In this paper, the fuzzy adaptive finite-time cooperative control with input saturation (FAFTCCIS) is designed to quickly accomplish the cooperation of nonlinear multiagent systems (MASs) without the risk of bumping among agents. At least one agent must communicate with the leader and the information of neighborhood agents is required to accomplish the assigned task. Each agent, including the leader and the followers, is first approximated by N fuzzy-based linear subsystems. To accomplish the null cooperation error in finite time, the proposed adaptive control possesses the switching surface with fraction order, a time-varying switching gain, and an on-line learning of the upper bound of the uncertainties in each fuzzy subsystem of agent j. The stability of all the cooperative uncertain systems is then verified by the Lyapunov stability theory. Finally, the application to the cooperative control of intelligent chef is presented to confirm the effectiveness, robustness and feasibility of the proposed FAFTCCIS. INDEX TERMS Adaptive law, Cooperative tracking control, Finite-time control, Fuzzy model, Intelligent chef, Lyapunov stability theory, Switching surface with fraction order, Time-varying switching gain. I. INTRODUCTION Recently, many consensus, containment or cooperative controls of multiagent systems (MASs) are published [1]-[33]. These typical studies are briefly discussed in the next three paragraphs. Afterwards, their features are summarized to lead to the motivations, objectives, and contributions of this paper. Based on local information that is measured or received from its neighbors and itself, a distributed consensus controller for each follower agent is designed by the fuzzy approximations of unknown nonlinear functions and one adaptive parameter to decay the effect of external disturbances [1]. Under a weighted undirected topology, the robust fixed-time consensus control for nonlinear MASs with uncertainties is investigated [2]. In [3], a multiagent collision avoidance problem is formulated by differential game to steer each agent from its initial position to a desired goal while avoiding collisions with obstacles and other agents. Despite unknown nonaffine dynamics and mismatched uncertainties, a distributed neural adaptive control for containment achieves the dynamic containment in finite time with sufficient accuracy [4]. In [5], a distributed adaptive containment control for nonlinear MASs with input quantization is developed by employing a matrix factorization and normalization technique. In [6], the output consensus problem of the heterogeneous stochastic nonlinear MASs with directed communication topologies is developed by the fuzzy approximation of the unknown nonlinear functions of agents. Under the directed communication topology, the distributed adaptive output feedback consensus problem for linear MASs with the matched nonlinear functions and actuator bias faults is designed [7]. In [8], a joint objective of the distributed formation tracking control and learning/identification of the