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Aug 10, 2022 · In this paper, a radial basis function neural network (RBFNN) learning control scheme is proposed to improve the trajectory tracking ...
This paper proposes a control method combining neural network and PID. In this study, Magnetorheological (MR) damper and motor are combined to provide ...
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This paper presents the application of adaptive neural networks to the trajectory tracking between plant, referent and adaptive neural networks. The proposed ...
Aug 1, 2022 · In this paper, a radial basis function neural network (RBFNN) learning control scheme is proposed to improve the trajectory tracking performance of a 3-DOF ...
This work investigates and contrasts two approaches for trajectory tracking control strategies for robotic operating systems.
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Jul 26, 2024 · System uncertainties and unknown dynamics can be exactly identified online by a self-constructing RBF neural network (SC-RBFNN) which is ...
Dec 1, 2023 · This paper proposes a data-driven NN-based ILC algorithm to address the trajectory tracking control problem of nonlinear repetitive discrete-time SISO systems ...
This paper presents a novel soft computing-based machine learning technique designed to enhance the trajectory tracking capabilities of mobile robots ...
Radial Basis Function (RBF) neural network control is designed to globally approximate the model uncertainties. Further, an itemized approximate RBF control ...
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A fixed-time trajectory tracking control method for uncertain robotic manipulators with input saturation based on reinforcement learning (RL) is studied.