Aeroservoelastic stability analysis for flexible aircraft based on a nonlinear coupled dynamic model

A unified theoretical aeroservoelastic stability analysis framework for flexible aircraft is established in this paper. This linearized state space model for stability analysis is based on nonlinear coupled dynamic equations, in which rigid and elastic motions of aircraft are both considered.The common body coordinate system is utilized as the reference frame in the deduction of dynamic equations, and significant deformations of flexible aircraft are also fully concerned without any excessive assumptions. Therefore, the obtained nonlinear coupled dynamic models can well reflect the special dynamic coupling mechanics of flexible aircraft. For aeroservoelastic stability analysis,the coupled dynamic equations are linearized around the nonlinear equilibrium state and together with a control system model to establish a state space model in the time domain. The methodology in this paper can be easily integrated into the industrial design process and complex structures.Numerical results for a complex flexible aircraft indicate the necessity to consider the nonlinear coupled dynamics and large deformation when dealing with aeroservoelastic stability for flexible aircraft.

Integrated fault estimation and fault-tolerant control for a flexible regional aircraft

The article focuses on the design and application of an active reconfigurable controller that mitigates the effects of gust load and actuator faults on a flexible aircraft.A novel integrated adaptive output feedback scheme is investigated to address the actuator faults.The real-time fault values provided by the fault estimation module are considered in the reconfigurable control law to improve the fault-tolerant capability.The estimate values of faults and control gains are calculated by analyzing the stability of the overall system.The proposed controller is simulated using a flexible aircraft model with a discrete‘1-cosine’gust,and the results show that it can effectively mitigate the wing root moments and recover the flight maneuver stability after the aircraft suffered from gusts.

A data driven approach for detection and isolation of anomalies in a group of UAVs

The use of groups of unmanned aerial vehicles（UAVs） has greatly expanded UAV＇s capabilities in a variety of applications, such as surveillance, searching and mapping. As the UAVs are operated as a team, it is important to detect and isolate the occurrence of anomalous aircraft in order to avoid collisions and other risks that would affect the safety of the team. In this paper, we present a data-driven approach to detect and isolate abnormal aircraft within a team of formatted flying aerial vehicles, which removes the requirements for the prior knowledge of the underlying dynamic model in conventional model-based fault detection algorithms. Based on the assumption that normal behaviored UAVs should share similar（dynamic） model parameters, we propose to firstly identify the model parameters for each aircraft of the team based on a sequence of input and output data pairs, and this is achieved by a novel sparse optimization technique. The fault states of the UAVs would be detected and isolated in the second step by identifying the change of model parameters.Simulation results have demonstrated the efficiency and flexibility of the proposed approach.