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Development Of An Autonomous Flight System For Small-scale Unmanned Helicopter Based On Model Predictive Control

Posted on:2009-09-03Degree:DoctorType:Dissertation
Country:ChinaCandidate:J F DuFull Text:PDF
GTID:1102360305456280Subject:Mechanical and electrical engineering
Abstract/Summary:PDF Full Text Request
Helicopters have superior flight characteristics that other aerocrafts haven't, such as vertical take-off, landing and hovering, and are widely used in aviation photography, reconnaisance and other application fields. Small-scale unmanned helicopters have small size, light quality, low cost, simple structure and can fly beyond visual range and finish tasks that traditional aerospace can't when furnished with autonomous flight control system. Thereby developing an autonomously flight control system is very significative and practical. However, helicopter models are under-actuated, strongly coupled, time-varying, multi-variate, time delay nonlinear, therefore, the design of an autonomous flight control system is a challenging task.In order to solve the problems encountered on current controller design, the author develops an autonomous flight control system based on explicit model predictive control (EMPC) for a single main rotor small-scale model helicopter with tail rotor. The contents of the paper will be discussed are listed as follows.1) Modeling of the small-scale unmanned helicopterCompared with large-scale helicopters, small scale unmanned helicopters haven't flapping hinges, but have stiffer and higher speed main rotors. The inertial effect of the main rotor is very significant and the main rotor should be modeled as rigid body besides the fuselage. Kinematical and dynamical equations are deduced by Kane method which can be realized by computer easily. In order to linearize the rotational kinematical equation more easily, Yaw-Pitch-Roll (YPR) angles are chosen as Euler angles during modeling.The author also analyzes and simplifies the model.2) Design of the flight controllerConventional controllers can't deal well with actuator constraints, output constraints and time delay, therefore the thesis proposes model predictive controller to control the unmanned helicopter. But ordinary model predictive controller needs online optimization and this leads to large computation complexity. Explicit model predictive control converts the online computation to offline and needs less computation complexity. It only looks up the control table and calculates the corresponding control input, so it is fit for embedded application. The flight controller consists of inner loop, which control the attitude, and outer loop, which control the position and velocity. The attitude controller, velocity controller and position controller are designed by the EMPC algorithms.3) Study on the robust stabilityHelicopters are usually disturbed during flight, so the robust stability of the flight controller is very important. In order to know the robust stability of EMPC, the controller undergoes control input noise, wind and the change of the mass of the helicopter. PID controller is also given for comparison.4) Validation of path tracking abilityIn order to validate the path tracking ability of the controller, the thesis simulate the controller to track rectangular path and "8" path. The simulation results show that explicit model predictive controller has better performance than PID controller.5) Real flight experimentsIn order to validate the performance of the flight controller in real environment, some real flight experiments are done in a flight lab. The results show that the helicopter can fly stably. The error of pitch angle doesn't exceed -2.9°~2.9°, roll angle not exceed -2.0°~2.0°and yaw angle not exceed -2.9°~4.6°. The position precision is fine. The longitudinal and lateral error is 0.15 m and the vertical error is only 0.05 m. This is enough for application.6) Decoupling of steering controlA compensation algorithm is presented in this paper which is used to improve the precision of position control during steering. Simulations and real flight experiments validate its performance. The results show that the improved MPC algorithm works well and can restrain the coupling.The main contributions of the dissertation are as follows: EMPC is presented for the control of the small-scale unmanned helicopter; the mathematical model is deduced using Euler YPR by Kane method; the attitude controller, velocity controller, and position controller are designed based on the mathematical model and EMPC; in addition, the coupling between the steering motion and plane motion is dealt with compensations. The controller is validated by simulations and real flight experiments and the results show that the controller has good performance. The method given in the dissertation is worth to be used for reference in theory and application.
Keywords/Search Tags:Unmanned helicopter, kinematics modeling, dynamics modeling, explicit model predictive control, robust
PDF Full Text Request
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