Newton-Based Extremum-Seeking Control for Building Equipment
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Solution Overview
Problem
Existing extremum-seeking control strategies for optimizing performance variables in energy systems, such as building equipment, often require model-based approaches and are inefficient in dynamically adjusting setpoints to achieve optimal performance without extensive data and configuration changes.
Innovation Solution
The implementation of Newton-based extremum-seeking control (NESC) systems that use a dither signal generator to perturb setpoints, a performance assessment system to determine gradients and Hessians, and apply adjustments based on these calculations to drive performance variables to extremum values, allowing for adaptive and efficient operation without a complete cost function model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If typical ESC controllers update equipment configurations before determining gradients, then the control system can optimize performance variables, but the optimization process is slow and requires extensive configuration changes and data collection
Solution Approach 1:
The system applies dither signals to the setpoint before determining gradients, allowing the performance assessment system to passively observe and calculate gradients and Hessians from the resulting equipment responses. This preliminary perturbation enables faster optimization by eliminating the need for extensive configuration changes and data collection during the optimization process
Solution Approach 2:
The invention replaces the traditional mechanical configuration change approach with a signal-based method. Instead of physically adjusting equipment configurations to determine gradients, the system uses dither signals and passive observation to calculate gradients and Hessians mathematically, significantly reducing the time required for optimization
2Reliability
If model-based approaches are used for extremum-seeking control, then the control strategy can optimize performance variables, but the system requires extensive data and configuration changes
Solution Approach 1:
The performance assessment system uses passive observation of equipment responses to dither signals to automatically calculate gradients and Hessians. The system serves itself by extracting optimization information from normal operational data without requiring external configuration changes or extensive data collection, reducing complexity while maintaining reliability
Solution Approach 2:
The system changes the setpoint parameter by applying dither signals, which induces measurable responses in the equipment. By analyzing these responses, the system calculates gradients and Hessians to determine optimal setpoint adjustments, achieving reliable optimization without requiring changes to equipment configurations or extensive data requirements
Data Source
AI summary
Systems and methods for monitoring and controlling a plant using extremum-seeking control. The method includes perturbing a setpoint for a controller by applying a dither signal to the setpoint. The controller uses a perturbed setpoint to generate one or more control inputs for the building equipment. Receiving, from the building equipment, an output signal and obtaining values of a performance variable based on the output signal, the values of the performance variable resulting from operating the building equipment based on the perturbed setpoint. The method includes determining a gradient of the performance variable with respect to the perturbed setpoint and a Hessian of the performance variable affected by the building equipment with respect to the setpoint. The method includes determining an adjustment to the setpoint predicted to drive the performance variable to an extremum based on the gradient and the Hessian of the performance variable.