In this paper, an incremental centralized formation system is developed for controlling the multirobot formation with joining robots, and a nonlinear model predictive control (NMPC) method is implemented as the controller. The incremental updating method is used to update the system's state in real time, when there is a new robot joining during the formation process. Then, an NMPC approach is developed to reformulate the formation system into a convex nonlinear minimization problem, which can be further transformed into a quadratic programming (QP) with constraints. Then, a general projection neural network (GPNN) is implemented for solving this QP problem online to get the optimal inputs. In the end, two examples of incremental multirobot formation are demonstrated to verify the effectiveness of this method.

中文翻译：

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基于广义投影神经网络的非线性模型预测控制方法增量更新多机器人编队

在本文中，开发了一种增量集中编队系统，用于控制带有加入机器人的多机器人编队，并采用非线性模型预测控制（NMPC）方法作为控制器。增量更新方法用于在编队过程中有新机器人加入时实时更新系统状态。然后，开发了一种 NMPC 方法，将编队系统重新表述为凸非线性最小化问题，该问题可以进一步转化为具有约束的二次规划 (QP)。然后，实现通用投影神经网络 (GPNN) 来在线解决此 QP 问题以获得最佳输入。最后，通过两个增量式多机器人编队实例验证了该方法的有效性。