Mesh refinement strategy for optimal control problems

2013 ◽  
Author(s):  
L. T. Paiva ◽  
F. A. C. C. Fontes
Keyword(s):  
Optimal Control ◽  
Optimal Control Problems ◽  
Mesh Refinement ◽  
Control Problems ◽  
Refinement Strategy

A rapid-based improvement on some mesh refinement strategies in solving optimal control problems

2019 ◽  
Vol 37 (2) ◽  
pp. 395-421
Author(s):  
Maedeh Souzban ◽  
Omid Solaymani Fard ◽  
Akbar H Borzabadi
Keyword(s):  
Optimal Control ◽  
Optimal Control Problems ◽  
Mesh Refinement ◽  
Computational Cost ◽  
The State ◽  
Computational Time ◽  
Control Problems ◽  
Arc Length ◽  
State Approximation ◽  
Refinement Strategy

Abstract Recently, a mesh refinement strategy is presented on pseudospectral methods for solving optimal control problems by using the relative curvature of the state approximation to choose the type of discretization change in each iteration. Nevertheless, this criterion requires a large amount of computational cost in terms of CPU time. The main goal of this paper is to draw attention to select a suitable criterion with fewer computational cost. To this end, we use the arc length of the state approximation in the mesh interval based on the relative error estimate that was recently provided. We also update the number of mesh intervals and the location of mesh points according to the behaviour of the arc length. Indeed, by implementing this criterion, we do not need to solve an optimization problem anymore, and so significantly reduce the computational time as well as CPU times. Finally, we illustrate the accuracy, efficiency and ability of the arc length criterion in comparison with the curvature by offering some numerical examples.

Warm Start Method for Solving Chance Constrained Optimal Control Problems Using Biased Kernel Density Estimators

2021 ◽  
Author(s):  
Rachel Keil ◽  
Mrinal Kumar ◽  
Anil V. Rao
Keyword(s):  
Optimal Control ◽  
Optimal Control Problems ◽  
Mesh Refinement ◽  
Kernel Density ◽  
Control Problems ◽  
Density Estimator ◽  
Constrained Optimal Control ◽  
Kernel Density Estimators ◽  
Warm Start ◽  
Density Estimators

Abstract A warm start method is developed for efficiently solving complex chance constrained optimal control problems. The warm start method addresses the computational challenges of solving chance constrained optimal control problems using biased kernel density estimators and Legendre-Gauss-Radau collocation with an $hp$ adaptive mesh refinement method. To address the computational challenges, the warm start method improves both the starting point for the chance constrained optimal control problem, as well as the efficiency of cycling through mesh refinement iterations. The improvement is accomplished by tuning a parameter of the kernel density estimator, as well as implementing a kernel switch as part of the solution process. Additionally, the number of samples for the biased kernel density estimator is set to incrementally increase through a series of mesh refinement iterations. Thus, the warm start method is a combination of tuning a parameter, a kernel switch, and an incremental increase in sample size. This warm start method is successfully applied to solve two challenging chance constrained optimal control problems in a computationally efficient manner using biased kernel density estimators and Legendre-Gauss-Radau collocation.

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