Exact Penalty Functions for Optimal Control Problems II: Exact Penalisation of Terminal and Pointwise State Constraints

TL;DR

This paper analyzes the exactness of penalty functions in optimal control problems with terminal and state constraints, showing conditions under which fixed-endpoint problems can be reduced to free-endpoint problems for linear and nonlinear systems.

Contribution

It provides new theoretical results on the exact penalization of terminal and state constraints, including local and global reduction conditions for linear and nonlinear systems.

Findings

Linear systems with convex constraints can be reduced to free-endpoint problems under certain conditions.

Nonlinear systems can be locally reduced if the linearised system is controllable.

State constraints can be exactly penalized using $L^{ abla}$ penalties under Slater's condition.

Abstract

The second part of our study is devoted to an analysis of the exactness of penalty functions for optimal control problems with terminal and pointwise state constraints. We demonstrate that with the use of the exact penalty function method one can reduce fixed-endpoint problems for linear time-varying systems and linear evolution equations with convex constraints on the control inputs to completely equivalent free-endpoint optimal control problems, if the terminal state belongs to the relative interior of the reachable set. In the nonlinear case, we prove that a local reduction of fixed-endpoint and variable-endpoint problems to equivalent free-endpoint ones is possible under the assumption that the linearised system is completely controllable, and point out some general properties of nonlinear systems under which a global reduction to equivalent free-endpoint problems can be achieved. In the case of problems with pointwise state inequality constraints, we prove that such problems for linear time-varying systems and linear evolution equations with convex state constraints can be reduced to equivalent problems without state constraints, provided one uses the $L^{\infty}$ penalty term, and Slater's condition holds true, while for nonlinear systems a local reduction is possible, if a natural constraint qualification is satisfied. Finally, we show that the exact $L^p$-penalisation of state constraints with finite $p$ is possible for convex problems, if Lagrange multipliers corresponding to the state constraints belong to $L^{p'}$, where $p'$ is the conjugate exponent of $p$, and for general nonlinear problems, if the cost functional does not depend on the control inputs explicitly.