Fuzzy PID Control for Stable Electrode Surface Density Measurement
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The existing battery manufacturing processes face challenges in parameter tuning and low robustness of closed-loop control, particularly in nonlinear and time-varying systems, leading to erroneous control loops and reduced production quality.
Innovation Solution
A genetic algorithm optimization-based fuzzy PID control method that includes stability monitoring, data sampling with median average filtering, fuzzification processing, and real-time fuzzy reasoning to optimize PID parameters, enhancing the robustness and accuracy of closed-loop control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional PID algorithm is used for closed-loop control, then ease of implementation and debugging is improved, but control accuracy and robustness deteriorate in nonlinear and time-varying systems
Solution Approach 1:
The patent introduces fuzzy logic as an intermediary layer between the traditional PID controller and the plant. The fuzzy logic controller processes the error and error rate inputs through linguistic rules and membership functions, transforming them into appropriate PID parameter adjustments. This intermediary handles the nonlinear and time-varying characteristics effectively while maintaining the simplicity of PID structure, thus improving control robustness without sacrificing ease of implementation.
Solution Approach 2:
The patent dynamically changes the PID parameters (Kp, Ki, Kd) based on the system's real-time state through fuzzy logic reasoning. Instead of using fixed PID parameters, the controller adjusts these parameters continuously according to the error magnitude and trend, enabling the system to adapt to nonlinear and time-varying characteristics while maintaining implementation simplicity.
2Ease of operation
If traditional PID algorithm is used for closed-loop control, then ease of debugging is improved, but control accuracy deteriorates in systems with many affecting factors
Solution Approach 1:
The fuzzy logic controller serves as an intermediary that handles complex decision-making based on multiple affecting factors through linguistic rules. It processes the error and error rate through membership functions and rule-based reasoning, transforming complex multi-factor influences into intuitive control actions. This maintains ease of debugging while significantly improving control accuracy in systems with many affecting factors.
Solution Approach 2:
The patent applies different control strategies locally based on the system state. The fuzzy logic controller divides the control space into different regions based on error magnitude and trend, applying appropriate control actions for each region. This local quality approach enables precise handling of different operating conditions while maintaining overall system ease of debugging.
3Device complexity
If manual adjustment of die gap is used, then device complexity is reduced, but production cycle time increases and material waste occurs
Solution Approach 1:
The patent implements a self-service control system where the closed-loop controller automatically adjusts the die gap based on real-time coating weight feedback. The system monitors the coating weight, compares it with the target value, and automatically modifies the die gap without manual intervention. This eliminates the trade-off between simplicity and productivity, as the automatic system maintains simple operation while dramatically reducing production cycle time and material waste.
Solution Approach 2:
The patent introduces a feedback mechanism that continuously monitors the coating weight and uses this information to automatically adjust the die gap. The closed-loop control system processes the measurement feedback through fuzzy logic reasoning and implements real-time parameter adjustments, enabling rapid response to deviations and eliminating the need for manual intervention, thus improving productivity without increasing complexity.
4Manufacturing precision
If closed-loop control is implemented with traditional PID, then coating uniformity is improved, but system adaptability to nonlinear and time-varying characteristics deteriorates
Solution Approach 1:
The patent dynamically changes the PID parameters (Kp, Ki, Kd) based on the system's real-time state through fuzzy logic reasoning. The controller adjusts these parameters continuously according to the error magnitude and trend, enabling the system to adapt to nonlinear and time-varying characteristics while maintaining coating uniformity. This parameter adaptation resolves the contradiction between precision and adaptability.
Solution Approach 2:
The patent transforms the static PID parameters into dynamic, state-dependent parameters through fuzzy logic control. The controller adapts its behavior based on the current operating conditions, making the control system dynamic and flexible. This enables the system to maintain coating uniformity across varying conditions while being highly adaptable to nonlinear and time-varying characteristics.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Disclosed are a genetic algorithm optimization-based fuzzy Proportional-Integral-Derivative (PID) control method, apparatus, and device. The method includes: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality, and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, setting a data sampling interval according to a preset system parameter of the surface density measurement system; S3, according to the data sampling interval, continuously sampling N pieces of data using a median average filtering algorithm, removing extreme values, calculating an arithmetic mean value of the N-2 pieces of data, and eliminating a sampling value deviation of the arithmetic mean value to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; and S5, according to the applicable PID parameters, controlling a servo motor of the surface density measurement system using a genetic algorithm. Therefore, the technical problems of difficulty in parameter tuning and low robustness of closed-loop control are solved.