Automated control-schedule acquisition within an intelligent controller
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Solution Overview
Problem
Current control systems lack the flexibility and intelligence to adapt and produce desired operational behaviors specified after controller design and implementation, particularly in dynamic environments where traditional design techniques fail to accommodate changing conditions.
Innovation Solution
The development of intelligent controllers equipped with automated control-schedule learning methods that allow them to receive sensor inputs, output control signals, and incorporate user inputs to refine control schedules over time, enabling adaptive behavior and optimal system operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional control design techniques are used, then the controller can ensure predetermined system behavior under normal operational conditions, but the controller lacks flexibility to adapt to changing conditions and produce desired operational behaviors specified after controller design and implementation
Solution Approach 1:
The controller performs self-learning by automatically acquiring control schedules from sensor data and user inputs without requiring external reprogramming. The machine learning module enables the controller to autonomously adapt to changing conditions and refine its control strategies over time, making the system self-improving and highly adaptable.
Solution Approach 2:
The controller continuously receives feedback from sensors and user inputs, processes this information through machine learning algorithms, and adjusts its control schedules accordingly. This closed-loop feedback mechanism enables the controller to learn from past performance and optimize future control actions, bridging the gap between predetermined behavior and adaptive responsiveness.
2Productivity
If traditional control systems are used, then the system structure remains fixed and predictable, but the system cannot learn from operational data or improve performance over time
Solution Approach 1:
The controller transitions from a static, predetermined control architecture to a dynamic, adaptive system. The control schedules are no longer fixed but evolve continuously as the machine learning module processes operational data and refines control strategies. This dynamic adaptation enables continuous performance improvement without requiring complete system redesign.
Solution Approach 2:
The patent replaces traditional mechanical control programming with intelligent machine learning algorithms. Instead of manually configuring control parameters and schedules, the system uses automated learning from operational data to generate and optimize control strategies, substituting complex manual configuration mechanisms with adaptive computational processes.
3Adaptability or versatility
If the controller incorporates learning capabilities to adapt to user preferences, then operational flexibility improves, but the computational requirements and processing time increase
Solution Approach 1:
The controller performs preliminary learning during periods when system adjustments are less critical, building knowledge bases and control schedules in advance. By proactively acquiring and processing operational data during low-demand periods, the system minimizes processing delays when real-time control decisions are needed, balancing adaptability with responsive performance.
Data Source
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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.