Introduction of the Foot Placement Estimator: A Dynamic Measure of Balance for Bipedal Robotics

2007 ◽  
Vol 3 (1) ◽  
Author(s):  
Derek L. Wight ◽  
Eric G. Kubica ◽  
David W. L. Wang
Keyword(s):  
Control System ◽  
Gait Cycle ◽  
State Machine ◽  
Gait Initiation ◽  
External Disturbances ◽  
Foot Placement ◽  
Simulation Based ◽  
Under Construction ◽  
Linear Controllers ◽  
Stability Results

The goal of most bipedal robotics research is to develop methods of achieving a dynamically balanced gait. Most current approaches focus on maintaining the balance of the system. This paper introduces a measure called the foot placement estimator (FPE) to restore balance to an unbalanced system. We begin by developing a theoretical proof to define when a biped is stable, as well as defining the region in which stability results are valid. This forms the basis for the derivation of the FPE. The results of the FPE are then extended to a complete gait cycle using the combination of a state machine and simple linear controllers. This control system is applied to a detailed and realistic simulation based on a physical robot currently under construction. Utilizing the FPE as a measure of balance allows us to create dynamically balanced gait cycles in the presence of external disturbances, including gait initiation and termination, without any precalculated trajectories.

scholarly journals Implementation of Deep Deterministic Policy Gradients for Controlling Dynamic Bipedal Walking

Biomimetics ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 28 ◽  
Author(s):  
Chujun Liu ◽  
Andrew Lonsberry ◽  
Mark Nandor ◽  
Musa Audu ◽  
Alexander Lonsberry ◽  
...  
Keyword(s):  
Control System ◽  
Sagittal Plane ◽  
Bipedal Walking ◽  
Distributed Control System ◽  
State Variables ◽  
Hip Joints ◽  
External Disturbances ◽  
Foot Placement ◽  
Policy Gradient ◽  
The Ideal

A control system for bipedal walking in the sagittal plane was developed in simulation. The biped model was built based on anthropometric data for a 1.8 m tall male of average build. At the core of the controller is a deep deterministic policy gradient (DDPG) neural network that was trained in GAZEBO, a physics simulator, to predict the ideal foot placement to maintain stable walking despite external disturbances. The complexity of the DDPG network was decreased through carefully selected state variables and a distributed control system. Additional controllers for the hip joints during their stance phases and the ankle joint during toe-off phase help to stabilize the biped during walking. The simulated biped can walk at a steady pace of approximately 1 m/s, and during locomotion it can maintain stability with a 30 kg·m/s impulse applied forward on the torso or a 40 kg·m/s impulse applied rearward. It also maintains stable walking with a 10 kg backpack or a 25 kg front pack. The controller was trained on a 1.8 m tall model, but also stabilizes models 1.4–2.3 m tall with no changes.

FLY System of Green Infrastructure for Coping with Climate Change

2012 ◽  
Vol 260-261 ◽  
pp. 1156-1157
Author(s):  
Goeun Choei ◽  
Jeon Geun Bae ◽  
Sang Min Shin ◽  
Heek Yung Park
Keyword(s):  
Climate Change ◽  
Control System ◽  
Green Infrastructure ◽  
Transition Layer ◽  
Cfd Simulation ◽  
Outdoor Temperature ◽  
External Disturbances ◽  
Indoor Temperature ◽  
Technical Feasibility

This study aims to examine technical feasibility of the FLY system that was developed for control indoor temperature against change of outdoor temperature based on principles for green infrastructure. The FLY system is a control system that protects inner system from external disturbances by making transition layer. The CFD simulation was used for analyzing change of temperature at transition layer and indoor. It was analyzed that the FLY system can reduce variability of indoor temperature against uncertain change of outdoor temperature.

A new principle of UHVDC line pilot protection based on trigger angle control characteristics

2020 ◽  
Vol 21 (4) ◽  
Author(s):  
Lu Wang ◽  
Zhenxing Li ◽  
Tan Hong ◽  
Hanli Weng ◽  
Zhenhua Li
Keyword(s):  
Control System ◽  
Trigger Delay ◽  
Internal Fault ◽  
Internal Parameters ◽  
Simulation Based ◽  
Transition Resistance ◽  
Change Tendency ◽  
Pilot Protection

AbstractA new principle of UHVDC Line pilot protection based on internal parameters of the trigger angle of converter is proposed, in order to improve the ability of the protection to withstand transition resistance. Through the analysis of UHVDC control system, it is found that, due to the regulation of control system, in terms of trigger delay angle of rectifier side and trigger leading angle of inverter side, the change tendency in internal fault and external fault is different. Thus, the protection criterion is constructed. Compared with the traditional protection principle using voltage and current to establish protection criteria, this principle uses internal parameters as protection quantity. The simulation based on PSCAD/EMTDC verifies the effectiveness of the protection principle.

scholarly journals Diseño de interfaz gráfica para analizar el ciclo de marcha del cuerpo humano en software libre

2020 ◽  
pp. 13-17
Author(s):  
Didia Carrillo-Hernández ◽  
Yered Uriel Terrones-Lara ◽  
Heraclio García-Cervantes ◽  
Alan David Blanco-Miranda
Keyword(s):  
Control System ◽  
Control Systems ◽  
Intelligent Control ◽  
Motor System ◽  
Gait Cycle ◽  
Health Sector ◽  
Lower Limbs ◽  
Essential Information ◽  
Intelligent Control System ◽  
Over Time

Currently in the country there are more than 27 thousand cases of annual amputations and more than 80% correspond to lower limbs, therefore, the demand for prosthetic equipment is greater than what the health sector institutions can provide. It should be noted that the equipment developed by these institutions is only passive equipment, so that only 10% of patients who receive a prosthetic equipment successfully complete their rehabilitation. The main problems that the patient faces when adapting to their prosthetic equipment is the response time and alignment vs the healthy limb, since it does not have an intelligent control system that allows them to respond in real time as the losted limb did. This causes gaps when performing your gait cycle, this over time can bring about abnormalities in your posture affecting the alignment of your motor system. This work allows us to analyze the range of motion of the ankles and knees, in addition to determining the angular velocity of both, it is essential information for the development of control systems necessary for active prosthetic equipment. The programming language where it was developed is the Python 3.7 software and additionally reproduce the simulation of the gait cycle.

Development of Gait Training System Powered by Antagonistic Mono-and Bi-Articular Actuators Using Contraction Model Control Scheme

2013 ◽  
Vol 393 ◽  
pp. 525-531 ◽  
Author(s):  
Mohd Azuwan Mat Dzahir ◽  
Tatsuya Nobutomo ◽  
Shin Ichiroh Yamamoto
Keyword(s):  
Control System ◽  
Cycle Time ◽  
Muscle Activation ◽  
Gait Cycle ◽  
Gait Training ◽  
Training System ◽  
Time Shift ◽  
Control Scheme ◽  
Model Control ◽  
User Friendly

The use of Pneumatic Muscle Actuator (PMA) in medical robots for rehabilitation has changed due to the requirements for a compliant, light weight and user-friendly robotic system. In this paper, a control system for controlling the bi-articular actuators (PMA) is proposed. Based on the information obtained from the positional input data (hip and knee joint angles), a contraction model is derived using mathematical equations to determine the contraction patterns of antagonistic mono-and bi-articular actuators, and then implemented it into the control system. Anterior and posterior muscle activation levels are introduced into the model to manipulate its magnitude. There are two tests for the control system; first is with antagonistic mono-articular actuators alone; second is along with antagonistic bi-articular actuators. The contraction model control scheme was tested on a healthy subject in a robot assisted walk test, and satisfactory performance was obtained. The result showed that, the cycle time of the gait training system is improved up to 3 seconds gait cycle compared to 5 seconds gait cycle used in previous research. However, a little time shift and inertia occurred when the controller is tested at faster gait cycle time of 2 seconds and 1 second. Thus, the potential field and iterative learning control are suggested to improve the gait cycle of the system.

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