Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control

Ravandi, Abbas Karamali; Khanmirza, Esmaeel; Daneshjou, K.

doi:10.1016/j.asoc.2018.05.048

Cited by 60 publications

(28 citation statements)

References 30 publications

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“…Similarly, from the analysis on the joint angle 2  as specified in Fig. 5(b), at 3.5ms, the actual 2  of SAGWO-FSMC is 2.98% deviated from the optimal 2  that is improved than the other schemes.…”

Section: B Analysis On Anglesmentioning

confidence: 83%

“…In 2018, Abbas et al [2] have established the hybrid position control of robotic manipulators functioning in tentative surroundings by incorporating the FLC with traditional SMC. The established AFSMC necessitates the least particulars regarding the dynamic structure of manipulator and physical features between the other hybrid control techniques offered until now.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Performance analysis on three angles of joints with respect to time namely, (a) 1  (b)2  and (c)3 C. Impact of External DisturbanceThe graphical demonstration of the examination on three displacements namely, x y z with time when applying external disturbance, is revealed inFig.…”

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confidence: 99%

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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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Section: B Analysis On Anglesmentioning

confidence: 83%

Section: Literature Reviewmentioning

confidence: 99%

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

2019

IJITEE

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“…Model-based and knowledge-based fault-tolerant control algorithms have several advantages, such as system knowledge, stability, robustness, and reliability, but they face a big challenge in the unlimited level of a faulty signal [17]. Hybrid fault-tolerant algorithms are used to address that issue [18,19]. The sliding mode technique can be an excellent candidate for a robust fault-tolerant control algorithm, but it must address the challenge of chattering.…”

Section: Introductionmentioning

confidence: 99%

“…The sliding mode technique can be an excellent candidate for a robust fault-tolerant control algorithm, but it must address the challenge of chattering. Various techniques have been proposed to attenuate chattering [16][17][18][19]. An adaptive fuzzy sliding mode estimation fault-tolerant control algorithm is suitable for reducing chattering and the effects of faults in robot manipulators.…”

Section: Introductionmentioning

confidence: 99%

Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators

Piltan

Kim

2019

Energies

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In this paper, an adaptive Takagi–Sugeno (T–S) fuzzy sliding mode extended autoregressive exogenous input (ARX)–Laguerre proportional integral (PI) observer is proposed. The proposed T–S fuzzy sliding mode extended-state ARX–Laguerre PI observer adaptively improves the reliability, robustness, estimation accuracy, and convergence of fault detection, estimation, and identification. For fault-tolerant control in the presence of uncertainties and unknown conditions, an adaptive fuzzy sliding mode estimation technique is introduced. The sliding surface slope gain is significant to improve the system’s stability, but the sliding mode technique increases high-frequency oscillation (chattering), which reduces the precision of the fault diagnosis and tolerant control. A fuzzy procedure using a sliding surface and actual output position as inputs can adaptively tune the sliding surface slope gain of the sliding mode fault-tolerant control technique. The proposed robust adaptive T–S fuzzy sliding mode estimation extended-state ARX–Laguerre PI observer was verified with six degrees of freedom (DOF) programmable universal manipulation arm (PUMA) 560 robot manipulator, proving qualified efficiency in detecting, isolating, identifying, and tolerant control for faults inherent in sensors and actuators. Experimental results showed that the proposed technique improves the reliability of the fault detection, estimation, identification, and tolerant control.

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Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

Khan,

Lee,

Suh

et al. 2024

Advanced Intelligent Systems

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This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end‐effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end‐effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque‐controlled robot end‐effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end‐effector deviates from the desired trajectory, the constant force‐tracking impedance control generates a compliance trajectory that regulates the end‐effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed‐loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force‐tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force‐tracking impedance control improves the end‐effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

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Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control

Cited by 60 publications

References 30 publications

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

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

Advanced Adaptive Fault Diagnosis and Tolerant Control for Robot Manipulators

Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

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