Robust Adaptive Control For Uncertain Nonlinear Systems And Its Applications To Aerospace Vehicles

The current research in developing next generation reusable flying vehicles is focused on aerospace vehicles (ASVs). The control systems of the ASVs pose several challenges due to their multi-mission profiles,large attitude maneuvers and complicated flight conditions. In this dissertation, three relative problems, i.e. modeling and analysis of a conceptual ASV,uncertain nonlinear system control and autonomous control system design, are studied.First of all, a six degree-of-freedom simulation model of a conceptual ASV is presented, which includes the whole of kinetic equations and motion equations. Aerodynamic force and moment coefficients are given as functions of angle of attack, Mach number and control surface deflections. The propulsion system covers a combination of an air-breathing engine and a variable thrust liquid propellant rocket engine. Rigid-body mass moments of inertia and center of gravity location are functions of time-varying vehicle weight. Open-loop dynamics and stability characteristics demonstrate that the proposed model can be used to allow research, refinement and evaluation of advanced guidance and control methods.Next, this thesis provides the design of a flight control system for the ASV based on Trajectory Linearization Control (TLC) method. The TLC method is a novel nonlinear tracking and decoupling control technique, which can be viewed as the ideal gain-scheduling controller designed at every point on the flight trajectory. In Chapter 3, its background and theoretical basis are reviewed at first. Two control laws are provided to the outer loop and inner loop respectively in terms of singular perturbation theory. Then, a simulation is presented for the ASV under hypersonic cruise condition. The simulation results demonstrate the good performance of the controllers.Theoretical analysis illustrates that the TLC method may exhibit poor performance in the presence of uncertainties. A novel control structure is developed by combining the current TLC method with the well-known compensation idea. A nonlinear disturbance observer enhanced TLC approach is implemented at first. The NDO is used to estimate the uncertainties, and then integrated with the TLC method. Stability and performance analysis of the composite closed-loop system is conducted using Lyapunov's direct method. By applications to control of a numerical example and the ASV, effectiveness of the proposed method is validated.Then, utilizing the universal approximation property of neural networks, several robust adaptive TLC (RATLC) approaches are established. The NN outputs are introduced to estimate the uncertainties, and robust adaptive terms are used to overcome the reconstruction errors. By Lyapunov's direct method, rigorous proofs demonstrate that the provided adaptive laws can guarantee ultimate boundedness of all the signals in the integrated system. Furthermore, we expand the idea of fuzzy disturbance observer technique and investigate two types of neural networks disturbance observers (NNDO). Subsequently, more RATLC schemes are obtained through integrating the current TLC with the NNDO outputs. Conditions are derived which guarantee ultimate boundedness of all the errors in the combined system. All the above RATLC algorithms are illustrated for the flight control application of the ASV. Simulation results show that each of the proposed algorithms can significantly improve uncertainty attenuation ability and performance robustness of the current TLC. So our philosophy is a promising way and greatly extends the current TLC method.At Last, architecture of autonomous control system for the ASV is designed based on the multi-agent system (MAS) thechnique. The architecture integrates several autonomous agents and completes the desired mission through coordination and cooperation. By analyzing work principles of the entire system, it can be seen that the provided architecture can improve the autonomy of the ASV, enhance its capability to solve complex problems and satisfy requirements of autonomous operations for long periods of time. Compared with the remote agent architecture of the deep space 1, the system has higher intelligence, better robustness, transplantation and expansibility.