2022
DOI: 10.3390/robotics11020047
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A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

Abstract: Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot … Show more

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Cited by 13 publications
(5 citation statements)
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“…Hence, they are not reasonable solutions for commercialization. Adaptive sliding mode [63] is a complex method to handle models with two degrees of freedom, with the ability to be adapted online. It is an appropriate method for managing uncertainties, but it is not a reliable solution to grasp different objects without training.…”
Section: Discussionmentioning
confidence: 99%
“…Hence, they are not reasonable solutions for commercialization. Adaptive sliding mode [63] is a complex method to handle models with two degrees of freedom, with the ability to be adapted online. It is an appropriate method for managing uncertainties, but it is not a reliable solution to grasp different objects without training.…”
Section: Discussionmentioning
confidence: 99%
“…Besides softening the interaction force by designing soft joints from a mechanical perspective, imposing desired behaviour on the robot can also be done from the software Fig. 2: A soft robotic arm is in a compressed state due to the collision with the environment [9] side [13]. To achieve this, in [14], Hogan presented two concepts, i.e.…”
Section: B Admittance and Impedancementioning
confidence: 99%
“…These choices were based on our previous research experience [20][21][22]. To control the SEAs of Serpens, a two-feedback-loop position-control algorithm was previously proposed by our research group [23][24][25]. The inner controller loop was implemented as a model reference adaptive controller (MRAC), while the outer control loop adopted a fuzzy proportional-integral controller (FPIC).…”
Section: Software Designmentioning
confidence: 99%
“…To tackle the challenges related to our previous work [23][24][25], an alternative approach based on the use of methods that do not assume a priori knowledge was successively presented [37]. In particular, a novel controller was presented based on the use of an artificial neural network (ANN) that was trained with reinforcement learning (RL) by applying proximal policy optimisation (PPO).…”
Section: Proposed Control Approach Based On Rlmentioning
confidence: 99%