Interactive and Worst-Case Optimized Robust Control for Potential Application to Guaranteeing Roll Stability for Intelligent Heavy Vehicle

2021 ◽  
Vol 22 (5) ◽  
pp. 1291-1303
Author(s):  
Yulong Liu ◽  
Xuewu Ji ◽  
Kaiming Yang ◽  
Xiangkun He ◽  
Shirou Nakano
Keyword(s):  
Robust Control ◽  
Potential Application ◽  
Heavy Vehicle ◽  
Worst Case

scholarly journals Robust Control for Renewable-Integrated Power Networks Considering Input Bound Constraints and Worst Case Uncertainty Measure

2019 ◽  
Vol 6 (3) ◽  
pp. 1210-1222 ◽  
Author(s):  
Ahmad F. Taha ◽  
Mohammadhafez Bazrafshan ◽  
Sebastian Adi Nugroho ◽  
Nikolaos Gatsis ◽  
Junjian Qi
Keyword(s):  
Robust Control ◽  
Uncertainty Measure ◽  
Power Networks ◽  
Bound Constraints ◽  
Worst Case

scholarly journals Robust collaborative object transportation using multiple MAVs

2019 ◽  
Vol 38 (9) ◽  
pp. 1020-1044 ◽  
Author(s):  
Andrea Tagliabue ◽  
Mina Kamel ◽  
Roland Siegwart ◽  
Juan Nieto
Keyword(s):  
Robust Control ◽  
Simulation Analysis ◽  
Unmodeled Dynamics ◽  
Physical Parameters ◽  
Parametric Uncertainties ◽  
Challenging Problem ◽  
Worst Case ◽  
Extensive Simulation ◽  
Object Transportation ◽  
Explicit Communication

Collaborative object transportation using multiple MAV with limited communication is a challenging problem. In this paper, we address the problem of multiple MAV mechanically coupled to a bulky object for transportation purposes without explicit communication between agents. The apparent physical properties of each agent are reshaped to achieve robustly stable transportation. Parametric uncertainties and unmodeled dynamics of each agent are quantified and techniques from robust control theory are employed to choose the physical parameters of each agent to guarantee stability. Extensive simulation analysis and experimental results show that the proposed method guarantees stability in worst-case scenarios.

scholarly journals A Stochastic Differential Game of Transboundary Pollution under Knightian Uncertainty of Stock Dynamics

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Chunyan Fu ◽  
Yongxi Yi ◽  
Susu Cheng
Keyword(s):  
Robust Control ◽  
Differential Game ◽  
Transboundary Pollution ◽  
Knightian Uncertainty ◽  
Stochastic Differential Game ◽  
Worst Case ◽  
Numerical Examples ◽  
Optimal Output ◽  
Control Framework ◽  
Pollution Accumulation

With the robust control framework of Hansen and Sargent (2001), this paper investigates a stochastic differential game of transboundary pollution between two regions under Knightian uncertainty of stock dynamics. Both regions are assumed to play a noncooperative and a cooperative game, and the worst-case pollution accumulation processes for discrete robustness parameters are characterized. Our objective is to identify both regions’ optimal output and emission levels and analyze the effects of the Knightian uncertainty of pollution stock dynamics on both regions’ optimization behavior. We illustrate the results with some numerical examples.

scholarly journals Robust Control Based Stability Analysis and Trajectory Tracking of Triple Link Robot Manipulator

2021 ◽  
Vol 54 (4) ◽  
pp. 641-647
Author(s):  
Mukul Kumar Gupta ◽  
Roushan Kumar ◽  
Varnita Verma ◽  
Abhinav Sharma
Keyword(s):  
Stability Analysis ◽  
Robust Control ◽  
Tracking Control ◽  
Robot Manipulator ◽  
Torque Control ◽  
Control Technique ◽  
Control Law ◽  
Worst Case ◽  
Control Techniques ◽  
The Stability

In this paper the stability and tracking control for robot manipulator subjected to known parameters is proposed using robust control technique. The modelling of robot manipulator is obtained using Euler- Lagrange technique. Three link manipulators have been taken for the study of robust control techniques. Lyapunov based approach is used for stability analysis of triple link robot manipulator. The Ultimate upper bound parameter (UUBP) is estimated by the worst-case uncertainties subject to bounded conditions. The proposed robust control is also compared with computer torque control to show the superiority of the proposed control law.

Finite-time optimized robust control with adaptive state estimation algorithm for autonomous heavy vehicle

2020 ◽  
Vol 139 ◽  
pp. 106616 ◽  
Author(s):  
Yulong Liu ◽  
Xuewu Ji ◽  
Kaiming Yang ◽  
Xiangkun He ◽  
Xiaoxiang Na ◽  
...  
Keyword(s):  
Robust Control ◽  
State Estimation ◽  
Finite Time ◽  
Estimation Algorithm ◽  
Heavy Vehicle

Methods to Compute Probabilistic Measures of Robustness for Structural Systems

1996 ◽  
Vol 2 (4) ◽  
pp. 447-463 ◽  
Author(s):  
R.V. Field ◽  
P.G. Voulgaris ◽  
L.A. Bergman
Keyword(s):  
Control System ◽  
Robust Control ◽  
Model Uncertainty ◽  
Structural Control ◽  
Closed Loop ◽  
Joint Probability ◽  
Joint Probability Distribution ◽  
Case Scenario ◽  
Worst Case ◽  
Small Gain

Model uncertainty, if ignored, can seriously degrade the performance of an otherwise well-designed control system. If the level of this uncertainty is extreme, the system may even be driven to instability. In the context of structural control, performance degradation and instability imply excessive vibration or even structural failure. Robust control has typically been applied to the issue of model uncertainty through worst- case analyses. These traditional methods include the use of the structured singular value (μ-analysis), as applied to the small gain condition, to provide estimates of controller robustness. However, this emphasis on the worst-case scenario has not allowed a probabilistic understanding of robust control. Because of this, an attempt to view controller robustness as a probability measure is presented. As a result, a much more intuitive insight into controller robustness can be obtained. In this context, the joint probability distribution is of dimension equal to the number of uncertain parameters, and the failure hypersurface is defined by the onset of instability of the closed-loop system in the eigenspace. A first-order reliability measure (FORM) of the system is computed and used to estimate controller robustness. It is demonstrated via an example that this FORM method can provide accurate results on the probability of failure despite the potential complexity of the closed-loop. In addition to the FORM method, a probabilistic measure of robustness is developed based on the fundamentals of μ-analysis. It is shown that the μ-analysis based method is inferior to the FORM method and can only have qualitative value when assessing control system robustness in a probabilistic framework.

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