As the key connecting part of the rigid flexible coupling manipulator, the structural dynamic characteristics of the bolted joint are analyzed by using the joint simulation technology of pro/e and ANSYS. Based on the spring equivalent principle, the finite element equivalent model of bolt joint is established, the relationship equation between contact surface pressure and bolt preload is derived, and its stress state is analyzed; Based on the micro convex deformation model and Hertz contact theory, the tangential stiffness equation and normal stiffness equation of the bolted joint are derived respectively. The three-dimensional model of the bolted joint is established by using pro/e and imported into ANSYS for joint simulation. The simulation experiments reveal the influence of bolt joint vibration characteristics under different conditions from the aspects of bolt diameter, pre tightening force, bolt group number and bolt distribution. The conclusions have important engineering value for the structural optimization of rigid flexible coupling manipulator.
Considering the control problems caused by uncertainties such as inaccurate modeling, external disturbance and joint flexibility, a neural network control method based on H∞ is proposed. By establishing the dynamic model of the free-floating space robot with flexible joints, according to its dynamic characteristics, it is split into a slow subsystem model representing the rigid characteristics and a fast subsystem model representing the flexible characteristics. Based on the H∞ robust control theory, a robust controller based on neural network is designed to realize the decoupling control of the rigid dynamic model, The designed weight adaptive learning rate can ensure the online and real-time adjustment of parameters. Based on Lyapunov theory, it is proved that the designed controller can ensure that the L2 gain of the system is less than the given index. A feedback controller based on velocity differential is designed to compensate the angle error caused by joint flexibility. The experimental simulation results verify that the proposed control method is effective and has good engineering application value.
As are considered, the body posture is controlled and position cannot control, space manipulator system model is difficult to be set up because of disturbance and model uncertainty. An IntroductionSpace robot has different dynamics and constraints with the ground robot: kinetics of mechanical arm and the base of the coupling, singular, limited fuel supply and limits of attitude control system. These factors lead to space robot show the strong nonlinear dynamics properties, as a result the dynamics and control of space robot than fixed ground robot is complex, not like ground fixed base of robots controlled by general method. For example, the dynamic model of manipulator mass, inertia matrix and load quality cannot be accurately acquired, and external disturbance signals have a certain impact on the controller. To eliminate these non-linear factors, many advanced control strategies such as robust control ref. However, in many cases, the desired trajectory is described in task-space and the robot is controlled by the torque input in joint-space, this is known as the task-space tracking problem.Ref.[16]-[17] bring forward adaptive control methods. In the process of designing, the parameters of dynamic equations need be linearized, so complicated pre-calculation is required. Ref. [18] proposes an fuzzy-neural control method which does not requires the exact model of robot. But much parameter is adjusted, that affects the real-time. Ref. [19] has presented a neural network control method, uncertain model can be identified adaptively by neural network, but this control scheme only can guarantee the system uniformly ultimately bounded (UUB).In this paper, an adaptive neural-network controller is proposed to deal with the task space tracking problem of space robot manipulators with uncertain kinematics and dynamics. The tracking controller is model-independent; this control method obtains control laws by the neural network online modeling technology. The neural network approximation errors and external bounded disturbances are eliminated by sliding mode variable structure controller. The control method neither requires an estimate of inverse dynamic model, nor requires a time-consuming training process. Based on the Lyapunov theory, this control method proves global asymptotic stability of the whole closed-loop system. The neural controller can not only achieve higher precision without calculating the inverse Jacobian matrix, so it reduces the calculation quantity, but also meet real-time requirements. So it has great value in engineering applications. Simulation results show that the controller can achieve higher precision.
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