Accelerating the Adoption and Benefits of Model-based Control in PEMD Applications

控制算法是操作许多系统的软件的核心，无论是洗衣机还是电动汽车。大多数应用程序都有相对基本的控制，不会从任何复杂的东西中获得太多好处。然而，对于更复杂、多方面的系统，如电动汽车，更先进的控制算法可以提供显着的好处，例如通过对系统进行优化的控制动作，可以提供更大的范围或更长的寿命组件。例如，电动汽车电池的寿命在很大程度上受到驾驶过程中充电或放电速度的影响，而一种复杂的控制算法可以主动减少这些循环，最终延长电池寿命。同样，当汽车在车流、交叉路口或起伏的道路上行驶时，一种算法可以最大限度地减少刹车和加速，从而实现电动汽车续航里程的最大化。在许多这样的情况下，可以通过加强控制来改进操作的某些方面。最优控制算法根据某些已定义的性能指标提供“最佳”控制动作，它使用内部数学模型来表示系统行为和优化例程。由于依赖于内部模型，这些通常被称为基于模型的控制器。这种先进的控制策略已广泛应用于炼油厂，在那里，系统的缓慢性质使复杂的算法有足够的时间进行计算。这种基于模型的控制现在在汽车和其他独立机器上强大的微处理器上运行的软件中是可行的。然而，这种控制算法的设计和部署确实需要很强的工程能力，因此，除了拥有大型研发团队的大型公司外，其他公司采用这种算法存在相当大的障碍。这项工作的目的是开发以应用为重点的培训材料，消除英国PEMD公司采用这些障碍，使他们能够理解和部署复杂的基于模型的控制方法，并为他们的产品提供竞争优势。

Control algorithms are at the core of the software that operates many systems, whether it is a washing machine or an EV car. Most applications have relative basic control and will not have much benefit from anything sophisticated. However, for more complex, multi-faceted systems, like an EV, a more advanced control algorithm can offer significant benefits, such as greater range or longer life components simply by having control actions that are optimised to the system. For example, the life of an EV battery is greatly influenced by how rapidly it is charged or discharged during driving, and a sophisticated control algorithm can actively minimise those cycles and ultimately extend battery life. Equally EV range can be maximised by an algorithm that minimises braking and accelerating as a car travels through traffic, junctions or undulating roads. There are many such situations where some aspect of operation can be improved by enhanced control.An optimal control algorithm provides the "best" control action based on some defined performance metrics, and it does this using an internal math model to represent the system behaviour and an optimisation routine. These are often referred to as model-based controllers due to the reliance on the internal model.Such advanced control strategies have been used widely in oil-refineries where the slow nature of the system allowed the complex algorithms plenty of time for their calculations. Such model-based control is now feasible within the software running on ever powerful microprocessors found in cars and other standalone machines. However, such control algorithms do require a significant engineering capability to design and deploy, and so there is a sizable barrier to adoption for all but the biggest of companies with large R&D teams.The aim of this work is to develop application focussed training materials that remove these barriers to adoption for UK PEMD companies to allow them to understand and deploy sophisticated model-based control methods and provide a competitive edge for their products.