18 results with keyword: 'robust adaptive control fuzzy neural network robot manipulators'
In [15], the authors suggested an adaptive fuzzy sliding mode control with nonlinear observer (AFSMCO) for the robot manipulators with unknown ex- ternal force. Here, by combining
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Cai, Robust position/force control of robot manipulators during constrained task, iEEE Proceedings on Control Theory and Applications, VoL 145, pp. Villani, Adaptive
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This chapter addressed sliding mode control (SMC) of n-link robot manipulators by using of intelligent methods including fuzzy logic and neural network strategies. In this
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Figure 4. Control torque of space robot joint 2 As can be seen from the figure 1,after the initial learning, neural network can get to complete learning and good approximation
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In this paper, an adaptive neural-network controller is proposed to deal with the task space tracking problem of space robot manipulators with uncertain kinematics and
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The control scheme is tested in different manipulation scenarios, namely, (i) trajectory tracking where the desired joint motions are predefined and (ii) command following where
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The control law for proposed controller is as (4) form. K gain of the corrective control u c is adjusted with fuzzy logic and is the equivalent control u eq is computed by
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The control input torque in (15) can be considered as a nonlinear PD controller with repetitive learning and neural network components.. Each term of the control input torque in
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University application consider for graduate studies, university of the graduation rates important investment content writer at university senior resident travel for the university
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This paper presents two neural fuzzy (NN/FZ) inference systems, namely, Fuzzy Adaptive Learning Control/Decision Network (FALCON) and Adaptive Network Based Fuzzy Inference
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In this paper, a robust neural network global sliding mode PID-controller is proposed to control a robot manipulator with parameter variations and external disturbances.. In
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Neural network controller is designed to approach the unknown nonlinear dynamics of the robot manipulators, unknown model upper of system uncertainties is not need; Sliding
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The joint trajectory tracking control simulations were carried out based on a simplified dynamic model of (3). The neural network controller was designed with three
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Production Control Process using Integrated Robust Data Envelopment Analysis and Fuzzy Neural Network..
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Then equation (1) would be an un- known switched nonlinear system, and the objective of this paper is to propose a direct adaptive fuzzy control strategy that can guarantee
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The simulation is carried out on a real redundant nonholonomic mobile modular manipulator, which has verified the effectiveness of the dynamic modeling method and the controller
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In this paper, a robust adaptive fuzzy neural network sliding mode (AFNNSM) control design is proposed to maximize the captured energy for a variable speed wind turbine and to
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In this paper, we have proposed a dynamic trajectory tracking control method for industrial robot manipulators using a linear feedback controller and a neural network
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