《多无人机编队的异常检测及其在控制中的应用(英文版)》 王建宏 著 冶金工业出版社 2021/6/1

内容简介：

In Chapter 1 the idea of multi UAVs formation anomaly detection is proposed there, and its relations with system identification, advanced control theory are also introduced. After formulating the problem of multi UAVs formation anomaly detection as one system identification problem, then two special cases are considered about its linear or nonlinear form respectively. From the detailed description on multi UAVs formation anomaly detection problem in previous Chapter 1, other interesting topics exist still, such as the nonlinear dynamic model and control strategy, so in Chapter 2 other two improved identification methods are proposed to improve the identification accuracy. Furthermore, an improved ellipsoid optimization is extended to advanced control theory. In Chapter 3, we want to study the optimal input design for multi UAVs formation anomaly detection. In order to extend the theory on optimal input design, we extend our derived theory in one control strategy-internal model control. In Chapter 4, we change to detect and identify the flutter model parameters for multi UAVs formation. After our detailed formulation, we find that this problem corresponds to one parameter identification problem too. The ground target positioning and tracking algorithm for cooperative detection of multi UAVs formation is studied in Chapter 5, where the problem of target tracking or state estimation is reduced to build ellipsoidal approximation of the considered state, whose inner and outer ellipsoidal approximations are derived through two semidefinite programs. Due to some optimization problems exist in above chapters, and as the best of our knowledge that the optimization problem is one important step in the advanced model predictive control strategy, so the mission of the Chapter 6 is to consider the same optimization problem in this model predictive control strategy. It means that system identification is combined with the model predictive control, and the interval predictor estimation is applied into robust model predictive control in case of the unmodeled noise or disturbance. Concluding remarks are provided at the end of each chapter, and In Chapter 7 we then provide a brief summary of the results presented in this monograph and an outlook to possible directions for future research on these topics.

目录：