Feedback Controller Calibration Using Kalman Filter Adaptation
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Solution Overview
Problem
Current methods for automating controller calibration in dynamic machines, such as autonomous vehicles and robots, are inadequate for continuous and safety-critical applications, as they rely on human expertise and trial-and-error approaches, which are not suitable for adapting to changing operating conditions.
Innovation Solution
The use of a modified Kalman filter to iteratively calibrate and update control parameters in real-time, allowing for adaptive control in dynamic environments by predicting and adjusting control parameters based on performance objectives and measurement models, while ensuring safety and stability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used for controller tuning, then control performance can be optimized for specific conditions, but the system cannot adapt to changing operating conditions over time and requires considerable manual effort
Solution Approach 1:
The patent transforms static control parameters into dynamic variables that evolve over time through a state-space model. The controller parameters are represented as state variables that can be updated recursively, allowing the system to adapt to changing operating conditions while maintaining optimal performance. This is achieved by defining a state transition model that predicts parameter evolution and a measurement model that corrects predictions based on actual performance feedback.
Solution Approach 2:
The system implements self-calibration by automatically adjusting its own control parameters without external intervention. The Kalman filter enables the controller to learn from its own performance measurements and autonomously update its parameters, eliminating the need for manual recalibration while maintaining optimal control across varying conditions.
2Extent of automation
If trial-and-error learning methods are used for controller calibration, then the system can learn from human expertise, but the method is not suitable for safety-critical machines and requires human demonstrators which limits automation
Solution Approach 1:
The patent implements a rigorous feedback mechanism where controller parameters are continuously updated based on performance measurements. The Kalman filter processes feedback data to recursively refine parameter estimates, enabling safe and reliable automated adaptation without requiring human demonstrators. This feedback-driven approach ensures that changes are data-driven and verifiable, making it suitable for safety-critical applications.
Solution Approach 2:
The patent replaces trial-and-error mechanical learning methods with a mathematical estimation framework. Instead of relying on human expertise and iterative trial-and-error, the system uses a Kalman filter-based state estimation approach that provides deterministic, verifiable parameter updates based on mathematical models and measured data, enhancing reliability for safety-critical machines.
3Device complexity
If control parameters are fixed in advance, then the controller design is simpler, but the controller becomes suboptimal when operating conditions change
Solution Approach 1:
The patent transforms static control parameters into dynamic variables that evolve over time through a state-space model. The controller parameters are represented as state variables that can be updated recursively, allowing the system to adapt to changing operating conditions while maintaining optimal performance. This is achieved by defining a state transition model that predicts parameter evolution and a measurement model that corrects predictions based on actual performance feedback.
4Ease of manufacture
If calibration is performed only at production stage, then the manufacturing process is simpler, but it becomes difficult to adjust the controller when operating conditions change over the machine's lifetime
Solution Approach 1:
The patent enables continuous calibration throughout the machine's operational lifetime rather than performing calibration only once at production. The recursive Kalman filter continuously updates controller parameters based on ongoing performance measurements, ensuring the controller remains optimal as operating conditions change. This continuous adaptation process maintains ease of manufacture while enabling lifelong adjustability.
Data Source
AI summary
A system for controlling an operation of a machine for performing a task is disclosed. The system submits a sequence of control inputs to the machine and receives a feedback signal. The system further determines, at each control step, a current control input for controlling the machine based on the feedback signal including a current measurement of a current state of the system by applying a control policy transforming the current measurement into the current control input based on current values of control parameters in a set of control parameters of a feedback controller. Furthermore, the system may iteratively update a state of the feedback controller defined by the control parameters using a prediction model predicting values of the control parameters and a measurement model updating the predicted values to produce the current values of the control parameters that explain the sequence of measurements according to a performance objective.