Abstract
Flight simulator is an important application in the field of aerospace as semi-physical simulation equipment. As it requires supreme control precision and stability, it is especially important to search the performance assessment of flight simulator servo system. The traditional researches on flight simulator control performance index is more about dynamic output tracking features but few on input characteristics and effects. Based on Linear Quadratic Gaussian (LQG) performance benchmark, this paper makes analyses on high precision flight simulator in three kinds of controller while considering the influences of input and output signals’ effect on controllers. After processing the input and output data, combined with the linear fitting method, we can obtain LQG performance tradeoff curve. Through comparing the controller’s actual performance with the optimal performance, we’ll gain the controller’s control performance index and its potential.
Highlights
The flight simulator servo system is a semi-physical simulation equipment in aerospace field where the design and development of aerospace aircraft, missiles play key roles
Control Performance Assessment (CPA) technology already ensures those in the whole process of industrial production of various controllers can run at good states
Control Performance Assessment’s goal is to estimate a lower bound performance of the designed control system by monitoring the running system and processing a range of data getting from the closed-loop system
Summary
The flight simulator servo system is a semi-physical simulation equipment in aerospace field where the design and development of aerospace aircraft, missiles play key roles. Control Performance Assessment (CPA) technology already ensures those in the whole process of industrial production of various controllers can run at good states. Control Performance Assessment’s goal is to estimate a lower bound performance of the designed control system by monitoring the running system and processing a range of data getting from the closed-loop system. Operators can judge the running state of the system and figure out whether the current control performance accords with the anticipated design goal. If it cannot meet the design requirements, CPA will locate the fault point in time and give reasonable suggestions
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
