Event triggered estimator based controller design for networked control system

• Published in: ISA transactions 2022-07, Vol.126, p.80-93
• Main Authors: K., Senthilkumar, Roy, Avinash Kumar, K., Srinivasan
• Format: Article
• Language: English
• Subjects: Estimator based controller; Event triggered mechanism; Lyapunov theory; Networked control system; Process and measurement noise
• ISSN: 0019-0578; 1879-2022
• DOI: 10.1016/j.isatra.2021.08.017

This paper presents an event-triggered estimator-based feedback control design for networked control systems (NCS) under the inevitable stochastic effects and correlated noises. Stochastic effects are modelled using random variables that obey Bernoulli distribution. There always exists a trade-off between control performance and usage of the resource. Thus, at the sensor node the relative error-based event-triggered mechanism (ETM) is incorporated to regulate the data transmission, thereby reducing energy consumption and network traffic. Here, a predicted sensor measurement is used by an estimator if data packet is lost and the event failed to trigger. This will improve the performance and reduces the computation burden. The proposed estimator estimates the state information using orthogonal projections that completes feedback control design. Sufficient conditions are provided for the mean-square stability of the proposed method using Lyapunov theory. Subsequently, the proposed method’s steady-state property, energy consumption, and computation burden are discussed in detail. Hence, it is worthy to note that the proposed work is suitable for online estimations because of its recursive structure. It is also suitable for a system with model mismatch and correlated noises. The efficacy of the proposed work is demonstrated using real-world applications in the simulation platform. •A discrete time event-triggered estimator based controller is proposed.•Prediction measurement based NCS is modelled to handle network imperfections.•The proposed method reduces energy consumption and computation burden.•Recursive estimator is suitable for online implementation.•It can handle model mismatch and it is suitable for noisy processes.