Linear Assist System And Robust MPC Design For 2-Dof Robotic Arm Model

In recent years,robotic arms have been widely used in various industries of the national economy,and research on motion control for robotic arms has received a lot of attention.It is noteworthy that the current research work in the literature that solves the static position,static force,and velocity problems of robotic arms with the help of robotic arm kinematic methods often does not take into account the performance of the optimized transient control process,which is particularly affected in the case of multivariate constraints.Relying on robotic arm dynamics modeling to guide the control design approach is a feasible idea to solve the above problems.However,the nonlinear and highly coupled characteristics of the robotic arm make its dynamics model complex,and simplifying the model complexity can provide a viable guarantee for the design of the support control.In view of this,this paper constructs an approximately linear auxiliary system method for a typical 2-Dof robotic arm,and accordingly designs state estimation,robust control,model predictive control,and resilient control methods to improve the reliability and safety of the transient control process.The main research contents are as follows:The establishment of the 2-Dof robotic arm linear assist system is studied.The EulerLagrange method is applied to derive the 2-Dof robotic arm dynamics equations.Next,the generalized nonlinear system model of the 2-Dof robotic arm is obtained by certain definitions.Finally,the LPV model with uncertainty characteristics is used to simplify the robotic arm model,and the generalized nonlinear system model of the robotic arm is reduced to the LPV model with convex polyhedral structure.In addition,the linear parameter variation discrete system model is further obtained after discretization.The robust control problem of 2-Dof manipulator LPV continuous system model under unconstrained conditions is studied.Firstly,a robust continuous unknown input estimator is designed by parametric method to estimate the system state,and the robustness and stability of the designed estimator are analyzed.Secondly,a robust controller is designed based on Lyapunov stability and bounded real lemma,and the robustness and stability conditions of the algorithm are given.Finally,considering the impact of preventing eavesdropping attacks,the privacy protection of the control system is carried out,and a resilient and robust control method based on an unknown input estimator is designed.Use MATLAB to perform simulations to verify the effectiveness of estimator and controller algorithms as well as encryption algorithms.The robust MPC control problem of 2-Dof manipulator LPV discrete system model under constraints is studied.Firstly,a robust discrete unknown input estimator is designed for the LPV discrete system model of the manipulator,which is used to estimate the system state.Secondly,by constructing the minimum optimization method,the constrained predictive control design of the 2-Dof manipulator linear discrete system is realized,and the robustness and stability of the designed controller are analyzed.Finally,considering that network attacks will steal system data,the output signal is encrypted to protect the network transmission data,and an elastic and robust MPC control method based on an unknown input estimator is constructed accordingly.Simulations are performed to verify the effectiveness of estimator and controller algorithms as well as encryption algorithms.