Networked Control Systems with Stochastic Communication Protocols

Networked control systems (NCSs) integrate physical processes, sensors, actuators and controllers through communication networks, enabling distributed and scalable control over long distances. The introduction of stochastic communication protocols, in which packet delivery, scheduling and timing are governed by probabilistic rules, addresses challenges of packet loss, variable delay and bandwidth constraints inherent to modern networks. Such protocols harness randomisation to balance network load, maintain system stability and optimise resource utilisation. By modelling delays and dropouts as stochastic processes—often via Bernoulli or Markov chains—researchers derive robust controllers that guarantee stability in the mean square sense or meet H∞ performance criteria despite unpredictable network behaviour. Practical implementations span autonomous vehicles, power grids, industrial automation and remote health monitoring, where resilience to uncertain communication is paramount. Key challenges include the design of efficient scheduling schemes, the handling of event‐triggered transmissions to reduce unnecessary traffic and the integration of security measures against denial-of-service attacks. The interplay between control theory and network science continues to drive advances in algorithmic design, theoretical guarantees and real-world deployment of NCSs under stochastic communication regimes.

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Recent studies have introduced scheduling and event‐triggered mechanisms to mitigate network constraints. In one approach, a round-robin protocol is applied to discrete-time Markov-jumping neural networks, granting exclusive channel access to individual actuators in cyclic order. This scheme reduces communication burden while ensuring stochastic stability, with controller gains synthesised via tractable linear matrix inequalities under varying activation functions. Complementary work on event-triggered H∞ output tracking control for nonlinear NCSs transforms variable sampling intervals and transmission delays into equivalent zero-order-hold refreshing intervals. By employing Lyapunov–Krasovskii functionals and solving associated matrix inequalities, these methods guarantee robust tracking performance and efficient bandwidth utilisation.

Another research thrust focuses on resilience to data loss and cyber-attacks. Advanced H∞ control frameworks model packet dropouts and denial-of-service events as Bernoulli processes with distinct probabilities during attack and quiescent intervals. An observer-based design compensates for intermittent sensor-to-controller and controller-to-actuator losses, deriving mean-square stability conditions and disturbance attenuation indices. Numerical examples corroborate the ability of these strategies to maintain closed-loop stability and robust performance in the face of probabilistic communication impairments.

Networked Control Systems with Stochastic Communication Protocols publication trend

The graph below shows the total number of articles in networked control systems with stochastic communication protocols across all publications each year (not limited to Nature Index journals).

Technical terms

Networked Control System (NCS): A control architecture in which sensors, controllers and actuators communicate over a shared network rather than via direct connections.

Stochastic Communication Protocol: A set of rules governing data transmission whose behaviour—such as timing, scheduling or packet delivery—is characterised by probability distributions.

Markov Chain: A mathematical model describing a sequence of random events in which the probability of each event depends only on the state attained in the previous event.

Event-Triggered Control: A transmission strategy where data are sent only when a predefined condition is met, reducing unnecessary network traffic compared with periodic sampling.

H∞ Control: A robust control methodology that seeks to minimise the worst-case gain from disturbance inputs to controlled outputs, ensuring performance in the presence of uncertainties.

Bernoulli Process: A sequence of independent random experiments each with two outcomes (success or failure), often used to model packet arrival or loss events.

References

1. Round-robin scheduling protocol-based stabilization for discrete-time Markov jumping neural networks. AIP Advances (2024).
2. An H∞ Output Tracking Control Approach to Sampled-Data Control for Nonlinear Networked Control Systems. IEEE Access (2020).
3. H∞ Control for the Nonlinear Markov Networked Control System in the Presence of Data Packet Loss and DoS Attacks via Observer. Mathematical Problems in Engineering (2023).