Distributed Kalman Consensus Control Under Noisy State Observations
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Solution Overview
Problem
Conventional cooperative control algorithms, such as the consensus algorithm, assume perfect knowledge of local state parameters, which are often corrupted by noise, leading to convergence issues when communicating devices receive data from only some other devices, and existing solutions either require global data exchange or increase noise corruption.
Innovation Solution
A method using a Kalman filter-based state observer that iteratively estimates an aggregated state parameter and controls the covariance matrix of process noise, allowing communicating devices to converge to a consensus value without increasing data exchange, by modeling process noise and adapting the covariance matrix based on convergence levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If Kalman filter-based state observers are used to de-noise local state parameter values, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex global de-noising problem into local segments by implementing independent Kalman filters at each communicating device. Each device performs state estimation locally using only its own measurements and received state values, avoiding the need for centralized processing while achieving effective noise reduction.
Solution Approach 2:
Each communicating device serves itself by implementing its own Kalman filter-based state observer. The device independently estimates its state parameter using local measurements and information from neighbors, without requiring external assistance or centralized coordination, thereby reducing overall system complexity.
2Measurement precision
If multi-hop communications are used to enable each communicating device to receive data from all other communicating devices, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The patent segments the communication network into local neighborhoods, where each device only needs to exchange data with its immediate neighbors. This local segmentation eliminates the need for multi-hop communications across the entire network, significantly reducing convergence time while maintaining adequate estimation accuracy through the distributed Kalman filter approach.
Solution Approach 2:
The patent applies partial action by having each device receive data only from a subset of neighbors rather than all other devices. This partial information exchange is sufficient for the local Kalman filter to achieve effective state estimation and consensus, avoiding the time-consuming global data collection required by traditional approaches.
3Measurement precision
If additional data such as covariance matrices are transmitted between communicating devices, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The patent extracts only the essential state parameter values for transmission between devices, removing the need to exchange additional data such as covariance matrices. The Kalman filter at each device uses this minimal exchanged information along with local measurements to compute state estimates, avoiding the noise corruption that would affect transmitted covariance data.
Solution Approach 2:
Instead of transmitting complex data structures like covariance matrices that are susceptible to noise, the patent uses simple copying of state parameter values between devices. This minimal data exchange is sufficient for the distributed Kalman filters to converge to consistent estimates without introducing additional noise corruption.
Data Source
AI summary
A method for controlling a state parameter by a communicating device, referred to as processing device, in a set of communicating devices, a subset of communicating devices being associated to said processing device, said subset including the processing device and the communicating devices from which the processing device receives data for updating a local value of the state parameter, the processing device estimating an aggregated state parameter aggregating the local values of the state parameter of the communicating devices of the subset by using a Kalman filter which applies a process model, an error introduced by the process model, the processing device updating its local value of the state parameter based on the estimated aggregated state parameter; wherein the processing device determines a convergence level of the local values of the state parameter and modifies a covariance matrix of the process noise based on the determined convergence level.