Re-examining the Legendre-Gauss-Lobatto Pseudospectral Methods for Optimal Control

TL;DR

This paper re-evaluates Legendre-Gauss-Lobatto pseudospectral methods for optimal control, introducing an augmented formulation that improves convergence and computational efficiency, especially for long-horizon problems.

Contribution

It presents an augmented LGL collocation approach, establishing its equivalence to integral formulations and demonstrating advantages over traditional methods.

Findings

Augmented LGL method improves convergence and accuracy.

Enhanced computational performance for long-horizon problems.

Theoretical validation of the method's equivalence and advantages.

Abstract

Pseudospectral methods represent an efficient approach for solving optimal control problems. While Legendre-Gauss-Lobatto (LGL) collocation points have traditionally been considered inferior to Legendre-Gauss (LG) and Legendre-Gauss-Radau (LGR) points in terms of convergence properties, this paper presents a rigorous re-examination of LGL-based methods. We introduce an augmented formulation that enhances the standard LGL collocation approach by incorporating an additional degree of freedom (DOF) into the interpolation structure. We demonstrate that this augmented formulation is mathematically equivalent to the integral formulation of the LGL collocation method. Through analytical derivation, we establish that the adjoint system in both the augmented differential and integral formulations corresponds to a Lobatto IIIB discontinuous collocation method for the costate vector, thereby resolving the previously reported convergence issues. Our comparative analysis of LG, LGR, and LGL collocation methods reveals significant advantages of the improved LGL approach in terms of discretized problem dimensionality and symplectic integration properties. Numerical examples validate our theoretical findings, demonstrating that the proposed LGL-based method achieves comparable accuracy to LG and LGR methods while offering superior computational performance for long-horizon optimal control problems due to the preservation of symplecticity.