Intelligent swarm algorithms for optimizing nonlinear sliding mode controller for robot manipulator

This work introduces an accurate and fast approach for optimizing the parameters of robot manipulator controller. The sliding mode control (SMC) was proposed as it documented effective tool designing robust controllers complex high-order linear nonlinear dynamic systems operating under uncertain conditions. In this Intelligent particle swarm optimization (PSO) social spider (SSO) were used obtaining best values to achieve consistency, stability robustness. Additional design integral (ISMC) implemented system high theory For optimizer processes, mean square error performances index considered. effectiveness tested with six degrees freedom by using (PUMA) robot. iteration SSO PSO algorithms objective function obtained, fitness =4.4876