Convexifying State-Constrained Optimal Control Problem

This article presents a method that convexifies state-constrained optimal control problems in the control-input space. The proposed method enables convex programming methods to find the globally optimal solution even if costs and control constraints are nonconvex in control and convex in state, dynamics is nonaffine in control and convex in state, and state constraints are convex in state. Under the above conditions, generic methods do not guarantee to find optimal solutions, but the proposed method does. The proposed approach is demonstrated in a 16-D navigation example.

Automated summary

This article presents a method to convexify state-constrained optimal control problems in the control-input space. The proposed method allows convex programming methods to find the globally optimal solution even when costs and control constraints are nonconvex in control and convex in state. The method is demonstrated in a navigation example with 16 dimensions.