A Systematic Approach to Model and Simulate Controlling Industrial Processes with Uncertainties
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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.