Research On Motion Control Of Robot Manipulators With Input And Output Constraints

In recent years,China's manufacturing industry develops towards intellectualization and informatization,which leads to rapid growth of robot manipulators,the motion control of robot manipulators has gradually become a hot issue.The central section of the manipulator are drive mechanism and control mechanism.Due to the limitations of physical components,the input constraints such as dead-zone,saturation and hysteresis inevitably exist in robot manipulator system.The existence of these input constraints will badly influence the performance of the manipulator system and even cause the system instability.At the same time,in consideration of safety and environmental protection,and in order to improve the transient-state and steady-state performance of the system,it is necessary to constraint the output of the system.Aiming at the robot manipulators system with input and output constraints,basing on back-stepping control and adaptive neural network control,the controller is designed to solve the problem of input and output constraints,which is verified by MATLAB simulation.The main contributions are as follows:Firstly,the dynamic model of the robot manipulators was established by using Lagrange equation,and its dynamic characteristics were also introduced.At the same time,adaptive control,neural network control and back-stepping control methods are elaborated,which become the basis of designing control algorithm in the following chapters.Secondly,to address the problem of unknown Prandtl-Ishiskiili hysteresis in robot manipulator,an adaptive neural network prescribed performance control based on back-stepping control is proposed.Adaptive control was used to solve the problem of unknown parameters in hysteresis.Neural network was used to approximate the model uncertainties in robot manipulators system.At the same time,prescribed performance control was used to constrain the output of the manipulators.The stability of the controller is proved by Lyapunov function and the effectiveness of the controller is verified by MATLAB simulation.Finally,an adaptive neural network full-state constraints control is proposed forrobot manipulators system with dead-zone and saturation.Nussbaum gain function was used to solve the problem of unknown control gain,and neural network was used to estimate the uncertainties of the system.Full state constraints were achieved by using the Barrier Lyapunov Function.And the system tracking error can converges to a prescribed compact set in a finite time by using speed function.The Lyapunov function stability theory was used to prove the stability of the controller,MATLAB simulation and Phantom Premium 1.5HF experiment are used to verify the effectiveness of the control method.