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Overview
The Dynamics and Control System Design Laboratory (EML 4314C) course at the University of Florida provided hands-on experience in designing and implementing control systems for dynamic mechanical systems. It involved a combination of modeling, system identification, and control system design using both classical and modern techniques. Key tools like LabVIEW and MATLAB were extensively employed to develop automated systems and analyze experimental data.
Key Skills
LabVIEW Programming: Worked with virtual instruments (VIs) for real-time data acquisition, signal processing, and control system implementation.
System Identification & Control: Modeled and characterized mechanical systems using both single-input/single-output (SISO) and multiple-input/multiple-output (MIMO) control systems.
Data Analysis & Automation: Utilized LabVIEW to automate data logging, signal processing, and feedback control in laboratory experiments.
Control System Design: Designed and implemented state-space and classical control methods for mechanical systems.
Work
Motor Flywheel System: Transfer Function and Frequency Response Analysis
Motor Flywheel System: Transfer Function and Frequency Response Analysis
Brief Summary:
Brief Summary:
In this lab, we analyzed a motor flywheel system by deriving transfer functions using both time-domain and frequency-domain methods. The time-domain approach involved step inputs, while the frequency-domain approach utilized sinusoidal waves. We modeled the system's dynamics through Bode plots and identified key parameters such as the DC gain and time constant. The lab demonstrated the system’s first-order behavior, using LabVIEW for data acquisition and MATLAB for theoretical model validation.
Key Skills: Data acquisition, Bode plot analysis, transfer function derivation
Group18_Lab1.pdfController Design and Error Minimization in Motor Systems
Controller Design and Error Minimization in Motor Systems
Brief Summary:
Brief Summary:
This lab focused on implementing different controllers, including bang-bang, PID, and full-state feedback controllers, to regulate a motor flywheel system. We tested each controller’s ability to minimize error between desired and actual motor rotation, using LabVIEW for real-time data acquisition. The lab also explored manual tuning methods to optimize the PID controller. The performance of each controller was compared, showcasing the trade-offs between stability and responsiveness.
Key Skills: PID controller tuning, full-state feedback control, performance comparison
Group28_Lab2.pdfDesign and Optimization of a Full-State Feedback Controller for a Furuta Pendulum System
Brief Summary:
Brief Summary:
This project focuses on designing a full-state feedback controller to optimize the performance of a Furuta pendulum system. Using both manual tuning and a Linear Quadratic Regulator (LQR) method, we aimed to achieve precise control of the pendulum's motion. By analyzing the system's performance through various tests, the study compares the effectiveness of the two approaches in minimizing errors while balancing control effort. The optimal solution was determined by evaluating Root-Mean-Square (RMS) values for the pendulum's motion and demonstrating successful control with minimal system errors.
Key Skills: LQR control, manual tuning of controllers, MATLAB system simulation, data analysis
Group28_Lab4.pdfPage updated
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