Intelligent Controller Schedule Learning for Adaptive Operation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing control systems lack the flexibility and intelligence to adapt and produce desired operational behaviors specified after design and implementation, particularly in dynamically changing environments or user preferences.
Innovation Solution
Intelligent controllers that learn and modify control schedules over time through schedule-creation and schedule-modification interfaces, using machine learning methods to encode desired operational behaviors into control schedules and sub-schedules, with aggressive and steady-state learning phases to optimize system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control systems are used with predetermined behavior, then system reliability is maintained, but adaptability to changing environments and user preferences deteriorates
Solution Approach 1:
The controller performs self-learning by automatically observing user interactions and environmental conditions to generate control schedules without manual programming. The system serves itself by acquiring knowledge from operational data and converting it into actionable control rules, eliminating the need for complex manual configuration while maintaining high adaptability
Solution Approach 2:
The system continuously monitors user manual adjustments and system performance, using this feedback to refine and update control schedules. The feedback loop enables the controller to learn from actual usage patterns and adapt its behavior dynamically, resolving the contradiction between simplicity and adaptability
2Ease of operation
If control schedules are manually programmed, then manufacturing precision is achieved, but ease of operation deteriorates due to complex configuration requirements
Solution Approach 1:
The controller automatically acquires control knowledge by observing user interactions and environmental conditions, eliminating the need for manual programming while preserving valuable operational insights. The system learns directly from usage patterns, making operation simple without losing control knowledge
Solution Approach 2:
The system performs preliminary learning during initial operation phases, gathering data about user preferences and environmental conditions before formal control schedule generation. This preliminary action ensures that control knowledge is captured early, preventing information loss while maintaining ease of operation
3Measurement precision
If frequent control inputs are used during learning phase, then measurement precision of user preferences is improved, but loss of time increases due to aggressive learning period
Solution Approach 1:
The system dynamically adjusts the learning phase duration and intensity based on accumulated data quality and user interaction patterns. As measurement precision improves, the learning phase naturally transitions to a maintenance phase, optimizing the balance between precision and time investment without fixed predetermined durations
Solution Approach 2:
The system uses feedback from observed user interactions to determine when sufficient precision has been achieved, automatically transitioning from aggressive learning to maintenance mode. This feedback-driven approach prevents excessive learning time while ensuring adequate precision in user preference detection
4Adaptability or versatility
If control schedules are frequently updated, then adaptability to changing conditions is improved, but device complexity increases due to schedule management overhead
Solution Approach 1:
The controller automatically manages schedule updates by detecting changes in user behavior patterns and environmental conditions, generating updated schedules without manual intervention. This self-service approach maintains high adaptability while minimizing the perceived complexity for users, as the system handles schedule management autonomously
Data Source
AI summary
The current application is directed to intelligent controllers that initially aggressively learn, and then continue, in a steady-state mode, to monitor, learn, and modify one or more control schedules that specify a desired operational behavior of a device, machine, system, or organization controlled by the intelligent controller. An intelligent controller generally acquires one or more initial control schedules through schedule-creation and schedule-modification interfaces or by accessing a default control schedule stored locally or remotely in a memory or mass-storage device. The intelligent controller then proceeds to learn, over time, a desired operational behavior for the device, machine, system, or organization controlled by the intelligent controller based on immediate-control inputs, schedule-modification inputs, and previous and current control schedules, encoding the desired operational behavior in one or more control schedules and/or sub-schedules.