Adaptive neural network output feedback robust control of electromechanical servo system with backlash compensation and disturbance rejection

In this paper, the high-performance tracking control of electromechanical servo systems is concerned. A novel neural network state observer is designed to observe the unknown states. Compared with existing neural network observers, the proposed observer has higher observation accuracy and better robustness. The addition of a fixed-weight single-node neural network can effectively improve the approximation ability of the double-layer neural network without adding a huge amount of calculation. With the addition of the new gain adjustment terms, the observer can still achieve high observation accuracy when the neural network approximation performance is poor, and the observation error can be kept arbitrarily small. To cope with the inherent explosion of the complexity problem in the classical backstepping method in controller design, a command filter is utilized. Compared with other results, the command filtering error has also been considered, and compensating signals are designed to eliminate it. The Lyapunov function is used to show the stability of the controller. Extensive comparative simulations and experimental results verify the effectiveness and advancement of the proposed control strategy compared with other controllers.