Predictive Feedback Control for Low-Calculation MPC
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Solution Overview
Problem
Model predictive control (MPC) methods face significant calculation challenges due to the need for extensive prediction processes and optimization problem solving, which increases computational demands.
Innovation Solution
A control device incorporating a controller, predictor, and corrector that reduces calculation by predicting output values based on input values and predictive models, and correcting control values using feedback mechanisms to maintain output values close to command values, thereby minimizing the need for complex optimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If model predictive control is used to achieve accurate control by predicting future behavior and solving optimization problems, then control precision is improved, but calculation amount significantly increases
Solution Approach 1:
The control process is divided into two distinct stages: a prediction stage that forecasts future output values without optimization, and a correction stage that adjusts control values based on prediction errors. This segmentation eliminates the need for complex real-time optimization while maintaining control precision through the correction mechanism.
Solution Approach 2:
The system performs preliminary prediction of future output values before actual control actions are taken. By predicting output values in advance using a predictive model and then correcting control values based on these predictions, the system avoids the need for complex real-time optimization calculations while achieving accurate control.
2Manufacturing precision
If extensive prediction processes and optimization solving are performed to minimize error area, then control accuracy is improved, but processing time increases
Solution Approach 1:
The invention extracts and removes the computationally intensive optimization solving process from the control loop, retaining only the prediction and correction stages. This extraction maintains control accuracy through the correction mechanism while dramatically reducing processing time by eliminating the time-consuming optimization calculations.
Solution Approach 2:
The system uses feedback from the difference between actual and predicted output values to correct control values. This feedback mechanism ensures control accuracy is maintained while avoiding the need for extensive real-time optimization, thereby reducing processing time.
3Manufacturing precision
If complex optimization problems are solved in real-time to minimize error, then control precision is improved, but device complexity increases
Solution Approach 1:
The control algorithm is segmented into simple prediction and correction components, removing the complex real-time optimization solver. This segmentation maintains control precision through the correction stage while significantly reducing the complexity of the control algorithm implementation.
Solution Approach 2:
The invention replaces the expensive and complex real-time optimization solver with simpler, computationally inexpensive prediction and correction calculations. This substitution achieves comparable control precision with much lower algorithmic complexity and computational resource requirements.
Data Source
AI summary
A control device includes a controller, a predictor, and a corrector. The controller generates a control value on the basis of a command value and an output value output from a control target. The predictor predicts the output value on the basis of an input value input to the control target and a predictive model of the control target and generates a prediction value indicating a result of predicting the output value. The corrector corrects the control value on the basis of the command value, the output value, and the prediction value. The input value input to the control target is the control value corrected by the corrector.