Affiliation:
1. Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611
2. Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus, OH 43210
Abstract
Abstract
A warm start method is developed for efficiently solving complex chance constrained optimal control problems using biased kernel density estimators and Legendre–Gauss–Radau collocation. 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.
Funder
Division of Civil, Mechanical and Manufacturing Innovation
Division of Mathematical Sciences
Subject
Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering