## 1 Introduction
This work has introduced smooth functions to compensate for the effect of quantization and bounded stuck faults. In [16], the formation tracking problem was designed relying only on the agents' local coordinate systems so that the centroid of the controlled formation tracks a given trajectory. In [17], a distributed model reference adaptive control (MRAC) design framework is proposed for containment control of heterogeneous general linear multi-agent systems. The main advantage of the model reference adaptive control (MRAC) is that adaptation takes place only when the system does not follow the reference model. This is particularly the case when there are uncertainties in the system under consideration. With respect to the distributed formation tracking control of multiple UUVs under a leader-follower architecture, the reference model is considered as the leading UUV. The remaining vehicles are considered as follow vehicles. For large-scale oceanic missions in hazardous environments, collision and obstacle avoidance have attracted much attention in recent years. In [18], a stable formation control law and collision avoidance have been investigated for multiple robots with single integrator dynamics under an undirected and connected graph. Formation control and collision avoidance are also presented in [19] for double-integrator systems, which is based on position estimation. In [20], the problem of formation control with collision avoidance for networked Lagrangian systems with uncertain parameters is investigated under directed network topology. In order to make a simulation of underwater drones, an open source tool that simulates the impact of communications in underwater robotics, namely, UWSim-NET [21], is presented. This is a new extension of UWSim [22], which is developed in the robot operation system (ROS). In [23], an extension of the open-source robotics simulator Gazebo to underwater scenarios is introduced, namely, Unmanned Underwater Vehicle Simulator (UUV-Simulator). It can simulate multiple underwater robots, and simultaneously allows modeling the underwater hydrostatic and hydrodynamic effects, thrusters, sensors, and external disturbances. In [24; 25], the SWARMs middleware architecture is presented for cooperative autonomous underwater vehicles at the mission level. Motivated by the above discussions, this paper has proposed and implemented a control framework for multiple low-cost underwater drones (i.e., BlueROV-1). Compared to the previous relative studies on the control framework of UUVs for formation tracking problem [1; 2; 3; 4; 5; 6; 7; 8], the proposed control framework can allow a group of UUVs to handle simultaneously and continuously the formation tracking problem and collision-obstacle avoidance. Compared with the collision-obstacle avoidance in [18; 19], which were studied for single and double integrator systems, the proposed control framework is subject to nonlinearity, parametric uncertainties of UUVs. More specifically, this proposed control framework is a combination of four terms: the consensus control, the neural network control, the robust control, the collision-obstacle avoidance control. Firstly, the consensus term is used to set up a desired formation. Secondly, the neural network control is employed to compensate for the unknown uncertainties. Thirdly, the robust control is presented, which is aimed to achieve asymptotic convergence of the tracking error. Finally, the collision and obstacle avoidance are mentioned to ensure that a group of BlueROV-1 can operate in hazardous environments. Specially, we have also implemented the control framework that uses only relative positions between underwater drones. This is reasonable in real terms that are only equipped with low-cost sensors (i.e., an inertial measurement unit sensor and a camera) as well as limitations of underwater communication. By emphasizing the realistic simulation aspects, several scenarios with a team of three low-cost underwater drones (BlueROV-1) are presented and discussed.