A dynamically-constructed fuzzy neural controller for direct model reference adaptive control of multi-input-multi-output nonlinear processes

Abstract

Conventional industrial control systems are in majority based on the single-input-single-output design principle with linearized models of the processes. However, most industrial processes are nonlinear and multivariable with strong mutual interactions between process variables that often results in large robustness margins, and in some cases, extremely poor performance of the controller. To improve control accuracy and robustness to disturbances and noise, new design strategies are necessary to overcome problems caused by nonlinearity and mutual interactions. We propose to use a dynamicallyconstructed, feedback fuzzy neural controller (DCF-FNC) from the input±output data of the process and a reference model, for direct model reference adaptive control (MRAC) to deal with such problems. The effectiveness of our approach is demonstrated by simulation results on a real-world example of cold mill thickness control and is compared with the performances of the conventional PID controller and the cascade correlation neural network (CCN). Exploiting the advantage of intelligent adaptive control, both the CCN and our DCF-FNC signi®cantly increases the control precision and robustness, compared to the linear PID controller, with our DCF-FNC giving the best results in terms of both accuracy and compactness of the controller, as well as being less computationally intensive than the CCN. We argue that our DCF-FNC feedback controller with both structure and parameter learning can provide a computationally ef®cient solution to control of many real-world multivariable nonlinear processes in presence of disturbances and noise.Keywords Feedback fuzzy neural controller, Dynamic controller structure, Direct MRAC, Cold mill thickness control IntroductionIndustrial processes are often multivariable and have strongly nonlinear and time varying behavior. There also exist strong mutual interactions between process variables. Conventional control of industrial processes usually uses simple linear or linearized models to approximate the processes. For multi-input-multi-output (MIMO) processes it is normally very dif®cult to derive accurate models, due to complex nonlinear relationships among variables, time dependent changes in process dynamics, and model uncertainties when dealing with some physical phenomena. The severe nonlinearity and complexity of the processes thus result in large robustness margins, and in some cases, extremely poor performance of the controller. It is therefore necessary to develop solid control methodologies that are capable of coping with both nonlinearities and interactions, as well as time varying processes under the strong in¯uence of disturbances and noise. In addition, nonlinear control schemes that employ more realistic and more complex descriptions for nonlinear processes require process models in the form of nonlinear differential equations. This limits its industrial applications, since such ®rst principles models are not readily available in industrial practice due to a chronic lack of detailed and ext...

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