Actuator Control Parameter Tuning Using Predicted Probability Distributions
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Solution Overview
Problem
Existing actuator control systems face challenges in optimally adjusting parameters to achieve precise control of actuators, particularly in adapting to different actuators and ensuring reliable operation, especially when parameters are not well adapted.
Innovation Solution
A method for automatically setting actuator control system parameters using a long-term cost function based on predicted temporal evolution of the probability distribution of the control variable, employing a Gaussian process model for prediction and numerical quadrature for approximation, with adaptive model adjustments and episodic training to improve the control strategy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control parameter adjustment methods are used, then the control system can operate with simple structure, but the adaptation precision to different actuators deteriorates
Solution Approach 1:
The patent changes the parameter representation from fixed control parameters to probability distribution parameters (mean and standard deviation) that can adapt to different actuators. By parameterizing the control approach through distribution characteristics rather than fixed values, the system achieves better adaptation precision while maintaining manageable complexity through statistical modeling.
Solution Approach 2:
The patent introduces a probability distribution as an intermediary between the control system and the actuator. Instead of directly controlling the actuator with fixed parameters, the system uses probability distributions to mediate the control signals, enabling adaptive control that accounts for uncertainties and variations in actuator behavior without requiring complex actuator-specific tuning.
2Reliability
If actuator-specific parameter adaptation is implemented, then the control precision improves, but the adjustment complexity increases
Solution Approach 1:
The patent transforms actuator-specific parameters into probability distribution parameters (mean and standard deviation) that capture the statistical characteristics of actuator behavior. This parameter transformation allows the system to adapt to different actuators by learning their distribution characteristics rather than manually tuning complex actuator-specific parameters, thereby improving reliability while reducing adjustment complexity.
Solution Approach 2:
The control system performs self-adaptation by automatically learning the probability distribution parameters from actuator behavior data. Instead of requiring manual adjustment of actuator-specific parameters, the system serves itself by autonomously adapting to different actuators through statistical learning, which improves operational reliability without increasing user-facing complexity.
3Adaptability or versatility
If fixed control parameters are used, then the system structure remains simple, but the adaptability to different actuators deteriorates
Solution Approach 1:
The patent changes from fixed control parameters to adaptive probability distribution parameters that can be learned and adjusted for different actuators. By representing control knowledge statistically through distributions rather than fixed values, the system gains versatility to work with different actuator types while maintaining structural simplicity through the unified probabilistic framework.
Solution Approach 2:
The probability distribution-based control approach serves as a universal interface that can adapt to multiple actuator types and scenarios. The same probabilistic control framework can be applied across different actuators by learning their specific distribution characteristics, providing multi-functionality and broad adaptability without requiring separate control systems for each actuator type.
Data Source
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AI summary
The invention relates to a method for automatically setting at least one parameter (Θ) of an actuator control system (45) which is designed to control a control variable (x) of an actuator (20) to a predefinable setpoint (xd). The actuator control system (45) is designed to generate a control variable (u) depending on the at least one parameter (Θ), the setpoint (xd) and the control variable (x) and to actuate the actuator (20) depending on this control variable (u). A new value (Θ*) of the at least one parameter (Θ) is selected depending on a long-term cost function (R), wherein this long-term cost function is determined depending on a predicted temporal evolution (F) of a probability distribution (p) of the control variable (x) of the actuator (20) and the parameter (Θ) is then set to this new value (Θ*).