Observation of Gradients, by Means of Algorithmic Differentiation

Changing environmental influences as well as changes in internal parameters of mechatronic systems deteriorate the system performance. A good means to ensure good performance despite these impediments is self-optimizing control. Optimization algorithms often depends on gradient informations. This paper presents an observer structure to achieve these gradients at run time. Therefore we make use of a method called algorithmic differentiation