Adaptive iterative correlation tuning for closed loop system with two parametrised controllers

In this paper, we study the problem of controller design for one closed loop system with feedforward controller and feedback controller simultaneously. After parametrised these two controllers by two unknown parameter vectors, iterative correlation tuning control is proposed to design these unknown controller parameters through one process of finding roots with respect to one correlation function. As no identification process is needed for the unknown plant in iterative correlation tuning control, the unknown controller parameters are identified by using only input–output measured data. Through applying the adaptive idea to guarantee iterative parameter estimators converge to their true values, MIT rule is regarded as a gradient scheme for the constructed correlation in the parameter adjustment mechanism. Further, Lyapunov stability is applied to derive one parameter adjustment law satisfying the stability for the whole adaptive system. From the point of adaptive analysis, some new results about the sensitivity functions are derived for three types of disturbances to consider tracking and regulation with independent object. Generally, iterative correlation tuning control can design controllers directly without any knowledge about the unknown plant and adjust the unknown controller parameters adaptively through one established parameter adjustment mechanism. Finally two simulation examples are performed to demonstrate the effectiveness of the theories.