MATLAB Simulation Analysis of Quadcopter Dynamics and Control

Quadcopter MATLAB simulation analysis typically involves modeling multiple core components and evaluating system-level performance. For stability and dynamic characteristics research, the simulation framework must integrate aerodynamic principles, control algorithms, and sensor feedback mechanisms.

Component Modeling Propulsion System: Motor and propeller models require consideration of thrust-RPM nonlinear relationships, typically implemented using experimental data fitting or simplified mathematical expressions. Battery models must simulate discharge curve effects on flight endurance. Sensor Modules: Cameras can simulate target tracking through image processing algorithms, while IMU (Inertial Measurement Unit) models need to integrate gyroscope and accelerometer noise characteristics using Gaussian noise functions.

Stability Analysis By establishing six-degree-of-freedom dynamic equations combined with PID or advanced control algorithms (such as LQR), wind disturbance resistance can be validated in Simulink. Lyapunov stability theory can be introduced for quantitative convergence assessment through eigenvalue analysis of system matrices.

Performance Evaluation Simulate motor response speed and energy consumption under different payloads to analyze efficiency bottlenecks during hover/maneuver operations. Parametric sweeping (e.g., propeller size variations) optimizes power distribution strategies using optimization toolbox functions.

Extension directions may include Hardware-in-the-Loop (HIL) test interface design or integration of reinforcement learning-based autonomous obstacle avoidance algorithms using MATLAB's Reinforcement Learning Toolbox.