PSO tuned PID controller for controlling camera position in UAV using 2-axis gimbal

R. Rajesh, C. Ananda
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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.
基于粒子群自整定PID的无人机二轴云台摄像机位置控制
本文设计了相机云台控制系统,控制无人机机载摄像机的位置,用于目标跟踪、监视、航空摄影、自主导航等多种应用。传统的跟踪系统笨重,难以安装在小型机身上。带相机的万向架取代了传统的跟踪系统,用于捕捉航空摄影,没有视频噪音和振动。因此,对框架进行轨迹规划和运动控制是十分必要的。采用不同的控制技术对相机云台进行控制,使其在不受阻尼柔度激励的情况下快速响应。为了实现控制,利用不同的机器人技术推导了运动学。本文设计了PID控制器,利用云台机构控制摄像机的位置。PID控制以其效率高、设计简单、可行性强等优点,成为目前工业上广泛应用的控制器。PID由三个可调参数组成,可以使用不同的技术进行调优。不建议使用手动调优,因为它既耗时又乏味,还会导致性能下降。本文采用传统的整定方法和进化算法/仿生算法对PID参数进行整定。粒子群算法具有稳定的收敛性、动静态性能和良好的计算效率,可以实现最小误差的系统性能,是一种常用的进化算法。本文比较了传统PID和PSO自整定PID的系统性能,并给出了最优解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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