针对四轴飞行器飞行性能不稳定和惯性测量单元(IMU)易受干扰、存在漂移等问题,利用惯性传感器MPU6050采集实时数据,以经典互补滤波为基础,提出一种可以自适应补偿系数的互补滤波算法,该算法在低通滤波环节加入PI控制器,依据陀螺仪测得...针对四轴飞行器飞行性能不稳定和惯性测量单元(IMU)易受干扰、存在漂移等问题,利用惯性传感器MPU6050采集实时数据,以经典互补滤波为基础,提出一种可以自适应补偿系数的互补滤波算法,该算法在低通滤波环节加入PI控制器,依据陀螺仪测得的角速度实时调节PI控制器补偿系数。飞行器姿态控制系统采用双闭环PID控制方法,姿态解算的欧拉角作为系统外环,陀螺仪角速度作为系统内环。最后,搭建以NI my RIO为核心控制器的四轴飞行器,通过Lab VIEW实现算法和仿真,实验结果表明,自适应互补滤波算法可以准确解算姿态信息,双闭环PID控制超调量小、反应灵敏,控制系统基本满足飞行要求。展开更多
This paper demonstrates the assembly of a servo-controlled platform with two degrees of freedom, empirical methods and a developed closed-loop control found in the system mathematical model. This control aims to stabi...This paper demonstrates the assembly of a servo-controlled platform with two degrees of freedom, empirical methods and a developed closed-loop control found in the system mathematical model. This control aims to stabilize and hold small objects on the platform. We parsed the step response in X and Y axes, hence we found the first and second-order models for each one. We did some further analyses to decide which one would better represent the behavior of the system. The MATLAB software provided step response for the model empirically obtained and latter compared it to experimental data acquired in the trials. Accelerometers and gyro sensors from the MPU-6050 sensor measured the angular position of platform on X and Y axes. In order to improve measurements accuracy and eliminate noise effects, we implemented the complementary filter to the firmware system. We used Arduino to control servomotors through PWM pulses and perform data acquisition.展开更多
文摘针对四轴飞行器飞行性能不稳定和惯性测量单元(IMU)易受干扰、存在漂移等问题,利用惯性传感器MPU6050采集实时数据,以经典互补滤波为基础,提出一种可以自适应补偿系数的互补滤波算法,该算法在低通滤波环节加入PI控制器,依据陀螺仪测得的角速度实时调节PI控制器补偿系数。飞行器姿态控制系统采用双闭环PID控制方法,姿态解算的欧拉角作为系统外环,陀螺仪角速度作为系统内环。最后,搭建以NI my RIO为核心控制器的四轴飞行器,通过Lab VIEW实现算法和仿真,实验结果表明,自适应互补滤波算法可以准确解算姿态信息,双闭环PID控制超调量小、反应灵敏,控制系统基本满足飞行要求。
文摘This paper demonstrates the assembly of a servo-controlled platform with two degrees of freedom, empirical methods and a developed closed-loop control found in the system mathematical model. This control aims to stabilize and hold small objects on the platform. We parsed the step response in X and Y axes, hence we found the first and second-order models for each one. We did some further analyses to decide which one would better represent the behavior of the system. The MATLAB software provided step response for the model empirically obtained and latter compared it to experimental data acquired in the trials. Accelerometers and gyro sensors from the MPU-6050 sensor measured the angular position of platform on X and Y axes. In order to improve measurements accuracy and eliminate noise effects, we implemented the complementary filter to the firmware system. We used Arduino to control servomotors through PWM pulses and perform data acquisition.
