Semi-Closed Control Parameter Calculation for Target Response Tracking
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Solution Overview
Problem
Conventional automatic adjustment techniques for controllers, such as FRIT and VRFT, are not optimized for semi-closed control systems and fail to accurately set parameters to achieve target responses, particularly in systems where the feedback parameter differs from the parameter defining the target response.
Innovation Solution
A calculation method and apparatus that calculate the optimal parameter ρ for a control system by using time-series data and a transfer function C(ρ) to minimize control errors between desired and actual outputs, allowing for precise adjustment of control inputs in semi-closed control systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional automatic adjustment techniques (FRIT/VRFT) are used for controller parameter adjustment, then the technique is suitable for fully closed control, but it fails to accurately set parameters for semi-closed control systems
Solution Approach 1:
The invention changes the fundamental parameter being adjusted from feedback amount to control input amount. By formulating the optimization objective around minimizing the difference between actual and target control inputs (rather than output errors), the technique becomes applicable to semi-closed control systems where output feedback is unavailable or insufficient.
Solution Approach 2:
The invention inverts the conventional control optimization approach. Instead of adjusting parameters to minimize output error (traditional feedback-based approach), it adjusts parameters to minimize the difference between actual and target control inputs. This inversion enables parameter optimization in semi-closed control systems by working backwards from the desired control action rather than forward from output error.
2Reliability
If the same parameter as the parameter defining the target response is used as the feedback amount, then fully closed control can be implemented, but semi-closed control cannot be properly handled
Solution Approach 1:
The invention changes the optimization parameter from output-related parameters to control input parameters. By defining the objective function in terms of control input differences rather than output errors, the system can handle cases where the feedback parameter differs from or is unrelated to the target response parameter, enabling semi-closed control application.
Solution Approach 2:
The invention introduces control input amount as an intermediary variable that bridges the gap between available measurements and target response. By optimizing based on control input rather than direct output feedback, the system can achieve accurate control even when the feedback parameter is different from or unavailable for the target response parameter.
3Manufacturing precision
If control parameters are adjusted to minimize output error, then target response tracking is improved, but the technique requires output feedback that is unavailable in semi-closed control
Solution Approach 1:
The invention extracts the essential control optimization function from the feedback loop. By formulating parameter adjustment based on control input characteristics rather than output error, it separates the parameter optimization task from the requirement for output feedback, enabling application in semi-closed control systems with reduced feedback requirements.
Solution Approach 2:
The invention redefines feedback from output error to control input difference. By using the difference between actual and target control inputs as the optimization criterion, the system maintains the beneficial effects of feedback-based parameter adjustment while reducing the feedback requirements to only what is necessary for calculating control input, making it suitable for semi-closed control.
Data Source
AI summary
A calculation method includes: acquiring one or more sets of time-series data including a sample u0 of control input u, a sample ym0 of first output ym, and a sample yf0 of second output yf; and calculating a value ρ* based on the time-series data, a transfer function C(ρ), and a model function Td that defines a normative value of the second output yf relative to a desired value r. The value ρ* is a value of parameter ρ that minimizes a control error and that is to be set to a control system. The control error is one of: an error between yf0 and a normative value of yf, the normative value of yf being calculated from C(ρ), Td, u0, and ym0; and an error between u0 and a normative value of the control input u, the normative value of u being calculated from C(ρ), Td, ym0, and yf0.