Neuro Adaptive Command Filter Control for Predefined-Time Tracking in Strict-Feedback Nonlinear Systems Under Deception Attacks

This paper presents a neural network enhanced adaptive control scheme tailored for strict-feedback nonlinear systems under the influence of deception attacks, with the aim of achieving precise tracking within a predefined time frame. Such studies are crucial as they address the increasing complexity of modern systems, particularly in environments where data integrity is at risk. Traditional methods, for instance, often struggle with the inherent unpredictability of nonlinear systems and the need for real-time adaptability in the presence of deception attacks, leading to compromised robustness and control instability. Unlike conventional approaches, this study adopts a Practical Predefined-Time Stability (PPTS) criterion as the theoretical foundation for predefined-time control design. By utilizing a novel nonlinear command filter, the research develops a command filter-based predefined-time adaptive back stepping control scheme. Furthermore, the incorporation of a switching threshold event-triggered mechanism effectively circumvents issues such as “complexity explosion” and control singularity, resulting in significant savings in computational and communication resources, as well as optimized data transmission efficiency. The proposed method demonstrates a 30% improvement in tracking accuracy and a 40% reduction in computational load compared to traditional methods. Through simulations and practical application cases, the study verifies the effectiveness and practicality of the proposed control method in terms of predefined-time stability and resilience against deception attacks.