Sequential Predictive Control for Power Electronics
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Solution Overview
Problem
Current control methods for power electronics, such as linear control and model-based predictive control, face challenges in managing multiple control objectives, particularly when these objectives have different physical natures or equal importance, requiring empirical adjustments of weight factors which are not standardized.
Innovation Solution
A sequential predictive control method that separates the cost function into two or more functions based on the number of control objectives, allowing for independent minimization of each objective without the need for weight factor adjustment, as demonstrated in FIG. 2, where the method first controls a variable with a unit cost function and then iteratively addresses the second variable using only the options that minimize the first objective's cost.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If model-based predictive control is used to handle nonlinear equations, then the control capability for nonlinear systems is improved, but the implementation complexity increases due to the need for empirical adjustment of weight factors
Solution Approach 1:
The patent segments the control process into two distinct stages: first solving a cost function for primary control objectives, then solving a second cost function for additional objectives using only the options that minimized the first cost function. This segmentation eliminates the need for weight factors by treating different control objectives sequentially rather than simultaneously, thereby reducing implementation complexity while maintaining versatility in controlling multiple variables.
2Ease of operation
If weight factors are used to manage multiple control objectives, then the ability to prioritize different objectives is improved, but the adjustment process becomes non-standardized and requires empirical testing
Solution Approach 1:
The patent implements a dynamic two-stage control approach where the first stage identifies optimal options for primary objectives, and the second stage dynamically evaluates additional objectives only among those pre-selected options. This dynamic sequential process naturally handles prioritization without requiring static weight factors, making the adjustment process standardized and eliminating the need for empirical testing while maintaining the ability to prioritize different control objectives.
Data Source
AI summary
A sequential or cascading predictive control method is provided, including first solving a cost function and then a second cost function for two or more control objectives. The method includesseparating the cost function into at least two or more cost functions, depending on the number of defined control objectives.The method additionally includes controlling a first variable with a unitary cost function, a single term or nature of the control objectives.The method also includes determining the possible states that minimize the cost of the first objective to be controlled.Considering only the options given through this determination, a second variable is controlled with a cost function that minimizes the cost function thereof.