SOCP Trajectory Optimization Documentation
The key functionality is the primal SOCP solver for Trajectory Optimization.
The SOCP trajectory optimization problem is formulated as follows.
\[\begin{aligned}
\underset{x_{1:N}, u_{1:N-1}}{\text{minimize}} \quad& \frac{1}{2} (x_N - x_{N, ref})^\top Q (x_N - x_{N, ref}) + \\
& \frac{1}{2} \Sigma_{n = 1}^{N-1} (x_n - x_{n, ref})^\top Q (x_n - x_{n, ref})
+ (u_n - u_{n, ref})^\top R (u_n - u_{n, ref})) \\
\text{subject to} \quad& x_{k + 1} = A x_k + B u_k + C \\
& H [x; u] ≤ h \\
& ||u_k|| ≤ u_{max}
\end{aligned}\]
Table of Contents:
TrajOptSOCPs.TrajOptSOCPs — ModuleTrajOptSOCPsA native Julia library to solve trajectory optimization problems that contain second-order cone constraints.
Author: Daniel Neamati (Summer 2020)
Advisor: Prof. Zachary Manchester (REx Lab at Stanford University)
Funding graciously provided by Caltech SURF program and the Homer J. Stewart Fellowship.