Model Predictive Control of Mobile Robots: Beyond Set Point Stabilization

该项目为未来科学家之间的科学互动提供了一个舞台，他们致力于模型预测控制(MPC)的当前趋势：互联系统的分布式预测控制与网络物理系统的自动控制有关。不同的方法主要是根据合作的强度(合作水平)来区分的。自我调整的MPC显示出额外的灵活性，从而扩大了潜在的应用范围。对互连的、空间分布的或不同耦合的系统的研究和分析开辟了新的视角，例如，在多智能体系统(移动机器人)、从工业4.0的观点来看在物流中是必不可少的，或者在可再生能源系统的控制中是必不可少的。在这种范式转变中，经济货币政策委员会发挥着重要作用。在这里，阶段成本直接取自各自的应用程序。因此，设定点或周期轨道是优化的结果，而不是先验的作为优化目标，因此，一个关键的目标是建立一个网络来解决既科学合理又适用于工业相关控制问题的联合创新研究项目。

The proposed project serves as a stage for scientific interaction between up-and-coming scientists, who are devoted to current trends in model predictive control (MPC):Distributed MPC of interconnected systems is concerned with automatic control of cyber physical systems. The different approaches are mainly distinguished with respect to the intensity of collaboration (level of cooperation).Self-tuning MPC exhibits additional flexibility and, thus, enlarges the range of potential applications. This allows for fault-tolerant control and the adaptation of the algorithm during its runtime to realize the control task.The study and analysis of interconnected, spatially distributed or differently coupled systems opens up new perspectives, which are, e.g., essential in multi-agent systems (mobile robots), in logistics from the viewpoint of Industrie 4.0 or within the control of renewable energy systems. Within this paradigm shift economic MPC plays a major role. Here, the stage costs are directly taken from the respective application. Hence, set points or periodic orbits are the outcome of the optimization and not set a priori as optimization objective.Thereby, a key objective is to build up a network to tackle joint, innovative research projects, which are both scientifical sound and applicable to industrially relevant control problems.