Finite Time Bilateral Control Of Teleoperation System In Cartesian Task Space

In recent years,teleoperation robot system can effectively replace operators to face dangerous and complex environment,so it is widely used.However,there are some problems in teleoperation robot system,such as nonlinear uncertainty,external interference,communication delay and disturbance,which will greatly affect the stability and transparency of the system.Aiming at the external disturbance,communication time-varying delay and communication disturbance existing in nonlinear bilateral teleoperation systems(NBTS),and the existence of unknown backslash hysteresis in the controller,based on the finite time theory,the controller design research is carried out,and the controller is generated in Cartesian coordinate task space,which increases the intuition and practicability of the system.The finite time controller designed in this thesis ensures the good transparency of the system,shortens the convergence time,improves the steady-state accuracy and achieves better transient performance.The main research contents of this thesis are as follows:(1)A new control algorithm of arctan Nonsingular Terminal Sliding Mode(ANTSM)with Arctan function is designed for bilateral teleoperation system with model uncertainty and timevarying communication delay.Radial basis function neural network(RBFNN)is used to reconstruct and estimate environmental forces,so as to avoid direct transmission of force signals in communication channels.The ANTSM controller is used to deal with the system uncertainty and external disturbance,and an adaptive compensator is designed to compensate the influence of time delay rate on the phase shift of the state.The finite-time stability theory is used to ensure the finitetime transient performance and steady-state accuracy of NBTS,and the effectiveness of the control strategy is verified by numerical simulation.(2)Considering the full-state error constraint of teleoperation system,a semi-global practical finite-time controller is proposed.An auxiliary control parameter system is introduced into the mixed error of position and velocity to realize the predetermined full-state funnel error constraint,and an adaptive back-stepping RBF finite-time(ABRF)control algorithm is derived by using the semi-global practical finite-time stability criterion to meet the requirements of limited-time performance control.A new state filter with robust compensation term is designed to deal with the influence of time delay and communication disturbance on the controller.The effectiveness of the control strategy is verified by numerical simulation and experimental platform,and compared with the existing research,the faster state tracking error convergence speed and better steady-state performance are realized under NBTS.(3)Considering the hysteresis effect caused by unknown backslash-like effect in NBTS controller,a finite-time controller based on neural learning is proposed.RBFNN is used to approximate the model uncertainty,and the robust adaptive term is used to compensate the lag caused by the unknown backslash-like effect.Using Lyapunov stability theory,it is proved that the semiglobal finite time of all deviation signals is practical and stable under mixed state errors.Finally,the effectiveness of the controller strategy is verified by simulation and experimental platform,and compared with the existing research,which ensures better transient performance and steady-state accuracy.