Prescribed-Time Performance-Based Data-Driven Control of Permanent Magnet Linear Motors: A Resilient Hierarchical Approach.
TL;DR
This article investigates coordinated velocity tracking control for heterogeneous permanent magnet linear motors under actuator faults and denial-of-service (DoS) attacks and proposes a prescribed-time performance-based hierarchical resilient adaptive data-driven control strategy.
Abstract
This article investigates coordinated velocity tracking control for heterogeneous permanent magnet linear motors (PMLMs) under actuator faults and denial-of-service (DoS) attacks. Parameter heterogeneity, actuator degradation, and communication failures jointly threaten system stability and tracking accuracy. A prescribed-time performance-based hierarchical resilient adaptive data-driven control strategy is proposed to solve the formulated issue. First, a distributed resilient prescribed-time observer is designed to estimate the time-varying virtual reference velocity despite intermittent communication failures caused by DoS attacks. Subsequently, using the dynamic linearization (DL) method, the ideal controller is converted into an equivalent data model that relies solely on input/output (I/O) data, bypassing the need for accurate PMLM models. By introducing a global prescribed-time performance function (PTPF), the constrained velocity tracking errors are transformed into equivalent unconstrained conditions. Based on the estimated reference signal, a decentralized controller-dynamic-linearization (CDL)-based fuzzy adaptive tracking controller is designed, in which the unknown fault-related nonlinear term is approximated using a fuzzy logic system to compensate for actuator fault effects. The prescribed-time stabilization of the observation error and velocity tracking error is validated by Lyapunov stability analysis and contraction mapping techniques. Finally, simulation examples confirm the effectiveness of the proposed scheme.