Human-robot cooperation for robust surface treatment using non-conventional sliding mode control

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

doi:10.1016/j.isatra.2018.05.013

Cited by 19 publications

(14 citation statements)

References 34 publications

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“…For instance, some works addressed the automation of surface treatment using robotic systems [5]- [7], whereas other works tackled some specific issues, such as detecting wether a polishing operation is complete or the polishing tool needs to be changed [8]. Furhtermore, [9], [10] proposed a human-robot collaboration strategy so that the user guides the robot to perform the treatment on a specific area.…”

Section: B Literature Reviewmentioning

confidence: 99%

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%

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

et al. 2020

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“…where b and g are positive constants. The switching control law obtained from equations (15) and (16) is given by…”

Section: Control Designmentioning

confidence: 99%

Sliding mode control design for stabilization of underactuated mechanical systems

Idrees

Ullah

Muhammad

2019

Advances in Mechanical Engineering

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Stabilization of underactuated mechanical systems is one of the fundamental benchmark problem in the field of control theory. In this article, a hierarchical sliding mode control based on state-dependent switching gain is proposed for stabilization of underactuated mechanical systems. This controller is based on the so called first-level and second-level sliding surfaces. The asymptotic stability of these surfaces is proved by the Lyapunov stability theory. The proposed control technique is applied to two nonlinear underactuated mechanical systems and its feasibility is verified by numerical simulation. The proposed controller efficiently tackles the bounded external disturbance and shows robust performance. The convergence rate of the proposed controller is much faster as compared with the conventional decoupled sliding mode control and the integral sliding mode control.

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“…SMC exists as an enhanced control technique that was extensively deployed due to its easiness in modeling, reliable performance and order diminution features [9]. It is the effective robust control method that offers invariance characteristics to the entire system since the system arrives at the reaching point of SMC [10] [11]. SMC has recognized itself as an effectual control method that was confirmed to be robust in opposition to external disturbances and system uncertainties.…”

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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Human-robot cooperation for robust surface treatment using non-conventional sliding mode control

Cited by 19 publications

References 34 publications

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

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

Sliding mode control design for stabilization of underactuated mechanical systems

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

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