Sliding Mode Controller (SMC) is a controller design method used for control systems, aimed at achieving robust and stable control of systems. To improve the performance of SMC, this paper applies a hybrid enhanced particle swarm optimization algorithm (HEPSO) to optimize the parameters, including \(c_{1}\) , \(c_{2}\) , \(\varepsilon\) and \(k\) , of SMC (HEPSO-SMC). The HEPSO integrates three strategies: adaptive inertia weightings (AIW), unified factor enhancement (UFE), and global optimal particle training (GOPT). The HEPSO is validated by simulation with CEC2022 which contains twelve benchmark functions, and the results show that the HEPSO is superior to the other variants of the PSO algorithm in terms of convergence speed and accuracy. The HEPSO-SMC is used as a 2-jointed manipulator for simulation verification. The simulation results, which are compared to PSO-SMC, IPSO-SMC, and UPS-SMC, are shown to illustrate the effectiveness and robustness of the HEPSO-SMC.
