Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints

Purwar, Shubhi; Kar, Indra Narayan; Jha, A. N.

doi:10.1109/fuzzy.2004.1375387

Cited by 16 publications

(27 citation statements)

References 18 publications

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“…(5), similar to Takagi-Sugeno fuzzy models (Takagi & Sugeno, 1985;Purwar et al, 2005). The m-th output of the MIMO with product inference, centroid defuzzifier and Bell membership functions is given by:…”

Section: Positive and Negative Association Rulesmentioning

confidence: 99%

Fuzzy System with Positive and Negative Rules

Nguyen¹,

Wu²

2010

Machine Learning

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Section: Positive and Negative Association Rulesmentioning

confidence: 99%

Fuzzy System with Positive and Negative Rules

Nguyen¹,

Wu²

2010

Machine Learning

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“…with selection of parameters T = 1 ms, K ν = diag [20,20], [5,5], Γ = diag[50, 10,10]. In simulation, the nominal robot parameter is θ = [2.9, 1.0, 0.9] T , with choice of its bound 0…”

Section: Computer Simulationmentioning

confidence: 99%

“…In [9][10][11], intelligent controllers are proposed for the compensation of effects yielded by nonlinearities and uncertainties. Some theses [8,12,13] use soft computer methods to approximate the whole robot dynamics rather than its nonlinear components as done by static intelligent means.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent Hybrid Control Strategy for Trajectory Tracking of Robot Manipulators

Zuo

Wang

Huang

et al. 2008

Journal of Control Science and Engineering

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We address the problem of robust tracking control using a PD-plus-feedforward controller and an intelligent adaptive robust compensator for a rigid robotic manipulator with uncertain dynamics and external disturbances. A key feature of this scheme is that soft computer methods are used to learn the upper bound of system uncertainties and adjust the width of the boundary layer base. In this way, the prior knowledge of the upper bound of the system uncertainties does need not to be required. Moreover, chattering can be effectively eliminated, and asymptotic error convergence can be guaranteed. Numerical simulations and experiments of two-DOF rigid robots are presented to show effectiveness of the proposed scheme.

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“…Therefore, to achieve the good tracking control performance for such complex process, several researchers have developed robust control approaches, most of which use the fuzzy logic control (FLC), sliding mode control (SMC), feedback linearization technique, Neural Network (NN), adaptive control, and H ∞ technique [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators

et al. 2018

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This paper develops a new robust tracking control design for n-link robot manipulators with dynamic uncertainties, and unknown disturbances. The procedure is conducted by designing two adaptive interval type-2 fuzzy logic systems (AIT2-FLSs) to better approximate the parametric uncertainties on the system nominal. Then, in order to achieve the best tracking control performance and to enhance the system robustness against approximation errors and unknown disturbances, a new control algorithm, which uses a new synthesized AIT2 fuzzy sliding mode control (AIT2-FSMC) law, has been proposed. To deal with the chattering phenomenon without deteriorating the system robustness, the AIT2-FSMC has been designed so as to generate three adaptive control laws that provide the optimal gains value of the global control law. The adaptation laws have been designed in the sense of the Lyapunov stability theorem. Mathematical proof shows that the closed loop control system is globally asymptotically stable. Finally, a 2-link robot manipulator is used as case study to illustrate the effectiveness of the proposed control approach.

show abstract

Adaptive control of robot manipulators using fuzzy logic systems under actuator constraints

Cited by 16 publications

References 18 publications

Fuzzy System with Positive and Negative Rules

Fuzzy System with Positive and Negative Rules

Intelligent Hybrid Control Strategy for Trajectory Tracking of Robot Manipulators

Robust Interval Type-2 Fuzzy Sliding Mode Control Design for Robot Manipulators

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