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This article considers finite-time trajectory tracking control problem for robotic manipulators with parameter uncertainties and external disturbances. A finite-time controller that achieves high precision and strong robustness is proposed without the requirement of the exact dynamic model. First, a novel finite-time model-assisted extended state observer is designed to compensate the system uncertainties with complex and uncertain dynamics. Then, a composite finite-time controller is developed for trajectory tracking control with the help of finite-time model-assisted extended state observer. Compared to the classic extended state observer, it is proved that the estimation error of finite-time modelassisted extended state observer can be stabilized in finite time. Meanwhile, the finite-time convergence of the closed-loop system with the proposed controller can also be proved through Lyapunov's stability theory. A variable structure term is employed to compensate the estimation errors of finite-time model-assisted extended state observer. The validity of the control scheme is demonstrated by simulations and experiments.

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Computed torque control is an effective control scheme for trajectory tracking of robotic manipulators. However, computed torque control requires precise dynamic models of robotic manipulators and is severely affected by uncertain dynamics. Thus, a new scheme that combines a computed torque control and a novel model-assisted extended state observer is developed for the robust tracking control of robotic manipulators subject to structured and unstructured uncertainties to overcome the disadvantages of computed torque control and exploit its merits. The model-assisted extended state observer is designed to estimate and compensate these uncertain dynamics as a lumped disturbance online, which further improves the disturbance rejection property of a robotic system. Global uniform ultimate boundedness stability with an exponential convergence of a closed-loop system is verified through Lyapunov method. Simulations are performed on a two degree-of-freedom manipulator to verify the effectiveness and superiority of the proposed controller.

Parametric interpolation for spline plays an increasingly important role in modern manufacturing. It is critical to develop a fast parametric interpolator with high accuracy. To improve the computational efficiency while guaranteeing low and controllable feedrate fluctuation, a novel parametric interpolation method based on prediction and iterative compensation is proposed in this article. First, the feedrate fluctuation and Taylor's expansion are analyzed that there are two main reasons to reduce the calculation accuracy including the truncation errors caused by neglecting the high-order terms and discrepancy errors between the original curve and the actual tool path. Then, to reduce these errors, a novel parametric interpolation method is proposed with two main stages, namely, prediction and iterative compensation. In the first stage, a quintic polynomial prediction algorithm is designed based on the historical interpolation knowledge to estimate the target length used in the second-order Taylor's expansion, which can improve the calculation accuracy and the convergence rate of iterative process. In the second stage, an iterative compensation algorithm based on the secondorder Taylor's expansion and feedrate fluctuation is designed to approach the target point. Therefore, the calculation accuracy is controllable and can satisfy the specified value through several iterations. When finishing the interpolation of current period, the historical knowledge is updated to prepare for the following interpolation. Finally, a series of simulations are conducted to evaluate the good performance in accuracy and efficiency of the proposed method.

Considering the joint elasticity, a novel dynamic parameter identification method is proposed for general industrial robots only with motor encoders. Firstly, the unknown parameters of the elastic joint dynamic model are analyzed and divided into two types. The first type is the motion-independent parameter only including the joint stiffness, which can be identified by the static force/torque-deformation experiments without the dynamic model. The second type is the motiondependent parameter composed of the rest of the parameters, which needs the dynamic excitation experiments. Therefore, these two types of parameters can be identified separately. Meanwhile, it is found that the rotor inertia parameters can be obtained from the manufacturer, which reduces the identification difficulty of other parameters. After obtaining the rotor inertia and joint stiffness, an approximate processing algorithm is proposed considering the motor friction to establish the linear identification model of other parameters. Hence, the least squares can be employed to identify the parameters, and the independence of the inertia and joint viscous friction parameters are not affected. Meanwhile, the exciting trajectories can be optimized throughout the robot workspace, which reduces the effect of measurement noise on identification accuracy. With the proposed separated identification strategy and approximate processing algorithm, the dynamic parameters can be obtained precisely without double encoders on each joint. Finally, a series of simulations are conducted to evaluate the good performance of the proposed method.

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