Reference Governor Search Range Optimization
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Solution Overview
Problem
The existing reference governor systems face high processing loads due to inefficient minimum value searches of the object function using the gradient method, particularly when updating target values for control outputs like supercharging pressure and EGR rate, as the search range consists of all real numbers, making it difficult to set suitable constants for effective convergence.
Innovation Solution
A plant control system is designed with a reference governor that performs a minimum value search using a binary search method, limiting the update range of the corrected target value to between r-0.5Rr and r, where Rr is the partial differential of the object function, and updates the target value using specific formulas to reduce processing load and improve convergence speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the gradient method is used to perform minimum value search of the object function, then the corrected target value can be updated, but the search range consists of all real numbers causing inefficient search and higher processing load
Solution Approach 1:
The patent segments the search range into two parts: the lower limit is set to r-0.5Rr and the upper limit is set to r, where r is the provisional target value and Rr is the partial differential value. This segmentation transforms the unbounded search space into a bounded interval, making the search more efficient and reducing processing load while still finding the optimal corrected target value that satisfies constraints.
2Speed
If a large constant is used in the gradient method, then the corrected target value moves faster, but the direction of movement changes frequently increasing the number of updates and processing load
Solution Approach 1:
The patent dynamically adjusts the search range based on the partial differential value Rr at each iteration. By setting the lower limit as r-0.5Rr and upper limit as r, the search interval adapts to the current gradient information, allowing faster convergence without excessive oscillations in the update direction, thus reducing the total number of updates required.
3Stability of the object's composition
If a small constant is used in the gradient method, then the corrected target value moves more stably, but the amount of movement per update is small requiring more updates and increasing processing load
Solution Approach 1:
The patent changes the search parameters by defining a dynamic search interval [r-0.5Rr, r] based on the partial differential value. This parameter change allows the system to take larger steps when the gradient is favorable while maintaining stability through the bounded interval, achieving both fast convergence and stable updates without requiring excessive iteration次数.
Data Source
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AI summary
A plant control system comprises a feedback controller 5 configured to determine a control input of a plant 6 so that one control output of the plant approaches a target value, a provisional target value calculating part 2 configured to calculate a provisional target value based on a predetermined parameter of the plant, and a reference governor 3 configured to perform a minimum value search of an object function by updating a corrected target value to thereby derive the target value from the provisional target value. The reference governor is configured to update the corrected target value only between r-0.5Rr and "r". Rr is a value of a partial differential for the corrected target value "w" of the object function when the corrected target value "w" is the provisional target value "r".