Adaptive Controller Reconfiguration Under Computation Complexity
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Solution Overview
Problem
Existing control systems for complex technical systems, such as robots and turbines, are often overly conservative and inflexible, requiring complex manual configuration of control parameters to adapt to changing operating conditions, leading to suboptimal performance.
Innovation Solution
A method and control device that capture state and complexity data to dynamically reconfigure control parameters using a control planner, optimizing performance measures like accuracy and speed through a second control loop, allowing for automatic adaptation to changing conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If control parameters are manually configured to satisfy criteria under all operating conditions, then the control system achieves comprehensive coverage of operating conditions, but the control parameters become unnecessarily conservative and the system loses flexibility
Solution Approach 1:
The control system dynamically adapts control parameters based on current operating conditions through a control planner that receives state data and complexity data, then determines optimized parameters in real-time. This replaces static manual configuration with dynamic adaptation, allowing the system to maintain optimal performance across varying conditions without being overly conservative.
Solution Approach 2:
The system implements feedback loops where state data from sensors and complexity data from the controller are continuously monitored and fed back to the control planner. This feedback mechanism enables the system to automatically adjust control parameters based on actual operating conditions and performance, resolving the contradiction between maintaining high control accuracy and adapting flexibly to changing conditions.
2Adaptability or versatility
If multiple sets of control parameters are used to account for changing control criteria, then the system can adapt to different operating conditions, but the ascertainment of such sets requires high complexity and rigid guidelines
Solution Approach 1:
The control planner automatically determines optimized control parameters based on received state data and complexity data without requiring manual intervention or rigid predefined guidelines. The system serves itself by autonomously selecting appropriate parameters from the parameter space, eliminating the need for complex manual management of multiple parameter sets while maintaining adaptability to changing conditions.
Solution Approach 2:
The system changes control parameters dynamically based on operating conditions by having the control planner select from a parameter space. This approach simplifies parameter management by using a unified parameter selection mechanism rather than managing multiple discrete parameter sets, reducing complexity while preserving adaptability through continuous parameter optimization.
3Measurement precision
If a more accurate control system is implemented, then control accuracy is improved, but computation complexity increases
Solution Approach 1:
The system dynamically balances control accuracy and computation complexity by having the control planner consider both state data and complexity data when determining control parameters. This dynamic optimization allows the system to achieve high control accuracy when computationally feasible while automatically adjusting to maintain acceptable performance when computation complexity becomes a constraint, rather than using a fixed high-accuracy approach that always increases computation load.
Solution Approach 2:
The control planner adjusts control parameters to optimize the trade-off between control accuracy and computation complexity by selecting from a parameter space. This parameter optimization enables the system to achieve accurate control when possible while reducing computational burden when necessary, resolving the contradiction by making accuracy adaptive rather than fixed.
4Productivity
If control parameters are optimized for all operating conditions, then overall performance is improved, but the configuration becomes rigid and cannot adapt to changing conditions
Solution Approach 1:
The control system achieves both optimized performance and adaptability through dynamic parameter determination. The control planner continuously receives state data and complexity data, then determines optimized parameters in real-time based on current conditions. This dynamic approach maintains high overall performance while remaining fully responsive to changing conditions, eliminating the rigidity of pre-configured parameter sets.
Data Source
AI summary
Provided is a state data of the technical system are captured and fed into a controller, which is configurable by control parameters, in order to control the technical system on the basis of the state data. Furthermore, complexity data quantifying a present computation complexity for the controller are captured and transmitted to a control planner. The control planner takes the complexity data as a basis for ascertaining an updated control parameter that renders the control currently more performant, according to a predefined performance measure, than as a result of the previous control parameter. The controller is then reconfigured by the updated control parameter.