Dynamic model has broad applications in motion planning, feedforward controller design, and disturbance observer design. Particularly, with the increasing application of model-based control in industrial robots, there has been a resurgence of research interest in accurate identification of dynamic models. However, on the one hand, most existing identification methods directly rely on least squares or weighted least squares (WLS), which suffer from outliers and could lead to physical infeasible solutions. On the other hand, nonlinearity of the friction model is seldom treated in a unified way with linear regression. Moreover, recent researches have shown that proper exciting trajectories are crucial to the identification accuracy, but few of previous works take measurement noise into consideration when optimizing the exciting trajectories. In this article, we propose an iterative approach which integrates WLS, iteratively reweighted least squares with linear matrix inequality constraints, and nonlinear friction models so that the above-mentioned issues can be properly solved altogether. Our research also reveals that performance can be improved by including priori knowledge of measurement noise in the optimization of exciting trajectories. The proposed approach is supported by experimental analysis of four different combinations within the framework on a 6-DoF industrial robot.

In this paper, a new discrete reaching law with improved quasi-sliding-mode domain (QSMD) is proposed and a sliding-mode controller is designed for discrete-time systems with uncertainties. By redefining the change rate as the second-order difference of the system uncertainties and adopting the continuous-approximate function, smaller width of the QSMD can be guaranteed. Moreover, the QSMD of the proposed reaching law is obtained and the system dynamics in and out the QSMD are theoretically analyzed. Perturbation estimation technique is employed to estimate the unknown uncertainties. Thus, no prior knowledge of the uncertainty bound is required. Both numerical simulations and experimental results on a piezoelectric actuator are presented to demonstrate the performance of the proposed method.

This paper presents the design, analysis, and verification of a novel reaching law for discrete-time sliding-mode control. The control gains of the reaching law are automatically regulated by the power function and the exponential term that dynamically adapts to the variation of the switching function. The reaching law also employs the perturbation estimation and the difference function to redefine the change rate as the second-order difference of the uncertainties. Compared with previous methods, the proposed reaching law has the capability to amend the control gains in a wise manner, further mitigate chattering and guarantee smaller width of the quasi-sliding-mode domain (QSMD). In order to describe the system dynamics in the reaching phase and the sliding phase with respect to control gain values, both the decrement band and the QSMD (ultimate band) of the proposed reaching law are theoretically analyzed. The reaching steps for the switching function to converge toward the sliding surface are also obtained. The effectiveness of the proposed method is verified through numerical simulations and experimental investigations on a piezoelectric actuator.

For a collaborative robot that shares the workspaces with humans, physical human-robot interactions are unavoidable. Therefore, it must be capable to detect collisions. Usual collision detection algorithms are based on the robot's dynamic model that frequently suffers from complex joint frictions. The uncertainty of commonly modeled frictions could lead to low sensitivity of the collision detection algorithm. This paper presents a novel collision detection scheme based on the robot's dynamic model that calculates the reaction force/torque at the bedplate. This dynamic model could avoid modeling joint frictions. To identify the dynamic model, a systematic procedure by measuring the force and torque at the robot's bedplate with a force/torque sensor is introduced. Moreover, a compensation method for the force/torque sensor signal coupling is developed. The proposed identification procedure has been demonstrated on a 6-DOF collaborative robot. Finally, collision detection experiments are conducted on a test-bed and humans. The results show that the proposed scheme is reliable and can effectively reduce the impact force with simple reaction strategies.