S-Function Implementation of a Single Neuron Adaptive PID Controller with Hebbian Supervision

This paper provides a comprehensive overview of the S-function implementation for a single neuron adaptive PID controller utilizing Hebbian supervision. We begin by examining the core algorithm principles and distinctive features of this control strategy, along with its practical applications in control systems. The algorithm employs a single neuron structure to adaptively adjust PID parameters through Hebbian learning rules, which modify connection weights based on neuronal activity patterns. Next, we discuss practical implementation aspects including parameter configuration methods and uncertainty handling techniques. The MATLAB S-function implementation involves defining system states, calculating proportional, integral and derivative terms, and updating weights using Hebbs learning rule (typically implemented through weight adjustment equations like Δw = η·x·y where η represents learning rate). Finally, we present real-world application performance metrics and explore potential improvement directions such as incorporating momentum terms for stable convergence or implementing gradient clipping for training stability. This work offers readers complete insights into developing and deploying Hebbian-supervised single neuron adaptive PID controllers through S-function implementations.