Active Disturbance Rejection Control Tuning for Dead-Time Stability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Motion control systems face challenges in achieving optimal performance and stability due to issues with controller tuning, particularly in systems with high dead time or phase lag, which can lead to undesirable oscillations and reduced robustness.
Innovation Solution
The system automatically determines suitable tuning parameters by establishing a relationship between plant parameters and controller parameters using robust stability analysis, specifically using the system gain and dead time to mitigate oscillations and improve performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If controller bandwidth is increased to improve response speed and disturbance rejection, then system performance is improved, but system stability deteriorates due to increased sensitivity to noise and reduced robustness
Solution Approach 1:
The patent applies parameter changes by systematically adjusting controller bandwidth and gain coefficients based on the relationship model. The controller bandwidth is optimized within a specific range (0.1 to 10 times the system bandwidth) and gain coefficients are tuned according to the model predictions, allowing the system to achieve optimal performance while maintaining stability through data-driven parameter selection rather than trial-and-error methods.
Solution Approach 2:
The patent implements preliminary action by pre-establishing a relationship model between controller parameters and system performance through robust stability analysis. This model is developed before actual controller deployment, enabling predictive determination of optimal tuning parameters. The model captures the relationship between controller bandwidth, gain coefficients, and system characteristics (dead time, time constant, gain), allowing practitioners to select optimal parameters in advance without requiring iterative tuning.
2Measurement precision
If controller bandwidth is increased to reduce tracking error, then tracking accuracy is improved, but system robustness deteriorates particularly in the presence of dead time and phase lag
Solution Approach 1:
The patent applies parameter changes by optimizing controller bandwidth within a specific range (0.1 to 10 times the system bandwidth) and adjusting gain coefficients based on the relationship model. This systematic parameter selection achieves high tracking accuracy while maintaining robustness against dead time and phase lag, as the model explicitly accounts for these system characteristics in determining optimal parameters.
Solution Approach 2:
The patent implements feedback by using the relationship model to predict system behavior and guide parameter selection. The model incorporates system characteristics including dead time, time constant, and gain, allowing the controller to be tuned with feedback about expected performance. This predictive feedback mechanism enables selection of parameters that maintain robustness while achieving high tracking accuracy.
3Productivity
If manual tuning methods are used to optimize controller parameters, then performance can be improved, but the tuning process complexity and time consumption increase
Solution Approach 1:
The patent applies self-service by enabling automatic determination of optimal controller parameters through the relationship model. Practitioners simply input system characteristics (dead time, time constant, gain) and the model automatically predicts optimal controller bandwidth and gain coefficients. This eliminates the need for manual trial-and-error tuning, significantly reducing tuning time and complexity while maintaining optimal performance.
Solution Approach 2:
The patent implements preliminary action by pre-developing the relationship model through robust stability analysis before actual controller deployment. This preliminary work captures the complex relationships between controller parameters and system performance, allowing rapid parameter determination during actual tuning. The model development phase performs the complex analytical work in advance, making the actual tuning process simple and efficient.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
A system for tuning a control system uses a simplified tuning procedure to generate robustly stabilizing tuning parameters that reduce or eliminate undesired system oscillations in the presence of long system dead times or phase lag. A control method is used to establish a relationship between the plant parameters of a controlled system and the tuning parameters of a parameterized active disturbance rejection controller determined to be optimal or substantially optimal for the control system. The plant parameters include the system gain, time constant, and dead time. Corresponding tuning parameters include the controller bandwidth and a system gain estimate. Using the system gain estimate as a tuning parameter can alleviate the influence of large dead times or phase lags on system response. Once established, these fixed relationships can be used to determine suitable tuning parameters for specific motion or process control applications based on the system gain and dominant constraints of the system.