Distributed Kalman Consensus Control Under Noisy Partial Communication
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Solution Overview
Problem
Conventional cooperative control algorithms, such as the consensus algorithm, face challenges in achieving convergence when local state parameter values are corrupted by noise, especially when communicating devices only receive data from a subset of other devices, leading to increased data exchange requirements and slower convergence.
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 towards a consensus value without increasing data exchange, by modeling process noise and adapting the process model to account for incomplete data receipt.
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 and data exchange requirements increase
Solution Approach 1:
The patent divides the global consensus problem into local sub-problems. Each communicating device independently implements a Kalman filter-based state observer using only its local measurements and local model, without requiring global system information. This segmentation allows noise filtering to be performed distributedly at each device, improving measurement precision while avoiding the complexity of centralized approaches.
Solution Approach 2:
Each communicating device serves itself by implementing the Kalman filter locally without relying on other devices for computational resources or global information. The state observer at each device uses its own local model and measurements to de-noise its state parameter values, eliminating the need for additional data exchange or centralized processing infrastructure.
2Measurement precision
If multi-hop communications are used to enable each device to receive data from all other devices, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent eliminates the need for multi-hop communications by segmenting the information requirements. Each device only needs its own local model and local measurements to run its Kalman filter, rather than requiring global information from all devices. This segmentation reduces communication hops to direct local exchanges only, significantly reducing convergence time.
Solution Approach 2:
The state models and Kalman filter parameters are pre-configured at each device before the consensus process begins. This preliminary preparation allows devices to immediately start filtering their local measurements without waiting for global information gathering, enabling faster convergence while maintaining measurement precision.
3Measurement precision
If additional data such as covariance matrices are transmitted between devices, then measurement precision is improved, but loss of information increases due to noise corruption of additional data
Solution Approach 1:
The patent extracts the essential information needed for noise filtering (local state measurements and local model parameters) and transmits only this minimal necessary data between devices. By taking out only the critical information and eliminating unnecessary data transmission, the system avoids the problem of additional data being corrupted by noise while still achieving precise state estimation through local Kalman filtering.
Solution Approach 2:
The Kalman filter acts as an intermediary that processes noisy measurements and produces clean state estimates without requiring direct transmission of intermediate computational data. The filter uses the local model as a mediator to interpret measurements, allowing precise state estimation to be achieved while transmitting only the final clean state values rather than intermediate noisy data or covariance matrices.
Data Source
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AI summary
The present disclosure relates to a method (40) for controlling a state parameter by a communicating device (20), referred to as processing device (20), in a set of communicating devices (20), 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 (S42) a local value of the state parameter, the processing device estimating (S41) 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 being modeled as a process noise, the processing device updating its local value of the state parameter based on the estimated aggregated state parameter; wherein the processing device (20) determines (S44) a convergence level of the local values of the state parameter and modifies (S45) a covariance matrix of the process noise based on the determined convergence level.