Event-triggered control optimal tuning through bio-inspired optimization in robotic manipulators

This paper proposes the tuning approach of the event-triggered controller (ETCTA) for the robotic system stabilization task where the reduction of the stabilization error and the data broadcasting of the control update are simultaneously considered. This approach is stated as a dynamic optimization problem, and the best controller parameters are obtained by using fourteen different bio-inspired optimization algorithms. The statistics results reveal that, among the tested bio-inspired optimization algorithms, the most reliable algorithm in the proposed tuning problem is the differential evolution variant DE/Best/1/Exp. The obtained result is validated both in numerical simulation as well as using a laboratory prototype. The simulation results indicate that the obtained control parameters can also deal with disturbances and reference changes not considered in the ETCTA's optimization problem formulation without significantly worsening the control design objective. Experimental results disclose that the proposed event-triggered control tuning approach provides the best trade-off between the number of control signal updates and the position error among other tuning approaches, decreasing the data broadcasting of the control update by around 86.33% with a non-significant increase in the stabilization error of around 26.53%.