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    Area of Science:

    • Control Systems Engineering
    • Signal Processing
    • Network Engineering

    Background:

    • Networked Control Systems (NCSs) are increasingly complex, integrating control theory with communication networks.
    • Challenges arise from integrating continuous-time linear multi-input multi-output (MIMO) systems with unstable poles and nonminimum phase (NMP) zeros into networks.
    • Communication constraints such as additive white noise (AWN), quantization, bandwidth limitations, and encoder-decoder issues significantly impact system performance.

    Purpose of the Study:

    • To investigate and explicitly represent the tracking performance limitations in NCSs.
    • To analyze the influence of internal plant characteristics and external communication constraints on system performance.
    • To provide a framework for understanding how various factors contribute to performance degradation in MIMO NCSs.

    Main Methods:

    • Modeling NCSs as continuous-time linear MIMO systems with random reference noises.
    • Evaluating system performance using tracking error energy and a two-degree-of-freedom (2DOF) controller.
    • Analyzing the impact of communication constraints including AWN, quantization noise, bandwidth, and encoder-decoder configurations.

    Main Results:

    • Tracking performance limitations are determined by plant characteristics (unstable poles, NMP zeros), reference noise power distribution (RNPD), and communication constraints (CNPD, QNPD, TBA, TEA).
    • Angular relationships between plant characteristics (NMP zeros, unstable poles) and noise/allocation directions critically affect performance.
    • For MIMO NCSs, bandwidth allocation and encoder-decoder strategies can be optimized for channel-specific performance.

    Conclusions:

    • The study provides an explicit representation of tracking performance limitations in NCSs.
    • Internal plant dynamics and communication channel characteristics are identified as key determinants of performance.
    • The findings offer insights into managing and optimizing NCS performance under various constraints.