In this study, a super-twisting sliding mode controller with a non-linear disturbance observer for a ball-screw servo system was designed to obtain a precise motion and fast convergent control performance. Unknown dynamics of the servo system were approximated into pre-assumed diagonal constants for rapid controller design in the real industry to avoid expensive and time consuming experimental identification process. Moreover, uncertainties due to nonlinear friction, axis misalignment and dead zone were estimated by a nonlinear disturbance observer, which is combined with the designed super-twisting controller. The designed controller and observer systems were applied to the 2-axis ball screw servo system to verify the efficacy of the proposed control system via simulation and experiment.

2-Axis Pan and Tilt Motion Platform, a complex multivariate non-linear system, may incur any disturbance, thus requiring system controller with robustness against various disturbances. In this study, we designed an adaptive backstepping compensated controller by estimating the disturbance and error using the Radial Basis Function Neural Network (RBF NN). In this process, Uniformly Ultimately Bounded (UUB) was demonstrated via Lyapunov and stability was confirmed. By generating progressive disturbance to the irregular frequency and amplitude changes, it was verified for various environmental disturbances. In addition, by setting the RBF NN input vector to the minimum, the estimated disturbance compensation process was analyzed. Only two input vectors facilitated compensatory function of RBF NN via estimating the modeling and control error values as well as irregular disturbance; the application of the process resulted in improved backstepping controller performance that was confirmed through simulation.