Direct adaptive neural network-based sliding mode control of a high-speed, ultratall building elevator using genetic algorithm

Abstract A direct adaptive sliding mode controller (SMC) based on radial basis function neural network (RBFNN) approximation is proposed for a high-speed, ultratall building elevator system using genetic algorithm (GA) to optimise the control parameters. The nonlinear dynamic model of the elevator system is described, with the RBFNN used to approximate the elevator system functions and external disturbance uncertainties. The RBFNN parameters are optimised using GA. The RBFNN-SMC was compared with a traditional sliding mode controller, nonlinear pseudo-derivative feedback (NPDF) controller and a nonlinear proportional-integral-derivative controller. The Lyapunov stability theorem is applied to develop the adaptive law, thereby guaranteeing the system stability. Performance of the proposed RBFNN-SMC has been evaluated using numerical simulations. The RBFNN-SMC achieved effective control of the elevator system. Although the RBFNN-SMC system achieved comparable pre-re-levelling control to its competitors, problematic chattering was observed due to sensor noise, suggesting that the system must be coupled with a noise-attenuating filter to avoid actuator damage. Following arrival of the cabins, an adaptive re-levelling operation was applied to reduce the distance between the cabins and the arrival floor. Although both SMC variants accomplished successful re-levelling, the NPDF-based controller achieved the best performance—adjusting the final cabin position to within 1 mm of the target floor in both considered displacement overshoot cases.