Aircraft Trajectory Control with Feedback Linearization for General Nonlinear Systems

Zhang, Sheng; Liao, Fei; Cheng, Yan-Qing; He, Kai

doi:10.1109/ccdc49329.2020.9164469

Cited by 7 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The dynamics of each agent are based on the model presented in the work by Anderson and Robbins. 24 Using the feedback linearization law described in the work by Zhang et al, 25 the best choice for the control input (velocity vector) that ensures stability of each agent is considered as in equation (53). Control law (31) is considered with ( K x = − [ 0 . 16 1 . 53 6 . 4 ] ) .…”

Section: Simulation Resultsmentioning

confidence: 99%

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Amirani

Bigdeli

Haeri

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

In this article, time-varying formation tracking problem for multi-agent systems is investigated. It is assumed the number of agents is changing and obstacles should be avoided along the trajectory. In addition, the tracking formation should be reached while the leader trajectory is unknown and communication data are noisy. To reach the desired formation tracking, first, a virtual leader is constructed using the local neighbouring data. Second, the reference signal is created based on the virtual leader and the leader data (which is available for some agents). The data fusion at this stage is done based on Kalman filtering to reduce the effect of communication noise. Third, an integral state feedback controller as a new consensus formation controller based on the relative position and velocity vectors of the agents and the estimated (virtual) leader are proposed to achieve the time-varying formation tracking. Next, closed-loop stability of the system under the proposed control law is investigated using the Lyapunov stability theorem. In order to implement the proposed control strategy, an extended state observer is designed to estimate the required velocity and accelerations. Finally, an obstacle avoidance strategy would be embedded to the controller via a repelling strategy. That is, once the leader identifies the obstacle and enters a repulsion region, its reference trajectory is modified to keep a safe distance from the obstacle. The agents modify their reference trajectory according to the new leader trajectory while trying to keep their formation. As an illustrative example, various simulations are performed for formation tracking of a unmanned aerial vehicle multi-agent system to evaluate its performance.

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Amirani

Bigdeli

Haeri

2022

Proceedings of the Institution of Mechanical Engineers, Part I:

View full text Add to dashboard Cite

show abstract

“…Therefore, the 3DoF model point mass model seems adequate for formation tracking purposes as requested in [36]. Equation (3) shows the model, in which the three first elements represent the kinematic equations and the following three are related to the dynamic equations as it has been considered in other works [36][37][38][39][40]. It should be emphasized that the fuel consumption dynamics has been neglected here, as it is considered of fixed rate first order dynamic and is independent of kinematics and dynamic variables.…”

Section: Uav Model Dynamics and Control Lawmentioning

confidence: 99%

“…Using the feedback linearization law described in Zhang et al [39], the best choice for the control input (velocity vector) that ensures stability of each agent is considered as in (8).…”

Section: Uav Model Dynamics and Control Lawmentioning

confidence: 99%

Distributed fault detection and isolation in time‐varying formation tracking UAV multi‐agent systems

Amirani

Bigdeli

Haeri

2022

Asian Journal of Control

View full text Add to dashboard Cite

The problem of distributed fault detection and isolation (DFDI) in conjunction with time‐varying formation control of unmanned aerial vehicle (UAV) multi‐agent systems with multiple‐leader leader–follower structure is studied, in this paper. It is assumed that the communication data of the agents are noisy, and faults may occur in either leaders or followers. The followers are not aware of the main trajectory and just follow the leaders. The first step in this paper is to reduce the communication noise. Besides, if a fault occurs in one of the leaders causing to exit the leader from the main trajectory, the followers should be able to detect the fault, isolate the leader, use the other leaders' data, and keep the main trajectory. Tracking the trajectory by followers in such a case is the second step of this paper. Preserving the time‐varying formation despite a fault occurring in one of the followers is the next step. To reach these goals, a distributed array of Kalman filters is used for noise cancellation and data extraction. Next, the combination of state vector data fusion and some type of χ2 test are employed for DFDI, in agents. Besides, a controller is implemented in each agent for formation tracking. In this controller, estimated relative position and velocity vectors of the agents are employed to keep the time varying formation while tracking the leaders. The closed‐loop stability is studied via Lyapunov stability theorem, and various simulations are carried out to demonstrate the capability of the proposed strategy.

show abstract

“…Linearization means that the nonlinear relation of the system is approximated by linear expression at the operating point of the system [41]. The stronger the nonlinearity of the system, the smaller the approximate range of linearization.…”

Section: B Linearization Of Nonlinear Modelsmentioning

confidence: 99%

A Linearization Model of Turbofan Engine for Intelligent Analysis Towards Industrial Internet of Things

et al. 2019

View full text Add to dashboard Cite

Big data processing technologies, e.g., multi-sensor data fusion and cloud computing are being widely used in research, development, manufacturing, health monitoring and maintenance of aero-engines, driven by the ever-rapid development of intelligent manufacturing and Industrial Internet of Things (IIoT). This has promoted rapid development of the aircraft engine industry, increasing the aircraft engine safety, reliability and intelligence. At present, the aero-engine data computing and processing platform used in the industrial Internet of things is not complete, and the numerical calculation and control of aero-engine are inseparable from the linear model, while the existing aero-engine model linearization method is not accurate enough to quickly calculate the dynamic process parameters of the engine. Therefore, in this paper, we propose a linear model of turbofan engine for intelligent analysis in IIoT, with the aim to provide a new perspective for the analysis of engine dynamics. The construction of the proposed model includes three steps: First, a nonlinear mathematical model of a turbofan engine is established by adopting the component modeling approach. Then, numerous parameters of the turbofan engine components and their operating data are obtained by simulating various working conditions. Finally, based on the simulated data for the engine under these conditions, the model at the points during the dynamic process is linearized, such that a dynamic real-time linearized model of turbofan engine is obtained. Simulation results show that the proposed model can accurately depict the dynamic process of the turbofan engine and provide a valuable reference for designing the aero-engine control system and supporting intelligent analysis in IIoT.INDEX TERMS Industrial Internet of Things, multi-sensor data fusion, cloud computing, turbofan engine, linearized model.

show abstract

Aircraft Trajectory Control with Feedback Linearization for General Nonlinear Systems

Cited by 7 publications

References 24 publications

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Time-varying formation tracking for noisy multi-agent systems with unknown leader trajectory

Distributed fault detection and isolation in time‐varying formation tracking UAV multi‐agent systems

A Linearization Model of Turbofan Engine for Intelligent Analysis Towards Industrial Internet of Things

Contact Info

Product

Resources

About