Indirect adaptive structure for multivariable neural identification and control of a pilot distillation plant

This paper describes the design and implementation of an indirect adaptive controller that uses neural networks both for identification and control of an experimental pilot distillation column containing a mixture of ethanol and water. The MATLAB platform is applied both for the neural identification and control of the distillation plant using the Levenberg–Marquardt approach, enabling also optimal input/output net configuration. The neural controller performance has been analyzed and illustrated via experimental tests on the pilot distillation column monitored under the LabVIEW platform. Both platforms have been linked together by constituting an integrated process control interface. The obtained experimental results demonstrate the effectiveness of the neural indirect adaptive control scheme as compared to proportional–integrative–derivative, when real-time multivariable control is demanded, even in presence of disturbances.

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► We develop an indirect control scheme based on neural networks.
► We integrate MATLAB and LabVIEW programming software for monitoring and control of a pilot plant.
► The neural design provides simplicity and flexibility during the controller design process.
► We highlight the application to a real time multivariable control such a distillation column.
► The intelligent programming methodology supposes a step ahead as compared with PID control.