Adaptive Neural Network Control of Biped Robots

Sun, Changyin; He, Wei; Ge, Wenjun; Cheng, Chih‐Chieh

doi:10.1109/tsmc.2016.2557223

Cited by 115 publications

(52 citation statements)

References 59 publications

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“…τ s = −K s e s − D sės (27) where e i = q i − q id is the tracking error, q id ∈ R n is the desired joint angle served as the reference command for the local PD controller, K ∈ R n×n and D ∈ R n×n are the symmetric positive definite matrices for the joint angle and angular velocity gains. The subscript "i" stands for "m" and "s", which denote the master device and the slave robot, respectively.…”

Section: A Basic Pd Control Designmentioning

confidence: 99%

“…In [26], fuzzy control was adopted to improve the performance of the automobile cruise system. In recent years, the applications of NN to the robot control system have become increasingly popular [27]- [31], due to the fact that the NN has the ability to emulate complicated nonlinearity and uncertain functions [32]- [34]. The RBF NN is a highly effective method and has been extensively used for control design of uncertain robot systems [35].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Yang

Wang

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

312

169

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Abstract-In this paper, a novel control scheme is developed for a teleoperation system, combining the radial basis function (RBF) neural networks (NNs) and wave variable technique to simultaneously compensate for the effects caused by communication delays and dynamics uncertainties. The teleoperation system is set up with a TouchX joystick as the master device and a simulated Baxter robot arm as the slave robot. The haptic feedback is provided to the human operator to sense the interaction force between the slave robot and the environment when manipulating the stylus of the joystick. To utilize the workspace of the telerobot as much as possible, a matching process is carried out between the master and the slave based on their kinematics models. The closed loop inverse kinematics (CLIK) method and RBF NN approximation technique are seamlessly integrated in the control design. To overcome the potential instability problem in the presence of delayed communication channels, wave variables and their corrections are effectively embedded into the control system, and Lyapunov based analysis is performed to theoretically establish the closed-loop stability. Comparative experiments have been conducted for a trajectory tracking task, under the different conditions of various communication delays. Experimental results show that in terms of tracking performance and force reflection, the proposed control approach shows superior performance over the conventional methods.

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Section: A Basic Pd Control Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Yang

Wang

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

312

169

View full text Add to dashboard Cite

show abstract

“…The NN approximation property is able to deal with systems with uncertainty and unknown dynamics [22] [23]. Thus, the approximation property has been widely applied in various fields.…”

Section: Introductionmentioning

confidence: 99%

Haptic Identification by ELM-Controlled Uncertain Manipulator

Yang

Huang

Cheng

et al. 2017

IEEE Trans. Syst. Man Cybern, Syst.

146

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Abstract-This paper presents an extreme learning machine (ELM) based control scheme for uncertain robot manipulators to perform haptic identification. ELM is used to compensate for the unknown nonlinearity in the manipulator dynamics. The ELM enhanced controller ensures that the closed-loop controlled manipulator follows a specified reference model, in which the reference point as well as the feedforward force is adjusted after each trial for haptic identification of geometry and stiffness of an unknown object. A neural learning law is designed to ensure finite-time convergence of the neural weight learning, such that exact matching with the reference model can be achieved after the initial iteration. The usefulness of the proposed method is tested and demonstrated by extensive simulation studies.

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“…In 1989, Lowe proposed the RBF neural network [5] which indicated that the parameters of the SLFNs can also be randomly selected in his articles. In 1992, Pao Y. H. et al proposed the theory of the random vector functional link network (RVFL) [6,7], and they presented that only one parameter of the output weights should be calculated during the training process.…”

Section: Introductionmentioning

confidence: 99%

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

Xiao

Mao

2017

Mathematical Problems in Engineering

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Extreme learning machine (ELM) is a rapid learning algorithm of the single-hidden-layer feedforward neural network, which randomly initializes the weights between the input layer and the hidden layer and the bias of hidden layer neurons and finally uses the least-squares method to calculate the weights between the hidden layer and the output layer. This paper proposes a multiple hidden layers ELM (MELM for short) which inherits the characteristics of parameters of the first hidden layer. The parameters of the remaining hidden layers are obtained by introducing a method (make the actual output zero error approach the expected hidden layer output). Based on the MELM algorithm, many experiments on regression and classification show that the MELM can achieve the satisfactory results based on average precision and good generalization performance compared to the two-hidden-layer ELM (TELM), the ELM, and some other multilayer ELM.

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Adaptive Neural Network Control of Biped Robots

Cited by 115 publications

References 59 publications

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Teleoperation Control Based on Combination of Wave Variable and Neural Networks

Haptic Identification by ELM-Controlled Uncertain Manipulator

A Multiple Hidden Layers Extreme Learning Machine Method and Its Application

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