Chapter 5: Filtering Estimation and Low-Level Control
Unmanned system control plays an important role in modern industry, agriculture, and national defense. Through unmanned system control, machinery, equipment, and management organizations can operate at high speed and efficiency, improving productivity and working conditions, and accelerating modernization. For an unmanned system to perform practical tasks, it must first be able to control its own motion, and accurate motion control depends on reliable knowledge of its current state. Control and filtering theory provides the theoretical foundation for unmanned system motion control, enabling the implementation of motion control through code to complete specific tasks.
The RflySim Toolchain provides rich control and filtering interfaces, allowing users to design and implement custom controllers and filters and use MATLAB for automatic code generation. After flashing to the flight controller, real aircraft experiments can be conducted. To help users become familiar with the control and filtering interfaces, the toolchain provides progressively structured interface examples. It offers interfaces for automatic code generation through MATLAB's Simulink PSP toolbox. For sensor calibration and filter design, raw sensor data can be accessed via the sensor interface. For controller design, filtered attitude/position information and RC control commands are available for generating motor control laws.
Session 4: Position Control and Filtering Estimation (Part 1)
Session 4: Position Control and Filtering Estimation (Part 2)
Session 4: Position Control and Filtering Estimation (Part 3)