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A systematic approach to model and simulate controlling industrial processes with uncertainties

机译：具有不确定性的工业过程建模和仿真的系统方法

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This paper investigates the design of adaptive controllers for such applications that could be modeled by low order dynamics with some parameters uncertainties. Some of the control applications that fall into this category include industrial processes (e.g. level, flow, pressure, etc.) and some automotive applications (e.g. active suspension). The paper introduces two different design techniques and compares them to traditional PID controllers. The first design is based on using a combination of state feedback and Lyapunov-based techniques. This proposed controller has the advantage of being applicable to both linear and nonlinear models. The key issue in the design is arriving at the best parameter update law that guarantees both stability and satisfactory transient performance. The second design technique makes use of the well-known gradient algorithm to identify the unknown parameter(s). A comprehensive comparison is then presented to highlight the advantages and disadvantages of the proposed strategies. Tradeoffs between stability and performance are carefully studied. A simulated first order process, using MATLAB, is used to exemplify the suggested techniques. Finally a conclusion is submitted with comments regarding real-time compatibility of the proposed controllers.
机译：本文研究了针对此类应用的自适应控制器的设计，该控制器可通过具有某些参数不确定性的低阶动力学建模。属于此类的一些控制应用包括工业过程（例如液位，流量，压力等）和一些汽车应用（例如主动悬架）。本文介绍了两种不同的设计技术，并将它们与传统的PID控制器进行了比较。第一个设计基于状态反馈和基于Lyapunov的技术的结合。提出的控制器具有可同时应用于线性和非线性模型的优点。设计中的关键问题是达到最佳参数更新法则，以保证稳定性和令人满意的瞬态性能。第二种设计技术利用众所周知的梯度算法来识别未知参数。然后进行了全面的比较，以突出提出的策略的优缺点。仔细研究了稳定性和性能之间的折衷。使用MATLAB进行的模拟一阶过程被用来举例说明所建议的技术。最后，提出了有关所建议控制器的实时兼容性的评论。

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