Iterative Learning Control for Collaborative Tracking: Point to Point Tasks and Constraint Handling

Abstract

Collaborative tracking of networked dynamical systems where a group of subsystems work together collaboratively to track a desired reference has important applications in a range of areas. To achieve high performance tracking, the idea of iterative learning control (ILC) has recently been applied with superior tracking performance. This paper considers two previously unexplored problems in ILC design of collaborative tracking, namely, point to point tracking tasks and constraint handling, which are of great practical relevance. We propose two new algorithms to solve the above problems using the idea of gradient based ILC and a projection based method. The proposed algorithms achieve monotonic convergence in the tacking error norm and can guarantee the satisfaction of system constraints. They can be applied to both homogenous and heterogenous networked systems where the subsystems might or might not have the same dynamics. Convergence properties of the algorithms are analysed in detail and numerical examples are presented to demonstrate their effectiveness.