## 1 Introduction  
Nowadays, many robotic applications are employed beyond the working capacity of a single robotic manipulator. Some applications [1] concerning manufacturing and automotive applications, for example, assembly, packaging, painting, and welding, usually require high flexibility and maneuverability. A single robot cannot manage these tasks well because of limitations of time and space in the single robot. A multi-robotic system gives the potential ability to handle them well. Some other motivations [2] for developing multi-robot systems are the following: (1) distributed properties; (2) more time/energy efficiency; (3) fewer system requirements; (4) strong robustness and adaptivity; and (5) high scalability and flexibility. The challenges in multi-robot systems are communication, synchronization, and tracking problems. The synchronization problem in multi-robot manipulators is a significant challenge that has attracted many researchers worldwide [3]. It ensures that multiple robots follow the same motion sequence simultaneously. In practice, both synchronous and tracking objectives are required in control design. In 2002, a synchronized controller [4] based on a cross-coupling approach and adaptive strategy was developed for a two-manipulator system. The multiple manipulators obtain both tracking and synchronous objectives by driving the cross-coupling error to zero. The effectiveness of the proposed method was verified by both simulation and experiments. In 2010, a sliding mode control [5] was designed for a dual-arm robotic system. The objectives of this study were safe load handling, transportation, and trajectory realization. In 2011, a fuzzy sliding mode control [6] was proposed for the cooperative motion of a dual-arm robot against parameter variations and external disturbances in load handling and transport applications. In 2014, a finite-time synchronous control [7] was developed for multiple manipulators under the presence of sensor saturation. The effectiveness of this approach was demonstrated by theory and simulation results. In 2014, nonlinear dynamics and synchronization controllers [8] were designed for a manipulator system against joint frictions and parameter variations. The four synchronous controllers are neuron synchronization controllers, an improved OPCL synchronization controller, an MRAC-PD synchronization controller, and an improved adaptive synchronization control. In 2015, a decentralized robust fuzzy adaptive control [9] was constructed for grasping an object of a dual-arm robot with unknown dead-zone input. The proposed control makes the motion and internal force track a reference trajectory against the parametric uncertainties and external disturbances. In 2015, an adaptive synchronous PI-type sliding mode control [10] was designed for two cooperative robotic manipulators to handle a lightweight beam application against uncertainties and external disturbances. The proposed control forced both tracking and synchronization errors to reach zero simultaneously. In 2016, an adaptive synchronized tracking control [11] was developed for multiple robotic manipulators under the presence of uncertain kinematics and dynamics. The tracking and coordinating objectives in the multiple robotic manipulator systems are guaranteed. In 2017, an adaptive neural network sliding mode control [12] was applied for a dual-arm manipulator (DAM) against external disturbance and parametric uncertainties. The radial basis function network is constructed to estimate the unknown dynamic model in the DAM. In 2019, an adaptive cooperative image-based approach [13] was constructed for coordinating manipulators with time-varying actuator constraints and uncertain dynamics. The challenges in this study are kinematic and dynamic uncertainties, uncalibrated camera models, and actuator constraints. A Nussbaum-type gain was conducted to manage the time-varying actuator constraints.