Designing Optimal Thresholds for Ternary Event-based State Estimation via Multi Objective Particle Swarm Optimizer
Somayeh Davar, Arash Mohammadi · 2018
The paper proposes a novel multi-objective approach for optimizing the threshold values in a ternary event-triggering (TET) mechanism using within event-based estimation architecture. In particular, Multi-Objective Particle Swarm Optimization (MOPSO) is employed as the optimization technique considering three objectives, i.e., the maximization of the rate of communicating quantized measurements together with the minimization of the number of idle and event epochs. In addition, the optimization process is subject to three constraints in order to guarantee the feasibility of the overall structure. The multi-objective optimizer has been utilized to automatically find the optimal design. The proposed method, referred to as the TEB-PSO, is capable of identifying a set of optimal values for the two thresholds within the TET to reduce the communication overhead. The simulation results confirm the effectiveness of the proposed method with the TET mechanism.