With the continuous deepening of research on consensus control problems for MASs, many scholars have found that formation control problems can be utilized to address consensus control issues. Reference [12] proposes an operator-based Newtonian trajectory optimization method to solve the problem of second-order, time-invariant optimal distributed formation tracking. Reference [13] employs adaptive optimal control parameters and neural networks to address the tracking control of optimal time-varying formation (TVF) under disturbances. Reference [14] presents a TVF control method that achieves specific formation shapes based on relative errors between agents, and has, consequently, attracted extensive attention from scholars in the field of TVF tracking control. Reference [15] investigates TVF tracking control for MASs with second-order integrator models under switching topologies and applies it to the practical application of quadrotor UAV flight. Reference [16] solves the formation control problem for a class of second-order MASs with communication delays based on a leader-follower approach. Reference [17] uses backstepping and simplified reinforcement learning methods to address a class of second-order uncertain MAS problems and employs an adaptive neural network state observer to handle unmeasurable state issues. However, both linear and nonlinear integrators have limited generality; in other words, they are applied in specific scenarios. By contrast, nonlinear systems are more common in practice, making it more valuable to design MASs that can be widely applied to nonlinear dynamic models. References [18; 19; 20; 21] all adopt leader-follower methods to study the consensus tracking control of MASs. Reference [22] proposes a class of MASs with multiple leaders to achieve TVF control. Reference [23] solves the problem of time-varying output formation control for heterogeneous MASs with multiple leaders under changing dynamic topologies by using distributed observers. Reference [24] addresses a sampled-data controlled MASs, completing TVF tracking control by designing a sampled-data controller with a larger sampling interval. However, it is evident that consensus control problems studied based on relative errors between agents and leader-follower methods are only special cases of formation control problems. In other words, most consensus control problems can be solved by formation control approaches. Additionally, References [22; 24] all consider linear system models to achieve TVF, while References [23; 25] design control laws for heterogeneous linear system models. Reference [26] proposes a consensus control method for heterogeneous nonlinear systems with arbitrary time-varying dynamics, where different agents contain distinct nonlinear terms. However, existing research on heterogeneous nonlinear systems remains limited. Furthermore, the control algorithms in References [22; 23; 24] do not achieve true distributed control, as they cannot complete formation control solely based on relative errors between adjacent agents. When facing large-scale MASs, these methods still require global topological information for processing, which is computationally challenging in practical scenarios. How to avoid global information and solve system problems using only local information remains a severe challenge. Reference [27] employs a low-gain feedback method to address adaptive TVF with input saturation, while Reference [28] uses observers to handle adaptive TVF with unilateral nonlinearities. Reference [29] considers adaptive TVF control under communication constraints. Reference [30] utilizes an adaptive feedback control method based on neural networks to solve formation control problems for a class of nonlinear MASs under directed graphs. Reference [31] solves a distributed optimal formation-tracking control problem using only relative information between agents and the cost functions of neighboring agents.