A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation

2020 ◽  
Author(s):  
Bo Pang ◽  
Zhong-Ping Jiang
Keyword(s):  
Infinite Horizon ◽  
Data Driven ◽  
Linear Quadratic ◽  
Linear Quadratic Regulation ◽  
Data Driven Approach

scholarly journals Data-driven Linear Quadratic Regulation via Semidefinite Programming

2020 ◽  
Vol 53 (2) ◽  
pp. 3995-4000
Author(s):  
Monica Rotulo ◽  
Claudio De Persis ◽  
Pietro Tesi
Keyword(s):  
Semidefinite Programming ◽  
Data Driven ◽  
Linear Quadratic ◽  
Linear Quadratic Regulation

Fast Algorithm for Estimating Control Horizon of Input Constrained Linear Quadratic Regulation

2012 ◽  
Vol 546-547 ◽  
pp. 1056-1062
Author(s):  
Wei Wei Zhang ◽  
Yong Sun
Keyword(s):  
Linear Programming ◽  
Upper Bound ◽  
Fast Algorithm ◽  
Linear Programming Problem ◽  
Infinite Horizon ◽  
Finite Horizon ◽  
Linear Quadratic ◽  
Lqr Problem ◽  
Linear Quadratic Regulation ◽  
On Line

A fast algorithm for estimating the control horizon of the input constrained linear quadratic regulation (LQR) problem is presented. It is known that there exists a finite horizon such that the infinite horizon constrained LQR problem can be solved as a finite horizon constrained LQR problem. An efficient algorithm to estimate the upper bound of the horizon is presented based on the linear programming. It only needs to solve a linear programming problem for on line application. Finally, the comparison among some methods is shown by an example. The proposed algorithm has less conservative than those of other algorithms.

A Data-Driven Approach for UAV Tracking Control

2017 ◽  
Author(s):  
Soumya Vasisht ◽  
Mehran Mesbahi
Keyword(s):  
Linear Models ◽  
Measurement Data ◽  
Numerical Data ◽  
Linear Quadratic Regulator ◽  
Data Driven ◽  
Target System ◽  
Linear Quadratic ◽  
Visual Sensing ◽  
Data Driven Approach ◽  
Improved Performance

This paper presents a simple data-driven approach to improve ground target tracking by an unmanned aerial vehicle (UAV) for certain classes of target trajectories from learned local linear models. The UAV is assumed to be a small fixed-wing aircraft equipped with a gimbaled camera for visual sensing. We attempt to build a controller from measurement data by building an augmented Linear Quadratic Regulator (LQR) system from an approximated linear operator that indirectly captures the properties of the target system. We evaluate the relative performance improvement gained by this data-driven approach versus the standard target following LQR system and provide bounds for this improved performance. We also consider the effect of sensor noise on the tracking performance resulting from the noisy and erroneous datasets. We demonstrate the performance of this method on a range of numerical data representing different classes of target trajectories.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

2012 ◽  
Author(s):  
Michael Ghil ◽  
Mickael D. Chekroun ◽  
Dmitri Kondrashov ◽  
Michael K. Tippett ◽  
Andrew Robertson ◽  
...  
Keyword(s):  
Dynamic Models ◽  
Data Driven ◽  
Stochastic Dynamic ◽  
Extended Range ◽  
Data Driven Approach ◽  
Low Dimensional

A Mostly Data-Driven Approach to Inverse Text Normalization

2017 ◽  
Author(s):  
Ernest Pusateri ◽  
Bharat Ram Ambati ◽  
Elizabeth Brooks ◽  
Ondrej Platek ◽  
Donald McAllaster ◽  
...  
Keyword(s):  
Data Driven ◽  
Data Driven Approach ◽  
Text Normalization
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