NC-SIMC: Neuro-Controller Simple Internal Model Control

Abstract

This study introduces a novel Neuro-Controller Simple Internal Model Control (NC-SIMC) paradigm. Our primary goal is to develop a neural network architecture that integrates the logical principles of Proportional-Integral-Derivative (PID) controller design, as outlined by the SIMC rules, into a model capable of generating control actions without the necessity for training with closed-loop data. This is achieved by employing the Simple Internal Model Control framework to effectively train the adapted neural network structure. We present a machine-learning approach that combines a specialized inductive bias node by using a custom new layer inside the neutral net structure to address this goal. Hence, it bridges traditional control theory and machine learning. A key innovation of our NC-SIMC approach is its ability to learn and internally formulate a control structure and parameters. This enables it to establish suitable control actions based on the system’s state, setpoint, and feedback without relying on a fixed control structure. Our paper demonstrates the efficacy of NC-SIMC in stabilizing control actions and its adaptability across various operating conditions without requiring retuning. This performance is shown to be favorably comparable to traditional SIMC rules. We conclude by discussing the potential for future improvements in dynamic performance and the integration of constraints directly into the inductive bias layer, opening new avenues for advanced control system design. The main advantage of our strategy is that no closed-loop data is needed to identify the NC-SIMC PID controller. Instead, the model learns from the SIMC rules.