Adaptive Neuro-Fuzzy Inference System (ANFIS)

An advanced control algorithm that uses a dynamic process model to predict future outputs and compute an optimal sequence of control moves over a finite receding horizon. Unlike ANFIS, which relies on data-driven fuzzy rules, MPC explicitly solves a constrained optimization problem at each time step. It excels in multi-variable processes with complex interactions and hard constraints on actuators or states. The receding horizon principle means only the first computed move is implemented before the optimization is repeated, providing inherent feedback to counteract model mismatch.

A non-parametric Bayesian inference method that models a distribution over possible functions to provide both predictions and calibrated uncertainty estimates for process variables. While ANFIS provides a deterministic output, Gaussian Process Regression quantifies the confidence of its predictions, making it invaluable for safety-critical adaptive control where knowing what the model doesn't know is crucial. It uses a kernel function to define similarity between data points, acting as a powerful surrogate model for Bayesian Optimization.

An autonomous software entity that learns an optimal control policy through trial-and-error interaction with a dynamic environment to maximize a cumulative reward signal. Unlike ANFIS, which typically learns from supervised input-output data, a Reinforcement Learning agent discovers control strategies from scratch. Algorithms like Deep Deterministic Policy Gradient (DDPG) are specifically designed for the continuous action spaces common in manufacturing, learning to adjust valve positions or motor speeds directly from process feedback.

The field of building mathematical models of dynamic systems from observed input-output data by estimating the parameters of a candidate model structure. ANFIS is itself a powerful tool for non-linear system identification, mapping complex relationships without requiring a first-principles physical model. Traditional techniques include:

• ARX and ARMAX models for linear time-invariant systems
• Subspace methods for multi-variable state-space models
• Neural network-based identification as a direct competitor to ANFIS

A model-agnostic control strategy that treats internal non-linearities and external disturbances as a total disturbance and estimates and cancels it in real-time via an extended state observer. ADRC shares ANFIS's goal of handling non-linear, uncertain processes without a precise mathematical model. However, ADRC achieves robustness through active estimation and cancellation rather than fuzzy rule interpolation. It is particularly effective for processes with significant unmodeled dynamics and external load variations.

A deep learning model where the loss function is regularized by the governing physical laws expressed as partial differential equations to ensure predictions obey known physics. While ANFIS learns purely from data, PINNs embed domain knowledge directly into the learning process. This hybrid approach prevents physically impossible predictions, a common risk with black-box models. In manufacturing, PINNs can model thermal dynamics or fluid flow where conservation laws must be strictly respected.