Self-Learning Motor Control for Sensorless Energy Optimization
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Solution Overview
Problem
Existing control systems for temperature and flow control in systems like pumps and fans often fail to optimize energy use and adapt to changing environments, relying on external sensors and manual configuration, which can lead to inefficiencies and increased costs.
Innovation Solution
A self-learning control system that detects input and system variables, updates a model to predict optimal operation points, and adjusts the operation of variably controllable motors to achieve setpoints without external sensors, optimizing energy use and system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external sensors and manual configuration are used in control systems, then measurement precision can be improved, but device complexity and operational costs increase
Solution Approach 1:
The control system uses its own operational data and built-in models to detect and optimize parameters without requiring external sensors. The system self-calibrates and self-optimizes by analyzing its own performance data, eliminating the need for additional measurement devices and reducing system complexity.
Solution Approach 2:
The patent replaces physical external sensors with virtual sensing capabilities implemented through software models and algorithms. The control system uses mathematical models to infer physical parameters from operational data, substituting mechanical sensing hardware with computational methods.
2Ease of operation
If existing control systems are used, then ease of operation is maintained, but energy optimization and adaptability to changing environments deteriorate
Solution Approach 1:
The control system dynamically adapts to changing environmental conditions and system performance by continuously updating its models and optimizing parameters in real-time. The system transitions from static pre-programmed control to dynamic self-learning control that responds to actual operating conditions.
Solution Approach 2:
The system implements continuous feedback loops where operational data is collected, analyzed, and used to automatically adjust control parameters. The feedback mechanism enables the system to learn from past performance and continuously optimize energy consumption without manual intervention.
3Measurement precision
If manual configuration and external sensors are required, then measurement accuracy is improved, but loss of time for setup and maintenance increases
Solution Approach 1:
The control system performs self-configuration and self-calibration by automatically detecting system parameters and optimizing control models without requiring manual setup or external calibration equipment. The system reduces maintenance time by using its own operational data for continuous self-optimization.
Data Source
AI summary
A control system for an operable system such as a flow control system or temperature control system. The system operates in a control loop to regularly update a model with respect at least one optimizable input variable based on the detected variables. The model provides prediction of use of the input variables in all possible operation points or paths of the system variables which achieve an output setpoint. In some example embodiments, the control loop is performed during initial setup and subsequent operation of the one or more operable elements in the operable system. The control system is self-learning in that at least some of the initial and subsequent parameters of the system are determined automatically during runtime.