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<p>This paper investigates an adaptive fuzzy quantized control strategy for an unmanned surface vehicle (USV) to address course tracking challenges under simultaneous signal quantization and input–output constraints effects. A uniform quantizer is employed to quantize the input signal, and the quantization process is linearly described to elucidate its mechanism, effectively reducing communication bandwidth requirements and execution frequency of the actuator while maintaining system responsiveness. Under the premise of considering input saturation, a fuzzy logic system is utilized to effectively estimate the uncertainty terms in the USV model, while introducing a log-type Barrier Lyapunov Function (BLF) ensures that the system states remain within predefined bounds. Based on Lyapunov stability theory, the proposed lemma demonstrates the boundedness of quantization errors and guarantees the stability of the developed control scheme. Finally, the simulation results demonstrate that under the constraint conditions, the designed adaptive fuzzy quantization control strategy can maintain stable course tracking performance.</p>

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Adaptive Fuzzy Course Tracking Control of USV with Signal Quantization and Output Constraints

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Liangtao Jin,
  • Jun Ning,
  • Xiaoyang Gao,
  • Tieshan Li

摘要

This paper investigates an adaptive fuzzy quantized control strategy for an unmanned surface vehicle (USV) to address course tracking challenges under simultaneous signal quantization and input–output constraints effects. A uniform quantizer is employed to quantize the input signal, and the quantization process is linearly described to elucidate its mechanism, effectively reducing communication bandwidth requirements and execution frequency of the actuator while maintaining system responsiveness. Under the premise of considering input saturation, a fuzzy logic system is utilized to effectively estimate the uncertainty terms in the USV model, while introducing a log-type Barrier Lyapunov Function (BLF) ensures that the system states remain within predefined bounds. Based on Lyapunov stability theory, the proposed lemma demonstrates the boundedness of quantization errors and guarantees the stability of the developed control scheme. Finally, the simulation results demonstrate that under the constraint conditions, the designed adaptive fuzzy quantization control strategy can maintain stable course tracking performance.