Adaptive Working Machine Control Using Updated Operation Models
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Solution Overview
Problem
Existing control systems for large-scale systems like chemical plants and construction sites struggle to flexibly respond to changes in evaluation values and controlled objects, such as frequent changes in productivity and machine updates, due to a lack of mechanisms for optimizing evaluation values and coping with dynamic work environments.
Innovation Solution
A control system that includes a control quantity calculating unit and a model updating unit to calculate and update operation models based on the state of the controlled object, allowing for flexible optimization of evaluation values and adaptation to changes in controlled objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a learning period is used to optimize evaluation values, then optimization accuracy is improved, but response time to changes deteriorates
Solution Approach 1:
The system pre-calculates and stores optimal control parameters for various work contents in a database before actual operation. When work content changes, the controller immediately retrieves the pre-prepared optimal parameters without requiring a learning period, thus achieving both optimization accuracy and fast response time
Solution Approach 2:
The system dynamically switches between different pre-stored optimal control parameters based on the current work content. The controller identifies the current work content and automatically selects the corresponding optimal parameters from the database, enabling real-time adaptation without learning delays
2Adaptability or versatility
If control parameters are manually adjusted, then adaptability to changes is improved, but operation complexity deteriorates
Solution Approach 1:
The controller automatically identifies the current work content and selects the corresponding optimal control parameters from the database without human intervention. The system serves itself by autonomously adapting to work content changes, eliminating the need for manual parameter adjustment while maintaining high adaptability
Solution Approach 2:
The system continuously monitors work content through sensors and automatically adjusts control parameters based on feedback from the database. When work content changes are detected, the controller retrieves updated parameters and applies them automatically, creating a closed-loop system that adapts without human input
3Manufacturing precision
If detailed control instructions are provided for autonomous machines, then control precision is improved, but system complexity deteriorates
Solution Approach 1:
The control system is segmented into distinct functional modules: work content identification module, database storage module, and parameter retrieval module. Each module handles a specific aspect of the control process, maintaining control precision while reducing overall system complexity through functional decomposition
Solution Approach 2:
The database stores universal optimal control parameters that can be applied to multiple working machines and various work contents. This single database serves multiple functions across different machine types and operations, reducing system complexity while maintaining control precision through standardized parameter management
Data Source
AI summary
A control system comprises: a control quantity calculating unit for calculating, on the basis of an operation target of a controlled object, an operation evaluation index, and an operation model, a control quantity for driving the controlled object, which is a working machine; and a model updating unit for updating the operation model on the basis of a state quantity of the controlled object. The control system may additionally be provided with: a command converting unit for converting the control quantity into a command value for a drive unit of the controlled object; and a conversion updating unit for updating a conversion characteristic from the control quantity to the command value, on the basis of the command value and a response of the controlled object.