Digital Twin of a Tribology Test Bench: The Adjoint Gradient Computation for Parameter Identification

In this article, a modified gradient approach, based on the adjoint method, is introduced, to deal with optimal control problems involving inequality constraints. A common way to incorporate inequality constraints in the adjoint approach is to introduce additional penalty terms in the cost functional. However, this may distort the optimal control due to weighting factors required for these terms and raise serious concerns about the magnitude of the weighting factors. The method in this article avoids penalty functions and can be used for the iterative computation of optimal controls. In order to demonstrate the key idea, first, a static optimization problem in the Euclidean space is considered. Second, the presented approach is applied to the tumor anti-angiogenesis optimal control problem in medicine, which addresses an innovative cancer treatment approach that aims to inhibit the formation of the tumor blood supply. The tumor anti-angiogenesis optimal control problem with free final time involves inequality and final constraints for control and state variables and is solved by a modified adjoint gradient method introducing slack variables. It has to be emphasized that the novel formulation and the special use of slack variables in this article delivers high accurate solutions without distorting the optimal control.     «

In this article, a modified gradient approach, based on the adjoint method, is introduced, to deal with optimal control problems involving inequality constraints. A common way to incorporate inequality constraints in the adjoint approach is to introduce additional penalty terms in the cost functional. However, this may distort the optimal control due to weighting factors required for these terms and raise serious concerns about the magnitude of the weighting factors. The method in this article a...     »