Hybrid Controller Architecture for Adaptive Hierarchical Control
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Solution Overview
Problem
Existing systems lack effective methods to optimize and supervise complex, hierarchical, or distributed control systems that operate under dynamic conditions, particularly when sub-systems exhibit misbehavior or unanticipated changes, leading to sub-optimal performance and failure to meet constraints and performance goals.
Innovation Solution
The implementation of a hybrid control system combining machine learning and control theory, known as the Hoffmann Optimization Framework (HOF), which dynamically monitors and adjusts system configurations, updates learning models, and modifies control parameters to ensure optimal performance and stability, using a governing controller to oversee subordinate hybrid controllers and adjust constraints and performance goals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a hybrid controller combining learned system models and dynamic components is used, then control performance and adaptability are improved, but system complexity increases
Solution Approach 1:
The controller is divided into distinct functional modules: a learned system model component that captures complex system behavior patterns, and a dynamic component that provides formal performance guarantees. This segmentation allows each component to specialize in different aspects of control, resolving the contradiction by organizing complexity into manageable, purpose-driven segments.
Solution Approach 2:
The patent merges two previously separate control approaches (learned models and dynamic control) into a unified hybrid controller. The learned system model and dynamic component work together synergistically, with the learned model handling adaptability and the dynamic component ensuring stability, thereby achieving improved control performance while managing complexity through integration.
2Measurement precision
If the hybrid controller is trained on specific conditions, then control accuracy improves for those conditions, but performance deteriorates under different conditions
Solution Approach 1:
The controller incorporates dynamic components that can adapt their behavior based on current operating conditions. The dynamic component adjusts control parameters in real-time based on system state and performance requirements, enabling the controller to maintain accuracy across varying conditions rather than being fixed to training conditions.
Solution Approach 2:
The hybrid controller uses feedback mechanisms where the dynamic component continuously monitors system performance and adjusts control actions accordingly. This feedback loop allows the controller to compensate for deviations from training conditions, maintaining control accuracy when operating conditions change by learning from real-time performance data.
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.