Neural Dynamic Programming for Automotive Engine Control

神经动态规划（NDP）是一种提供动态规划近似解的方案，其中神经网络被用作函数逼近的工具。这种方案适用于最小化/最大化成本的问题，但由于成本函数通常是未知的，传统的动态规划方法是不可行的。在本项目中，将研究和实现NDP在汽车发动机控制中的应用。汽车工业面临的一个具有挑战性的问题是设计出能够满足联邦政府未来排放规定的汽车。这里的挑战是最小化排放，同时实现更好的燃油经济性和车辆驾驶性能。在过去的几年里，汽车行业一直致力于开发发动机控制算法，以产生符合政府排放标准的排放。汽车排放的废气是美国空气污染的主要来源之一。从理论上讲，通过控制发动机的燃烧过程，使空气和燃料按一定的比例混合，可以将排放控制到尽可能低的水平。众所周知，这个控制问题是很难解决的。这部分是由于现代汽车发动机的复杂性和由于燃料燃烧过程的复杂性。除了实现更低的排放，汽车行业也在努力设计具有更好的驾驶性能和更少的燃料消耗的汽车。ndp的实施可以通过添加一个1-10美元的芯片来完成，该芯片是在之前的NSF SBIR资助下开发的，通过额外的培训，也可以降低燃料灵活性的成本，这是一项迫切的战略需求。

Neural dynamic programming (NDP) is a scheme that provides approximate solutions to dynamic programming in which neural networks are used as a tool for function approximation. Such a scheme is applicable to problems that minimize/maximize a cost but for which a traditional dynamic programming approach is not feasible since the cost function is usually not known. In the present project, applications of NDP to automotive engine control will be studied and implemented. A challenging problem facing the automotive industry is to design vehicles that generate emissions satisfying the federal government's future emission regulations. The challenge here is to minimize the emission and at the same time to achieve better fuel economy and vehicle driveability. In the past few years, the automotive industry has engaged in efforts to develop engine control algorithms that will generate emissions satisfying the government's emission standards. Emissions generated by automobiles are one of the major sources for air pollution in the United States. Theoretically, emissions can be controlled to a minimum possible level by controlling the engine combustion process so that the air and fuel are mixed at certain desired ratio. This control problem, as it is known, turns out to be very difficult to solve. This is partly due to the complexity of modern automotive engines and due to the complexity of the fuel combustion process. In addition to achieving lower emissions, the automotive industry has also engaged in efforts to design cars that have better driveability and consume less fuelNDP implementation can be accomplished by adding a $1-10 chip developed under a previous NSF SBIR grant that, with additional training, could also be to reduce the cost of fuel flexibility an urgent strategic need.