Sensor networks with distributed event-triggered scheme for T–S fuzzy system with dissipativity analysis. (May 2023) - Aslam, Muhammad Shamrooz; Qaisar, Irfan
Highlights: This study proposes a distributed event–triggered system of communication. A communication network is used to physically distribute the sensor nodes and the filter nodes. Depending upon the sensor topology, the sensor nodes acquire data from their underlying neighbors, aggregate the data using preset sensor topologies, and then sample the raw data at regular intervals, before broadcasting it to the remote filter node. By monitoring events, we can determine whether we need to broadcast/transmit current sampled data for each of our sensor nodes. Nodes can choose to broadcast data only if a prescribed threshold is exceeded between their sampled outputs and the most recent message. An evaluation of distributed event-triggered dissipative filters and their design criteria is undertaken. A communication system with multiple delays is created as a result of event–triggered communications, which includes communication delay information. Dissipative dispersion filters can be specified as asymptotically stable with prescribed weighting average dissipative disturbance attenuation performances as long as the requirements are met. The analysis of the tradeoff between weighing average dissipative performance and communication resource occupancy is conducted in conjunction with the theoretical results obtained. New fuzzy LKFs are derived for single and double integral matrices, which are less liberal regarding the existence of dissipative extended fuzzy filters that depend on the delay. Abstract: In this article, the problem of distributed extended dissipativity consensus filtering over the mobile sensor environment in which each sensor communicates through an event is investigated. We address the fuzzy filtering problem through a unified approach that considers H ∞, L 2 − L ∞ and dissipative performance constraints. The event-triggering scheme is determined by event-based data processors (EDPs), which determine whether the sampled–data needs to be transmitted. A filter built on event data is applied at each node based on the collected samples from a sensor and its neighbors. By transforming it into an error system with an interval time-varying delay, the distributed event-triggered dissipative consensus filtering problem is solved. A new fuzzy Lyapunov–Krasovskii approach is derived to solve the filtering problem in the form of Linear Matrix Inequality (LMIs) . Finally, using an engineering example, a unified algorithm for co-designed filter gains as well as for threshold parameters at the event condition is presented.