## 1 Introduction  
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. As a fundamental problem of distributed cooperative control, consensus problem requires all agents to reach an agreement on the states of interest, and various consensus problems have been thoroughly studied, such as leader-following consensus [3], group consensus [4], finite-time consensus [5], and so on. Iterative learning control (ILC), a classic learning control strategy, is designed to improve tracking performance by applying information obtained from the past control experiment [6; 7; 8]. The current input signal is usually generated by the previous input signal plus the tracking error (either the derivative of tracking error or the integral of the tracking error). ILC is used to accomplish control tasks that repeat on finite-time intervals, and it does not require an accurate system model. Due to this attractive feature of ILC, it has been widely used in practical systems and engineering practice, for example, industrial robots that perform repetitive tasks such as welding and handling [9], servo systems whose command signals are periodic functions [10], etc. Many industry issues require repeated execution and coordination among several subsystems, for example, cooperative control of several robotic arms on an industrial production line. Recently, the consensus tracking problem of multiagent systems by ILC strategy has attracted the attention of researchers, since many industry issues can be solved under the ILC algorithm [11; 12; 13; 14]. Unfortunately, existing results on consensus problems by ILC algorithm are mostly given for homogeneous multiagent systems, of which all the agents have the same dynamics. In some 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. [PERSON] et al. (2015) 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. [PERSON] (2016) considered a heterogeneous multiagent system composed of first- and second-order dynamics, and the leader was assumed to have second-order dynamics. Different protocols have been designed for the heterogeneous following agents, so that all the following agents tracked the state of the leader asymptotically ([PERSON], 2016). As an important evaluation index of the ILC algorithm, the convergence rate refers to the speed at which the multiagent systems approach the reference trajectory, and has stimulated research interest for a long time. In order to accelerate the convergence rate of the ILC algorithm, taking the proportional differential (PD)-type learning law as an example, an acceleration correction algorithm with variable gain and adjustment of learning interval was designed for the linear time-invariant system ([PERSON] et al., 2017). [PERSON] et al. proposed an interpolating algorithm to regulate the reference trajectory in order to achieve faster convergence rate ([PERSON] et al., 2017). The convergence rate of closed-loop ILC algorithm is obviously faster than that of open-loop ILC algorithm, due to real-time performance of closed-loop ILC algorithm ([PERSON] et al., 2017). The convergence rate was greatly improved by introducing adaptive gains into the ILC algorithm ([PERSON] et al., 2018). [PERSON] et al. used the terminal converging strategy for ILC algorithm, and reached the finite-time convergence ([PERSON] et al., 2018).