Data-Driven Robust Control Systems for Sustainability

随着我们进入无处不在的人工智能和自动化时代，基于机器学习的自适应鲁棒控制和优化的发展可以极大地提高工程系统的可持续性。其目标是提高系统自动化水平，实现自主运行，同时保持大规模的安全运行，并为子孙后代保护环境。虽然人工智能，更准确地说，机器学习，最近在图像识别和自然语言处理等许多领域实现了令人难以置信的性能提升，但其固有的自适应黑匣子体系结构使其难以用于安全关键的工程应用，如飞机飞行控制或自动驾驶车辆，因为它的决策的可解释性，以及它与闭环系统动态的不可预测的相互作用效果。因此，这种基于机器学习的安全关键系统的认证目前在例如航空航天应用中即使不是完全不可能的话也是非常困难的。然而，机器学习提供了一条通向所有人都在准备的自主未来的道路。因此，拟议的工程研究计划的主要目标是开发创新的、数据驱动的、可证明稳健的和可持续的自主控制系统。应用范围包括电动汽车(EV)、电动自动驾驶汽车(EAV)、可再生能源管理系统(EMS)、建筑供暖、通风和空调(HVAC)系统以及电气化工业流程。经典的控制和优化技术在提高生产机械、工业机器人、地面车辆、飞机和工业过程等单个系统的效率和性能方面非常成功。然而，这样的控制系统可能不能很好地适应剧烈变化的条件和快速变化的动态。例如，在当前自动客运和运输车辆快速发展的背景下，这种自动驾驶车辆将在不断变化的条件下运行，远远超过基于稳健、自适应和模型预测控制方法的常规控制系统的能力，以使车辆始终处于稳定、安全的运行区域。另一方面，无处不在的互联自主网络物理系统(如AV)提供了来自操作对等系统的海量新鲜数据，这些数据可用于改善本地系统的行为，并使其能够响应网络更高效率目标的需求，例如机器人出租车车队。机器学习技术已被证明适合于基于实时数据捕获快速变化的动态环境的近似模型，例如在自动驾驶中。因此，我们建议研究机器学习和反馈控制技术的融合，以获得两个世界的最佳结果，即适应不断变化的条件的具有良好鲁棒性的高性能控制系统。

As we are entering the era of ubiquitous artificial intelligence and automation, the sustainability of engineered systems can greatly benefit from the development of machine-learning-based adaptive robust control and optimization. The goal is to increase system automation levels towards autonomous operation while maintaining safe operation at scale and preserving the environment for future generations. While AI, or more precisely machine learning, has recently enabled incredible performance enhancement in many fields such as image recognition and natural language processing, its inherent adaptive black-box architecture has made it difficult to use in safety-critical engineering applications such as aircraft flight control or autonomous vehicles with respect to the explainability of its decision-making and its unpredictable interaction effects with the system dynamics in closed loop. Thus, the certification of such machine-learning-based safety-critical systems is currently very difficult if not outright impossible in, e.g., aerospace applications. Yet, machine learning offers a path to the autonomous future that all are preparing for. The main goal of the proposed engineering research program is thus to develop innovative, data-driven, provably robust and sustainable autonomous control systems. The applications range from electric vehicles (EV), electric autonomous vehicles (EAV), renewable energy management systems (EMS), building heating, ventilation and air conditioning (HVAC) systems, and electrified industrial processes. Classical control and optimization techniques have been very successful at increasing the efficiency and performance of individual systems such as production machines, industrial robots, ground vehicles, aircraft and industrial processes. However, such control systems may not adapt well to wildly variable conditions and rapidly changing dynamics. For instance, in the current context of fast development of automated passenger and transport vehicles, the changing conditions under which such autonomous vehicles will be operating far surpasses the ability of regular control systems based on robust, adaptive and model predictive control methods to keep the vehicle in a stable, safe zone of operation at all times. On the other hand, the ubiquity of connected autonomous cyberphysical systems such as AV offers an enormous amount of fresh data from operating peer systems that can be used to improve the behavior of the local system and allow it to respond to the demand of higher efficiency objectives of the network, such as a fleet of robo-taxis. Machine learning techniques have proven apt at capturing approximate models of fast changing dynamical environments based on real-time data, e.g., in autonomous driving. Thus, we propose to investigate the merging of machine learning and feedback control techniques to get the best of both worlds, that is, provenly-robust high-performance control systems that adapt to changing conditions.