Particle Filter Motion Model Control Under Changing Dynamics
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Model-based control systems face challenges when dealing with uncertain motion models, particularly when system dynamics are partially unknown or changing, leading to suboptimal performance or instability.
Innovation Solution
A modified particle filter approach is introduced, where each particle includes a motion model, a measure of uncertainty, and a weight to reflect the joint fit with the measurement model, allowing for the estimation of both the state and motion model, using a Gaussian process to model uncertainty and updating the weights of basis functions to simplify the learning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If adaptive or learning-based MPC is used to estimate unknown parameters, then control performance is improved, but the method is insufficient when system dynamics are changing
Solution Approach 1:
The particle filter is updated recursively in real-time, allowing the motion model estimates to adapt dynamically as new measurements become available. This enables the system to track changing dynamics rather than assuming static unknown parameters
Solution Approach 2:
The measurement model provides feedback to update the particle weights and resample particles, creating a closed-loop identification scheme that continuously refines the motion model estimates based on actual system behavior
2Measurement precision
If a particle filter includes motion model uncertainty to estimate both state and motion model, then accuracy is improved, but computational complexity increases
Solution Approach 1:
The motion model is decomposed into a weighted combination of basis functions, separating the estimation problem into learning weights for each basis function rather than estimating the entire complex motion model at once
Solution Approach 2:
The Gaussian process parameters (mean and covariance) are updated recursively using measurement data, transforming the computational problem from evaluating the entire Gaussian process to updating a smaller set of parameters that define the distribution
Data Source
AI summary
A system is controlled using particle filter executed to estimate weights of a set of particles based on fitting of the particles into a measurement model, wherein a particle includes a motion model of the system having an uncertainty modeled as a Gaussian process over possible motion models of the system and a state of the system determined with the uncertainty of the motion model of the particle, wherein a distribution of the Gaussian process of the motion model of one particle is different from a distribution of the Gaussian process of the motion model of another particle. Each execution of the particle filter updates the state of the particle according to a control input to the system and the motion model of the particle with the uncertainty and determines particle weights by fitting the state of the particle in the measurement model subject to measurement noise.