Intelligent Process Control with Uninformative Data

Intelligence control leverages the power of artificial intelligence (AI). Traditional simulation techniques limited by their dependence on accurate and complete data. To address the issues of a lack of such data in practice, this paper proposes an intelligent system engineering approach to modelling and control. In particular, we introduce the use of a set of local transfer functions to brace a nonlinear system for well-fitted models. Additionally, a networked PID controller is designed using the variable parameter transfer function obtained to adapt to changing operating conditions. Simulations in Simulink are conducted for an AI-based air conditioning control system to meet the required precision. The AI-based approach is seen to be able to model air conditioning temperature control systems accurately, and offers potential to improve control accuracy in real-world applications.