Cooperative transport tasks with robots using adaptive non-conventional sliding mode control

Gracia, Luis; Solanes, J. Ernesto; Muñoz-Benavent, Pau; Esparza, Alicia; Miró, Jaime Valls; Tornero, Josep

doi:10.1016/j.conengprac.2018.06.005

Cited by 12 publications

(9 citation statements)

References 49 publications

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“…Human-robot cooperation has been the main focus of a growing number of research contributions in the last years. For instance, [14]- [16] consider human-robot cooperation in the context of multi-agent systems, whereas in [17], [18] it is used to cooperatively perform transport tasks.…”

Section: B Literature Reviewmentioning

confidence: 99%

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Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

et al. 2020

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This article develops a human-robot cooperation to carry out treatments such as sanding, polishing, etc. on the surface of a known rigid object. For this purpose, a vision system is considered to get the object location to ensure not only the perpendicularity of the robot tool to the object surface but also a smooth approach of the tool to the surface. In order to add flexibility, the proposal includes the simultaneous combination of automatic and manual modes of operation. Thus, the human user can guide the robot tool to treat arbitrary areas (manual mode) and, when the operator releases the tool, the robot goes into the automatic mode to treat prior established areas. The method uses a task prioritization framework and three types of controllers: an admittance controller for the tool guidance; a hybrid controller to modify the tool orientation and, in the automatic mode, the tool position; and a sliding mode controller to limit the velocity at which the tool approaches the object surface. The applicability and efficacy of the proposed method is demonstrated experimentally using a conventional 6R robot arm. INDEX TERMS Cooperative sanding, robot cooperation, vision system I. INTRODUCTION A. MOTIVATION

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Section: B Literature Reviewmentioning

confidence: 99%

“…In order to avoid taking the time derivative of J n for the last term in (18), the following hybrid control equation is considered for Level 2:…”

Section: ) Manual Modementioning

confidence: 99%

Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

et al. 2020

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“…The formulation of the optimal joint angle is revealed in Eq. (7), in which  refers to the control waveforms from SMC and fuzzy K signifies the constant portrayed by fuzzy system. Accordingly, the optimal joint angle is determined on the basis of the control signals that are generated from SMC.…”

Section: Fig 2 Solution Encodingmentioning

confidence: 99%

“…In addition, it is much complex to accomplish better performance while the control design is entirely dependent on the robotic plant design [5] [6]. Numerous techniques were tracked to deal with this issue such as model predictive control, feedback linearization, and SMC [7] [8]. SMC exists as an enhanced control technique that was extensively deployed due to its easiness in modeling, reliable performance and order diminution features [9].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Fuzzy Sliding Mode Controller for Robot Manipulator: Effect on External Interferences

2019

IJITEE

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In the previous decades, the SMC approach has attained unique consideration as this technique offers a systematic model to maintain robust performance and asymptotic stability. As robotic manipulators turn out to be gradually more significant in industrial automation, robotic manipulators by means of SMC have raised as a significant region of research. Hence, this paper intends to model and establish an adaptive sliding mode controller (SMC) for robotic manipulator. As it is not feasible to match up the SMC functions with the system model each time, this paper implements a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. Consequently, it derives the obtained features as fuzzy rules. In the subsequent phase, it signifies the derived fuzzy rules depending on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the external disturbances and noise and validates the effectiveness of the proposed and conventional models. IV. RESULTS AND DISCUSSIONS A. Experimental ProcedureThe proposed SAGWO-FSMC scheme that adopts the fuzzy model to support the SMC to control the robotic manipulator was simulated in MATLAB, and the outcomes were attained. The count of iteration was fixed as 100. For analyzing the performance of the proposed model, it was distinguished with the traditional experimental schemes such as SMC [1], FSMC [19], and GWO-SMC [17] in case of external disturbances and noises. The basic Simulink model of the SAGWO-FSMC was shown in Fig. 3 and the SAGWO block was broadly modeled in Fig. 4.

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“…Several robust control systems for the LLE have been developed to handle the external disturbances and uncertainties [21]- [23]. For instance, He et al [24] applied an adaptive neural network to estimate the unknown model of a two DoF rehabilitation robot by reducing the tracking error and effective interactions between human and robot.…”

Section: Introductionmentioning

confidence: 99%

Initialized Model Reference Adaptive Control for Lower Limb Exoskeleton

2019

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In this paper, a Proportional-Integral-Derivative (PID) controller tuning scheme by Initialized Model Reference Adaptive Control (IMRAC) for a Lower Limb Exoskeleton (LLE) is presented. Mathematical expression of the LLE structure is determined using Lagrangian and Kirchoff's equations. The transfer function of the structure based on the physical features of the links, and actuators is established under Range of Motion (RoM) condition. The PID controller of the LLE is tuned in a closed-loop control system using Ziegler-Nichols (Z-N) for initializing parameters of IMRAC. Adjustment mechanism is a gradient based method for real-time adaptation of tuned PID controller. A Lyapunov function has been applied to confirm the stability of IMRAC. The proposed IMRAC shows faster convergence in comparison with conventional non-initialized model reference adaptive control. It can be ascertained the proposed tuning scheme is applicable for real-time tuning of PID controller of LLE.

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Cooperative transport tasks with robots using adaptive non-conventional sliding mode control

Cited by 12 publications

References 49 publications

Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

Human-Robot Cooperation for Surface Repair Combining Automatic and Manual Modes

Adaptive Fuzzy Sliding Mode Controller for Robot Manipulator: Effect on External Interferences

Initialized Model Reference Adaptive Control for Lower Limb Exoskeleton

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