作者：
吴伟仁田玉龙黄翔宇Institute for Pattern Recognition and Artificial Intelligence
State Key Lab. for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074 China Deep Space Exploration Research Center
Harbin Institute of Technology Harbin 150001 Chinahe image elements of earth-center and moon-center are obtained by processing the images of earth and moon these image elements in combination with the inertial attitude information and the moon ephemeris are utilized to obtain the probe initial position relative to earth and the Levenberg-Marquardt algorithm is used to determine the accurate probe position relative to earth and the probe orbit relative to earth is estimated by using the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simulation.
The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized ...
详细信息
The image elements of earth-center and moon-center are obtained by processing the images of earthand moon, these image elements in combination with the inertial attitude information and the moon ephemerisare utilized to obtain the probe initial position relative to earth, and the Levenberg-Marquardt algorithm is usedto determine the accurate probe position relative to earth, and the probe orbit relative to earth is estimated by u-sing the extended Kalman filter. The autonomous optical navigation algorithm is validated using the digital simu-lation.
In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is compose...
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ISBN:
(纸本)0780347781
In this paper, a precise trajectory tracking method for mobile robot using a vision system is presented. In solving the problem of precise trajectory tracking, a hierarchical control structure is used which is composed of a path planer, vision system, and dynamic controller. When designing the dynamic controller, non-ideal conditions such as parameter variation, frictional force and external disturbance, are considered. The proposed controller can learn bounded control input for repetitive or periodic dynamics compensation which provides robust and adaptive learning capability. Moreover, the vision system allows the robot to compensate the cumulative location error which exists when a relative sensor, like encoder, is used to locate the robot. The effectiveness of the proposed control scheme is shown through experiment as well as computer simulation.
An adaptive PID learning controller which consists of an adaptive PID feedback control scheme and a feedforward input learning scheme is proposed for learning of periodic robot motion. In the learning controller, the ...
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An adaptive PID learning controller which consists of an adaptive PID feedback control scheme and a feedforward input learning scheme is proposed for learning of periodic robot motion. In the learning controller, the adaptive PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. In addition, the proposed adaptive PID learning controller is compared with the fixed PID learning controller in terms of the stability condition of lower PID gain bound, the performance of tracking, and the convergence rate of desired learning input.
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