Lesson 5 pose control and filter estimation.
Unmanned system control plays an important role in the fields of modern industry, agriculture and national defense. through the use of unmanned system control, machinery, equipment and management organizations can operate at a high speed and efficiently, improve production efficiency and improve working conditions, speed up modernization. In order for an unmanned system to perform practical tasks, it first needs to be able to control its own motion, and accurate motion control must be based on the current known state of its own. Control and filtering theory can provide theoretical support for motion control of unmanned system, and realize motion control through code, and then complete specific tasks. RflySim provides rich control and filtering interfaces, so that users can design and implement custom controllers and filters, and use MATLAB to generate code automatically. After burning to flight control, real machine experiments can be carried out. In order to make users more familiar with the control and filtering interface, the RflySim platform sets up interface routines from shallow to deep. The RflySim platform provides an interface for automatic code generation through MATLAB's Simulink PSP toolbox. For sensor calibration and filter design, it is necessary to obtain the original data of the sensor and use the sensor interface. For the controller, it is necessary to obtain the filtered attitude position information and the control instructions of the remote controller, so that the motor control law can be generated.
Issue 4: Position control and filter estimation (Part 1)
Issue 4: Position control and filter estimation (Part 2)
Issue 4: Position Control and Filter Estimation (Part 3)