Sensor State Estimation Without State Equation Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
In real-time control systems for robots and construction machines, accurately determining the state equation is challenging, making it difficult to effectively filter out noises and estimate true values without diverging from the expected operation.
Innovation Solution
An estimation apparatus and method that perform linear prediction on time-series data from sensors to calculate an estimate value before update, without requiring a pre-determined state equation, using a prediction section and update section to refine the estimate value using observation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the Kalman filter is used for filtering observation data, then the filtering performance is improved in linear systems with Gaussian noises, but the device complexity increases due to the need for accurate state equation modeling
Solution Approach 1:
The patent extracts and removes the requirement for state equation modeling from the filtering process. Instead of using the Kalman filter which requires a state space model, the invention uses a simpler filtering approach that directly processes observation data without needing to determine system parameters like inertia moment or friction resistance.
Solution Approach 2:
The patent replaces the complex, resource-intensive state equation modeling approach with a simpler, more lightweight filtering method. This disposable approach does not require persistent system identification or complex model maintenance, achieving filtering with reduced computational overhead.
2Reliability
If the state equation is determined accurately by obtaining parameters like inertia moment and friction resistance, then the Kalman filter can be effectively applied, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent removes the requirement for measuring difficult physical parameters like inertia moment and friction resistance. The filtering method is designed to work directly with observation data from sensors without needing these hard-to-measure system parameters, thereby eliminating the measurement difficulty while maintaining reliability.
3Device complexity
If a simple filtering method is used without state equation, then the device complexity is reduced, but the measurement precision deteriorates due to inability to cancel noises effectively
Solution Approach 1:
The patent implements a feedback mechanism where the filtering process continuously uses recent observation data to adapt to changing system conditions. This feedback approach allows the simple filter to effectively cancel noises by leveraging temporal correlations in the data without requiring complex forward models.
Solution Approach 2:
The patent employs a dynamic filtering approach that adapts to changing system conditions in real-time. Rather than relying on a fixed state equation, the filter dynamically adjusts its behavior based on the statistical properties of the observation data, maintaining noise cancellation capability while keeping the system simple.
Data Source
AI summary
In order to provide an estimation apparatus estimating a true value from observation data without determining a state equation for a target to be controlled, the estimation apparatus includes a prediction section and an update section. The prediction section executes linear prediction on time-series data, which includes observation values acquired from a sensor attached to the target to be controlled, to calculate an estimate value before update relating to a state of the target to be controlled. The update section updates the estimate value before update by using the observation value acquired from the sensor. The prediction section may calculate the estimate value before update by weighted linear prediction on the time-series data of the observation values.