Uncertainty-Aware Control Signal Optimization for Sensor-Driven Systems
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Solution Overview
Problem
Existing control systems for industrial machinery struggle with inaccuracies in sensor inputs, leading to misleading control signals due to uncertainties in environmental and operational conditions, which can deteriorate the performance of model predictive control (MPC) systems.
Innovation Solution
A controller comprising an input module for reading sensor data, a configuration module for setting parameters for optimization and uncertainty quantification modules, an optimization module for generating control signals, and an uncertainty quantification module for determining uncertainty ranges and analyzing performance values through computer-aided simulations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If model predictive control is used to control industrial machinery, then control performance is improved, but sensor inaccuracies and uncertainties lead to misleading control signals
Solution Approach 1:
The patent introduces an intermediary processing layer between sensor inputs and the MPC controller. This layer includes data validation mechanisms, uncertainty quantification modules, and potential sensor fusion algorithms that process raw sensor data before feeding it to the MPC system, thereby filtering out inaccuracies and preventing misleading control signals
Solution Approach 2:
The patent implements enhanced feedback mechanisms that continuously monitor sensor data quality and system performance. By incorporating feedback loops that detect and correct sensor inaccuracies in real-time, and by using performance feedback to adjust control strategies, the system maintains reliable control despite measurement uncertainties
2Reliability
If robust model predictive control is implemented to handle uncertainties, then control reliability under uncertain conditions is improved, but system complexity and computational burden increase
Solution Approach 1:
The patent segments the control system into distinct functional modules: sensor data validation module, uncertainty quantification module, MPC control module, and performance monitoring module. This segmentation allows each module to handle specific aspects of uncertainty management independently, making the overall complex system more manageable and maintainable while achieving robust control
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting control parameters and uncertainty thresholds based on operating conditions. By changing parameters adaptively rather than using fixed complex models, the system achieves robust control with reduced computational burden and simpler implementation
Data Source
AI summary
A controller and a method for providing an optimized control signal for controlling a technical system is provided. The controller includes: —an input module configured to read in sensor data, —a configuration module configured to provide a first configuration parameter for configuring an optimization module and a second configuration parameter for configuring an uncertainty quantification module, —the optimization module configured to provide a control signal depending on the first configuration parameter, —the uncertainty quantification module configured —to provide an uncertainty range or statistical distribution for the control signal depending on the second configuration parameter, and —to determine performance values for control signals within the uncertainty range or the statistical distribution by a computer-aided simulation —to analyze the respective performance values, and —to provide an analysis result to the optimization module, and —an output module configured to output the optimized control for controlling the technical system.