Benchmarking Time-Delay Estimation Strategies for Nonlinear Control System Design

Abstract

Time-delay estimation in nonlinear control systems represents a critical challenge in modern control engineering, significantly impacting system stability and performance. This study investigates and benchmarks various time-delay estimation strategies across different nonlinear control system configurations in Madhya Pradesh industrial facilities. The research focuses on evaluating the effectiveness of gradient-based optimization methods, correlation-based approaches, machine learning techniques, and adaptive algorithms. Our methodology encompasses comparative analysis of five primary estimation techniques: Extended B-polynomial methods, nonlinear least squares, rational approximations, LSTM-based predictive models, and Short-Time Fourier Transform approaches. Through comprehensive experimental validation using data from 150 industrial control systems, we demonstrate that LSTM-based methods achieve superior accuracy with 15.2% lower estimation error compared to traditional approaches. The gradient-based sequential optimization shows 23% faster convergence rates, while correlation methods exhibit robustness in noisy environments. Results indicate that hybrid approaches combining multiple strategies offer optimal performance with 18.7% improvement in overall system stability. These findings provide crucial insights for control system designers in selecting appropriate time-delay estimation strategies based on specific application requirements and system characteristics