A Novel Penalty Approach for Nonlinear Dynamic Optimization Problems With Inequality Path Constraints

Xinggao Liu, Yunqing Hu, Jianghua Feng, Kean Liu

Abstract: The control vector parameterization (CVP) approach is popular to solve nonlinear dynamic optimization problems, but handling inequality path constraints within its framework is a challenge. This study deals with inequality path constraints by using the $l_{1}$ penalty function and a novel smoothing technique. After clarifying some theories of the proposed approach, a concomitant algorithm is also put forward and verified.