Control Master

Overview

Advanced control algorithms are the core of drone performance optimization. In this project, you will learn how to implement LQR and MPC control algorithms to improve drone flight stability and response speed.

What You’ll Learn

• LQR (Linear Quadratic Regulator) principles
• MPC (Model Predictive Control) principles
• System modeling and identification
• Controller design and implementation

Materials Needed

Item	Quantity	Notes
Fully Assembled Drone	1	-
Computer	1	With VS Code + ESP-IDF environment
USB Cable	1	For programming
MATLAB or Python	1	For algorithm design and simulation

Step 1: Understand Advanced Control Algorithms

LQR (Linear Quadratic Regulator)

Design optimal controllers by minimizing quadratic cost functions.

MPC (Model Predictive Control)

Predict future states based on system models and optimize control sequences.

Step 2: System Modeling

1. Establish linearized model of the drone
2. Include attitude dynamics and position dynamics
3. Use MATLAB or Python for system identification and model validation

Step 3: Algorithm Design

Use MATLAB’s lqr() function or Python’s control library to design LQR controller:

from control import lqr

# System matrices
A = [[...]]  # State matrix
B = [[...]]  # Input matrix
Q = [[...]]  # State weight matrix
R = [[...]]  # Control weight matrix

# Calculate LQR gain
K, S, E = lqr(A, B, Q, R)

Step 4: Code Implementation

Create a new controller file in components/core/crazyflie/modules/src/controller:

controller_lqr.c

void controllerLQR(control_t *control, setpoint_t *setpoint, const state_t *state) {
    // Calculate state error
    float error[12];
    error[0] = setpoint->position.x - state->position.x;
    error[1] = setpoint->position.y - state->position.y;
    error[2] = setpoint->position.z - state->position.z;
    // ... other state errors

    // LQR control law: u = -K * error
    float control_output[4];
    for (int i = 0; i < 4; i++) {
        control_output[i] = 0;
        for (int j = 0; j < 12; j++) {
            control_output[i] -= K[i][j] * error[j];
        }
    }

    // Apply control output
    control->thrust = control_output[0];
    control->roll = control_output[1];
    control->pitch = control_output[2];
    control->yaw = control_output[3];
}

Step 5: Compile, Flash and Test

1. Compile and flash the code
2. Flight test, compare performance differences between advanced controller and PID controller

Troubleshooting

Controller unstable

• Check if system model is accurate
• Adjust weight matrices Q and R

Slow response

• Increase state weight Q
• Decrease control weight R

Achievement

Congratulations! You have implemented advanced control algorithms, which is the core technology of drone control!

Next Steps

In the next project, you will learn how to develop multi-sensor data fusion systems.