基于模型神经网络预测的CSTR建模与控制 控制

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摘要翻译：
提出了一种基于神经网络模型的对象预测控制策略，并将其应用于连续搅拌釜式反应器(CSTR)中。该系统是一个高度非线性的过程；因此，一种非线性预测方法，如神经网络预测控制，可以更好地匹配控制系统动力学。本文描述了神经网络模型及其在一定预测范围内预测CSTR过程行为的方法，并对优化过程提出了一些意见。将预测控制算法应用于连续搅拌釜式反应器(CSTR)的浓度控制中，通过求解二次型性能指标来最优地确定反应器的参数。只有建立准确的模型，才能有效地控制cstr中的产品浓度。本文尝试利用神经网络等人工智能技术来缓解建模困难。仿真结果验证了NNMPC技术的可行性和有效性。
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英文标题：
《Modeling and Control of CSTR using Model based Neural Network Predictive
  Control》
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作者：
Piyush Shrivastava
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Computer Science        计算机科学
二级分类：Neural and Evolutionary Computing        神经与进化计算
分类描述：Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络，连接主义，遗传算法，人工生命，自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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一级分类：Physics        物理学
二级分类：Adaptation and Self-Organizing Systems        自适应和自组织系统
分类描述：Adaptation, self-organizing systems, statistical physics, fluctuating systems, stochastic processes, interacting particle systems, machine learning
自适应，自组织系统，统计物理，波动系统，随机过程，相互作用粒子系统，机器学习
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英文摘要：
  This paper presents a predictive control strategy based on neural network model of the plant is applied to Continuous Stirred Tank Reactor (CSTR). This system is a highly nonlinear process; therefore, a nonlinear predictive method, e.g., neural network predictive control, can be a better match to govern the system dynamics. In the paper, the NN model and the way in which it can be used to predict the behavior of the CSTR process over a certain prediction horizon are described, and some comments about the optimization procedure are made. Predictive control algorithm is applied to control the concentration in a continuous stirred tank reactor (CSTR), whose parameters are optimally determined by solving quadratic performance index using the optimization algorithm. An efficient control of the product concentration in cstr can be achieved only through accurate model. Here an attempt is made to alleviate the modeling difficulties using Artificial Intelligent technique such as Neural Network. Simulation results demonstrate the feasibility and effectiveness of the NNMPC technique.
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PDF链接：
https://arxiv.org/pdf/1208.3600

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关键词：神经网络 神经网 Optimization Presentation Difficulties 浓度 提出 Network based 控制算法