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This paper gives an extension to the Iterative Feedback Tuning (IFT) approach that ensures the performance improvement of state feedback control systems for single input processes. IFT employs sensitivity functions and the experiments conducted on the real-world control system in order to provide an efficient way to deal with the nonlinear or ill-defined processes when the model-dependent Linear-Quadratic Regulator (LQR) is not successful. An experimental setup is suggested to implement the real-time iterative calculation of the gradients in the minimization of the LQR’s objective function. The experimental results validate the performance of the proposed IFT algorithm in a mechatronics application which deals with the angular position controller for a DC servo system with actuator dead zone and control signal saturation. The results show the reduction of the LQR’s objective function for a single input process application.

Experiment-based Performance Improvement of State Feedback Control Systems for Single Input Processes