Forecast-Based Control for Uncertain Target System Parameters
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Solution Overview
Problem
Existing automated control systems face challenges in managing uncertainty in future parameter values, particularly in making timely control decisions with limited information, which affects the efficiency and longevity of systems like batteries and network traffic management.
Innovation Solution
The implementation of a Collaborative Distributed Decision (CDD) system that uses forecasting techniques to predict future parameter values, combining multiple models and data sets for improved accuracy, and adjusting control actions based on these forecasts to optimize system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional automated control systems are used, then control decisions can be made with current information, but uncertainty in future parameter values cannot be effectively managed
Solution Approach 1:
The system performs preliminary forecasting of future parameter values using multiple forecasting models before control decisions are made. This allows the control system to anticipate future states and prepare appropriate control actions in advance, rather than reacting to current states only
Solution Approach 2:
The system dynamically adapts between different forecasting models based on their performance and changing conditions. It continuously updates model weights and selects the most appropriate models for current situations, making the forecasting system flexible and responsive to changing environments
2Measurement precision
If multiple forecasting models are combined, then forecasting accuracy improves, but system complexity increases
Solution Approach 1:
The system changes parameters by dynamically adjusting the weights and confidence levels of different forecasting models based on their performance metrics. This allows the system to optimize forecasting accuracy by emphasizing more accurate models while downweighting less accurate ones, rather than treating all models equally
Solution Approach 2:
The system implements feedback mechanisms where forecasting results are continuously evaluated against actual outcomes. This feedback is used to update model performance assessments, adjust model weights, and improve future forecasting accuracy, creating a self-improving system
3Productivity
If real-time data processing is implemented, then control decisions are timely, but computational resources are consumed
Solution Approach 1:
The system applies partial action by selectively processing only the most critical forecasting models and parameters for each control decision, rather than exhaustively processing all available models. This reduces computational overhead while maintaining sufficient forecasting accuracy for effective control
Data Source
AI summary
Techniques are described for implementing automated control systems to control operations of specified physical target systems. In some situations, the described techniques include forecasting future values of parameters that affect operation of a target system, and using the forecasted future values as part of determining current automated control actions to take for the target system—in this manner, the current automated control actions may be improved relative to other possible actions that do not reflect such forecasted future values. Various automated operations may also be performed to improve the forecasting in at least some situations, such as by combining the use of multiple different types of forecasting models and multiple different groups of past data to use for training the models, and/or by improving the estimated internal non-observable state information reflected in at least some of the models.