Modeling and Control for Giant Magnetostrictive Actuators with Rate-Dependent Hysteresis
Ping Liu,Zhen Zhang,Jianqin Mao +2 more
TLDR
A relevance vector machine (RVM) model is proposed for describing the hysteresis nonlinearity under varying input current and a proportional integral derivative (PID) control scheme combined with a feedforward compensation is implemented on a giant magnetostrictive actuator for real-time precise trajectory tracking.Abstract:
The rate-dependent hysteresis in giant magnetostrictive materials is a major impediment to the application of such material in actuators. In this paper, a relevance vector machine (RVM) model is proposed for describing the hysteresis nonlinearity under varying input current. It is possible to construct a unique dynamic model in a given rate range for a rate-dependent hysteresis system using the sinusoidal scanning signals as the training set input signal. Subsequently, a proportional integral derivative (PID) control scheme combined with a feedforward compensation is implemented on a giant magnetostrictive actuator (GMA) for real-time precise trajectory tracking. Simulations and experiments both verify the effectiveness and the practicality of the proposed modeling and control methods.read more
Citations
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A survey on hysteresis modeling, identification and control
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Event-Triggered Neural Control of Nonlinear Systems With Rate-Dependent Hysteresis Input Based on a New Filter
TL;DR: In this paper, a second-order filter is proposed to overcome the design conflict between the quantized networked control signal and the rate-dependent hysteresis characteristics, and a novel adaptive control strategy is developed from a neural network technique and a modified backstepping recursive design.
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A Comprehensive Dynamic Model for Magnetostrictive Actuators Considering Different Input Frequencies With Mechanical Loads
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Active vibration control based on modal controller considering structure-actuator interaction
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References
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Journal ArticleDOI
Sparse bayesian learning and the relevance vector machine
TL;DR: It is demonstrated that by exploiting a probabilistic Bayesian learning framework, the 'relevance vector machine' (RVM) can derive accurate prediction models which typically utilise dramatically fewer basis functions than a comparable SVM while offering a number of additional advantages.
Proceedings Article
Variational Relevance Vector Machines
TL;DR: This paper shows how the RVM can be formulated and solved within a completely Bayesian paradigm through the use of variational inference, thereby giving a posterior distribution over both parameters and hyperparameters.
Journal ArticleDOI
Neural networks for nonlinear internal model control
Kenneth J. Hunt,D. Sbarbaro +1 more
TL;DR: In this paper, a novel technique, directly using artificial neural networks, is proposed for the adaptive control of nonlinear systems, where the ability of neural networks to model arbitrary nonlinear functions and their inverses is exploited.
Journal ArticleDOI
Recurrent least squares support vector machines
TL;DR: This paper introduces SVM's within the context of recurrent neural networks and considers a least squares version of Vapnik's epsilon insensitive loss function related to a cost function with equality constraints for a recurrent network.
Journal ArticleDOI
Semilinear Duhem model for rate-independent and rate-dependent hysteresis
JinHyoung Oh,Dennis S. Bernstein +1 more
TL;DR: This paper considers rate-independent and rate-dependent semilinear Duhem models with provable properties with sufficient conditions for convergence to a limiting input-output map.
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