Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators

Cheng, Long; Liu, Weichuan; Hou, Zeng-Guang; Yu, Junzhi; Tan, Min

doi:10.1109/tie.2015.2455026

Cited by 235 publications

(103 citation statements)

References 36 publications

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“…2.3. While there are many other issues that need to be resolved by further theoretical and experimental research, the authors suggest comparison of the present work with various other applications (e.g., Cheng et al [30]) as a topic of future research. Fig.…”

Section: Summary Conclusion and Future Workmentioning

confidence: 91%

Neural Probabilistic Forecasting of Symbolic Sequences With Long Short-Term Memory

Hauser

Phoha

et al. 2018

Journal of Dynamic Systems, Measurement, and Control

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This paper makes use of long short-term memory (LSTM) neural networks for forecasting probability distributions of time series in terms of discrete symbols that are quantized from real-valued data. The developed framework formulates the forecasting problem into a probabilistic paradigm as h H : X Â Y ! [0, 1] such that P y2Y h H ðx; yÞ ¼ 1, where X is the finite-dimensional state space, Y is the symbol alphabet, and H is the set of model parameters. The proposed method is different from standard formulations (e.g., autoregressive moving average (ARMA)) of time series modeling. The main advantage of formulating the problem in the symbolic setting is that density predictions are obtained without any significantly restrictive assumptions (e.g., second-order statistics). The efficacy of the proposed method has been demonstrated by forecasting probability distributions on chaotic time series data collected from a laboratory-scale experimental apparatus. Three neural architectures are compared, each with 100 different combinations of symbol-alphabet size and forecast length, resulting in a comprehensive evaluation of their relative performances.

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Section: Summary Conclusion and Future Workmentioning

confidence: 91%

Neural Probabilistic Forecasting of Symbolic Sequences With Long Short-Term Memory

Hauser

Phoha

et al. 2018

Journal of Dynamic Systems, Measurement, and Control

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“…In order to get a high approximate accuracy, we choose four RBF neural networks to reconstruct M (q), C(q,q), G(q), F (q), respectively. This neural network approximation approach can also be used in many other fields, such as piezoelectric actuators [22]. The structure of the four RBF neural networks is shown in Fig.…”

Section: Control Design and Stability Analysismentioning

confidence: 99%

“…Using (23) and considering the boundedness of W * , ε, τ d and the definition of ξ, (22) can be deduced as followṡ…”

Section: Control Design and Stability Analysismentioning

confidence: 99%

An RBF-based neuro-adaptive control scheme to drive a lower limb rehabilitation robot

Cui

Bian

Hou

et al. 2015

2015 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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In this paper, a novel robust adaptive control scheme is proposed for a lower limb rehabilitation robot designed by our laboratory. The proposed control strategy is based on the radial basis function (RBF) neural networks and the parameters of the system dynamics are unknown. The weights of the RBF neural networks are updated by an adaptive law according to the Lyapunov stability analysis. The robustness against possible variations of the system dynamics and the external disturbance are considered in the control design. The proposed control strategy can not only avoid the complex procedure of system parameters identification, but also guarantee high robustness, small trajectory tracking errors and the assistance of the patient's voluntary participation. Using this control algorithm, the robot can regulate its exerted torque to adapt to the patient's active torque in real time during rehabilitation. The effectiveness of our control method is demonstrated by a simulation.

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“…Huang, Na, Wu, Liu, & Guo, 2015; T. Lee, Koh, & Loh, 1996;G.-H. Yang & Ye, 2006). In the field of adaptive control, neural networks (NNs) are always considered as an efficient way to handle the uncertain or poorly known dynamics due to their universal approximation capabilities (Cheng, Liu, Hou, Yu, & Tan, 2015;Hou, 2001;Kennedy & Chua, 1988;Z. Li, Li, & Feng, 2016).…”

Section: Introductionmentioning

confidence: 99%

An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions

Xia

Zhang

Yang

et al. 2019

International Journal of Systems Science

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Conventional Neural Network (NN) control for robots use radial basis function (RBF) and for n-link robot with online control, the number of nodes and weighting matrix increases exponentially, which requires a number of calculations to be performed within a very short duration of time. This consumes a large amount of computational memory and may subsequently result in system failure. To avoid this problem, this paper proposes an innovative NN robot control using a dimension compressed RBF (DCRBF) for a class of n-degree of freedom (DOF) robot with full-state constraints. The proposed DCRBF NN control scheme can compress the nodes and weighting matrix greatly and provide an output that meets the prescribed tracking performance. Additionally, adaption laws are designed to compensate for the internal and external uncertainties. Finally, effectiveness of the proposed method has been verified by simulations. The results indicate that the proposed method, integral Barrier Lyapunov Functions (iBLF) avoids the existing defects of Barrier Lyapunov Functions (BLF) and prevents the constraint violations.

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Neural-Network-Based Nonlinear Model Predictive Control for Piezoelectric Actuators

Cited by 235 publications

References 36 publications

Neural Probabilistic Forecasting of Symbolic Sequences With Long Short-Term Memory

Neural Probabilistic Forecasting of Symbolic Sequences With Long Short-Term Memory

An RBF-based neuro-adaptive control scheme to drive a lower limb rehabilitation robot

An improved adaptive online neural control for robot manipulator systems using integral Barrier Lyapunov functions

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