Teaching intelligent control using a laboratory-scaled process mini-plant

This paper discusses an experiment-based approach to teaching intelligent control methodologies for students conducted at the Engineering Physics Department, Institut Teknologi Bandung, Indonesia. The teaching process conducted in the form of student final project involving several tasks, i.e. plant analysis and modeling, building up intelligent control structures for on-line purposes, implementing real-time control, and investigating the results of control performances. A laboratory-scaled process mini-plant, which has strongly inherent mechanical nonlinearity due to its mechanical components which is made difficult to control, has been used for the experiment. The objective of control is to maintain the fluid level in a tank to a specified level. Intelligent control scheme using two neural network structures was developed for this purpose, namely the feedforward neural network, which is employed as the plant identifier, and the diagonal recurrent neural network as the controller. The neural models are developed based on an on-line learning process so that the plant parameters can be adapted to the changes occurred at the plant. Results of control implementation demonstrate the applicability and the performance of the developed intelligent control scheme and has deepened students understanding and capability to implement intelligent control strategy in real-time environment.