Kernel recursive maximum correntropy with variable center

2021 ◽  
pp. 108364
Author(s):  
Xiang Liu ◽  
Chengtian Song ◽  
Zhihua Pang
Keyword(s):  
2019 ◽  
Vol 26 (8) ◽  
pp. 1212-1216 ◽  
Author(s):  
Badong Chen ◽  
Xin Wang ◽  
Yingsong Li ◽  
Jose C. Principe

2018 ◽  
Vol 10 (8) ◽  
pp. 168781401877863 ◽  
Author(s):  
Ran Jiao ◽  
Wusheng Chou ◽  
Rui Ding ◽  
Mingjie Dong

The control of quadrotor equipped with a robotic arm has received growing challenges. This article proposes a new adaptive control strategy of quadrotor equipped with a 2-degree-of-freedom robotic arm. To consider the positional variety of the center of gravity caused by the motion of the robotic arm, the kinematic and dynamic models are built. Based on the presented models, a backstepping and sliding mode controller with a terminal sliding mode manifold is first applied to cope with the condition in which the robotic arm is motionless relative to the quadrotor. As the evolvement of the backstepping and sliding mode controller, a novel adaptive backstepping and sliding mode controller is then designed for the vehicle with the robotic arm wavering. The robustness and effectiveness of the proposed control law are investigated through both simulations and flight tests. With the proposed control laws, several simulations are conducted in conditions of both a variable and a constant center of gravity, and the performance of hovering is tested with a variable center of gravity in an experiment. Overall results show that the proposed adaptive backstepping control could estimate and compensate the variable center of gravity which may seriously influence the stabilization of quadrotor flying in the air.


Entropy ◽  
2020 ◽  
Vol 22 (1) ◽  
pp. 70
Author(s):  
Fei Dong ◽  
Guobing Qian ◽  
Shiyuan Wang

The complex correntropy has been successfully applied to complex domain adaptive filtering, and the corresponding maximum complex correntropy criterion (MCCC) algorithm has been proved to be robust to non-Gaussian noises. However, the kernel function of the complex correntropy is usually limited to a Gaussian function whose center is zero. In order to improve the performance of MCCC in a non-zero mean noise environment, we firstly define a complex correntropy with variable center and provide its probability explanation. Then, we propose a maximum complex correntropy criterion with variable center (MCCC-VC), and apply it to the complex domain adaptive filtering. Next, we use the gradient descent approach to search the minimum of the cost function. We also propose a feasible method to optimize the center and the kernel width of MCCC-VC. It is very important that we further provide the bound for the learning rate and derive the theoretical value of the steady-state excess mean square error (EMSE). Finally, we perform some simulations to show the validity of the theoretical steady-state EMSE and the better performance of MCCC-VC.


1982 ◽  
Vol 154 (3) ◽  
pp. 515-523 ◽  
Author(s):  
Ariel Prunell ◽  
Roger D. Kornberg

2011 ◽  
Vol 23 (14) ◽  
pp. 989-991 ◽  
Author(s):  
Yasuki Sakurai ◽  
Masahiro Kawasugi ◽  
Yuji Hotta ◽  
M. D. Saad Khan ◽  
Hisashi Oguri ◽  
...  

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