Tracking control over a finite interval for multi-agent systems with a time-varying reference trajectory

Iterative learning control with input sharing for multi-agent consensus tracking

“…Consider the most general control law (24). Since the communication topology is iteration-varying, we adopt the following iteration-varying learning gains…”

“…As long as an appropriate learning gain is chosen, the perfect tracking can be achieved even when the system parameters are unknown. Recently, a number of ILC works for formation control and consensus tracking problems have been reported in the literature [16][17][18][19][20][21][22][23][24]. The distributed ILC laws in these works have a common structure, that is, each individual agent maintains its own learning process, and the correction term is synthesized by the local measurement (extended tracking error).…”

Bipartite coordination problems on networks of multiple mobile agents

“…Without loss of generality, we consider CB of full-row rank to meet the basic requirement of our following established learning algorithms on the system structure of agents (see also [30], [31]). …”

“…With this fact, we can develop the remainder of this proof by following the same way as used in the proof of [30,Theorem 12], which is thus not detailed here.…”

“…It has been shown that ILC can be incorporated into the algorithm design of each agent by employing only relative state or output information between it and its neighbors (see, e.g., [25]- [33]). They have contributed to developing ILC-based algorithms for coordination tasks of formation keeping in [25]- [29] and consensus tracking in [30]- [33], respectively. Moreover, ILC-based algorithms not only create a fundamentally two-dimensional structure with respect to independent time and iteration axes but also are in distributed manners with local state or output information only needed in their constructions, which are different from the classical ILC algorithms requiring the global state or output information of the controlled system as well as an available reference trajectory as prior knowledge.…”