针对多无人艇系统经过狭窄水域等复杂环境的情形，提出一种结合人工势场法与模型预测控制的编队控制与避障算法。首先，对传统的人工势场法进行了改进，采用饱和形式的引力势场和分区式的斥力势场，提升无人艇编队在复杂环境中的避障精度和队形保持能力。然后，结合模型预测控制的多步预测优化能力，利用势场力生成的期望轨迹，动态调整控制输入，进而实现编队的稳定控制和高效避障，避免了传统人工势场法在狭窄水域的路径振荡问题。仿真实验结果表明，改进算法在避障成功率、编队稳定性和路径规划效率等方面均优于传统方法，能够有效避免局部最优，保持队形稳定，顺利通过复杂水域，从而验证了算法的有效性。

For multi-unmanned surface vehicle (USV) systems navigating through narrow channels and other complex environments, a formation obstacle-avoidance control algorithm is presented that integrates the artificial potential field (APF) method with model predictive control (MPC). To mitigate these problems，the traditional APF approach is improved, including the use of a saturated gravitational potential field and a partitioned repulsive potential field. These modifications aim to improve the precision of obstacle avoidance and the ability to maintain formation coherence in complex environments. Furthermore, by leveraging the multi-step predictive optimization capabilities of MPC, the proposed algorithm dynamically adjusts control inputs based on desired trajectories generated from potential field forces, which ensures the stability of formation control and the effectiveness of obstacle avoidance, thereby avoiding the path oscillation issues encountered by traditional artificial potential field methods in narrow waters. Simulation results demonstrate that the proposed algorithm outperforms traditional methods in terms of obstacle-avoidance success rate, formation stability, and path planning efficiency. And the improved algorithm can avoid local minima, maintains formation integrity, and ensure smoothly passage through narrow channels, thereby validating its effectiveness.