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<p>The classical Ziegler-Nichols (Z-N) method is a cornerstone for PID parameter self-tuning. However, its performance is highly sensitive to the selection of the critical gain (<i>K</i><sub>c</sub>). Improper <i>K</i><sub>c</sub> selection often leads to asymmetric oscillation waveforms during the tuning process, which severely degrades the control accuracy and stability of industrial systems. To address this fundamental issue, this paper introduces two key innovations: a quantitative metric termed relative asymmetry (α) to objectively assess waveform quality, and a model-free fast waveform convergence algorithm. First, rigorous experimental analyses are conducted to establish a clear negative correlation between α and system performance. Higher asymmetry results in larger steady-state errors. Subsequently, the proposed algorithm dynamically monitors the upper (<i>A</i><sub>rise</sub>) and lower (<i>A</i><sub>fall</sub>) amplitudes of the oscillation waveform and intelligently adjusts the tuning parameter <i>K</i><sub>c</sub>, thereby driving the waveform to rapidly approach symmetry. Experimental validation on a high-precision temperature control system (equipped with a K-type thermocouple and a CS1237 24-bit ADC) demonstrates that when relative asymmetry is reduced to below 0.3 via the algorithm (typically within 4–5 iterations), the steady-state maximum control deviation of the system is constrained to within ± 0.2&#xa0;°C. This model-free method provides a novel and effective solution to the waveform asymmetry problem in PID self-tuning, offering significant practical value for high-precision industrial process control.</p>

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High-precision PID tuning via waveform asymmetry correction with fast convergence for industrial applications

  • 期刊论文

  • 发表日期：

    2026年5月27日

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  • Liao Jie

摘要

The classical Ziegler-Nichols (Z-N) method is a cornerstone for PID parameter self-tuning. However, its performance is highly sensitive to the selection of the critical gain (Kc). Improper Kc selection often leads to asymmetric oscillation waveforms during the tuning process, which severely degrades the control accuracy and stability of industrial systems. To address this fundamental issue, this paper introduces two key innovations: a quantitative metric termed relative asymmetry (α) to objectively assess waveform quality, and a model-free fast waveform convergence algorithm. First, rigorous experimental analyses are conducted to establish a clear negative correlation between α and system performance. Higher asymmetry results in larger steady-state errors. Subsequently, the proposed algorithm dynamically monitors the upper (Arise) and lower (Afall) amplitudes of the oscillation waveform and intelligently adjusts the tuning parameter Kc, thereby driving the waveform to rapidly approach symmetry. Experimental validation on a high-precision temperature control system (equipped with a K-type thermocouple and a CS1237 24-bit ADC) demonstrates that when relative asymmetry is reduced to below 0.3 via the algorithm (typically within 4–5 iterations), the steady-state maximum control deviation of the system is constrained to within ± 0.2 °C. This model-free method provides a novel and effective solution to the waveform asymmetry problem in PID self-tuning, offering significant practical value for high-precision industrial process control.