Neural State Prediction for Wireless Feedback Control Latency
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Solution Overview
Problem
Existing feedback control systems face challenges in migrating to wireless networks for systems with fast dynamics and high reliability requirements, as wireless networks introduce latency that can lead to performance degradation or instability, particularly in Class B and C controlled systems.
Innovation Solution
The implementation of a predictive wireless feedback control system using a state prediction neural network that accounts for communication latencies in wireless networks, enabling the use of wireless time-sensitive networks to synchronize data and predict future states of the target system, allowing for timely control signal generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If wireless networks are used to convey control signals and measurements, then installation flexibility and mobility are improved, but latency increases leading to performance degradation or instability
Solution Approach 1:
The patent applies preliminary action by predicting the future state of the controlled system before the control signal is applied. The neural network predicts system state at time t+Δt, where Δt is the total latency, so that the control signal is based on anticipated future conditions rather than outdated past measurements, thereby maintaining stability in wireless control systems
Solution Approach 2:
The patent introduces a neural network as an intermediary component between the measurement receiver and the control signal generator. This neural network processes delayed measurements and predicts future system states, acting as a mediator that compensates for wireless network latency and enables reliable control despite the wireless connection
2Adaptability or versatility
If wireless networks are used in control systems, then device mobility is improved, but control precision deteriorates due to latency
Solution Approach 1:
The system performs preliminary prediction of the controlled system's state at the time the control signal will be effective. By predicting state at t+Δt where Δt accounts for wireless latency, the control precision is maintained despite using mobile wireless connections instead of wired connections
3Reliability
If neural networks are used to predict system states, then control performance in wireless systems is improved, but computational complexity increases
Solution Approach 1:
The patent uses a neural network to create a computational model (copy) of the controlled system's dynamics. This neural network model replicates the system's behavior to predict future states, enabling accurate control predictions without requiring complex real-time simulations or iterative calculations during actual control operation
Data Source
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AI summary
Example wireless feedback control systems disclosed herein include a receiver to receive a first measurement of a target system via a first wireless link. Disclosed example systems also include a neural network to predict a value of a state of the target system at a future time relative to a prior time associated with the first measurement, the neural network to predict the value of the state of the target system based on the first measurement and a prior sequence of values of a control signal previously generated to control the target system during a time interval between the prior time and the future time, and the neural network to output the predicted value of the state of the target system to a controller. Disclosed example systems further include a transmitter to transmit a new value of the control signal to the target system via a second wireless link.