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Mechanical Multi-agent Maneuvering Using Noncooperative DMPC

机译：非合作式DMPC的机械多主体机动

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Distributed Model Predictive Control is used to coordinate agents in multi-agent systems by managing set-points and coupling constraints. The coordination of multi-agent systems concept regards all type of control algorithms dependent on information interchange between subsystems. The control algorithms are developed to solve a series of static optimization problems with nonlinear coupling constraints by means of a typical receding horizon policy applied in predictive control design. For distributed scenarios, the motion of each agent is determined by the distributed algorithm as function of the information shared with the other agents making the individual behavior implicitly dependent on a global outcome or cost. The control algorithm is used to maneuver dynamically decoupled mechanical agents in a two dimensional scenario with obstacles avoidance. The found solution is meaningful to realize how Predictive Control can be an alternative to other solutions obtained through Dynamic Games, where the agents play an important role, in a strategic space, as game players or Computational Intelligence technique, where the agents present a self-organized behavior. Hence, the developed algorithm is useful to maneuver unmanned vehicles in mazes, formations and also for collision avoidance.
机译：分布式模型预测控制用于通过管理设定点和耦合约束来协调多主体系统中的主体。多代理系统概念的协调涉及所有类型的控制算法，这些控制算法取决于子系统之间的信息交换。开发了控制算法，以通过在预测控制设计中应用的典型后退水平策略来解决一系列具有非线性耦合约束的静态优化问题。对于分布式方案，每个代理的运动由分布式算法确定，该函数是与其他代理共享的信息的函数，从而使单个行为隐含地依赖于全局结果或成本。该控制算法用于在有障碍物避开的二维情况下操纵机械解耦的动态代理。找到的解决方案对于实现预测控制如何替代通过动态游戏获得的其他解决方案是有意义的，在动态解决方案中，代理在战略空间中扮演重要角色，如游戏玩家或计算智能技术，在这种情况下，代理展现自我有组织的行为。因此，所开发的算法对于在迷宫，编队中操纵无人驾驶车辆以及避免碰撞都很有用。

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