Research On Trajectory Tracking Control Of 3-dof Humanoid Manipulator Based On Iterative Learning Algorithm

The research contents of this paper consist of dynamic modeling,trajectory tracking control and virtual prototype simulation of a 3-DOF humanoid manipulator.The humanoid manipulator is composed of a 2-DOF parallel mechanism which forms the elbow and a 1-DOF serial mechanism which forms the wrist.Its unique mechanical structure leads to a more complex mathematical model and more difficult control.Therefore,it is of great theoretical value and practical significance to study the modeling and control strategy of the 3-DOF humanoid manipulator.First,the pseudo-underactuated dynamics model of the system is established by using the Lagrange method combined with a generalized variable expansion.The unmodeled dynamics and external uncertainty disturbances of the system are further considered to determine the complete dynamic model of the 3-DOF humanoid manipulator.Secondly,the complete dynamic model of the system is transformed into the state equation of a class of nonlinear time-varying systems.Based on this,a PD self-tuning iterative learning controller with variable forgetting factor is designed to realize the trajectory tracking control of humanoid manipulator.In order to improve the robustness and tracking accuracy of the system,the average value of multiple iterative learning errors is introduced into the design of the forgetting factor.At the same time,the gain auto-tuning term is added into the algorithm,which not only achieves the acceleration convergence effect,but also solves effectively the blindness problem of control gain selection.The convergence analysis of the system is carried out based on the norm theory.The simulation results demonstrate the effectiveness of the control strategy in the trajectory tracking control of the 3-DOF anthropomorphic manipulator.Thirdly,a fuzzy adaptive iterative learning controller with initial state learning is designed to solve the problem that the manipulator cannot meet the global continuous Lipschitz condition and strictly the same initial conditions in practical applications.The addition of the initial state learning relaxes the strict repetition of the initial state of the system.At the same time,the fuzzy controller is used to adjust the control parameters in the adaptive iterative learning process in real time and improve the convergence speed of the system.The convergence of the system is analyzed by designing the Lyapunov function,and the simulation achieves precise trajectory tracking control of the 3-DOF humanoid manipulator.Finally,the virtual prototype of the 3-DOF humanoid manipulator was established through SolidWorks and ADAMS.Combining the motion curve of human arm throwing motion and the PD self-tuning iterative learning control strategy with variable forgetting factor to obtain the final trajectory tracking curve.The curve was introduced into ADAMS for kinematics simulation analysis to further verify the correctness of the virtual prototype of the 3-DOF humanoid manipulator.