Abstract
This paper deals with the consensus tracking problem of heterogeneous linear multiagent systems under the repeatable operation environment, and adopts a proportional differential (PD)-type iterative learning control (ILC) algorithm based on the fractional-power tracking error. According to graph theory and operator theory, convergence condition is obtained for the systems under the interconnection topology that contains a spanning tree rooted at the reference trajectory named as the leader. Our algorithm based on fractional-power tracking error achieves a faster convergence rate than the usual PD-type ILC algorithm based on the integer-order tracking error. Simulation examples illustrate the correctness of our proposed algorithm.
Highlights
In the past decades, the cooperative control problem of multiagent systems [1,2] has been extensively studied due to its wide applications in many engineering fields, e.g., multirobot cooperative control, formation control of unmanned aerial vehicles, traffic control, smart grid, and so on
Yang et al [15] proposed an Iterative learning control (ILC) algorithm to solve the consensus tracking problems of homogeneous and heterogeneous multiagent systems, respectively, and the output consensus conditions have been obtained based on the concept of graph-dependent matrix norm
This paper focuses on the output consensus tracking problem of heterogeneous linear multiagent systems, in which the following agents are required to track the output trajectory of the leader
Summary
The cooperative control problem of multiagent systems [1,2] has been extensively studied due to its wide applications in many engineering fields, e.g., multirobot cooperative control, formation control of unmanned aerial vehicles, traffic control, smart grid, and so on. Algorithms 2019, 12, 185 real engineering applications subjected to various restrictions or to reach the goals with lowest costs, the dynamics of cooperating agents are required to be distinct, e.g., coordination control of unmanned aerial vehicles and unmanned ground vehicles, so the study on heterogeneous multiagent systems is more practical and meaningful. Yang et al [15] proposed an ILC algorithm to solve the consensus tracking problems of homogeneous and heterogeneous multiagent systems, respectively, and the output consensus conditions have been obtained based on the concept of graph-dependent matrix norm. For the consensus tracking problem of multiagent systems, Yang et al proposed an ILC algorithm with input sharing, i.e., each agent exchanged its input information to the neighbors, and obtained a faster convergence rate [22]. Sufficient consensus condition, which depends on the control parameters, is obtained based on the operator theory
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