Hybrid Controller Architecture for Real-Time Constraint Adaptation
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Solution Overview
Problem
Existing systems lack the ability to optimally control complex, hierarchical, or distributed systems with loosely or closely coupled sub-systems, particularly in dynamic operational conditions, and fail to adjust performance goals or constraints in real-time to address suboptimal behaviors or unanticipated events.
Innovation Solution
The implementation of hybrid control systems that combine machine learning and control theory, using dynamic optimization with targeted constraints and performance goals to enhance the responsiveness of machine-learned systems, allowing for predictive modeling and real-time adjustments of learning and control parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a controller includes a system-modeling component trained to represent the system under control, then the controller can provide better control outputs that take into account learned information about the complex structure of the controlled system, but the controller performs sub-optimally under conditions that differ from those used to generate the training data
Solution Approach 1:
The patent implements a governing controller that dynamically adjusts the parameters and constraints of the hybrid controller based on real-time system performance and changing conditions. This allows the control system to adapt to different operational scenarios beyond the training data conditions, resolving the contradiction between maintaining reliable control performance and adapting to varying conditions.
Solution Approach 2:
The governing controller continuously monitors system output and uses this feedback to adjust the hybrid controller's parameters and constraints. This feedback mechanism enables the system to maintain optimal performance under changing conditions by learning from actual system behavior and adjusting accordingly, rather than relying solely on pre-trained models.
2Productivity
If a hybrid controller combines dynamic components and learned system model components, then the controller provides better control outputs with minimal compute resources, but the hybrid controller exhibits suboptimal behavior when the system or environment differs significantly from development conditions
Solution Approach 1:
The governing controller dynamically modifies the hybrid controller's configuration based on environmental conditions and system performance. This allows the system to maintain high control efficiency while adapting to environmental changes that differ from development conditions, resolving the contradiction between productivity and adaptability.
Solution Approach 2:
The governing controller adjusts parameters of the hybrid controller including learning rates, constraint values, and model configurations based on real-time conditions. This parameter adaptation enables the system to maintain optimal performance across different environments without requiring complete retraining, thus preserving productivity while improving adaptability.
3Adaptability or versatility
If an additional governing controller is provided to monitor and govern the hybrid controller, then the system can adjust to different conditions, but the device complexity increases
Solution Approach 1:
The governing controller and hybrid controller are integrated into a unified control architecture where the governing controller supervises and adjusts the hybrid controller's parameters. This merging approach enables adaptability to different conditions while managing complexity through a structured hierarchical relationship rather than completely separate systems.
Solution Approach 2:
The governing controller serves multiple functions including monitoring system performance, adjusting hybrid controller parameters, updating constraints, and selecting appropriate learning models. This multi-functionality reduces overall system complexity by consolidating control functions into a single supervisory component rather than requiring separate specialized controllers for each function.
Data Source
AI summary
Example embodiments allow for networks of hybrid controllers that can be computed efficiently and that can adapt to changes in the system(s) under control. Such a network includes at least one hybrid controller that includes a dynamic sub-controller and a learned system sub-controller. Information about the ongoing performance of the system under control is provided to both the hybrid controller and to an over-controller, which provides one or more control inputs to the hybrid controller in order to modify the ongoing operation of the hybrid controller. These inputs can include the set-point of the hybrid controller, one or more parameters of the dynamic controller, and an update rate or other parameter of the learned system controller. The over-controller can control multiple hybrid controllers (e.g., controlling respective sub-systems of an overall system) and can, itself, be a hybrid controller.