Microcomputer Fuzzy Control in TIG Inverter Power Source

Literature Overview

The paper by Wang Jianping from Shenzhen University, published in Electronics Application (Volume 26, Issue 2, 2000), presents the application of fuzzy control algorithms to arc current regulation in TIG welding inverter power sources. The author develops a dual-input single-output fuzzy controller that uses arc current and its rate of change as inputs, with the phase-shifted PWM pulse width adjustment voltage as the output. This work represents an early application of fuzzy logic to welding power source control, addressing the challenge of maintaining stable welding parameters under varying process conditions.

Core Technical Content

Fuzzy control is a control methodology that uses fuzzy logic to handle imprecision and uncertainty in the control system. Unlike traditional PID control, which requires an accurate mathematical model of the controlled process, fuzzy control can be designed based on expert knowledge and linguistic rules. This makes it particularly suitable for welding processes, which are inherently nonlinear, time-varying, and difficult to model precisely.

The control architecture described in this paper consists of:

1. Sensors: Real-time measurement of arc current
2. Microcomputer: Executes the fuzzy control algorithm
3. Fuzzy controller: Processes inputs and generates control outputs
4. PWM modulator: Converts the control output to switching signals for the inverter
5. Inverter power stage: Delivers the controlled welding current

Fuzzy Control Design

The fuzzy controller design involves several steps:

1. Fuzzification: Converting the measured arc current deviation and its rate of change into fuzzy sets using membership functions.
2. Rule base: Establishing a set of IF-THEN rules that encode expert knowledge about the control action required under different conditions.
3. Fuzzy inference: Applying the rule base to determine the fuzzy output.
4. Defuzzification: Converting the fuzzy output into a crisp control signal.

The rule base for arc current control typically includes rules such as:

• IF current deviation is large positive AND rate of change is positive, THEN decrease pulse width significantly.
• IF current deviation is small negative AND rate of change is zero, THEN increase pulse width slightly.
• IF current deviation is zero AND rate of change is large positive, THEN decrease pulse width moderately.

The use of both the current deviation and its rate of change as inputs provides the controller with information about both the present state and the trend, enabling more responsive and stable control.

Engineering Practice Considerations

The application of fuzzy control to TIG welding power sources offers several advantages over traditional control methods:

1. Robustness: Fuzzy control does not require a precise mathematical model of the welding process, making it robust to variations in workpiece geometry, material properties, and welding position.
2. Adaptability: The control rules can be easily modified to accommodate different welding conditions without redesigning the entire control system.
3. Nonlinearity handling: Fuzzy control naturally handles the nonlinear characteristics of the welding process, which can be challenging for linear control methods.

However, several practical challenges must be addressed:

• Computational speed: The fuzzy control algorithm must execute within the control sampling period, typically 1-10 ms. Microcomputers with adequate processing speed are required.
• Noise rejection: Arc current measurements are subject to electrical noise, which can affect the fuzzy control performance. Filtering and signal conditioning are necessary.
• Rule tuning: The fuzzy rule base and membership functions must be tuned for specific welding applications, which requires expertise and experimentation.

Study Insights and Reflections

This paper represents an early application of fuzzy logic to welding power source control, predating the widespread adoption of more sophisticated control methods. The approach demonstrates that fuzzy control can be effectively implemented with relatively simple hardware (microcomputers) and provides a practical solution for arc current regulation.

In retrospect, the principles described in this paper have evolved into more sophisticated adaptive and intelligent control systems. Modern welding power sources often incorporate multiple control loops, including arc length control, arc current control, and welding speed control, all working together to maintain optimal welding conditions. The fuzzy control approach described here can be seen as a foundation for these more advanced systems.

The key insight from this work is that welding process control does not always require complex mathematical models. Expert knowledge encoded in fuzzy rules can provide effective control, particularly in situations where the process is difficult to model or where the operating conditions vary significantly. This principle remains relevant today, as modern control systems often combine model-based and knowledge-based approaches.

For engineers developing welding power sources, this paper provides a valuable reference for understanding the fundamentals of fuzzy control application. The dual-input single-output architecture is simple and effective, and the approach can be extended to include additional inputs such as arc voltage, welding speed, or workpiece temperature for more comprehensive process control.

Summary

This paper presents a practical application of fuzzy control to TIG welding inverter power sources, demonstrating the effectiveness of knowledge-based control methods for welding process regulation. The dual-input fuzzy controller provides robust arc current control without requiring a precise mathematical model of the welding process. While the technology has evolved since 2000, the fundamental principles remain relevant for modern welding power source development.