Heating, ventilation, and air conditioning system controller
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Solution Overview
Problem
HVAC system controllers face complexity in determining operating parameters to maintain desired set points and comfort ranges due to multiple interconnected variables, requiring significant time, computational effort, and expertise, and often neglecting dynamic disturbances without modeling.
Innovation Solution
A non-model based, generic multivariable controller that determines operating parameters for HVAC systems by receiving approximate relationships between controlled and manipulated variables, using a gain matrix to tune and optimize settings without requiring high skill or knowledge of the system, and embedding easily within existing systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional control methods are used to determine operating parameters for multiple manipulated variables, then the controlled variables can be maintained at desired set points, but the process becomes time consuming and computationally complex
Solution Approach 1:
The patent segments the control problem by dividing manipulated variables into two groups: those directly affecting the primary controlled variable and those affecting secondary controlled variables. This segmentation allows the controller to first determine operating parameters for the primary group, then use those results to determine parameters for the secondary group, breaking down a complex simultaneous optimization problem into sequential, simpler steps that reduce computational time while maintaining control precision.
2Manufacturing precision
If traditional control methods are used to determine operating parameters for multiple manipulated variables, then the controlled variables can be maintained at desired set points, but the device complexity increases
Solution Approach 1:
The controller structure is segmented into distinct functional modules: a primary controlled variable tuning module that determines operating parameters based on the primary variable and manipulated variables, and secondary controlled variable tuning modules that determine parameters based on secondary variables and the previously determined operating parameters. This modular segmentation simplifies the controller architecture while maintaining the ability to control multiple variables accurately.
Solution Approach 2:
The controller performs preliminary action by first determining operating parameters for manipulated variables that directly affect the primary controlled variable before determining parameters for variables affecting secondary controlled variables. This sequential approach eliminates the need for complex simultaneous equations and iterative solutions, reducing controller complexity while achieving precise control of all variables.
3Ease of operation
If a non-model based approach is used, then the controller can be generic and easier to implement, but system dynamics and disturbances are neglected
Solution Approach 1:
The patent incorporates feedback mechanisms where the controller receives actual measurements of controlled variables and compares them to desired set points. Based on this feedback, the controller adjusts the operating parameters of manipulated variables to minimize deviations. This feedback loop enables the generic non-model-based controller to respond to actual system conditions and disturbances, improving reliability without requiring complex system models.
Data Source
AI summary
Heating, ventilation, and air conditioning (HVAC) controllers are described herein. One method includes receiving an approximate relationship between each of a number of controlled and manipulated variables of an HVAC system, designating one of the number of controlled variables as a primary controlled variable, determining operating parameters for each of the number of manipulated variables that maintain the primary controlled variable based, at least in part, on the approximate relationship between the primary controlled variable and each respective manipulated variable, and determining operating parameters for each of the number of manipulated variables that maintain each of the other controlled variables based, at least in part, on the approximate relationship between each respective other controlled variable and each respective manipulated variable and the determined operating parameters for each of the number of manipulated variables that maintain the primary controlled variable.