Robustness of the dynamic walk of a biped robot subjected to disturbing external forces by using CMAC neural networks

2005 ◽  
Vol 51 (2-3) ◽  
pp. 81-99 ◽  
Author(s):  
Christophe Sabourin ◽  
Olivier Bruneau
Keyword(s):  
Neural Networks ◽  
Biped Robot ◽  
External Forces

A force-resisting balance control strategy for a walking biped robot under an unknown, continuous force

Robotica ◽  
2014 ◽  
Vol 34 (7) ◽  
pp. 1495-1516
Author(s):  
Yeoun-Jae Kim ◽  
Joon-Yong Lee ◽  
Ju-Jang Lee
Keyword(s):  
External Force ◽  
Control Strategy ◽  
Balance Control ◽  
Biped Robot ◽  
Second Step ◽  
Double Support ◽  
Zero Moment ◽  
External Forces ◽  
Single Support Phase ◽  
Support Phase

SUMMARYIn this paper, we propose and examine a force-resisting balance control strategy for a walking biped robot under the application of a sudden unknown, continuous force. We assume that the external force is acting on the pelvis of a walking biped robot and that the external force in the z-direction is negligible compared to the external forces in the x- and y-directions. The main control strategy involves moving the zero moment point (ZMP) of the walking robot to the center of the robot's sole resisting the externally applied force. This strategy is divided into three steps. The first step is to detect an abnormal situation in which an unknown continuous force is applied by examining the position of the ZMP. The second step is to move the ZMP of the robot to the center of the sole resisting the external force. The third step is to have the biped robot convert from single support phase (SSP) to double support phase (DSP) for an increased force-resisting capability. Computer simulations and experiments of the proposed methods are performed to benchmark the suggested control strategy.

scholarly journals Deriving external forces via convolutional neural networks for biomedical image segmentation

2019 ◽  
Vol 10 (8) ◽  
pp. 3800 ◽  
Author(s):  
Yibiao Rong ◽  
Dehui Xiang ◽  
Weifang Zhu ◽  
Fei Shi ◽  
Enting Gao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Biomedical Image ◽  
External Forces

scholarly journals A Disturbance Rejection Control Method Based on Deep Reinforcement Learning for a Biped Robot

2021 ◽  
Vol 11 (4) ◽  
pp. 1587
Author(s):  
Chuzhao Liu ◽  
Junyao Gao ◽  
Dingkui Tian ◽  
Xuefeng Zhang ◽  
Huaxin Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Disturbance Rejection ◽  
Humanoid Robot ◽  
Control Method ◽  
Learning Algorithm ◽  
Biped Robot ◽  
Forward Direction ◽  
Disturbance Rejection Control ◽  
Table Model ◽  
External Forces

The disturbance rejection performance of a biped robot when walking has long been a focus of roboticists in their attempts to improve robots. There are many traditional stabilizing control methods, such as modifying foot placements and the target zero moment point (ZMP), e.g., in model ZMP control. The disturbance rejection control method in the forward direction of the biped robot is an important technology, whether it comes from the inertia generated by walking or from external forces. The first step in solving the instability of the humanoid robot is to add the ability to dynamically adjust posture when the robot is standing still. The control method based on the model ZMP control is among the main methods of disturbance rejection for biped robots. We use the state-of-the-art deep-reinforcement-learning algorithm combined with model ZMP control in simulating the balance experiment of the cart–table model and the disturbance rejection experiment of the ASIMO humanoid robot standing still. Results show that our proposed method effectively reduces the probability of falling when the biped robot is subjected to an external force in the x-direction.

scholarly journals Adaptive fractional PID control of biped robots with time-delayed feedback

2019 ◽  
Vol 277 ◽  
pp. 01007 ◽  
Author(s):  
◽  
P Joel Perez ◽  
Jose P. Perez ◽  
Mayra Flores Guerrero ◽  
Ruben Perez P. ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Fractional Order ◽  
Degrees Of Freedom ◽  
Tracking Error ◽  
Reference Signal ◽  
Biped Robot ◽  
Control Laws ◽  
Adaptive Neural Networks ◽  
Fractional Order Pid

This paper presents the application of Fractional Order Time- Delay adaptive neural networks to the trajectory tracking for chaos synchronization between Fractional Order delayed plant, reference and Fractional Order Time-Delay adaptive neural networks. The proposed new control scheme is applied via simulations to control of a 4-DOF Biped Robot [1]. The main methodologies, on which the approach is based, are Fractional Order PID the Fractional Order Lyapunov-Krasovskii functions methodology. The structure of the biped robot is designed with two degrees of freedom per leg, corresponding to the knee and hip joints. Since torso and ankle are not considered, it is obtained a 4-DOF system, and each leg, we try to force this biped robot to track a reference signal given by undamped Duffing equation. The tracking error is globally asymptotically stabilized by two control laws derived based on a Lyapunov-Krasovski functional.

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