them with traditional laboratories. It was found that using advanced level of online screen experiments improved students understanding and also helped them score well in their laboratory assessments. Virtual simulations cannot replace traditional laboratories since many experiments require hands-on tool abilities, smelling, seeing colored complexes, and trial-error strategies. It was also observed that experiments that require costly instruments can be carried as a remote trigger, paving the way to augment virtual experiments in the future chemistry laboratory. The chapter discusses the pedagogical design for graduate students in a virtual chemistry laboratory and live demonstrations performed in the traditional laboratory. In this chapter chemistry experiments based on organic synthesis, stereochemistry and spectroscopy on a virtual screen have been studied. The different modalities of conducting virtual screen experiments, assessment strategies, and government initiatives are also highlighted. This chapter concludes with current and future trends of virtual chemistry laboratories with highlights on the barriers, opportunities, and key measures to resolve them.