Finite-time tracking control for uncertain robotic manipulators using backstepping method and novel extended state observer

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 model-assisted 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.

Trajectory planning method of robot sorting system based on S-shaped acceleration/deceleration algorithm

To improve the sorting accuracy and efficiency of sorting system with large inertia robot, this article proposes a novel trajectory planning method based on S-shaped acceleration/deceleration algorithm. Firstly, a novel displacement segmentation method based on assumed maximum velocity is proposed to reduce the computational load of velocity planning. The sorting area can be divided into four parts by no more than three steps. Secondly, since the positions of workpieces are dynamically changing, a dynamic prediction method of workpiece picking position has been presented to consider all the possible positions of the robot and the workpiece, so as to realize the picking position prediction of the workpiece at any positions. Each situation in this method can constitute an equation with only one solution, and the existence of the solution can be verified by the proposed graphical method. The simulations of the motion time of the sorting process show that the proposed method can significantly shorten the sorting time and improve the sorting efficiency compared with the previous method. Finally, this method was applied to the Selective Compliance Assembly Robot Arm (SCARA) robot for experiments. In the physical picking experiment, the missing-pick rate was less than 1%, which demonstrates the efficiency and effectiveness of this method.

Model-assisted extended state observer-based computed torque control for trajectory tracking of uncertain robotic manipulator systems

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.

PSO-Lyapunov motion/force control of robot arms with model uncertainties

A method for motion/force control of robot arms with model uncertainties is presented. Tracking control of complex trajectories is guaranteed using a Lyapunov approach with high-precision performance ensured using a particle swarm optimization (PSO) algorithm. Tracking performance and robustness are simulated for a robotic device for limb rehabilitation that is designed to be adapted easily to different subjects by considering model parameter uncertainties. Controller parameters are optimized offline using the PSO algorithm with Lyapunov stability conditions considered as inequality constraints. Using the control scheme, the robot can guide limbs on smooth and non-smooth trajectories, under model uncertainties and measurement noise.

The Inverted Pendulum Benchmark in Nonlinear Control Theory: A Survey

Abstract For at least fifty years, the inverted pendulum has been the most popular benchmark, among others, in nonlinear control theory. The fundamental focus of this work is to enhance the wealth of this robotic benchmark and provide an overall picture of historical and current trend developments in nonlinear control theory, based on its simple structure and its rich nonlinear model. In this review, we will try to explain the high popularity of such a robotic benchmark, which is frequently used to realize experimental models, validate the efficiency of emerging control techniques and verify their implementation. We also attempt to provide details on how many standard techniques in control theory fail when tested on such a benchmark. More than 100 references in the open literature, dating back to 1960, are compiled to provide a survey of emerging ideas and challenging problems in nonlinear control theory accomplished and verified using this robotic system. Possible future trends that we can envision based on the review of this area are also presented.