Adaptive Gait Phase Segmentation Based on the Time-Varying Identification of the Ankle Dynamics: Technique and Simulation Results

2020 ◽  
Author(s):  
Juan C. Perez-Ibarra ◽  
Adriano A. G. Siqueira ◽  
Hermano I. Krebs
Keyword(s):  
Time Varying ◽  
Gait Phase ◽  
Adaptive Gait ◽  
Simulation Results

scholarly journals Time-Varying Impedance Control of Port Hamiltonian System with a New Energy-Storing Tank

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Min Zheng ◽  
Tangqing Yuan ◽  
Tao Huang
Keyword(s):  
Control Method ◽  
Impedance Control ◽  
Conservative System ◽  
Time Varying ◽  
Control Performance ◽  
Singularity Problem ◽  
Numerical Examples ◽  
New Energy ◽  
Simulation Results ◽  
New Framework

In order to guarantee the passivity of a kind of conservative system, the port Hamiltonian framework combined with a new energy tank is proposed in this paper. A time-varying impedance controller is designed based on this new framework. The time-varying impedance control method is an extension of conventional impedance control and overcomes the singularity problem that existed in the traditional form of energy tank. The validity of the controller designed in this paper is shown by numerical examples. The simulation results show that the proposed controller can not only eliminate the singularity problem but can also improve the control performance.

Iterative learning algorithm with a quadratic criterion for linear time-varying systems

2002 ◽  
Vol 216 (3) ◽  
pp. 309-316 ◽  
Author(s):  
S N Huang ◽  
K K Tan ◽  
T H Lee
Keyword(s):  
Learning Algorithm ◽  
Linear Time ◽  
Tracking Error ◽  
Iterative Learning ◽  
Tracking Performance ◽  
Time Varying ◽  
Control Scheme ◽  
Time Varying Systems ◽  
Simulation Results ◽  
Learning Law

A novel iterative learning controller for linear time-varying systems is developed. The learning law is derived on the basis of a quadratic criterion. This control scheme does not include package information. The advantage of the proposed learning law is that the convergence is guaranteed without the need for empirical choice of parameters. Furthermore, the tracking error on the final iteration will be a class K function of the bounds on the uncertainties. Finally, simulation results reveal that the proposed control has a good setpoint tracking performance.

Traffic Fuzzy Controller's Improvement Based on Self-Adaptive Optimal Algorithm

2012 ◽  
Vol 562-564 ◽  
pp. 1414-1417
Author(s):  
Zhi Yi Xu ◽  
Da Lu Guan ◽  
Ai Long Fan
Keyword(s):  
Mathematical Model ◽  
Large Scale ◽  
Fuzzy Controller ◽  
Optimal Algorithm ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Time Varying ◽  
Large Scale Systems ◽  
Simulation Results ◽  
Self Adaptive

The transport system is a nonlinear, time-varying, lagging large-scale systems. Fuzzy control does not need to build a precise mathematical model, can be easily integrated people's thinking and experience, and is suitable for applications in the traffic signal control system. Here,a self-adaptive optimal algorithm was used to improve the traditional fuzzy controller. Simulation results show that the improved system has higher availability.

Adaptive Synchronization in Unknown Stochastic Chaotic Neural Networks with Mixed Time-Varying Delays

2011 ◽  
pp. 289-313
Author(s):  
Jian-an Fang ◽  
Yang Tang
Keyword(s):  
Neural Networks ◽  
Sufficient Conditions ◽  
Stochastic Perturbation ◽  
Adaptive Synchronization ◽  
Output Coupling ◽  
Time Varying ◽  
Connection Weight ◽  
Chaotic Neural Networks ◽  
Simulation Results ◽  
Feedback Techniques

Neural networks (NNs) have been useful in many fields, such as pattern recognition, image processing etc. Recently, synchronization of chaotic neural networks (CNNs) has drawn increasing attention due to the high security of neural networks. In this chapter, the problem of synchronization and parameter identification for a class of chaotic neural networks with stochastic perturbation via state and output coupling, which involve both the discrete and distributed time-varying delays has been investigated. Using adaptive feedback techniques, several sufficient conditions have been derived to ensure the synchronization of stochastic chaotic neural networks. Moreover, all the connection weight matrices can be estimated while the lag synchronization and complete synchronization is achieved in mean square at the same time. The corresponding simulation results are given to show the effectiveness of the proposed method.

scholarly journals Sampled-Data-Based Adaptive Group Synchronization of Second-Order Nonlinear Complex Dynamical Networks with Time-Varying Delays

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Bo Liu ◽  
Jiahui Bai ◽  
Yue Zhao ◽  
Chao Liu ◽  
Xuemin Yan ◽  
...  
Keyword(s):  
Adaptive Strategy ◽  
Second Order ◽  
Complex Dynamical Networks ◽  
Time Varying ◽  
Sampled Data ◽  
Dynamical Networks ◽  
Coupling Strengths ◽  
Simulation Results ◽  
Group Synchronization ◽  
The Given

This paper studies the adaptive group synchronization of second-order nonlinear complex dynamical networks with sampled-data and time-varying delays by designing a new adaptive strategy to feedback gains and coupling strengths. According to Lyapunov stability properties, it is shown that the agents of subgroups can converge the given synchronous states, respectively, under some conditions on the sampled period. Moreover, some simulation results are given.

Numerical Simulation by CGM for Moving Force Identification

2012 ◽  
Vol 238 ◽  
pp. 826-829
Author(s):  
Zhen Chen ◽  
Jun Ling Han
Keyword(s):  
Numerical Simulation ◽  
Time Domain ◽  
Bending Moment ◽  
Identification Accuracy ◽  
Time Varying ◽  
Acceptable Solution ◽  
Moving Force ◽  
Ill Posed ◽  
Simulation Results ◽  
The Time Domain

The conjugate gradient method (CGM) is compared with the time domain method (TDM) in the paper. The numerical simulation results show that the CGM have higher identification accuracy and robust noise immunity as well as producing an acceptable solution to ill-posed problems to some extent when they are used to identify the moving force. When the bending moment responses are used to identify the time-varying loads, the identification accuracy is more obviously improved than the TDM, which is more suitable for the time-varying loads identification.

Impact of different and time-varying land cover data sets in a regional climate model on regional and local climate over Europe

2020 ◽  
Author(s):  
Mingyue Zhang ◽  
Jürgen Helmert ◽  
Merja Tölle
Keyword(s):  
Land Cover ◽  
Regional Climate Model ◽  
Climate Model ◽  
Regional Climate ◽  
Data Sets ◽  
Time Varying ◽  
Data Set ◽  
Local Climate ◽  
Land Cover Data ◽  
Simulation Results

<p>According to IPCC, Land use and Land Cover (LC) changes have a key role to adapt and mitigate future climate change aiming to stabilize temperature rise up to 2°C. Land surface change at regional scale is associated to global climate change, such as global warming. It influences the earth’s water and energy cycles via influences on the heat, moisture and momentum transfer, and on the chemical composition of the atmosphere. These effects show variations due to different LC types, and due to their spatial and temporal resolutions.  Thus, we incorporate a new time-varying land cover data set based on ESACCI into the regional climate model COSMO-CLM(v5.0). Further, the impact on the regional and local climate is compared to the standard operational LC data of GLC2000 and GlobCover 2009. Convection-permitting simulations with the three land cover data sets are performed at 0.0275° horizontal resolution over Europe for the time period from 1992 to 2015.</p><p>Overall, the simulation results show comparable agreement to observations. However, the simulation results based on GLC2000 and GlobCover 2009 (with 23 LC types) LC data sets show a fluctuation of 0.5K in temperature and 5% of precipitation. Even though the LC is classified into the same types, the difference in LC distribution and fraction leads to variations in climate simulation results. Using all of the 37 LC types of the ESACCI-LC data set show noticeable differences in distribution of temperature and precipitation compared to the simulations with GLC2000 and GlobCover 2009. Especially in forest areas, slight differences of the plant cover type (e.g. Evergreen or Deciduous) could result in up to 10% differences (increase or decrease) in temperature and precipitation over the simulation domain. Our results demonstrate how LC changes as well as different land cover type effect regional climate. There is need for proper and time-varying land cover data sets for regional climate model studies. The approach of including ESACCI-LC data set into regional climate model simulations also improved the external data generation system.</p><p>We anticipate this research to be a starting point for involving time-varying LC data sets into regional climate models. Furthermore, it will give us a possibility to quantify the effect of time-varying LC data on regional climate accurately.</p><p><strong>Acknowledgement</strong>:</p><p>1: Computational resources were made available by the German Climate Computing Center (DKRZ) through support from the Federal Ministry of Education and Research in Germany (BMBF). We acknowledge the funding of the German Research Foundation (DFG) through grant NR. 401857120.</p><p>2: Appreciation for the support of Jürg Luterbacher and Eva Nowatzki.</p><p> </p>

Global Analysis and Simulation of Mechanical Systems with Time-Varying Topologies

1993 ◽  
Vol 115 (4) ◽  
pp. 817-821 ◽  
Author(s):  
Yu Wang
Keyword(s):  
Global Analysis ◽  
Mapping Method ◽  
Time Varying ◽  
Numerical Techniques ◽  
Time Model ◽  
Discrete Time Model ◽  
Its Regions ◽  
Regions Of Stability ◽  
Simulation Results ◽  
Backward Mapping

We apply the discrete time model of a mechanical system with time-varying topologies to determine its regions of stability. The backward mapping method is applied and the associated numerical properties are discussed. The model is combined with conventional numerical techniques to form a computer simulation system which is more reliable and accurate in handling the topological changes. The simulation results of an example system are presented.

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