Improved Adaptation Speed of the Robust Fixed Point Transformation-based Controller Using Virtual Response Forecast

Robust Fixed Point Transformation-based adaptive control design was introduced in 2009, in order to overcome some of the difficulties of certain Lypunov function-based adaptive control design techniques. This control method was developed for trajectory tracking applications for nonlinear second order systems. The RFPT controller, instead of refining some kind of available dynamic model, that is neiter precise nor complete, targets the deterministic behavior of the trajectory tracking error, by finding the correct control signal in an iterative manner. In each control cycle a single step of iteration is made. In this paper a variation of the RFPT control method is introduced, where concurrent adaptive control and model identification is applied, based on Recursive Least Square Algorithm and a Hammerstein model. In the proposed control scheme the estimated control response is used to apply multiple iterative steps, that way increasing the adaptation speed of the controller. The proposed solution was tested on experimental basis in a DC motor control application. The increased adaptation speed of the proposed method resulted in more precise trajectory tracking.