Novel Parameter Estimation Strategies for Time-Varying Systems via Real-Time Non-Linear Receding Horizon Control in Chaotic Environments

TL;DR

This paper introduces a real-time nonlinear receding horizon control method for accurately estimating parameters in chaotic systems with both time-invariant and time-varying dynamics, demonstrating robustness and effectiveness.

Contribution

It develops a novel real-time parameter estimation approach using non-linear receding horizon control without iterative approximations, suitable for chaotic environments.

Findings

Successfully applied to chaotic systems with varying parameters

Demonstrates robustness against bounded noise

Provides real-time parameter estimation without iterative methods

Abstract

In this paper, based on real-time nonlinear receding horizon control methodology, a novel approach is developed for parameter estimation of time invariant and time varying nonlinear dynamical systems in chaotic environments. Here, the parameter estimation problem is converted into a family of finite horizon optimization control problems. The corresponding receding horizon control problem is then solved numerically, in real-time, without recourse to any iterative approximation methods by introducing the stabilized continuation method and backward sweep algorithm. The significance of this work lies in its real-time nature and its powerful results on nonlinear chaotic systems with time varying parameters. The effective nature of the proposed method is demonstrated on two chaotic systems, with time invariant and time varying parameters. At the end, robustness performance of the proposed algorithm against bounded noise is investigated.