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相关概念视频

Internal Combustion Engine01:20

Internal Combustion Engine

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The internal combustion engine is a heat engine that uses the byproducts of combustion as the working fluid instead of using a heat transfer medium to transfer heat. The combustion is done in a way that produces high-pressure combustion products that can be expanded through a turbine or piston to create work. Internal combustion engines can again be categorized into three kinds: (1) spark ignition gasoline engines, most commonly used in automobiles, (2) compression ignition diesel engines that...
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PD Controller: Design01:26

PD Controller: Design

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In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...
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Otto and Diesel Cycle01:27

Otto and Diesel Cycle

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An Otto engine is a four-stroke engine that uses a mixture of gasoline and air as the working fuel. The fuel is injected into the cylinder, and the piston is moved completely down so that the cylinder is at maximum volume. By moving the piston up, adiabatic compression takes place. The spark plug ignites the gasoline-air mixture, and the burning fuel adds heat to the system at a constant volume. The heated mixture expands adiabatically and gets further cooled by exhausting heat, and this cyclic...
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Reinforcement01:23

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Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
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Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

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Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Implementation of Portable Emissions Measurement Systems PEMS for the Real-driving Emissions RDE Regulation in Europe
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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
PubMed
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.

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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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.