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Novel maximum coentropy ellipsoid set membership filtering method

A technology of collective filtering and ellipsoid, which is applied in the direction of instruments, adaptive control, control/regulation systems, etc.

Active Publication Date: 2020-11-24
ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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Problems solved by technology

However, in many practical applications, such as the motion state space model system of autonomous mobile robots, the dynamic systems studied are generally nonlinear and non-Gaussian, so it is necessary to seek a method that can estimate the state variables or system parameters of the dynamic system online and in real time from system observations. An effective method for the statistical properties of the error, which is an important challenge faced by scientists and technicians

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  • Novel maximum coentropy ellipsoid set membership filtering method
  • Novel maximum coentropy ellipsoid set membership filtering method
  • Novel maximum coentropy ellipsoid set membership filtering method

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[0215] Applying the method of the present invention to the scene of the autonomous mobile robot, the motion state variable X of the autonomous mobile robot can be designed k =[x k ,y k ,θ k ] T , establish the motion model of the autonomous mobile robot as,

[0216]

[0217] where T is the sampling period, v k and w k are the instantaneous linear velocity and angular velocity of the center of mass movement of the robot at time k, respectively, which can be expressed by Calculated, ε=[ε x ,ε y ,ε θ ] T is the system noise item, a and b respectively represent the horizontal distance and vertical distance between the sensor and the center of mass of the robot, assuming that the movement speed of the left and right wheels of the robot is the input quantity u k =(v R,k ,v L,k ) T . The mobile robot uses the external sensor installed on itself to obtain the observation information of the environmental map to construct the map. Here, the distance and direction senso...

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Abstract

The invention provides a novel maximum coentropy ellipsoid set membership filtering method, which is used for improving the calculation stability and calculation precision of state parameters of a state space model of a robot system. The invention discloses an ellipsoid set membership filtering algorithm based on a minimum mean square error criterion. A nonlinear system state space model is oriented. The method comprises steps: introducing a novel maximum coentropy MCC criterion in an observation updating step; obtaining a new system noise expression by jointly implementing system model extension operation through a nonlinear system prediction noise error and observation noise; and constructing an error cost function expressed by a second-order information potential energy formula according to a maximum coentropy criterion between a prediction vector and an observation vector based on a system state variable, thereby designing an observation updating calculation process of an ellipsoidset membership filtering algorithm. When the method is used for carrying out land-based robot pose calculation simulation verification, the calculation precision is improved, and the calculation stability is obviously improved compared with a traditional ellipsoid set membership filtering algorithm.

Description

technical field [0001] The invention relates to the technical field of filtering state parameters of a motion state space model of an autonomous mobile robot, in particular to a novel maximum coentropy ellipsoid member filtering method. Background technique [0002] Filtering technology is a technology that studies how to accurately estimate unknown real signals or system state parameters from signal observations subject to various interferences. Due to the random nature of both interference and signals, this estimation technique can only be solved using statistical methods. It realizes the accurate estimation and calculation of the signal according to a certain estimation criterion and a certain statistical method. Different estimation criteria, different observation sequences and observation signal methods will lead to different estimation methods. Because of this, the filter theory technology has experienced the development of the least square theory, Wiener filter theo...

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Application Information

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IPC IPC(8): G05B13/04
CPCG05B13/042
Inventor 丁国强赵朋朋凌丹赵素娜刘娜娄泰山张焕龙王晓雷
Owner ZHENGZHOU UNIVERSITY OF LIGHT INDUSTRY
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