Synchronization Control Of Multiple Robotic Manipulators Based On Sliding Mode Control

With the wide application of multiple robotic manipulator systems,the control requirements of multiple robotic manipulator systems have gradually changed from stability and reliability to rapidity,high steady-state accuracy and strong robustness.In the actual control,multiple robotic manipulator often has unmodeled parts,unknown parameters or models,which makes the design of control algorithm more difficult.Therefore,according to the different information of the multiple robotic manipulator systems,the targeted design of the synchronous control algorithm has important significance for improving the control precision and stability of the system.Therefore,in this thesis,the multiple robotic manipulator system as the research object,and studies the position synchronization control algorithm and overall design scheme.The main work is as follows:1.The dynamic model and characteristic analysis of multiple robotic manipulator system are presented in this thesis.Firstly,the dynamic model of a single manipulator is established by using Lagrange dynamic equation method,then the multiple robotic manipulator model is deduced and the dynamic characteristics of the manipulator are briefly analyzed.Then,according to the existing synchronization control structure of multiple robotic manipulators,a mean coupling synchronization control strategy is proposed.The theoretical analysis shows that the synchronization control strategy has the advantages of simple operation form and fast feedback speed,which can achieve the synchronization convergence of system tracking error and synchronization error.2.For multiple robotic manipulators with model uncertainties and external disturbances,an improve global sliding mode synchronization control scheme based on extended nonlinear disturbance observer is proposed.Firstly,an extended nonlinear disturbance observer is designed to estimate the composite disturbance and the disturbance change rate of the system.Since the information of the interference change rate can be effectively utilized in the disturbance estimation process,the accuracy of the estimation is improved.Then,based on the mean coupling synchronization control strategy,an improved global sliding surface is designed,and then the sliding mode control law is obtained to make the synchronization convergence of the position tracking error and synchronization error of the system.Finally,the simulation results show that the control algorithm has strong robustness and interference suppression ability.3.For multiple robotic manipulator systems with unknown parameters and lumped disturbances,a fixed time sliding mode controller based on adaptive parameter identification is designed.Firstly,the unknown parameter estimation error information is extracted by redefining the known regression matrix and a series of filtering operations in the multiple robotic manipulator system,and then a fixed time adaptive law with the parameter estimation error correction term is constructed,so that the unknown parameters of the system can converge to the true value in a fixed time.Then,by designing an adaptive fixed time sliding mode controller,the position tracking error and synchronization error of the system converge at a fixed time,and the upper bound of the convergence time is independent of the initial state of the system.Finally,simulation results show that the proposed algorithm can accurately estimate the unknown parameters of the system.4.For multiple robotic manipulator systems with unknown dynamics model,a fixed-time sliding mode position synchronization scheme based on neural network is proposed.Combined with the mean coupling synchronization control strategy,the fixed time sliding mode surface and controller are designed to make the tracking error and synchronization error converge in a fixed time,and the upper bound of convergence time is independent of the initial state of the system.At the same time,the RBF neural network weight update law is designed to estimate the system unknown nonlinear dynamics model.This method does not need prior knowledge of the system model parameters,and can effectively reduce the chattering problem.The fixed time convergence of the system is verified by simulation.