## 1 Introduction  
Formation control [1, 2] is crucial for multi-agent systems. Previously, switching topology [3, 4], actuator faults [5], noise resistance [6], time constraints [7, 8], connectivity maintenance [9], and communication delay [10] have been widely studied. Inspired by this research, a stable and feasible controller is critical for the formation control of multi-agent systems. Although various controllers have been proposed, nonlinearity caused by maneuvering [11, 12, 13] and saturation [5, 14] is still the challenge. Besides, communication interference will lead to bad control due to the lack of reference information, which means extra strategies should be applied for stable formation [4]. Therefore, a comprehensive survey of bounded controllers considering maneuvering and communication interference is necessary. Previously, optimization-based methods and consensus algorithms with graph theory have been widely studied for controller design. As for controller design, [PERSON]. [14] proposed the inverse optimality method (IOM) with Hamilton-Jacobi-Bellman (HJB) equations, which provides a feasible bounded controller for formation control theoretically, but HJB equations are difficult to be solved for a simple control law. In [15, 16], intelligent algorithms have been used to optimize velocities using centralized computing systems. However, time consumption is a big challenge for intelligent algorithms and saturation constraints are hard to be incorporated into the objective functions. Recently, model predictive control (MPC) [17, 18] has been widely used for formation control, various requirements can be converted into inequality constraints. MPC is easy to implement programmatically, the challenge is the central computer will assume a great burden with a large number of agents. Then, distributed MPC (DMPC) has been proposed [19, 20] for low time cost. With less computation and the advantage of bounded outputs, DMPC has attracted much attention now. The problem with DMPC is that there must exist nonlinear constraints considering dynamic constraints, such as maximum velocity and angular rate, which might cause complex computation. Furthermore, communication interferences will affect MPC and DMPC due to the lack of sufficient information for objective functions and some constraints. Recently, consensus algorithms (CA) [12; 21; 22] with graph theory have been extensively researched. With the Laplacian matrix, relationships among agents can be easy to be incorporated into stability analysis with Lyapunov functions (LF), thus the formation switch and stability can be solved simultaneously. CA can apply measurement besides the interacted data to design control laws; what needs to be done is obtaining reference information with limited sources under communication interference [23]. The practicability of CA lies in linear control with linear feedback technique (LFT), with adaptive parameters [5] and sufficient information, the formation can move stably as required. Therefore, insufficient measuring data [1] might cause instability without cooperation, and strategies should be proposed for sufficient reference. To tackle this problem, rigid theory [1; 8; 11] has been introduced for limited sensors, the formation is expected to be predefined well for obtaining relative bearings or distances solely. However, communication systems and sensors might be equipped for stability in hostile environments, and strategies for obtaining expected information with limited sources can be useful in this situation. More importantly, agents encountering communication break needs to know how many sensing sources are required at least. Motivated by the above analysis, this paper focuses on the formation control with the following purposes: (1) Establishing a control law with definitely bounded outputs based on LFT technology; thus, the problem of saturation can be avoided by adjusting parameters.