Iterative learning control for tracking varying-amplitude and morphologically similar targets. (March 2022) - Chen, Chunjun
To break through the limitation of conventional iterative learning control algorithm (ILC) that requires a fixed target, a new ILC algorithm is designed for tracking the varying-amplitude but morphologically similar targets. First, the problem is formulated, in which the tracking target is selected as varying-amplitude and biased-measured. Then, the process of the ILC algorithm based on a variable-gain-proportional-integral scheme is discussed, in which the magnitude coefficient is defined and calculated by orthogonal projection and utilised to redefine the error and build the refreshment algorithm of the input. Next, the convergence of the algorithm is analysed and the applicational simulation is conducted. Results show that the new ILC algorithm has the ability in dealing with the trajectory of the varying but morphologically similar target and the applicational scenario of the ILC algorithm is expanded.