Abstract
In this paper, we consider iterative learning control for trajectory tracking of robotic manipulator with uncertainty. An improved quadratic-criterion-based iterative learning control approach (Q-ILC) is proposed to obtain better trajectory tracking performance for the robotic manipulator. Besides of the position error information, which has been used in existing Q-ILC methods for robotic control, the velocity error information is also taken into consideration such that a new norm-optimal objective function is constructed. Convergence and error sensitivity properties for the proposed method are also analyzed. To deal with uncertainty, the Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are incorporated for estimation of uncertain parameters by constructing extended system states. The performances between the two filters are also compared. Simulations on a 2DOF Robot manipulator demonstrate that the improved Q-ILC with parameter estimators can achieve faster convergence and better transient performance compared to the original Q-ILC, in the presence of measurement noise and model uncertainty.
Highlights
Robot manipulators are widely used in the modern manufacturing such as injection molding, automobile assembly industry and spinning, where there are plenty of repetitive operations
This paper proposed an improved quadratic-criterion-based iterative learning control approach (Q-iterative learning control (ILC)) algorithm for trajectory tracking of uncertain robot manipulators, the velocity information is considered compared to the original Q-ILC
The trajectory tracking error of the previous batch is more fruitfully used as the feedforward signal of the Q-ILC algorithm, which is helpful for improving the control performance
Summary
Robot manipulators are widely used in the modern manufacturing such as injection molding, automobile assembly industry and spinning, where there are plenty of repetitive operations. M. Zhu et al.: Estimation-Based Quadratic ILC for Trajectory Tracking of Robotic Manipulator the proportional-derivative feedback structure was proposed in [13]. Moore [22] issued to update the input signal of the operation by solving an quadratic norm-optimal problem that constructed by tracking error This algorithm was later referred as the Q-ILC. Compared to existing methods that only use the position error information for industrial robots, the new scheme utilizes more available observations such that the control performance can be improved, as verified by both theoretical analysis and simulation studies. The integration of improved Q-ILC algorithm and the UKF-based dual estimation is able to greatly improve the trajectory tracking performance of uncertain robot systems, maintain robustness to noise, disturbance and model errors.
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