Adaptive Control Parameter Planning Under Computing Load
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Solution Overview
Problem
Existing control systems for complex technical systems, such as robots and turbines, often require conservative control parameters that are inflexible and require significant effort to adjust, failing to adapt optimally to changing operating conditions and computing efforts.
Innovation Solution
A method and control device that dynamically reconfigures control parameters based on real-time status and computing effort data, using a control planner to optimize performance measures like accuracy and speed, with the ability to automatically adjust control methods in multiple loops.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control parameters are manually determined by experts to meet criteria under all operating conditions, then reliability is improved, but device complexity and ease of operation deteriorate due to conservative parameters and significant adjustment effort
Solution Approach 1:
The control system performs self-optimization by automatically determining control parameters based on recorded status data and cost data, without requiring manual expert intervention. The control planner autonomously evaluates different control parameters and selects those that optimize the performance measure, enabling the system to serve itself in parameter optimization.
Solution Approach 2:
The control parameters are made dynamic and adaptable to changing operating conditions. The system continuously records status data and cost data, and the control planner dynamically adjusts control parameters in response to changing criteria such as computing effort, energy consumption, or performance requirements, rather than using fixed manual parameters.
2Adaptability or versatility
If different sets of control parameters are used to account for changing control criteria, then adaptability is improved, but ease of operation deteriorates due to rigid specifications and significant determination effort
Solution Approach 1:
The control system automatically determines optimal control parameters through the control planner, which evaluates status data and cost data to select parameters that satisfy changing control criteria. This eliminates the need for manual determination effort while maintaining adaptability to different operating conditions.
Solution Approach 2:
The system implements a feedback mechanism where cost data (quantifying computing effort, energy consumption, or other criteria) and status data are continuously recorded and fed back to the control planner. The control planner uses this feedback to automatically adjust control parameters, enabling the system to adapt to changing criteria without manual intervention.
3Manufacturing precision
If more precise control is implemented to improve performance, then manufacturing precision is improved, but use of energy deteriorates due to higher computational effort
Solution Approach 1:
The system dynamically changes control parameters based on the current operating conditions and cost data. When high precision is required, the control planner selects parameters that achieve the necessary precision while minimizing computational effort and energy consumption. The parameter optimization balances precision requirements against energy costs.
Solution Approach 2:
The control system applies different levels of control precision locally based on specific operating conditions. The control planner evaluates which control parameters provide sufficient precision for the current task while minimizing computational effort, rather than applying maximum precision uniformly across all operating conditions.
4Productivity
If control parameters are optimized for specific operating conditions, then productivity is improved, but adaptability deteriorates due to rigid control specifications
Solution Approach 1:
The control system is made dynamic through automatic parameter optimization. The control planner continuously evaluates status data and cost data to determine optimal control parameters for the current operating conditions, enabling the system to adapt to different conditions while maintaining high performance. This dynamic approach replaces rigid fixed-parameter control with adaptive optimization.
Data Source
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Figure 2
AI summary
State data (SD) of the technical system (TS) are acquired and fed into a controller (CTL) configurable by control parameters (MP) in order to control the technical system (TS) based on the state data (SD). Furthermore, computational effort data (AD), quantifying the current computational effort of the controller (CTL), are acquired and transmitted to a control planner (PL). Based on the computational effort data (AD), the control planner (PL) determines an updated control parameter (MPS), which, according to a predefined performance measure (PM), makes the control system more efficient than with the previous control parameter (MP). The controller (CTL) is then reconfigured using the updated control parameter (MPS).