Adaptive Control of Space Robot Manipulators with Task Space Base on Neural Network

Abstract

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.

System Identification and LMI Based Robust PID Control of a Two-Link Flexible Manipulator

“…A good agreement between modelling and experiments has been achieved [2]. Zhou et.al [3] presents the neural network online modelling technology to approximate the system uncertain model a space manipulator. Dogan and Istefanopulos [4] have developed the finite element models to describe the deflection of a planar two-link flexible robot manipulator.…”