Sensitivity-Based Iterative State-Feedback Tuning for Nonlinear Systems

Abstract

In this paper, a new approach to tuning and optimisation of controlled systems regarding the tracking behaviour is presented. This approach can be understood as an extension to the iterative feedback tuning (IFT) approach known from the literature. Motivated by the sensitivity concept, the IFT algorithm is extended to both linear and nonlinear systems in state-space description with static state-feedback control, resulting in the proposed iterative state-feedback tuning (ISFT) method. The main contribution consists of the derivation of a closed sensitivity function for quadratic cost functions, which is typical for control optimisation problems. The proposed approach results in a gradient-based iterative algorithm for control parameter adaptation. In the end, two exemplary simulation results will be presented for a linear and a nonlinear system, demonstrating the usability of this new approach and a slight improvement w.r.t. the classical IFT.