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提供了基于BP(Back Propagation)神经网络结合PID(比例-积分-微分)控制策略的Simulink仿真模型。该模型旨在实现对杨艺所著论文《基于S函数的BP神经网络PID控制器及Simulink仿真》中的理论进行实践验证。在Matlab 2016b环境下开发,经过测试,确保能够正常运行,适合学习和研究神经网络在控制系统中的应用。 特点 集成BP神经网络:模型中集成了BP神经网络用于提升PID控制器的性能,使之能更好地适应复杂控制环境。 PID控制优化:利用神经网络的自学习能力,对传统的PID控制算法进行了智能调整,提高控制精度和稳定性。 S函数应用:展示了如何在Simulink中通过S函数嵌入MATLAB代码,实现BP神经网络的定制化逻辑。 兼容性说明:虽然开发于Matlab 2016b,但理论上兼容后续版本,可能会需要调整少量配置以适配不同版本的Matlab。 使用指南 环境要求:确保你的电脑上安装有Matlab 2016b或更高版本。 模型加载: 下载本仓库到本地。 在Matlab中打开.slx文件。 运行仿真: 调整模型参数前,请先熟悉各模块功能和输入输出设置。 运行整个模型,观察控制效果。 参数调整: 用户可以自由调节神经网络的层数、节点数以及PID控制器的参数,探索不同的控制性能。 学习和修改: 通过阅读模型中的注释和查阅相关文献,加深对BP神经网络与PID控制结合的理解。 如需修改S函数内的MATLAB代码,建议有一定的MATLAB编程基础。
翻译Agent 𝑐 𝑖 . In this paper, we regard each charging station 𝑐 𝑖 ∈ 𝐶 as an individual agent. Each agent will make timely recommendation decisions for a sequence of charging requests 𝑄 that keep coming throughout a day with multiple long-term optimization goals. Observation 𝑜 𝑖 𝑡 . Given a charging request 𝑞𝑡 , we define the observation 𝑜 𝑖 𝑡 of agent 𝑐 𝑖 as a combination of the index of 𝑐 𝑖 , the real-world time 𝑇𝑡 , the number of current avail able charging spots of 𝑐 𝑖 (supply), the number of charging requests around 𝑐 𝑖 in the near future (future demand), the charging power of 𝑐 𝑖 , the estimated time of arrival (ETA) from location 𝑙𝑡 to 𝑐 𝑖 , and the CP of 𝑐 𝑖 at the next ETA. We further define 𝑠𝑡 = {𝑜 1 𝑡 , 𝑜2 𝑡 , . . . , 𝑜𝑁 𝑡 } as the state of all agents at step 𝑡. Action 𝑎 𝑖 𝑡 . Given an observation 𝑜 𝑖 𝑡 , an intuitional design for the action of agent𝑐 𝑖 is a binary decision, i.e., recommending 𝑞𝑡 to itself for charging or not. However, because one 𝑞𝑡 can only choose one station for charging, multiple agents’ actions may be tied together and are difficult to coordinate. Inspired by the bidding mechanism, we design each agent 𝑐 𝑖 offers a scalar value to "bid" for 𝑞𝑡 as its action 𝑎 𝑖 𝑡 . By defining 𝑢𝑡 = {𝑎 1 𝑡 , 𝑎2 𝑡 , . . . , 𝑎𝑁 𝑡 } as the joint action, 𝑞𝑡 will be recommended to the agent with the highest "bid" value, i.e., 𝑟𝑐𝑡 = 𝑐 𝑖 , where 𝑖 = arg max(𝑢𝑡)
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