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Intelligent control of a nonlinear tank reactor based on Lyapunov direct method

机译:基于Lyapunov直接法的非线性储罐反应器的智能控制。

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In this paper, intelligent control of the outlet flow concentration of a non-thermic catalytic continuous stirred tank reactor (CSTR) is addressed. Control command is the sum of two control commands: steady state and transient commands. A fuzzy controller generates transient control command pushing the system towards the reference (desired situation). Steady state control command is generated to maintain steady state situation (based on the concept of 'control equilibrium point'). This research is performed in simulation environment; however, the mathematical model of the system is not used during stability analysis to keep the methodology useful in case of considerable uncertainties; instead, some obvious facts about the system in the form of fuzzy rules are used for stability analysis, namely 'fuzzy rough model'. Using this technique, Lyapunov asymptotic stability of control system is proved. For comparison, the case study is also controlled by neuro-predictive algorithm. The studied CSTR is known as a very good example of neuro-predictive control application; however, the newly offered hybrid intelligent method leads much better reference tracking performance as well as less change in control command (which is very important in implementation).
机译:在本文中,解决了非热催化连续搅拌釜反应器(CSTR)出口流量浓度的智能控制问题。控制命令是两个控制命令的总和:稳态命令和瞬态命令。模糊控制器生成瞬态控制命令,将系统推向参考点(期望的情况)。生成稳态控制命令以维持稳态情况(基于“控制平衡点”的概念)。这项研究是在仿真环境中进行的;但是,在稳定性分析过程中不会使用系统的数学模型来保持该方法在不确定性较大的情况下的有效性。取而代之的是,使用一些模糊规则形式的关于系统的明显事实进行稳定性分析,即“模糊粗糙模型”。利用该技术,证明了Lyapunov控制系统的渐近稳定性。为了进行比较,该案例研究还由神经预测算法控制。所研究的CSTR被认为是神经预测控制应用的一个很好的例子。但是,新提供的混合智能方法带来了更好的参考跟踪性能以及更少的控制命令变化(这在实现中非常重要)。

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