Bin Xi’s research while affiliated with Xiamen University and other places

In applications of the L1 adaptive flight control system, we found two limitations to be extended: (1) the system cannot meet the demands of engineering in terms of nonlinearity and adaptation in most flight scenarios; (2) the adaptive control law generates a transient response in the tracking error, hindering the system from reaching the steady-state error, and ultimately decreasing control accuracy. In response to these problems, an extended flight control system for L1 adaptive theory is proposed and rigorously proved. This system involves considering the nonlinear function matrix of state variables, which serves as an extension of the regression matrix in the original L1 adaptive control system, thus enhancing its nonlinear characteristics. The problem of calculating the adaptive laws, caused by the extended regression matrix, is solved by using the pseudo-inverse matrix. To eliminate the transient response, the state vector and its estimate are recorded and employed just like an integrator. Finally, the proposed system is verified on a high-subsonic flight subject to nonlinear uncertainties, with simulation results showing improved control accuracy and enhanced robustness. The proposed system resolves the limitations of the L1 adaptive control system in nonlinearity, providing the possibility for further theoretical development to improve the performance of adaptive control systems.

This paper describes the design of a nonlinear L1 adaptive control system based on angle and angular velocity feedback for the longitudinal attitude control of an unmanned aerial vehicle. An integral-form adaptive law based on Lyapunov's stability theorem is applied to estimate both the linear and nonlinear uncertainties. A modified algorithm is proposed based on the transformation between energy and autocorrelation function to solve the parameter drift problem caused by nonlinear terms in the adaptive law. The absolute value of the function is minimized by selecting an appropriate step length to eliminate the influence of noise on the adaptive law. A parameter design method for several common forms of noise is proposed. Compared with existing robust modification techniques, autocorrelation modification is demonstrated using MATLAB simulations to solve the parameter drift problem and ensure the robustness of the system. Flight tests examining the stability of the controller and the performance robustness of the nonlinear L1 adaptive control system to sensor noise and actuator failure are described. The variable gain control system is verified through flight tests. Comparative experiments show that the proposed modification suppresses the system oscillations caused by noise and solves the parameter drift problem within 5 s.