Designing Robust Software Sensors for Nonlinear Systems via Neural Networks and Adaptive Sliding Mode Control

2025-07-09

AI Summary

To address the challenges of unmeasurable states, imprecise models, and absence of ground-truth labels in nonlinear systems, this paper proposes a physics-informed adaptive sliding-mode neural observer. The method integrates neural networks with adaptive sliding-mode control, where a neural network dynamically learns time-varying gain matrices—eliminating requirements for system linearization, differentiability assumptions, or exact modeling. Crucially, it introduces the first end-to-end training framework that enforces physical equations as hard constraints without access to true state labels. The resulting architecture exhibits enhanced robustness against severe measurement noise, nonsmooth dynamics, and weak observability. Simulation results demonstrate rapid convergence and high-accuracy state estimation, validating its effectiveness and generalizability under complex operating conditions.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Machine Learning: Neuro-Symbolic Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate knowledge of the state variables in a dynamical system is critical for effective control, diagnosis, and supervision, especially when direct measurements of all states are infeasible. This paper presents a novel approach to designing software sensors for nonlinear dynamical systems expressed in their most general form. Unlike traditional model-based observers that rely on explicit transformations or linearization, the proposed framework integrates neural networks with adaptive Sliding Mode Control (SMC) to design a robust state observer under a less restrictive set of conditions. The learning process is driven by available sensor measurements, which are used to correct the observer's state estimate. The training methodology leverages the system's governing equations as a physics-based constraint, enabling observer synthesis without access to ground-truth state trajectories. By employing a time-varying gain matrix dynamically adjusted by the neural network, the observer adapts in real-time to system changes, ensuring robustness against noise, external disturbances, and variations in system dynamics. Furthermore, we provide sufficient conditions to guarantee estimation error convergence, establishing a theoretical foundation for the observer's reliability. The methodology's effectiveness is validated through simulations on challenging examples, including systems with non-differentiable dynamics and varying observability conditions. These examples, which are often problematic for conventional techniques, serve to demonstrate the robustness and broad applicability of our approach. The results show rapid convergence and high accuracy, underscoring the method's potential for addressing complex state estimation challenges in real-world applications.

Problem

• Design robust software sensors for nonlinear systems using neural networks and adaptive SMC.
• Estimate state variables without direct measurements under less restrictive conditions.
• Ensure real-time adaptation and robustness against noise and system variations.

Innovation

• Neural networks integrate with adaptive SMC
• Time-varying gain matrix for real-time adaptation
• Physics-based constraints guide observer training