FRIT Control System Parameter Optimization via Feedforward Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing control systems using the FRIT technique struggle to accurately design controller parameters to match target responses due to errors in parameter calculation, leading to suboptimal control performance.
Innovation Solution
A method that calculates and optimizes both the ρ and θ parameters of a control system using time-series data and evaluation functions, incorporating a feedforward controller to correct deviations and improve control accuracy, thereby reducing errors relative to target responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the FRIT technique is used to adjust controller parameters, then the control output can approach the target response, but errors in parameter calculation occur leading to suboptimal control performance
Solution Approach 1:
The patent divides the parameter optimization into two independent stages: first optimizing ρ while fixing θ at its initial value, then optimizing θ while fixing ρ at its calculated value. This segmentation allows each parameter to be optimized independently without interference from the other, resolving the contradiction between parameter calculation accuracy and control performance reliability.
Solution Approach 2:
The patent performs preliminary optimization of the ρ parameter before proceeding to optimize the θ parameter. By establishing the optimal ρ value first and fixing it during the second optimization stage, the system ensures that the parameter calculation is accurate before using it to determine the final controller parameters, thereby improving both measurement precision and control performance reliability.
2Device complexity
If only feedback control is used, then the control system is simple, but the accuracy in achieving target responses is insufficient
Solution Approach 1:
The patent merges feedforward control and feedback control into a unified control system. The feedforward controller uses the pre-calculated optimal parameters (ρ* and θ*) to compute a preliminary control output, while the feedback controller adjusts this output based on actual system response. This combination achieves high target response accuracy while maintaining reasonable system complexity through the integration of both control approaches.
Data Source
AI summary
A design method includes: acquiring time-series data of a set of a sample um of a control input u and a sample ym of a control output y; calculating, based on the time-series data, a value ρ* of a parameter ρ that minimizes a value of an evaluation function J(ρ,θ,um,ym) in a state where θ is set to an initial value θini corresponding to a target response yr; calculating, based on the time-series data, a value θ* of θ that minimizes the value of J(ρ,θ,um,ym) in a state where ρ is set to ρ*; and designing the control system based on θini and θ* and ρ*. A feedforward controller of the control system receives input of the desired value r and calculates r*=G(θini,θ*)r following a transfer function G(θini,θ*). The feedback controller calculates u=C(ρ*)(r*−y) following C(ρ=ρ*) based on a deviation r*−y. The transfer function G(θini,θ*) is a transfer function Td(θini)/Td(θ*).