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安全的深度强化学习在柴油发动机排放控制中

Armin Norouzi¹, Saeid Shahpouri¹, David Gordon¹

¹Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada.

Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering

|September 11, 2023

概括

一种新的安全强化学习方法有效减少柴油发动机的氧化排放和燃料消耗. 这种方法通过在没有完整的系统模型的情况下学习最佳控制来优于传统控制器,尽管它需要安全约束的基本模型.

科学领域:

* 内部燃烧发动机的使用
* 工程中的人工智能
* 环境控制技术 * 环境控制技术

背景情况:

*控制压缩点火柴油发动机的排放对于环境遵守和燃油效率至关重要.
* 传统的控制方法在适应动态发动机条件和复杂的减排目标方面存在局限性.
* 深度增强学习 (DRL) 为自适应和优化发动机控制提供了一个有前途的途径.

研究的目的:

* 调查用于控制柴油发动机排放的深度强化学习应用.
*为了减少发动机的氧化 (NOx) 排放,并尽量减少燃料消耗.
* 为了实现准确跟踪参考发动机负载.

主要方法:

*使用实验数据在GT-Power中开发和校准基于物理的发动机模拟模型.
* 在GT-Power/Simulink共同模拟环境中实现深度决定性政策梯度算法.
* 整合安全过器,在DRL训练过程中强制执行输出限制.

主要成果:

*安全强化学习 (SRL) 控制器与非线性模型预测控制 (NMPC) 相比,显示出更高的NOx减少.
* SRL精确跟踪任意参考负载输入,超过了代学习控制器 (ILC) 的局限性.
*虽然SRL实现了较低的排放,但它的负载跟踪误差和燃料消耗略高于NMPC.

结论:

*安全增强学习是减少柴油发动机NOx排放和燃料消耗的可行和有效策略.
*SRL可以直接学习最佳控制策略,减少对复杂系统模型的依赖,但需要一个简化模型来执行约束.
* SRL在适应性和直接学习方面比ILC和NMPC有优势,尽管在负载跟踪和燃油效率方面存在较小的权衡.

关键词:

机器学习是机器学习.深度学习是一种深度学习.柴油发动机柴油发动机的使用情况排放控制系统的排放控制系统代学习控制代学习控制强化学习是一种强化学习.安全的学习安全的学习

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