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Four-dimensional guidance of atmospheric vehicles

JOURNAL OF GUIDANCE control AND DYNAMICS 1996年第1期19卷 113-122页

作者： StiharuAlexe, I OShea, J POLYTECH UNIV BUCHARESTROMANIA

The nonlinear control theories based upon the model predictive control and the nonlinear inverse control are used to develop four-dimensional guidance controllers, the conversion of the primary trajectory being assumed by a specific solution to the inverse kinematics problem, The control task is simplified by using the forced singular perturbation theory, overall system dynamics being separated into the fast and slow reduced-order systems and separately controlled. The algorithm performances are evaluated in the case of a conventional transport aircraft that tracks a four-dimensional trajectory defined by flight levels, waypoints, and an estimated time of arrival. The predictive control and dynamic inversion are compared as basic algorithms for the fast-dynamics control in view of establishing performances and limits.

关键词： Nonlinear Dynamic Inversion Avionics Aeronautical Engineering Aeronautics Boeing predictive control method Inverse Kinematics Transport Aircraft Algorithm Performance Aerodynamic Moments

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Optimal control Framework for a Centralized Approach to Separation Management

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JOURNAL OF GUIDANCE control AND DYNAMICS 2014年第3期37卷 1033-1038页

作者： Walter, Leif Kostina, Ekaterina French German Inst St Louis F-68301 St Louis France Univ Marburg Fac Math & Comp Sci D-52425 Marburg Germany

I. Introduction With the increase in demand and capacity, Air Traffic Management (ATM) will face further challenges to keep delay considerably low while increasing safety standards at a cheaper cost. When addressing these challenges imposed by future ATM programs (SESAR, NextGen), controllers will need sophisticated decision support tools to deal with upcoming traffic flows in an efficient way. As it was pointed out in the Performance Review Report 2010 , [12], 5 % of all flights in Europe are held on the ground to manage en-route congestion, resulting in more than 50 % of all delay minutes within the system. In the future, air navigation service providers (ANSPs) will need to deliver more capacity, in a cheaper, safer, and environmental-friendlier way. This goal will not be achieved without the aid of sophisticated and automated tools that will assist the human air traffic controller (ATCO). One of the air traffic management fields that can contribute to provide to future capacity is separation management . In the context of this paper, separation management is the task of separating aircraft in an allocated airspace volume as efficiently as possible. The aim of this paper is to present the application of optimal control theory in a central- ized approach to separation management, where we use information of the current traffic situation in a sector to derive an optimal control problem. The solution of this control problem consecutively provide conflict-free trajectories for all users within the regarded airspace. Solutions are obtained by using a direct method that is composed of multiple shooting discretization and a sequential quadratic programming (SQP) method to solve the resulting nonlinear optimization problem.

关键词： Optimal control Airspace Bank Angle Flight Tracking ATM Sequential Quadratic Programming Air Traffic controller predictive control method Knots Indicated Airspeed Base of Aircraft Data

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Software-enabled receding horizon control for autonomous unmanned aerial vehicle guidance

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JOURNAL OF GUIDANCE control AND DYNAMICS 2006年第3期29卷 680-694页

作者： Keviczky, Tamas Balas, Gary J. Univ Minnesota Control Sci & Dynam Syst Ctr Minneapolis MN 55455 USA Univ Minnesota Dept Aerosp Engn & Mech Minneapolis MN 55455 USA

This paper describes autonomous unmanned aerial vehicle (UAV) guidance technologies developed and demonstrated in a flight test sponsored by the DARPA Software Enabled control program. The flight experiment took place in June 2004 using a Boeing UAV testbed and demonstrated important autonomy capabilities enabled by a receding horizon guidance controller and fault detection filter. The receding horizon controller (RHC) design process is presented in detail as well as demonstration scenarios which were designed to exercise and evaluate the primary functionalities;of the control system. Simulation results of the key capabilities are shown and compared with recorded flight data for evaluation purposes. Hardware-in-the-loop simulations and other high-fidelity test run results illustrate secondary capabilities such as controller reconfiguration due to actuator fault and maneuvering limit enforcement using output constraints in the receding horizon approach.

关键词： Experimental Flight Test Fault Detection Filter Aerospace Company Defense Advanced Research Projects Agencies Aileron Actuators Hardware in the Loop Simulation Avionics Software control Systems predictive control method Autopilot

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A New Active Fault Tolerant control System: predictive Online Fault Estimation

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IEEE ACCESS 2021年 9卷 118461-118471页

作者： Bavili, Robab Ebrahimi Mohammadzadeh, Ardashir Tavoosi, Jafar Mobayen, Saleh Assawinchaichote, Wudhichai Asad, Jihad H. Mosavi, Amir H. Sahand Univ Technol Dept Elect Engn Tabriz *** Iran Univ Bonab Fac Engn Dept Elect Engn Bonab *** Iran Ilam Univ Dept Elect Engn Ilam *** Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu 64002 Yunlin Taiwan King Mongkuts Univ Technol Thonburi Fac Engn Dept Elect & Telecommun Engn Bangkok 10140 Thailand Palestine Tech Univ Kadoorie Fac Appl Sci Dept Phys Tulkarm Palestine Tech Univ Dresden Fac Civil Engn D-01069 Dresden Germany J Selye Univ Dept Informat Komarno 94501 Slovakia Obuda Univ Inst Software Design & Dev H-1034 Budapest Hungary

This study presents a new approach for active fault-tolerant controller (FTC) design for constrained nonlinear multi-variable systems. The proposed approach utilize the nonlinear model predictive controller (NMPC) and fault estimation method which is on basis of extended kalman filters (EKFs). The deficiency of actuators and sensors and also the plant states measurement errors are estimated by the suggested approach. A supervisor unit using the fault information and fault modeling per sampling time, corrects the predictor model of the controller and compensates actuator and sensor faults in control system. Furthermore, by the presented feedback compensation, the robustness of the designed method against plant faults and uncertainties is ensured. The important advantages of the proposed method are: (1) The suggested FTC scheme based on NMPC leads to calculate more accurate control action than MPC in nonlinear processes, (2) it is comprehensive in fault accommodation point of view because it is able to compensate all types of faults in control systems simultaneously, (3) it has low computational cost because of using NMPC by analytical solution, (4) it can handle control and states constraints to prevent of actuator saturations and unsafe situations, (5) the simplicity and effectiveness of the designed FTC scheme for real applications is more significant. Simulation results on continuous stirred tank reactor process verifies the superiority and capability of the designed approach.

关键词： Actuators Sensors Fault tolerant systems Fault tolerance Uncertainty Trajectory predictive models Fault-tolerant controller fault modeling uncertainty predictive control method robustness

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Fast and Near-Optimal Guidance for Docking to Uncontrolled Spacecraft

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JOURNAL OF GUIDANCE control AND DYNAMICS 2017年第12期40卷 3138-3154页

作者： Ventura, Jacopo Ciarcia, Marco Romano, Marcello Walter, Ulrich Tech Univ Munich Inst Astronaut Boltzmannstr 15 D-85748 Garching Germany Naval Postgrad Sch Dept Mech & Aerosp Engn 700 Dyer RdWatkins Hall Monterey CA 93940 USA Tech Univ Munich Inst Astronaut Boltzmannstr 15 D-85748 Garching Germany

This paper proposes a guidance scheme for autonomous docking between a controlled spacecraft and an uncontrolled tumbling target in circular orbit. The onboard trajectory planning consists of a direct optimization method based on the inversion of the system dynamics. The trajectory components of the controlled spacecraft are imposed by using polynomial functions. Some of the polynomial coefficients are constrained to satisfy path constraints, whereas the remaining coefficients are varied parameters to be optimized. The optimal control problem is converted into a nonlinear programming problem by inverting the system dynamics. The proposed guidance scheme, based on the closed-loop implementation of this optimization problem, is applied to several scenarios. The resulting trajectories closely match the solutions of the correspondent optimal control problems. The guidance scheme is shown to perform precise maneuvers in most maneuvering situations, even in the presence of orbital perturbations. The sensitivity analysis to system uncertainties shows that the guidance is sensitive to noise on the target angular velocity, being the critical disturbance for this type of maneuvers.

关键词： Spacecrafts Trajectory Optimization predictive control method Nonlinear Programming Sensitivity Analysis Autonomous Docking Target Spacecraft Guidance System Robust Guidance Algorithm Standard Gravitational Parameter

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Laboratory Experimentation of Spacecraft Robotic Capture Using Deep-Reinforcement-Learning-Based Guidance

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JOURNAL OF GUIDANCE control AND DYNAMICS 2022年第11期45卷 2138-2146页

作者： Hovell, Kirk Ulrich, Steve Carleton Univ Dept Mech & Aerosp Engn 1125 Colonel Dr Ottawa ON K1S 5B6 Canada

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Multiple Target Tracking Via Dynamic Point Clustering on a UAV Platform

Multiple Target Tracking Via Dynamic Point Clustering on a U...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Holsinger, Seth Sharma, Rajnikant Univ Cincinnati Aerosp Engn Cincinnati OH 45221 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

This research focuses on solving the problem of tracking multiple ground targets from a single or multiple UAV's utilizing a dynamic clustering algorithm. The location of each of the targets can be ascertained by using a geolocation method with a camera as the primary sensor. Each UAVis assumed to have only one camera that is mounted on a two axis gimbal. The targets will not be able to transmit any of their location data to the UAVs. An extended Kalman filter is used to estimate the location and velocity of the targets with the afore mentioned geolocation used as the update step. The algorithm uses the estimated target locations and the distance from the UAV to different point locations to find the maximum number of targets that the camera can view at once. These clusters of points are then subject to a cost function where the goal is the minimize the overall uncertainty of all of the system. While also staying below a maximum allowable uncertainty for individual targets. The approach taken by this method is scalable with the number of UAVs and ground targets that it can track to allow for it to be used in a wide variety of circumstances.

关键词： Extended Kalman Filter Sensor Fusion Geolocation Monte Carlo Simulation Track Algorithm MATLAB Earth predictive control method Gaussian White Noise Autonomous Systems

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Bridge Arms Current Tracking predictive control of Modular Multilevel Converter for HVDC system 7

Bridge Arms Current Tracking Predictive Control of Modular M...

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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Liu, Xiangjie Xu, Huimin Zhang, Xuedong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China China Elect Power Univ Sch Energy & Power Engn North China Elect Power Univ Baoding Beijing Peoples R China

ISBN: (纸本)9781479986460

in order to suppress circulation current, a predictive control method based on error model of system is proposed. Firstly, we set up the discrete model of the controlled system by Eulor's formula, and then derive the tracking error discrete model, finally, the desired AC current is tracked completely by the actual current. the merits of the error model predictive control is that the error model eliminates the repeat errors in the time index and the predictive control and Kalman filter remove the random error. the calculated amount is too heavy to be implemented by the computer in the model predictive control for modular multilevel converters when the numbers of the sub-modular is high, a explicit optimal grouping method is bring forward according to the mean inequality, and reduce the computer burden. the simulation result show the proposed control strategy is of feasibility and superiority

关键词： predictive control method modular multilevel converters error model desired bridge arms current optimal grouping method

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Uncertainty Aware Geo-localization of Multi-Targets with Multi-UAV using Neural Network and Extended Kalman Filter

Uncertainty Aware Geo-localization of Multi-Targets with Mul...

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AIAA Science and Technology (SciTech) Forum and Exposition

作者： Khanapuri, Eshaan Sharma, Rajnikant Univ Cincinnati Dept Aerosp Engn & Engn Mech Cincinnati OH 45221 USA

ISBN: (数字)9781624105784

ISBN: (纸本)9781624105784

In this paper we propose a Neural Network based approach to improve the accuracy in the estimated positions of multiple targets and minimize their uncertainty using multiple small fixed wing Unmanned Aerial Vehicle (UAV). The UAVs are equipped with a gimbal camera to track the targets. To Geo-localize the targets they should remain inside the Field of View (FOV) of the camera. Extended Kalman Filter (EKF) is used to estimate target's positions. As long as the targets are in the FOV the error in the estimated positions of the targets will be low. The challenge here is to predict the azimuth and elevation angles of gimbal cameras such that multiple targets remain inside the FOV. This is achieved by training a Neural Network with backpropogation to predict the required azimuth and elevation angles for each UAV. In certain cases when multiple targets cannot be kept in the FOV of the camera, neural network predicts gimbal angles for each UAV such that they can observe atleast one target each. Using simulation results we can see the effectiveness of the proposed approach and also show that the estimated position and uncertainty of targets are better with Neural Network compared to Model predictive control method.

关键词： Neural Network methods Extended Kalman Filter Unmanned Aerial Vehicle predictive control method Computer Vision Geolocation Robust Algorithm Pressure Sensors Yaw

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Multi-objective Optimal predictive control of Axial Field Flux-Switching Permanent Magnet Machine 4

Multi-objective Optimal Predictive Control of Axial Field Fl...

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4th International Conference on Smart Power and Internet Energy Systems (SPIES)

作者： He, Jianbiao Zhang, Wei Fan, Haojie Nantong Univ Sch Elect Engn Nantong Jiangsu Peoples R China

ISBN: (纸本)9781665489577

A multi-objective optimal predictive control method is proposed in this paper in order to improve the operating efficiency of axial field flux-switching permanent magnet machine (AFFSPMM) while suppressing the torque pulsation caused by the cogging torque. The method is based on the mathematical model of the machine considering iron loss to calculate the minimum excitation currents of iron and copper consumption. On the one hand, the excitation currents corresponding to the minimum loss of the machine are obtained by the interval current seeking. On the other hand, the d-q axis compensation currents are calculated according to the results of the finite element analysis of the cogging torque. Finally, based on the model predictive control principle, the value function is designed jointly by using the loss minimum excitation currents and compensation currents to accomplish the optimal switching states, so that the multi-objective optimal control with low torque pulsation and high efficiency of AFFSPMM is realized. The experimental results verify the feasibility and effectiveness of the proposed multi-objective optimal predictive control method for AFFSPMM.

关键词： axial field flux-switching permanent magnet machine (AFFSPMM) multi-objective optimal minimum loss predictive control method

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