Research On The Related Technologies Of The Experimental Platform Of Biomimetic Sensing Robot

The aim of the research of biomimetic sensing robot is to overcome the key technologies about bionic sensing and control and to develop a demonstration system for miniature remote-sensing space-operated biomimetic robot. This micro-spacecraft has small price and the effectiveness characteristics. The key technology of biomimetic sensing robot is autonomous soft-landing system. One of the most important factors is how to control its speed and attitude precisely and fulfill soft-landing automatically in the region designated. So, as a pre-study project of this smart small flying robot, the prerequisite and guarantees of the biomimetic sensing robot research project is to configure a autonomous soft-landing experimental platform which is equipped with multi-sensor, data process and control systems, and it can be used on the biomimetic sensing robot in condition of limited payload.This project is supported by IIM (Institute of Intelligent Machines of Hefei, the Chinese Academy of Sciences) Innovation Fund projects - "Biomimetic Sensing Robot". In this thesis, we chose the model helicopter as flight platform and combined the robotics, intelligent sensor and control technologies to research the key technologies of autonomous soft-landing system which include the platform configuration based on the autonomous soft-landing system, system identification of the model helicopter and related control methods.1. The platform configuration based on the autonomous soft-landing task:One of the key technologies of biomimetic sensing robot is autonomous soft-landing system. For the biomimetic sensing robot, the most important factor is how to control its speed and attitude precisely and fulfill soft-landing automatically in the region designated. This thesis describes a most effective autonomous soft-landing platform which is equipped with the core board Autopilot MP2128, multi-sensors, data process and radio sub-system using model helicopter as flight platform. It can control the helicopter conveniently and adjust the parameters of the helicopter's flight state online by virtual of related control software which run on the ground control station.2. The study on the system identification of model helicopter:The model of helicopter is a multi-input multi-output, strong coupled, and nonlinear time-varying system. These features make it very difficult to get the mathematical model of helicopter. From the beginning of the last century, many domestic and foreign research institutes and universities have been studying and developing the unmanned helicopter system, but the results of dynamic system identification research for unmanned helicopter are very limited.In this thesis, the components of the model helicopter such as main rotor, tail rotor, fuselage, horizontal stabilizer, vertical fin and other components' aerodynamic models have been analyzed in detail and a relatively complete mathematical nonlinear dynamics model of helicopter is established at first. On the basis of some reasonable assumptions, this model is been simplified to a linear mathematical model. Then, after analyzing and comparing varieties of commonly used identification algorithms in the model helicopter, the subspace identification method is been applied to the system identification of model helicopter at the first time. Finally, the identified yaw-vertical coupled model and full state model of helicopter are simulate by MATLAB. The simulation results show that helicopter model getting by subspace identification algorithm is more precise and it is effective for the identification of model helicopter.3. The yaw-vertical coupled state control of model helicopter with adaptive neural network PID:It is the most challenge and difficulty to control unmanned model helicopter for the complexity of its structure and aerodynamics. At first, the existing varieties of control methods and technologies are compared and analyzed in this thesis. Because the yaw-vertical coupled flight state is one of the most important processes of soft-landing, the feature of model helicopter in this state is analyzed. Then, an adaptive neural network PID controller is designed for the model helicopter which is in yaw -vertical coupled flight state. Finally, the results of this controller are simulated by MATLAB and the outputs show that the controller can quickly and accurately track the input signals.