Research On Control Strategy Of Dual-motor Servo System Based On Backstepping Metho

With the continuous promotion of industry 4.0 and the continuous upgrading of manufacturing industry for personalized customization,servo systems need to face increasingly complex control tasks.On the other hand,the single motor system is difficult to meet the requirements of large inertia load because of power constraints.While the dualmotor system can be applied to different occasions because of its advantages such as high power,large inertia and high control performance,which meets the high requirements of special working conditions.Especially for some large inertia systems,it is necessary to study how to control the high-speed,high-precision and high synchronization operation of dual-motor systems.Nowadays,some substantial achievements have been made in relevant research.But for the problems of multi-motor mechanical backlash,low-speed nonlinear friction,high-order system full-state constraints and so on,the high-performance control methods of dual-motor servo systems still need to be further studied and discussed.Aiming at the dual-motor servo system,combined with neural network control,fuzzy logic system and adaptive control,this paper deduces and designs high-performance tracking and synchronization controller based on backstepping.The relevant control scheme is given,and the stability is analyzed under different conditions by combining Lyapunov stability theory in the system.The main work can be summarized as follows:1.For the dual-motor servo system with disturbance torque,the integral sliding mode control based on command filter backstepping is studied.This method can avoid the problem of "explosion of complexity".The filtering error can be reduced by utilizing the error compensation mechanism at the same time,improving the servo precision of the system.The integral sliding mode surface of each subsystem is designed in the derivation process of command filtering backstepping method.And the saturation function is introduced into the control law.The ways reduce the steady-state error and chattering phenomenon of the system.Adaptive control is used to estimate and compensate the disturbance torque,which reduces the influence of the disturbance torque on the dual-motor system and improves the anti-disturbance performance.With the help of Lyapunov function,the input control law and adaptive law of the system are derived.By choosing appropriate control law and adaptive law parameters,the system error converges to a small neighborhood of the origin.2.For the dual-motor servo system considering backlash and friction simultaneously,an adaptive fuzzy controller based on command filter backstepping is designed.The fuzzy logic system is used to approximate the Lu Gre friction nonlinearity in the system.And the adaptive law of the system is given combining with adaptive control to reduce the influence of nonlinear friction on the control accuracy of the system.In addition,the nonlinear backlash model of the transmission structure is analyzed.The backlash is considered as the combination function of linear part and bounded disturbance.It is convenient for controller design and stability analysis in the system.The controller of the system is derived and the stability is proved by devising Lyapunov function.3.For the dual-motor servo system considering full-state constraints and asymmetric dead-zone,the adaptive neural network control based on command filter backstepping is studied.For the non-differentiable asymmetric dead-zone model,the smooth derivative approximation function is used.And the nonlinear function is approximated by utilizing neural network control in order to improve the control accuracy of the system.For the fullstate constraints of the dual-motor system,the time-varying obstacle Lyapunov function is utilized to ensure that the system states are within the constraint conditions.According to this function,the input control law and adaptive law of the system are derived,and the stability of the system is proved at the same time.