Stochastic Point-to-Point Iterative Learning Tracking Without Prior Information on System Matrices
Iterative learning control is concerned with tracking a reference trajectory defined over a finite time duration, and is applied to systems which perform this action repeatedly. In this paper iterative learning schemes are developed to address the case in which the output is only critical at certain time instants. This freedom makes it possible to incorporate both hard and soft constraints into the control scheme. Experimental results confirm practically and performance.