{"title":"PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal","authors":"R. Rajesh, C. Ananda","doi":"10.1109/ICPACE.2015.7274930","DOIUrl":null,"url":null,"abstract":"In this paper, camera gimbal control is designed which controls the on board camera position used in UAV for various applications such as target tracking, Surveillance, Aerial photography, autonomous navigation and so on. Traditional tracking systems are heavy and large to mount on small airframes. Gimbal with camera replaces traditional tracking systems and used to capture aerial photography without video noise and vibrations. So, the gimbal trajectory planning and its motion control are necessary. The controlling of camera gimbal is designed using different controlling techniques which respond quickly without excitation of damping flexibility. In order to develop the control, kinematics is derived using different robotics techniques. In this paper PID controller is designed to control camera position using gimbal mechanism. PID control is the popular controller used in industries for its effectiveness, simplicity of design and its feasibility. PID consists of three tuning parameters which can be tuned using different techniques. Manual tuning is not preferred since it is time consuming, tedious and leads to poor performance. Here, traditional tuning methods and evolutionary algorithms/bio-inspired algorithms are used to tune PID parameters. PSO is the evolutionary algorithm used because of its stable convergence, dynamic and static performance, good computational efficiency due to which system performance with minimum errors can be achieved. In this paper, performance of system with conventional PID and PSO tuned PID are compared and optimum solution is implemented.","PeriodicalId":6644,"journal":{"name":"2015 International Conference on Power and Advanced Control Engineering (ICPACE)","volume":"30 1","pages":"128-133"},"PeriodicalIF":0.0000,"publicationDate":"2015-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"24","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on Power and Advanced Control Engineering (ICPACE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPACE.2015.7274930","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 24
Abstract
In this paper, camera gimbal control is designed which controls the on board camera position used in UAV for various applications such as target tracking, Surveillance, Aerial photography, autonomous navigation and so on. Traditional tracking systems are heavy and large to mount on small airframes. Gimbal with camera replaces traditional tracking systems and used to capture aerial photography without video noise and vibrations. So, the gimbal trajectory planning and its motion control are necessary. The controlling of camera gimbal is designed using different controlling techniques which respond quickly without excitation of damping flexibility. In order to develop the control, kinematics is derived using different robotics techniques. In this paper PID controller is designed to control camera position using gimbal mechanism. PID control is the popular controller used in industries for its effectiveness, simplicity of design and its feasibility. PID consists of three tuning parameters which can be tuned using different techniques. Manual tuning is not preferred since it is time consuming, tedious and leads to poor performance. Here, traditional tuning methods and evolutionary algorithms/bio-inspired algorithms are used to tune PID parameters. PSO is the evolutionary algorithm used because of its stable convergence, dynamic and static performance, good computational efficiency due to which system performance with minimum errors can be achieved. In this paper, performance of system with conventional PID and PSO tuned PID are compared and optimum solution is implemented.