Study Of Nonlinear Adaptive Attitude And Trajectory Control For Near Space Vehicles

The development of near space vehicle (NSV) is related to national security and peaceful use of space, and it is a new vehicle which attracts increasing attention of many military powers. The NSV in flight shows characteristics such as large flight envelope, multi-flight status and multi-tasking mode. Moreover, due to its special flight environment, the NSV also possesses objects features of serious nonlinearity, fast time variation, strong uncertainty and intense coupling.?Therefore, the flight control system design of the NSV has become an innovative and challenging issue. Around this problem, the dissertation carries out an intensive study in the NSV modeling and analysis, and the nonlinear adaptive control of attitude and trajectory system in uncertain environments. The main results are as follows:First, we establish a flight motion model of the NSV under the influence of changing wind field. Airflow attitude angles are regarded as the main state variables of this model. The state equations not only include thrust vector control, but also contain interference terms of the wind field. This work provides a more comprehensive nonlinear model at home for specific flight control design. Furthermore, a systematic design idea is applied to the design of NSV nonlinear flight control system. According to physical characteristics of the NSV, we employ series control structure to design nonlinear adaptive attitude and trajectory controllers. Also, we conduct demand analysis, system design and control methods selection based on develop procedures of actual flight control systems. This approach is further close to the actual system development work.Then, the functional link artificial neural network (FLANN) is introduced into the area of uncertain control and flight control for the first time. The FLANN bears a smaller computational burden than the usual multi-layer NN. It is suitable for the real-time approximation of parameter uncertainties and external disturbances. In this dissertation, FLANN disturbance observer (FLNDO) is designed to learn the uncertainties and disturbances, and FLNDO-based nonlinear generalized predictive control (NGPC) method is presented for the NSV attitude control. Additionally, the Lyapunov stability theory is first utilized to derive FLANN adaptive control law.Later, a new nonlinear adaptive control method is proposed. That is the stable adaptive NGPC method based on the partially feedback FLANN. And a robust control item with an adaptive gain is used to improve the accuracy of approximating dynamic lumped disturbances. The adaptive laws of network weights and robust gain are derived by the Lyapunov theory and they can ensure that system errors are uniformly ultimately bounded. Simulation results show that good attitude control effect is attained with the presented method, and the thrust misalignment disturbance and dynamic parameters uncertainties are well surpressed.Further, B-spline recurrent FLANN (BRFLN) is designed to learn high-order nonlinear functions. On this basis, PD-correction BRFLN adaptive NGPC method is proposed for the slow-loop attitude system, and then the whole attitude controller is obtained. Simulation results show satisfactory attitude control performance for high-altitude wind turbulence, large torque disturbance and dynamic parameter uncertainties.Finally, aiming at the trajectory control problem of the NSV, we construct the BRFLN adaptive NGPC trajectory controller to track the airspeed and altitude based on the attitude controller. A new swarm intelligence optimization algorithm, i.e. improved cooperative particle swarm optimizer (ICPSO), is presented to learn self-feedback coefficients of the BRFLN. Simulation results display that the trajectory control scheme is effective and the outputs can track the given instructions of the NSV which is subjected to gusts and attitude disturbances.