Control Loop Tuning Using Simulation-Based Performance Indicators
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Solution Overview
Problem
Designing and optimizing control loops in control and automation technology is challenging due to the need to balance contradictory requirements such as control and disturbance behavior, with existing methods being restrictive and lacking objective measures for parameter selection, especially for non-linear systems and varying interference conditions.
Innovation Solution
A system-independent design of control loops using standardized multidimensional control performance indicators (CPIs) for assessing control quality, allowing for simulation-based optimization of controller parameters and structures to ensure high-quality behavior across all operating conditions, with search-based testing for robustness and efficient use of computing power.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual design and parameter selection by development engineers is used, then control loop design flexibility is maintained, but the optimization process becomes extremely time-consuming and lacks objective measures
Solution Approach 1:
The system enables automated self-assessment of control loop designs through simulation-based evaluation. The performance assessment unit automatically evaluates control quality using standardized CPIs without requiring manual engineer intervention, allowing the system to self-optimize parameters while maintaining objective measurement capabilities
Solution Approach 2:
The system systematically varies control parameters and controller structures in simulation runs to find optimal configurations. By automatically changing parameters and evaluating their impact on control quality metrics, the system accelerates the optimization process while providing objective assessment through standardized performance indicators
2Productivity
If automated tuning methods are used for linear control loops, then parameter optimization is accelerated, but the methods become restrictive and require specification of essential characteristics
Solution Approach 1:
The performance assessment system uses standardized control performance indicators that are system-independent and applicable to various types of control loops. The simulation-based approach can evaluate different controller structures and parameters across multiple test cases, making the method universally applicable to both linear and non-linear systems without requiring system-specific adaptations
Solution Approach 2:
The system dynamically adapts to different control loop configurations by automatically adjusting evaluation parameters and test scenarios based on the specific system being assessed. This allows the automated method to maintain versatility across different systems while still providing rapid optimization through simulation-based evaluation
3Reliability
If robust design for disturbance compensation is performed on simple ARMA models, then optimization is possible with uncertainty ranges, but the assumption of known disturbances and linear time-invariant models limits applicability
Solution Approach 1:
The system handles non-linear and time-varying control loops by dynamically adjusting simulation parameters and evaluation criteria. The performance assessment adapts to different system behaviors across multiple operating conditions, allowing robust design for complex non-linear systems without requiring simplifying assumptions about linearity or time-invariance
Solution Approach 2:
The system performs preliminary simulation-based evaluation across multiple test cases and operating conditions before final controller deployment. By pre-assessing performance under various scenarios including uncertain disturbances, the system ensures robustness is built into the design before implementation, rather than requiring simplified models
4Measurement precision
If control quality is assessed using multiple standardized indicators, then objective and system-independent evaluation is achieved, but the complexity of parameter optimization increases
Solution Approach 1:
The performance assessment system breaks down control quality evaluation into multiple standardized control performance indicators, each measuring a specific aspect of control behavior. This segmentation allows comprehensive objective assessment while managing complexity by evaluating each indicator separately through automated simulation runs rather than requiring complex manual analysis
Data Source
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AI summary
Method (40, 50) for designing a control loop (R1, R2, 41), characterized by the following features: - a simulation (42, 52) of the closed control loop (R1, R2, 41) is carried out, - based on the simulation (42, 52) the control loop (R1, R2, 41) is subjected to a performance evaluation (10, 20, 30, 43, 53) and - by feedback (44, 45, 46, 54, 55, 56) of the performance evaluation (10, 20, 30, 43, 53) to the design the control loop (R1, R2, 41) is improved.