首页|New Findings from Manipal Academy of Higher Education in Evolvable Hardware Prov ides New Insights (Evolvable Hardware-based Optimal Position Control of Quadcopt er)

New Findings from Manipal Academy of Higher Education in Evolvable Hardware Prov ides New Insights (Evolvable Hardware-based Optimal Position Control of Quadcopt er)

扫码查看
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Evolvable Hardware. According to news reporting out of Manipal,India,by NewsRx editors,research stated,"Trading off performance metrics in control design fo r position tracking is unavoidable. This has severe consequences in mission-crit ical systems such as quadcopter applications." Our news journalists obtained a quote from the research from the Manipal Academy of Higher Education,"The controller area and propulsion energy are conflicting design parameters,whereas the reliability and tracking speed are related metri cs to be optimized. In this research,a switching-based position controller was co-simulated with the quadcopter model. Performance analysis of the Field Progra mmable Gate Array (FPGA)-based controller validates a better scheme for tracking speed,propulsion energy,and reliability optimization under similar error perf ormance. To improve the computation power and controller area,the dynamic parti al reconfiguration(DPR) approach has been adapted and implemented on FPGA using the Vivado Integrated Development Environment (IDE),where a ranking-based appro ach brings into action either proportional derivative,sliding mode,or model pr edictive controllers for each dimension of position tracking. It is verified by analyzing the cumulative tracking speed,reliability,controller area,and propu lsion energy metrics that the proposed controller can optimize all these metrics within three successive iterations of tracking either in the same direction or in any combination of directions."

ManipalIndiaAsiaEmerging Technolog iesEvolvable HardwareMachine LearningManipal Academy of Higher Education

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Mar.29)