Servo MPC Weighting Setup from Desired Time Response
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In model prediction control for servo control, determining optimal weighting coefficients is challenging due to a lack of direct correlation with the time response of the control target, requiring numerous trial-and-errors, and existing methods cannot effectively handle servo control scenarios.
Innovation Solution
A method is introduced to set weighting coefficients for model prediction control by linearizing the prediction model near the terminal end and using the ILQ design method, which includes a control device with a first integrator to track a target command, utilizing a virtual control target and Riccati equation to calculate state feedback and integral gains, and subsequently determine weighting coefficients based on desired time responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If model prediction control is used for servo control, then tracking performance is improved, but determining optimal weighting coefficients becomes difficult requiring numerous trial-and-errors
Solution Approach 1:
The patent transforms the difficult-to-set weighting coefficients into easily adjustable time response parameters (rise time, settling time, overshoot). By changing the parameter representation from abstract weighting coefficients to intuitive time response specifications, users can achieve optimal tracking performance without extensive trial-and-errors.
Solution Approach 2:
The patent introduces an intermediary optimization module that acts as a bridge between the user-friendly time response parameters and the internal weighting coefficients. This intermediary automatically calculates the corresponding weighting coefficients based on the specified time response characteristics, eliminating the need for users to directly manipulate complex weighting parameters.
2Manufacturing precision
If weighting coefficients are adjusted to improve time response, then tracking accuracy is improved, but system complexity increases
Solution Approach 1:
The patent creates a simplified virtual model of the control system that mirrors the essential dynamics. This virtual model allows users to specify desired time response characteristics without dealing with the full complexity of the actual control system, making parameter adjustment intuitive while maintaining high tracking accuracy.
Solution Approach 2:
The patent replaces complex weighting coefficient parameters with intuitive time response parameters (rise time, settling time, overshoot). This parameter transformation maintains the ability to achieve high tracking accuracy while significantly reducing the complexity of system configuration and adjustment.
3Reliability
If trial-and-error method is used to determine weighting coefficients, then optimal parameters can be found, but time consumption increases
Solution Approach 1:
The patent performs preliminary calculations to establish the relationship between time response specifications and weighting coefficients before actual control operation. By pre-computing the optimal weighting coefficients based on desired time response characteristics, the system eliminates the need for time-consuming trial-and-error adjustments during deployment.
Solution Approach 2:
The patent replaces the manual trial-and-error adjustment process with an automated computational algorithm. The optimization module automatically calculates the optimal weighting coefficients based on the specified time response parameters, substituting the mechanical trial-and-error process with an efficient computational approach that dramatically reduces parameter setting time.
Data Source
Figure 1A~1B
Figure 1C~2
Figure 3
AI summary
A setting method according to the present invention determines a desired time response in an optimum servo control structure corresponding to a servo control structure of a control target, calculates a predetermined gain corresponding to the desired time response, and calculates a first weighting coefficient Qf, a second weighting coefficient Q, and a third weighting coefficient R of a predetermined Riccati equation according to the Riccati equation on the basis of the predetermined gain. The first weighting coefficient Qf, the second weighting coefficient Q, and the third weighting coefficient R are set as a weighting coefficient corresponding to a terminal cost, a weighting coefficient corresponding to a state quantity cost, and a weighting coefficient corresponding to a control input cost, respectively, in a predetermined evaluation function for model prediction control.