<h2 id="description"><a aria-hidden="true" tabindex="-1" href="#description"><span class="icon icon-link"></span></a>description:</h2>
<p>A PID (Proportional, Integral, Derivative) control system is a widely used feedback control mechanism in industrial control systems and other applications that require continuously modulated control. It is designed to minimize the error between a desired setpoint and a measured process variable by adjusting the output of the system. The PID controller achieves this by combining three separate control actions: proportional, integral, and derivative.</p>
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<p>Proportional Control (P): The proportional term is responsible for producing an output that is proportional to the current error value. The proportionality constant, known as the proportional gain (Kp), determines how much output changes in response to an error. A high Kp value results in a large change in output for a given error, while a low Kp value results in a smaller change.</p>
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<p>Integral Control (I): The integral term accumulates the error over time and produces an output based on the accumulated error. This helps eliminate steady-state errors that may persist with only proportional control. The integral gain (Ki) determines how quickly the accumulated error affects the output.</p>
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<p>Derivative Control (D): The derivative term considers the rate of change of the error and produces an output based on this rate. This helps anticipate future errors and can improve system stability by counteracting rapid changes in error. The derivative gain (Kd) determines how much influence the rate of change of error has on the output.</p>
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<p>The overall output of a PID controller is calculated using these three terms:</p>
<p>Output = Kp * Error + Ki * Integral(Error) + Kd * Derivative(Error)</p>
<p>Tuning a PID controller involves adjusting the gains (Kp, Ki, Kd) to achieve optimal performance for a specific system or process. Proper tuning can result in reduced overshoot, faster settling times, and improved stability.</p>
<p>PID controllers are used in various applications such as temperature control systems, motor speed controllers, chemical process controllers, and robotics. They are popular due to their simplicity, robustness, and ability to handle a wide range of control problems.</p>
<h2 id="projects"><a aria-hidden="true" tabindex="-1" href="#projects"><span class="icon icon-link"></span></a>projects:</h2>
<p><a href="/projects/Autonomous%20CyRide%20Micro-scale%20Bus" title="Autonomous CyRide Micro-scale Bus">Autonomous CyRide Micro-scale Bus</a></p>
<h2 id="classes"><a aria-hidden="true" tabindex="-1" href="#classes"><span class="icon icon-link"></span></a>Classes:</h2>
<p><a href="/academics/EE%20224%20Signals%20and%20Systems%20I" title="EE 224 Signals and Systems I">EE 224 Signals and Systems I</a></p>

Abstract Classes OOP

JavaDocs

Convolutions

Image Sorting

Agile Methodologies

Code Linters

Automated GitHub Actions

Carla Car Simulator

Continuous Integration Continuous Delivery (CI CD)

Differential Equations

Conda Environments

Cisco Any Connect

C (Programming Language)

LTSpice

Fusion360

Generic Classes OOP

FPGA

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High Pass Filters

Gradients

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Impedance

JSON

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Electrical Admittance

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PID Control Systems

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Agile Methodologies 1

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ROS2

Neovim

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Three-phase Circuit Analysis

VSCode IDE

Windows Powershell

Obsidian MD

Video Signals

WebEx

Multivariable Calculus

`.m` files

Shunt impedance

Windows Subsystem for Linux

Transfer Functions

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AGEDS 461 Innovation Diffusion and the Role of Agricultural and Extension Education 1

AGEDS 461 Innovation Diffusion and the Role of Agricultural and Extension Education

CHEM 167 General Chemistry for Engineering Students

EE 303 Power Systems and Electronics

EE 394 Program Exploration

PHYSICS 231 Introduction to Classical Physics I

CS 228 Introduction to Data Structures

EE 285 Problem Solving Methods and Tools for Electrical Engineering

Math 267 Elementary Differential Equations and Laplace Transforms

EE 201 Electric Circuits

MATH265

EE 230 Electronic Circuits and Systems

PHYSICS 232 Introduction to Classical Physics II

EE 224 Signals and Systems I

CS 227 Introduction to Object-Oriented Programming

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goals

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Temperature Sensing Circuit Design Project Lab EE230

EE 230 Lab 7

Electrical Engineering 185 Lab 7

Enigma Machine in C + MATLAB

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Dog Fetch AI Machine

Autonomous CyRide Micro-scale Bus

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Earth Extension of Agriculture

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