A constraint approach for UWB and PDR fusion

2012 ◽  
Author(s):  
Francisco Zampella ◽  
Alessio De Angelis ◽  
Isaac Skog ◽  
Dave Zachariah ◽  
Antonio Jimenez
Keyword(s):  
Constraint Approach

Effect of Social Norm on Consumer Demand: Multiple Constraint Approach

2020 ◽  
Vol 22 (1) ◽  
pp. 41-60
Author(s):  
Sungjee Choi ◽  
Inwoo Nam ◽  
Jaehwan Kim
Keyword(s):  
Social Norm ◽  
Consumer Demand ◽  
Multiple Constraint ◽  
Constraint Approach

A hierarchical constraint approach for dynamic modeling and trajectory tracking control of a mobile robot

2021 ◽  
pp. 107754632199918
Author(s):  
Rongrong Yu ◽  
Shuhui Ding ◽  
Heqiang Tian ◽  
Ye-Hwa Chen
Keyword(s):  
Mobile Robot ◽  
Dynamic Modeling ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
Wheeled Mobile Robot ◽  
Structural Constraints ◽  
Control Force ◽  
Trajectory Tracking Control ◽  
Motion Constraints ◽  
Constraint Approach

The dynamic modeling and trajectory tracking control of a mobile robot is handled by a hierarchical constraint approach in this study. When the wheeled mobile robot with complex generalized coordinates has structural constraints and motion constraints, the number of constraints is large and the properties of them are different. Therefore, it is difficult to get the dynamic model and trajectory tracking control force of the wheeled mobile robot at the same time. To solve the aforementioned problem, a creative hierarchical constraint approach based on the Udwadia–Kalaba theory is proposed. In this approach, constraints are classified into two levels, structural constraints are the first level and motion constraints are the second level. In the second level constraint, arbitrary initial conditions may cause the trajectory to diverge. Thus, we propose the asymptotic convergence criterion to deal with it. Then, the analytical dynamic equation and trajectory tracking control force of the wheeled mobile robot can be obtained simultaneously. To verify the effectiveness and accuracy of this methodology, a numerical simulation of a three-wheeled mobile robot is carried out.

scholarly journals Stochastic Non-Pharmaceutical Optimal Control Strategies to Mitigate COVID-19

2021 ◽  
Author(s):  
Paolo Scarabaggio ◽  
Raffaele Carli ◽  
Graziana Cavone ◽  
Nicola Epicoco ◽  
Mariagrazia Dotoli
Keyword(s):  
Control Strategies ◽  
Optimal Strategies ◽  
Real Data ◽  
Chance Constraint ◽  
Control Actions ◽  
Regional Strategies ◽  
Italian Regions ◽  
Predictive Controller ◽  
Definition Of ◽  
Constraint Approach

This paper proposes a stochastic non-linear model predictive controller to support policy-makers in determining robust optimal strategies to tackle the COVID-19 secondary waves. First, a time-varying <i>SIRCQTHE </i>epidemiological model (considering Susceptible, Infected, Removed, Contagious, Quarantined, Threatened, Healed, and Extinct compartments of individuals) is defined to get reliable predictions on the pandemic dynamics on a regional basis. A stochastic Model Predictive Control problem is then formulated to select the necessary control actions to minimize the arising socio-economic costs. <br>In particular, considering the unavoidable uncertainty characterizing this decision-making process, we ensure that the capacity of the network of regional healthcare systems is not violated in accordance with a chance constraint approach.<br>Furthermore, since the infection rate depends on people’s mobility, differently from the related literature, we model the control actions as interventions affecting the mobility levels associated to different socio-economic categories.<br><div>The effectiveness of the presented method in properly supporting the definition of diversified regional strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data from the Italian Civil Protection Department. However, provided the availability of reliable data, the proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale.</div><div><br></div><div>Preprint of paper submitted to IEEE Transactions on Automation Science and Engineering (<em>T-ASE</em>)</div>

Sign in / Sign up

Export Citation Format

Share Document