HVAC Multivariable Controller for Fast Set Point Tuning
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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 resources, and expertise, and often need to account for dynamic disturbances without generating system models.
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 can be easily integrated into existing HVAC 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 complex multivariable control problem into simpler sub-problems by using iterative optimization methods that adjust one manipulated variable at a time while holding others constant, thereby reducing computational complexity while maintaining control precision
Solution Approach 2:
The patent changes the approach from solving complex simultaneous equations to using parameter-based optimization methods that iteratively adjust operating parameters based on performance criteria, reducing computational burden while achieving desired control accuracy
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 process becomes difficult and requires significant expertise
Solution Approach 1:
The patent implements self-service by enabling the control system to automatically determine optimal operating parameters through iterative optimization algorithms, eliminating the need for operator expertise in complex multivariable control while maintaining precise control of all controlled variables
Solution Approach 2:
The patent transforms the control approach from requiring expert knowledge of complex variable interactions to using automated parameter optimization that systematically adjusts manipulated variables based on measured performance, making the system easy to operate while maintaining high control precision
3Device complexity
If a non-model based approach is used, then the controller can be easily integrated into existing HVAC systems with low computational footprint, but the controller must ignore system dynamics and disturbances
Solution Approach 1:
The patent applies feedback by continuously measuring controlled variables and using the measured deviations to iteratively adjust manipulated variables, achieving reliable control without requiring an explicit system model, thereby maintaining low controller complexity while improving reliability through closed-loop operation
Solution Approach 2:
The patent changes from model-based parameter prediction to empirical parameter optimization based on actual system measurements, allowing the controller to adapt to real system behavior without requiring complex dynamic models, thus maintaining simplicity while achieving reliable control performance
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.