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    <title>PID vs MPC Control | About</title>
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        <a href="index.html">← Back to Simulation</a>
        <a href="mech.html">History of Mechatronics →</a>
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    <main>
        <h1>SYSTEM_DOCS_V2.0</h1>
        <section>
            <h2>01. PROJECT_OVERVIEW</h2>
            <!-- <img src="/gifs/smart1.webm" class="fun-gif" alt="smart gif"> -->
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            <p><strong>The Challenge:</strong> "Classical" control (PID) and "Modern" control (MPC) are fundamentally different philosophies. One reacts to the past; the other predicts the future. Seeing the difference in real-time is rare.</p>
            <p><strong>The Solution:</strong> I engineered a comparative physics simulation. It allows users to switch between a standard <span style="color: cyan">PID Controller</span> and a <span style="color: #00ff00">Model Predictive Controller (MPC)</span> to visualize how they handle <span style="color: yellow">stochastic disturbances</span> and <span style="color: cyan">orbital gravity</span> differently.</p>
        </section>

        <section>
            <h2>02. SOFTWARE_ARCHITECTURE</h2>
            <!-- <img src="/gifs/frustration1.gif" class="fun-gif" alt="frustration gif"> -->
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            <p>The system now runs two distinct control engines derived from first principles:</p>
            <ul>
                <li><strong>PID Engine:</strong> A reactive loop using error-based feedback ($K_p$, $K_i$, $K_d$) featuring integral anti-windup to handle steady-state drift, and a low-pass filter (exponential moving average) to smooth out derivative noise on the telemetry graphs.</li>
                <li><strong>MPC Engine:</strong> A predictive solver that looks $N$ seconds into the future. It calculates the exact force vector required to zero out velocity and position, utilizing <strong>Feedforward Cancellation</strong> to mathematically negate external wind/gravity forces before they affect the drone.</li>
                <li><strong>Performance:</strong> Both algorithms run within the 60FPS render budget of the Three.js loop.</li>
            </ul>
        </section>

        <section>
            <h2>03. INFRASTRUCTURE_&_IOT</h2>
            <!-- <img src="/gifs/lain1.gif" class="fun-gif" alt="computer gif"> -->
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            <p>Unlike standard cloud hosting, this project is a <strong>self-hosted hybrid system</strong> running entirely on hardware in my home lab.</p>
            <ul>
                <li><strong>Host:</strong> Raspberry Pi 4B (Linux/Debian).</li>
                <li><strong>Network:</strong> Exposed securely to the public internet via a Tailscale Funnel (Reverse Proxy).</li>
                <li><strong>Data Pipeline:</strong> A custom Python service polls a DHT22 sensor via GPIO pins and writes asynchronous telemetry data to the frontend.</li>
            </ul>
        </section>

        <section>
            <h2>04. LIVE_TELEMETRY</h2>
            <!-- <img src="/gifs/science1.gif" class="fun-gif" alt="science gif"> -->
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                <strong>SERVER_STATUS:</strong> ONLINE<br>
                <strong>LOCATION:</strong> HOME_LAB // VANCOUVER<br>
                --------------------------------<br>
                LAB_TEMP: <span id="lab-temp" style="color: cyan;">--</span>°C<br>
                LAB_HUM: <span id="lab-hum" style="color: cyan;">--</span>%<br>
                CPU_LOAD: <span id="lab-cpu" style="color: cyan;">--</span>%<br>
                RAM_LOAD: <span id="lab-ram" style="color: cyan;">--</span>%
            </div>
            <p style="font-size: 0.8em; color: gray;">* Server vitals polled every 5s. Simulation power usage (Watts) calculated real-time in client.</p>
        </section>

        <section>
            <h2>05. LEARNING_OUTCOMES</h2>
            <!-- <img src="/gifs/writing1.gif" class="fun-gif" alt="writing gif"> -->
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            <p>Implementing the "Duel" revealed key engineering trade-offs:</p>
            <ul>
                <li><strong>Efficiency vs. Complexity:</strong> PID is computationally cheap but struggles with "sag" under gravity. MPC is expensive but offers perfect tracking via feedforward physics.</li>
                <li><strong>Systems Integration:</strong> Managed the complexity of switching control laws dynamically without destabilizing the physics engine.</li>
                <li><strong>Full Stack:</strong> Bridged the gap between hardware sensors (Python), 3D graphics (Three.js), and real-time control logic.</li>
            </ul>
        </section>

        <section>
            <h2>06. ASSET_CREDITS</h2>
            <p><small>
                "<a href="https://skfb.ly/pxRGB" target="_blank" style="color: #00ff00;">Hatsune Miku Plushie</a>" by revsworks is licensed under
                <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank" style="color: #00ff00;">Creative Commons Attribution</a>.
            </small></p>
            <p><small>
                "<a href="https://skfb.ly/pzJqp" target="_blank" style="color: #00ff00;">Poteto</a>" by indoingard is licensed under
                <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank" style="color: #00ff00;">Creative Commons Attribution</a>.
            </small></p>
        </section>

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