Control Systems and Robotics Outreach to Middle-school Girls: Approach, Results, and Suggestions

We conducted a three-day outreach camp focused on control systems and robotics for 8th grade girls from economically disadvantaged families. The overall objective of the camp was motivating the young girls to consider pursuing a career in engineering and sciences. The main focus of the camp were hands-on labs using LEGO Mindstorms EV3 kit. Students learned about programming, sensors, motors and put their skills to test by creating a mobile robot that took part in three contests: car racing, line following, and parallel parking. A pre- and post-camp survey indicated that although program did not predominantly change the girls’ excitement towards careers in engineering and sciences, it increased the girls’ knowledge and excitement towards robotics and control systems. Our results indicate that short camps help kindle the interests of young girls, but are not able to sway them to take on engineering/science careers. In the latter case, we hypothesize that long-term STEM-based programs (e.g., a quarter or year-long robotics course) might be more effective.Cockrell School of Engineerin

A novel track-drive mobile robotic framework for conducting projects on robotics and control systems

This paper presents a novel robotic framework to help students to practically grasp the concepts of Robotics and Control Systems in a laboratory environment. The framework is centered on a robotic rover having two tank-like tracks which permit locomotion on uneven terrains. The sensory system consists of encoders for position feedback while the actuation system comprises of six precise DC motors. To enhance the learning outcomes of students and to permit readily realizaion of applications, developed software library supports three different command levels. The efficacy of the framework has been demonstrated by presenting a list of projects conducted on the framework. In particular, as a case-study, a project titled tether tracking and control of robotic rover has been detailed in the paper with presentation of experimental results. The pilot study indicated that incorporating the framework in robotics laboratory resulted in an efficient methodology of imparting interdisciplinary knowledge to engineering students. Additionally, the framework finds its potential in research of advanced robotic and control algorithms

Enhancing the Performance of Power System under Abnormal Conditions Using Three Different FACTS Devices

In this paper, a comparison between Flexible Alternating Current Transmission System (FACTS) devices including Static Synchronous Compensator (STATCOM), Static Synchronous Series Compensator (SSSC) and Unified Power Flow Controller (UPFC) for providing a better adaptation to changing operating conditions and improving the usage of current systems. The power system using FACTS devices is presented under different conditions such as single phase fault and three phase fault. A digital simulation using Matlab/Simulink software package is carried out to demonstrate the better performance including the voltage and the current of the presented system using FACTS that located between buses B1 and B2 under different faults types. The results obtained investigate that the presented system gives better response with FACTS as compared to not using them under abnormal conditions besides, the UPFC gives better performance of power system under several faults as compared to STATCOM or SSSC as It can absorb reactive power in a manner which significantly reduced the fault current. It is demonstrated that UPFC can reduce the peak fault current at bus B1 ‎to 63.85% of its value without ‎using FACTS devices under line to ground fault and 79.18% under three line to ‎ground fault whereas STATCOM and SSSC reduce it ‎to (75.21, 94.35%) and (75.40, 94.68%), respectively

Performance Enhancement of a Variable Speed Permanent Magnet Synchronous Generator Used for Renewable Energy Application

The paper aims to develop an improved control system to enhance the dynamics of a permanent magnet synchronous generator (PMSG) operating at varying speeds. The generator dynamics are evaluated based on lowing current, power, and torque ripples to validate the effectiveness of the proposed control system. The adopted controllers include the model predictive power control (MPPC), model predictive torque control (MPTC), and the designed predictive voltage control (PVC). MPPC seeks to regulate the active and reactive power, while MPTC regulates the torque and flux. MPPC and MPTC have several drawbacks, like high ripple, high load commutation, and using a weighting factor in their cost functions. The methodology of designed predictive voltage comes to eliminate these drawbacks by managing the direct voltage by utilizing the deadbeat and finite control set FCS principle, which uses a simple cost function without needing any weighting factor for equilibrium error issues. The results demonstrate several advantages of the proposed PVC technique, including faster dynamic response, simplified control structure, reduced ripples, lower current harmonics, and decreased computational requirements when compared to the MPPC and MPTC methods. Additionally, the study considers the integration of blade pitch angle and maximum power point tracking (MPPT) controls, which limit wind energy utilization when the generator speed exceeds its rated speed and maximize wind energy extraction during wind scarcity. In summary, the proposed PVC enhanced control system exhibits superior performance in terms of dynamic response, control simplicity, current quality, and computational efficiency when compared to alternative methods

Comparison between Compensated and Uncompensated PD with Cascade Controller Design of PMDC Motor: Real Experiments

In this paper, we compare two different kinds of position controllers, namely PD (both compensated and uncompensated PD) and Cascade controller (both compensated and uncompensated Cascade).  SIMULINK software is used to implement lumped parameters estimation with UKF. The Proportional-Derivative (PD) controller design by root locus and Cascade controller design have been chosen for our feedback system, and coulomb friction as disturbance is taken into account in the estimation model. The aim of this paper is to find out which controller is better (both compensated and uncompensated PD controller vs. both compensated and uncompensated Cascade controller). Therefore, the objective is to show how useful the parameter estimation for controller design with compensation. Through comparing two position controller designs, the experiment results show that both Compensated Proportional Derivative (PD) controller and Cascade controller Design have much better performance than the Uncompensated ones

Forward and Inverse Kinematics Solution of A 3-DOF Articulated Robotic Manipulator Using Artificial Neural Network

In this research paper, the multilayer feedforward neural network (MLFFNN) is architected and described for solving the forward and inverse kinematics of the 3-DOF articulated robot. When designing the MLFFNN network for forward kinematics, the joints' variables are used as inputs to the network, and the positions and orientations of the robot end-effector are used as outputs. In the case of inverse kinematics, the MLFFNN network is designed using only the positions of the robot end-effector as the inputs, whereas the joints’ variables are the outputs. For both cases, the training of the proposed multilayer network is accomplished by Levenberg Marquardt (LM) method. A sinusoidal type of motion using variable frequencies is commanded to the three joints of the articulated manipulator, and then the data is collected for the training, testing, and validation processes. The experimental simulation results demonstrate that the proposed artificial neural network that is inspired by biological processes is trained very effectively, as indicated by the calculated mean squared error (MSE), which is approximately equal to zero. The resulted in smallest MSE in the case of the forward kinematics is 4.592×10^(-8) in the case of the inverse kinematics, is 9.071×10^(-7). This proves that the proposed MLFFNN artificial network is highly reliable and robust in minimizing error. The proposed method is applied to a 3-DOF manipulator and could be used in more complex types of robots like 6-DOF or 7-DOF robots

Self-Tuning PID Controller for Quadcopter using Fuzzy Logic

Tracking has become a necessary feature of a drone. This is due to the demand for drones, especially quadcopters, to be used for activities such as surveillance, monitoring, and filming. It is crucial to ensure the quadcopters perform the tracking with stable flight. Despite the advantages of having VTOL ability and great maneuverability, quadcopters require an effective controller to overcome their under-actuation and instability behavior. Even though a PID controller is commonly used and promising with its simple mechanism, it requires very proper tuning to ensure the stability of the system is not affected. In this paper, a simple Fuzzy algorithm is proposed to be incorporated into a PID controller to form a self-tuning Fuzzy PID controller. The Fuzzy logic controller works as the self-adjuster to the PID parameters. A mathematical model of the DJI Tello quadcopter is derived with position and attitude control loops that are designed to track a variety of trajectories with stable flight. The proposed method uses a simple architecture where the ranges of PID parameters are used as scaling factors for Fuzzy controller outputs. The results of the simulations show the tracking error performance metrics, which are IAE, ISE, and RMSE, are smaller compared to the values of the PID controller. Beyond its impact on quadcopter control, the proposed self-tuning approach holds promise for broader applications in nonlinear systems

Intelligent Controller Based on Artificial Neural Network and INC Based MPPT for Grid Integrated Solar PV System

Solar photovoltaic (PV) systems have become an integral part of today's advanced energy infrastructure due to its low kinetic energy, its abundance availability, and its freedom from human interference. Solar PV systems have the potential to greatly reduce our reliance on fossil fuels, but their intermittent nature means they cannot provide a constant source of electricity. The system's security should be well thought out, and it should be able to withstand a lot of abuse. The current energy system faces a significant difficulty in ensuring continuous supply. In this study, a three-phase, two-stage photovoltaic system that is managed by artificial neural networks (ANN). A DC-DC boost converter with maximum power point tracking (MPPT) based on the incremental conductance (INC) method is incorporated in the first stage. In the next step, an ANN-based controller optimizes the performance of a three-phase switching PWM inverter that is connected to the grid by controlling currents along the d-q axis. Comprehensive simulations were carried out using MATLAB or Simulink to evaluate the system's performance under various illumination and temperature conditions. Results show that the suggested approach outperforms the baseline in a number of areas. Better dynamic reactions, accurate tracking of reference currents within permissible bounds, and quick settling periods after startup are all displayed by it. These findings show that our method has the potential to greatly improve the efficiency and dependability of solar PV systems. The results of this study have implications for renewable energy in general and present a viable path toward enhancing the resilience and sustainability of energy infrastructure