Adaptive Control for Strict-Feedback Nonlinear Systems with Input Saturation and Output Hysteresis via Multi-dimensional Taylor Networks

The rapid advancement of industrial systems has outpaced traditional mathematical modeling methods, making it difficult for conventional control techniques to manage complex processes effectively. This article delves into the challenges of controlling nonlinear systems with input saturation and output hysteresis, which can severely degrade system performance if not properly addressed. The study introduces an innovative adaptive control strategy that leverages multi-dimensional Taylor networks (MTNs) to approximate unknown functions, ensuring accurate tracking and real-time performance. Unlike neural networks and fuzzy logic systems, MTNs offer a simpler topology, faster computational speed, and fewer parameters to tune, making them ideal for practical, real-world applications. The proposed method combines backstepping techniques with MTNs to handle input saturation and output hysteresis, providing a robust solution for strict-feedback nonlinear systems. Through detailed simulations and practical examples, the article demonstrates the superior tracking performance and stability of the MTN-based controller, highlighting its potential to revolutionize control system design in industrial settings.