###### Abstract
Cooperative system frameworks are used in many areas such as increasing performance of a sensory network in localization of data [12] or calculating the communication topology between dynamic agents within the cooperative system [13]. It is also used in designing controllers for agents within a network [14]. All the applications have one thing in common, which is the distributed modeling of the cooperative system and the distributed calculation of the variables to reach the desired objective. Autonomous aerial systems have become essential in numerous practical fields, such as in public safety, as a surveillance tool and first response [15; 16; 17]. It has also been studied for the delivery of goods by the industry [18; 19]. A critical task during the operation of an aerial system is docking maneuver, where it might be approaching a stationary or a moving platform [20; 21] to drop/load cargo. In addition, aerial vehicles require refueling or recharging to extend their workspace [22]. Docking is an intricate maneuver that necessitates the awareness of the docking path's constraints regarding flight safety and the docked platform's attitude [23]. In addition, nonlinear effects such as the wake of the leading agent due to the proximity flight must be considered [24] as well as the ground effect [25]. Thus it is clear that the characteristic of this maneuver requires the agent to handle certain constraints and uncertainties. A popular control method to handle previously described constraints and calculate control actions is model-predictive control (MPC). An MPC reaches the desired control action by minimizing a given objective using linear and non-linear optimization theory, as applied in the case of a tracking controller [26]. MPC also allows the integration of secondary tasks into decision-making, as in [27], which makes it a unified strategy to handle docking maneuvers without requiring ad-hoc integration of multiple frameworks that increase complexity. Implementing MPC-based docking strategies for space applications exists in case studies, where line of sight constraints are satisfied, energy-saving strategies are pursued, and docking on tumbling objects are executed [28; 29; 30]. Autonomous docking for aerial vehicles requires state information of the docked platform, which receives the docking agent. Therefore it certainly requires an on-board or external mechanism to sense and estimate states. On-board sensors such as cameras or LiDAR are widely implemented solutions (see [31]), yet they are bounded with range limitations. External mechanisms to obtain state information about the docked platform are sensors placed on the agents, except for the docking agent or external observers. As a result, this information is shared over a communication grid as illustrated in [32]. Besides state information, some of the autonomous docking literature can be grouped in terms of cooperation at the controller level. In one of the works, a central controller calculates lateral and longitudinal velocity commands for both of the docking maneuver devices: an uncrewed ground vehicle (UGV) and an uncrewed aerial vehicle (UAV). Then, these control signals are fed into these agents [33]. In the extension of this work, an MPC strategy is utilized on the same system, where both agents are cooperatively trying to execute docking maneuvers [34]. In another work, a multirotor docks onto a fixed-wing platform, where only the multirotor executes the docking maneuver [21]. The aerial refueling problem is addressed as a docking problem in [22]. However, only the docking platform and the boom are manipulated. Recent work studies an uncrewed sea surface vehicle (USV) landing and designs an MPC controller for a multirotor, where cooperative docking is executed [35]. However, the docking trajectory calculation is calculated on the multirotor and shared back to the USV.