Research On Adaptive Command Filter Backstepping Control Of Multi-manipulator System

With the development of modern science and technology,manipulator has gradually been applied to all aspects of people’s production and life,which has greatly promoted the rapid development of industry and science and technology.In the industrial production process,compared with a single manipulator,a networked system composed of multiple manipulators has the advantages of low power consumption and high efficiency,which has become one of the hot topics for scholars.This paper mainly combines the command filtered backstepping technology,adaptive technology and neural network approximation theory,etc.,and proposes an adaptive command filtered backstepping control strategy for multiple manipulator systems.In addition,the uncertain parameters and unknown backlash are considered,as well as the multiple manipulator systems with unknown control directions.The main research contents of the paper are as follows:Firstly,the manipulator system based on neural network adaptive backstepping control is considered.The command filtered backstepping technology,adaptive and neural network approximation theory are applied to the manipulator system,and its stability is proved according to Lyapunov theory.Based on this,the finite-time adaptive backstepping control of the manipulator system with uncertain parameters and unknown control backlash is studied,the command filter,virtual signal,adaptive update law and error compensation form are designed.The simulation is performed in Matlab/Simulink.The model of two-link manipulator and the PUMA 560 manipulator are selected.Simulation experiments show that the proposed method has better control effect.Secondly,the multiple manipulator system based on neural network adaptive command filtering backstepping control is considered.The command filtered backstepping technique is used to avoid the computational complexity caused by the traditional backstepping control,and the error compensation signal is used to compensate the error caused by the command filtering.In addition,the neural network approximation theory is used to effectively approximate the uncertain nonlinear terms of the system,and the stability proof is given based on Lyapunov theory.In the case of multi-manipulator system with uncertain parameters and external disturbances,Matlab/Simulink simulation experiments prove that the control strategy of the multiple manipulator system can achieve good joint position consistency tracking.Thirdly,the multiple manipulator system with uncertain parameters and unknown control directions based on neural network adaptive command filtering control is considered.When the control direction and system nonlinear dynamics are unknown,the Nussbaum function,command filtered backstepping technology,adaptive technology,and neural network approximation theory are used.The designed neural network adaptive command filtered backstepping controller can not only deal with the computational complexity caused by traditional backstepping,but also can make the consensus tracking error converge into a sufficiently small neighborhood.Finally,the effectiveness of the strategy was verified by Matlab/Simulink.