Data-driven observer-based model-free adaptive discrete-time terminal sliding mode control of rigid robot manipulators

Esmaeili, Babak; Salim, Mina; Baradarannia, Mahdi; Farzamnia, Ali

doi:10.1109/icrom48714.2019.9071819

Cited by 13 publications

(9 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This salient merit not only decreases the design cost of the controller but also attenuates the undesired effect of unmodeled dynamics on the closed-loop behavior. In addition, unlike the conventional model-free adaptive DSMC (see Esmaeili et al, 2019b; Wang et al, 2016, for example) which leads to having consistent tracking performance without any amelioration even after several executions, owing to the use of the iterative learning process in MFA-ILITSMC, its tracking ability is enhanced along the iteration axis. This is a substantial privilege for repetitive tasks.…”

Section: Controllermentioning

confidence: 99%

“…In the model-free case, a fast form of TSMCs based on integer-order and fractional-order surfaces have been, respectively, developed in Wang et al (2020b) and Wang et al (2018) to control the cable-driven manipulators, where the time-delay estimation approach has been used to estimate the lumped dynamics. Furthermore, by taking nonlinear sliding surfaces into account, DL-based finite-time DSMCs have been proposed for rigid manipulators driven by PAMs, and the electrically actuated ones in Esmaeili et al (2019a) and Esmaeili et al (2019b), respectively, to gain higher precision and finite-time convergence. A constrained fast-terminal sliding surface–based MFAC has also been developed in Esmaeili et al (2020).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

Esmaeili

Madani

Salim

et al. 2021

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

This article addresses the reference tracking issue of multi-degree-of-freedom robotic exoskeletons under exogenous perturbations. First, by considering the concept of iteration-dependent full-format dynamic linearization, the exoskeleton robot’s dynamics is reformulated as a linear data model in an iterative manner. Then, based upon an iterative sliding variable, a novel data-based model-free adaptive iterative learning integral terminal sliding mode control is designed. The superiority of the proposed study is that the reference tracking problem is just solved by using the measured input/output information of exoskeletons. In addition, its finite-iteration convergence is also affirmed by mathematical analysis. The simulation investigation together with the compared results also clarifies the efficiency of the developed learning-based control algorithm.

show abstract

Section: Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

Esmaeili

Madani

Salim

et al. 2021

Journal of Vibration and Control

Self Cite

View full text Add to dashboard Cite

show abstract

“…For the rigid robot in [27], the researchers proposed an observer-based model-free adaptive terminal sliding mode controller based on data driving. The controller uses the concept of fully dynamic linearization to convert the nonlinear dynamics of the robot into an equivalent linear data model that only depends on the I/O data of the robot manipulator.…”

Section: Model-based Control and Model-free Controlmentioning

confidence: 99%

Model-Based and Model-Free Robot Control: A Review

Zhang

Liu

2021

Lecture Notes in Mechanical Engineering

View full text Add to dashboard Cite

Robot control is one of the key aspects of robotics research. Models are essential tools in robotics, such as robot's own body dynamics and kinematics models, actuator/motor models, and the models of external controllable objects. In this paper, we review the latest advances in model-based and model-free approaches with a strong focus on robot control. Based on the designed search strategy, several prevailing control approaches are classified and discussed according to their control strategies. An insight into the gripper control is also explored. Then the research problems and applicability of the control methods are discussed by investigating their merits and demerits. Based on the discussion, we summarize the challenges and future research trends of robot control.

show abstract

“…In Wang, Wang & Wang (2019) they designed an impedance controller based on torque feedback. For the rigid robot in Esmaeili et al (2019) , the researchers proposed an observer-based data-driven model-free adaptive terminal sliding mode controller. In addition, reinforcement learning is also applied to model-free control.…”

Section: Related Workmentioning

confidence: 99%

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Zhang¹,

Liu²

2021

PeerJ Computer Science

View full text Add to dashboard Cite

The robot controller plays an important role in controlling the robot. The controller mainly aims to eliminate or suppress the influence of uncertain factors on the control robot. Furthermore, there are many types of controllers, and different types of controllers have different features. To explore the differences between controllers of the same category, this article studies some controllers from basic controllers and advanced controllers. This article conducts the benchmarking of the selected controller through pre-set tests. The test task is the most commonly used pick and place. Furthermore, to complete the robustness test, a task of external force interference is also set to observe whether the controller can control the robot arm to return to a normal state. Subsequently, the accuracy, control efficiency, jitter and robustness of the robot arm controlled by the controller are analyzed by comparing the Position and Effort data. Finally, some future works of the benchmarking and reasonable improvement methods are discussed.

show abstract

Data-driven observer-based model-free adaptive discrete-time terminal sliding mode control of rigid robot manipulators

Cited by 13 publications

References 27 publications

Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

Model-free adaptive iterative learning integral terminal sliding mode control of exoskeleton robots

Model-Based and Model-Free Robot Control: A Review

Control and benchmarking of a 7-DOF robotic arm using Gazebo and ROS

Contact Info

Product

Resources

About