An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastic model. The adaptive filter has six states with a time variable transition matrix. ...An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastic model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good estimates of the states. When the system is in the high dynamic mode and the bias has converged to an accurate estimate, the attitude calculation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance.展开更多
MEMS(micro electro mechanical systems) inertial navigation system(MINS) has been wildly used in robots for its low-cost.The MINS and magnetometers are commonly the component parts of the attitude and heading reference...MEMS(micro electro mechanical systems) inertial navigation system(MINS) has been wildly used in robots for its low-cost.The MINS and magnetometers are commonly the component parts of the attitude and heading reference systems(AHRS),which provide pitch and roll angles relative to the earth gravity vector,and heading angle relative to the north.However,the performance of sensors with low cost AHRS is not so good.The gyros are not sensitive enough to observe the earth angular velocity,so the traditional technique like alignment algorithm is invalid.The measurements of gyros become useless to determine the initial attitude matrix from navigation frame to body frame.The alignment algorithm is computed by the accelerometers and magnetometers.The process is established as an optimization problem of finding the maximum eigenvector.Meanwhile the sensitive analysis with respect to the biases of accelerometers is proposed.Then the recursive least squares algorithm(RLSA) is introduced.The comparison between the proposed method and RLSA is provided.The results demonstrate its accuracy favorably and verify the feasibility of the proposed algorithm.展开更多
In low-cost Attitude Heading Reference Systems (AHRS), the measurements made by Micro Electro-Mechanical Systems (MEMS) type sensors are affected by uncertainties, noises and unknown disturbances. In this paper, consi...In low-cost Attitude Heading Reference Systems (AHRS), the measurements made by Micro Electro-Mechanical Systems (MEMS) type sensors are affected by uncertainties, noises and unknown disturbances. In this paper, considering the robustness of sliding mode observers against structured and unstructured uncertainties, and also exogenous inputs, the process of design and implementation of a sliding mode observer (SMO) is proposed based on a linearized model of the AHRS. To decrease the chattering phenomenon is the main difficulty of the SMO. Through smoothing the discontinuity term, the tracking performance of the observer is attenuated. Boundary layer technique, for example, using a saturation term, is the common smoother to remove the chattering drawbacks. However, through poor tracking performance, the high range chattering could not be removed by this method. Therefore, a knowledge-based Mamdani-type fuzzy SMO (FSMO) is proposed to decrease the chattering effects intelligently, which in turn could obtain the high accuracy tracking performance of the SMO. Following proving the stability of the proposed SMOs based on direct Lyapunov’s method, the performance of the proposed observers is compared with that of the extended Kalman filter through simulation and real experiments of an AHRS.展开更多
This article proposes a new inner attitude integration algorithm to improve attitude accuracy of the strapdown inertial attitude and heading reference system （SIAHRS） , which, by means of a Kalman filter, integrates...This article proposes a new inner attitude integration algorithm to improve attitude accuracy of the strapdown inertial attitude and heading reference system （SIAHRS） , which, by means of a Kalman filter, integrates the calculated attitude from the accelerometers in inertial measuring unit （IMU） , called damping attitudes, with those from the conventional IMU. As vehicle＇ s acceleration could produce damping attitude errors, the horizontal outputs from accelerometers are firstly used to judge the vehicle＇ s motion so as to determine whether the damping attitudes could be reasonably applied. This article also analyzes the limitation of this approach. Furthermore, it suggests a residual chi-square test to judge the validity of damping attitude measurement in real time, and accordingly puts forward proper information fusion strategy. Finally,the effectiveness of the proposed algorithm is proved through the experiments on a real system in dynamic and static states.展开更多
针对一种下肢柔性助力机器人,开发了一种基于航姿参考模块(attitude and heading reference system,AHRS)设计的人体下肢运动学信息采集系统.该系统的AHRS具有体积小、功耗低的特点,便于通过绑带与人体保持紧致连接,并且能够长时间穿戴,...针对一种下肢柔性助力机器人,开发了一种基于航姿参考模块(attitude and heading reference system,AHRS)设计的人体下肢运动学信息采集系统.该系统的AHRS具有体积小、功耗低的特点,便于通过绑带与人体保持紧致连接,并且能够长时间穿戴,AHRS的设计融合了卡尔曼滤波算法(Kalman filter,KF)以提高数据的精度,多通道的数据通过ZigBee技术组建的无线传感器网络进行通信,并传输至上位机.然后,建立人体下肢运动学模型,推出AHRS测量角度和人体下肢姿态角度转换关系.同时,考虑在人体不同的步态活动下机器人助力的情况不同,设计了决策树分类器以对不同的步态活动进行识别分类,进一步地协助下肢柔性助力机器人对人体进行有效助力.在多种步态活动下,通过将该系统所采集的数据与Vicon系统采集的数据进行对比可知,该系统所采数据具有较高的精度,也验证了系统具有良好的稳定性及可靠性,另外利用大量的步态数据训练决策树分类器,并通过对未知步态活动进行的分类实验验证了步态识别的准确性.展开更多
文摘An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastic model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good estimates of the states. When the system is in the high dynamic mode and the bias has converged to an accurate estimate, the attitude calculation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance.
基金Supported by the National Natural Science Foundation of China(No.60905056)
文摘MEMS(micro electro mechanical systems) inertial navigation system(MINS) has been wildly used in robots for its low-cost.The MINS and magnetometers are commonly the component parts of the attitude and heading reference systems(AHRS),which provide pitch and roll angles relative to the earth gravity vector,and heading angle relative to the north.However,the performance of sensors with low cost AHRS is not so good.The gyros are not sensitive enough to observe the earth angular velocity,so the traditional technique like alignment algorithm is invalid.The measurements of gyros become useless to determine the initial attitude matrix from navigation frame to body frame.The alignment algorithm is computed by the accelerometers and magnetometers.The process is established as an optimization problem of finding the maximum eigenvector.Meanwhile the sensitive analysis with respect to the biases of accelerometers is proposed.Then the recursive least squares algorithm(RLSA) is introduced.The comparison between the proposed method and RLSA is provided.The results demonstrate its accuracy favorably and verify the feasibility of the proposed algorithm.
文摘In low-cost Attitude Heading Reference Systems (AHRS), the measurements made by Micro Electro-Mechanical Systems (MEMS) type sensors are affected by uncertainties, noises and unknown disturbances. In this paper, considering the robustness of sliding mode observers against structured and unstructured uncertainties, and also exogenous inputs, the process of design and implementation of a sliding mode observer (SMO) is proposed based on a linearized model of the AHRS. To decrease the chattering phenomenon is the main difficulty of the SMO. Through smoothing the discontinuity term, the tracking performance of the observer is attenuated. Boundary layer technique, for example, using a saturation term, is the common smoother to remove the chattering drawbacks. However, through poor tracking performance, the high range chattering could not be removed by this method. Therefore, a knowledge-based Mamdani-type fuzzy SMO (FSMO) is proposed to decrease the chattering effects intelligently, which in turn could obtain the high accuracy tracking performance of the SMO. Following proving the stability of the proposed SMOs based on direct Lyapunov’s method, the performance of the proposed observers is compared with that of the extended Kalman filter through simulation and real experiments of an AHRS.
基金Aeronautical Science Foundation of China(20080852011,20070852009)
文摘This article proposes a new inner attitude integration algorithm to improve attitude accuracy of the strapdown inertial attitude and heading reference system （SIAHRS） , which, by means of a Kalman filter, integrates the calculated attitude from the accelerometers in inertial measuring unit （IMU） , called damping attitudes, with those from the conventional IMU. As vehicle＇ s acceleration could produce damping attitude errors, the horizontal outputs from accelerometers are firstly used to judge the vehicle＇ s motion so as to determine whether the damping attitudes could be reasonably applied. This article also analyzes the limitation of this approach. Furthermore, it suggests a residual chi-square test to judge the validity of damping attitude measurement in real time, and accordingly puts forward proper information fusion strategy. Finally,the effectiveness of the proposed algorithm is proved through the experiments on a real system in dynamic and static states.
