Actuator Control Parameter Tuning With Gaussian Process Prediction
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Solution Overview
Problem
Existing actuator control systems face challenges in optimally setting parameters to efficiently control actuators to predefined target values, especially in predicting and adapting to the temporal evolution of probability distributions of control variables.
Innovation Solution
A method is introduced that automatically sets 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 that adapts to the manipulated and control variables, and utilizes numerical quadrature for efficient approximation, allowing for successive improvement of the model and control system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control parameter adjustment methods are used, then the control system can be implemented with simple computation, but the control precision and adaptability to different actuators are insufficient
Solution Approach 1:
The patent applies preliminary action by pre-adapting the Gaussian process model to the specific actuator characteristics before control execution. The model is trained offline using actuator data to learn the probability distribution of control variables, so that during actual control operations, the pre-adapted model can provide accurate predictions without requiring complex real-time computations. This resolves the contradiction by performing the computationally intensive adaptation work in advance.
Solution Approach 2:
The patent changes parameters by using a Gaussian process model to dynamically determine optimal control parameters based on the predicted probability distribution of control variables. Instead of using fixed control parameters, the system continuously adapts parameters according to the learned actuator characteristics and current state, achieving high precision control while the computational complexity is managed through the efficient Gaussian process framework.
2Adaptability or versatility
If the control parameters are adjusted frequently to adapt to different actuators, then the adaptability improves, but the time consumption for parameter setting increases
Solution Approach 1:
The patent performs preliminary adaptation of the Gaussian process model to each actuator type during an offline training phase. By pre-learning the actuator-specific probability distributions and control characteristics, the system eliminates the need for time-consuming parameter adjustment during actual control operations. The pre-adapted model enables immediate deployment with high adaptability, resolving the time loss contradiction.
Solution Approach 2:
The patent creates a probabilistic model copy of the actuator's behavior characteristics using Gaussian processes. This model copy captures the essential dynamics and uncertainties of the physical actuator, allowing the control system to work with the simplified model rather than repeatedly adapting to the complex physical system. The model copy enables fast adaptation while maintaining high fidelity to the actual actuator behavior.
3Measurement precision
If a detailed model is used to predict the temporal evolution of probability distribution, then the prediction accuracy improves, but the computational complexity increases
Solution Approach 1:
The patent uses a Gaussian process model with carefully selected parameters to represent the temporal evolution of probability distributions. By changing the parameterization approach to use Gaussian process hyperparameters (length scale, signal variance, noise variance) instead of detailed dynamic models, the system achieves high prediction accuracy while keeping the model computationally tractable. The Gaussian process framework provides a balance between model fidelity and computational efficiency.
Data Source
AI summary
Method for automatically setting at least one parameter of an actuator control system, which is set up for controlling a control variable of an actuator to a predefinable target value, wherein the actuator control system is set up, depending on the at least one parameter, the target value and the control variable, to generate a manipulated variable and depending on this manipulated variable to control the actuator, wherein a new value-of the at least one parameter is selected depending on a long-term cost function, wherein this long-term cost function is determined depending on a predicted temporal evolution of a probability distribution of the control variable of the actuator and the parameter is then set to this new value.