Adaptive trajectory tracking neural network control with robust compensator for robot manipulators

Cuong, Pham Van; Wang, Nan

doi:10.1007/s00521-015-1873-4

Cited by 88 publications

(32 citation statements)

References 33 publications

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“…The research results and mathematical methods of neural basic theory are more complete. The performance of the neural network model is superio [7]. Network algorithms and performance are studied in depth, such as stability, convergence, fault tolerance, and robustness.…”

Section: State Of the Artmentioning

confidence: 99%

Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller

Pan

2018

Polish Maritime Research

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To study the autonomous learning model of the learning robot for marine resource exploration, an adaptive neural network controller was applied. The motion characteristics of autonomous learning robots were identified. The mathematical model of the multilayer forward neural network and its improved learning algorithm were studied. The improved Elman regression neural network and the composite input dynamic regression neural network were further discussed. At the same time, the diagonal neural network was analysed from the structure and learning algorithms. The results showed that for the complex environment of the ocean, the structure of the composite input dynamic regression network was simple, and the convergence was fast. In summary, the identification method of underwater robot system based on neural network is effective.

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Section: State Of the Artmentioning

confidence: 99%

Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller

Pan

2018

Polish Maritime Research

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“…He [24] studied the adaptive fuzzy neural networks for trajectory designing of the mobile manipulators using impedance learning, which effectively integrate the robot arm and the surrounding environment and induce the robotic manipulators to arrive the destination freely. In [25], the adaptive neural network scheme with radial basis function (RBF) was developed to be applied to precision trajectory tracking of distributed movable manipulator device. After the widespread with Gradient recurrent neural network (GNN) [26], Chen et al [27] proposed a Jacobian-matrix-adaption method based on Zhang neural network for path schematization of the manipulators only considering the input and output parameter information of the robotic models.…”

Section: Introductionmentioning

confidence: 99%

A Repeatable Optimization for Kinematic Energy System with Its Mobile Manipulator Application

et al. 2019

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For repeatable motion of redundant mobile manipulators, the flexible base platform and the redundant manipulator have to be returned to the desired initial position simultaneously after completing the given tasks. To remedy deviations between initial position and desired position of each kinematic joint angle, a special kind of repeatable optimization for kinematic energy minimization based on terminal-time Zhang neural network (TTZNN) with finite-time convergence is proposed for inverse kinematics of mobile manipulators. It takes the advantages that each joint of the manipulator is required to return to the desired initial position not considering the initial orientation of itself for realizing repeatable kinematics control. Unlike the existed training methods, such an optimization of kinematic energy scheme based on TTZNN can not only reduce the convergent position error of each joint to zero in finite time, but also improve the convergent precision. Theoretical analysis and verifications show that the proposed optimal kinematic energy scheme accelerates the convergent rate, which is tended to be applied in practical robot kinematics. Simulation results on the manipulator with three mobile wheels substantiate the timeliness and repetitiveness of the proposed optimization 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.

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Adaptive trajectory tracking neural network control with robust compensator for robot manipulators

Cited by 88 publications

References 33 publications

Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller

Exploration and Mining Learning Robot of Autonomous Marine Resources Based on Adaptive Neural Network Controller

A Repeatable Optimization for Kinematic Energy System with Its Mobile Manipulator Application

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

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