Task Space Control of an Autonomous Underwater Vehicle Manipulator System by Robust Single-Input Fuzzy Logic Control Scheme

Londhe, P. S.; Mohan, Santhakumar; Patre, B. M.; Waghmare, L. M.

doi:10.1109/joe.2016.2548820

Cited by 36 publications

(26 citation statements)

References 42 publications

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“…On the other hand, and due to the universal approximation property of FSs [26], fuzzy control methods have been largely studied and successfully implemented in control problem by many researchers [27][28][29][30][31][32]. However, type-1 FSs (T1-FSs), in the presence of linguistic uncertainties and measurement errors, are not able to process excellently such perturbations.…”

Section: Introductionmentioning

confidence: 99%

Design of a robust interval type-2 fuzzy adaptive super twisting control for a given class of disturbed MIMO nonlinear systems

Nafia¹,

El²,

Ayyad³

et al. 2020

FME Transactions

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This study deals with the tracking control problem of a large class of multi input multi output (MIMO) nonlinear systems with unknown dynamics and subject to unknown disturbances. First, two interval type-2 adaptive fuzzy systems (IT2-AFSs) are constructed to efficiently estimate the unknown nonlinear dynamics. Then, based on IT2-AFSs and super-twisting algorithm (STA), a new robust adaptive fuzzy-reaching STC law (AF-RSTCL) has been added to the global control law to improve the robustness of the studied systems in the presence of approximation errors and unknown disturbances. In order to avoid the chattering phenomenon and guarantee simultaneously the best tracking performance, the gains of the designed AF-RSTCL are optimally online estimated. The adaptive parameters of the global synthesized control law are deduced from the stability analysis in the sense of Lyapunov. Finally, an example of simulation is used to confirm the effectiveness of the developed method in achieving the predetermined objectives of the tracking control.

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Section: Introductionmentioning

confidence: 99%

Design of a robust interval type-2 fuzzy adaptive super twisting control for a given class of disturbed MIMO nonlinear systems

Nafia¹,

El²,

Ayyad³

et al. 2020

FME Transactions

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show abstract

“…Over the past years, intelligent algorithms using fuzzy logic systems (FLSs) are increasingly used and successfully applied in control problem of robot manipulators in the presence of dynamic uncertainties and unknown disturbances [12][13][14]. However, conventional type-1 fuzzy logic system (T1-FLS) cannot directly handle rule and measurement uncertainties because it uses T1-fuzzy sets (T1-FSs) that are certain.…”

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.

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“…Generally, aiming at the problem of AUV trajectory tracking caused by the uncertain factors of ocean current disturbance and dynamic modeling uncertainty, some adaptive control algorithms for trajectory tracking of AUV have been proposed, such as fuzzy adaptive control [4,5], neural network adaptive control [6][7][8][9], which overcome the relevant trajectory tracking control problems to some certain extent. Among them, in Refs.…”

Section: Introductionmentioning

confidence: 99%

“…Among them, in Refs. [4,5], the fuzzy control is used to estimate the uncertain factors, but the acquisition of fuzzy rules depends on the knowledge of the designers. Ref.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle

Yang

Yao

Zhang

2018

Chin. J. Mech. Eng.

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The trajectory tracking control problem is addressed for autonomous underwater vehicle (AUV) in marine environment, with presence of the influence of the uncertain factors including ocean current disturbance, dynamic modeling uncertainty, and thrust model errors. To improve the trajectory tracking accuracy of AUV, an adaptive backstepping terminal sliding mode control based on recurrent neural networks (RNN) is proposed. Firstly, considering the inaccurate of thrust model of thruster, a Taylor's polynomial is used to obtain the thrust model errors. And then, the dynamic modeling uncertainty and thrust model errors are combined into the system model uncertainty (SMU) of AUV; through the RNN, the SMU and ocean current disturbance are classified, approximated online. Finally, the weights of RNN and other control parameters are adjusted online based on the backstepping terminal sliding mode controller. In addition, a chattering-reduction method is proposed based on sigmoid function. In chattering-reduction method, the sigmoid function is used to realize the continuity of the sliding mode switching function, and the sliding mode switching gain is adjusted online based on the exponential form of the sliding mode function. Based on the Lyapunov theory and Barbalat's lemma, it is theoretically proved that the AUV trajectory tracking error can quickly converge to zero in the finite time. This research proposes a trajectory tracking control method of AUV, which can effectively achieve high-precision trajectory tracking control of AUV under the influence of the uncertain factors. The feasibility and effectiveness of the proposed method is demonstrated with trajectory tracking simulations and pool-experiments of AUV.

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Task Space Control of an Autonomous Underwater Vehicle Manipulator System by Robust Single-Input Fuzzy Logic Control Scheme

Cited by 36 publications

References 42 publications

Design of a robust interval type-2 fuzzy adaptive super twisting control for a given class of disturbed MIMO nonlinear systems

Design of a robust interval type-2 fuzzy adaptive super twisting control for a given class of disturbed MIMO nonlinear systems

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

Adaptive Backstepping Terminal Sliding Mode Control Method Based on Recurrent Neural Networks for Autonomous Underwater Vehicle

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