Stability Analysis and Optimization Strategy of PLC Ship Lock System Based on Fuzzy Logic Control

The article first analyzes commonly used stability analysis methods, including Lyapunov stability theory, frequency domain analysis method, and time domain simulation method. For the programmable logic controller (PLC) ship lock system based on fuzzy logic control (FLC), a comprehensive stability analysis method combining Lyapunov stability theory and time domain simulation method is proposed. By establishing the state space equation of the system and constructing Lyapunov function, the asymptotic stability of the system is verified by using MATLAB/Simulink for time domain simulation. In the aspect of optimization strategy, the paper focuses on the realization method of fuzzification and defuzzification process. In the process of fuzzification, the triangular membership function is selected, and according to the actual operation range of PLC lock system, the input variables such as water level height are divided into multiple fuzzy sets to improve the accuracy of fuzzification. In the process of defuzzification, the center of gravity method is adopted, and the weighting mechanism is introduced, and the output is weighted and averaged according to the importance of fuzzy rules to enhance the control stability and accuracy of the system. The effectiveness of the optimization strategy is verified by simulation analysis. The simulation results show that the time response curve of the system is asymptotically stable, and the water level height and lock opening and closing degree gradually tend to stable values after initial disturbance. The comparison data of system performance before and after optimization shows that after the optimization of fuzzification and defuzzification process, all performance indexes of the system have been significantly improved, including the reduction of water level height error, lock opening error, the shortening of system response time and stabilization time, and the reduction of energy consumption.

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Publication Details

Published
2025-03-26
DOI
https://doi.org/10.1109/edpee65754.2025.00259
Citations
1
Primary Topic
Industrial Automation and Control Systems
Type
article
Field-Weighted Citation Impact
1.21
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article

Stability Analysis and Optimization Strategy of PLC Ship Lock System Based on Fuzzy Logic Control

Ruihe Li, Ganquan Cui
1 citations
Industrial Automation and Control Systems
1.21
article

Stability Analysis and Optimization Strategy of PLC Ship Lock System Based on Fuzzy Logic Control

Ruihe Li, Ganquan Cui
article en
1 citations

Abstract

The article first analyzes commonly used stability analysis methods, including Lyapunov stability theory, frequency domain analysis method, and time domain simulation method. For the programmable logic controller (PLC) ship lock system based on fuzzy logic control (FLC), a comprehensive stability analysis method combining Lyapunov stability theory and time domain simulation method is proposed. By establishing the state space equation of the system and constructing Lyapunov function, the asymptotic stability of the system is verified by using MATLAB/Simulink for time domain simulation. In the aspect of optimization strategy, the paper focuses on the realization method of fuzzification and defuzzification process. In the process of fuzzification, the triangular membership function is selected, and according to the actual operation range of PLC lock system, the input variables such as water level height are divided into multiple fuzzy sets to improve the accuracy of fuzzification. In the process of defuzzification, the center of gravity method is adopted, and the weighting mechanism is introduced, and the output is weighted and averaged according to the importance of fuzzy rules to enhance the control stability and accuracy of the system. The effectiveness of the optimization strategy is verified by simulation analysis. The simulation results show that the time response curve of the system is asymptotically stable, and the water level height and lock opening and closing degree gradually tend to stable values after initial disturbance. The comparison data of system performance before and after optimization shows that after the optimization of fuzzification and defuzzification process, all performance indexes of the system have been significantly improved, including the reduction of water level height error, lock opening error, the shortening of system response time and stabilization time, and the reduction of energy consumption.

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Industrial Automation and Control Systems
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