Robust adaptive neural control for pure-feedback stochastic nonlinear systems with Prandtl–Ishlinskii hysteresis

Wang, Huanqing; Shen, Haikuo; Xie, Xue‐Jun; Hayat, Tasawar; Alsaadi, Fuad E.

doi:10.1016/j.neucom.2018.04.023

Cited by 21 publications

(11 citation statements)

References 57 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this section, the back-stepping design approach is carried out for the reduced system (14), and the design processes are given as follows: Step 1: Let z 1 = x 1 − x 1d be the output tracking error between given reference x 1d and system displacement feedback x 1 . The time derivative of z 1 along the first equation of (14) is…”

Section: Back-stepping Control Law Design Augmented With Esomentioning

confidence: 99%

“…Remark 6: Pressure sensors are required, in theory, to calculate the non-linear control gain β n which involves R 1 and R 2 (see the definitions of β n , R 1 , and R 2 before (5) and (14)]. In practice, the developed control strategy can be implemented just with the displacement feedback.…”

Section: Co-simulationsmentioning

confidence: 99%

“…Feedback linearisation method is an effective tool to handle the non-linear problem. Feedback linearisation method achieves the desired dynamics by eliminating the undesired non-linear terms [14,15]. To cope with the problem of parametric uncertainties, the adaptive control schemes have been developed for hydraulic servo systems [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Disturbance observer‐based output feedback control of hydraulic servo system considering mismatched uncertainties and internal pressure dynamics stability

Wang

Zhang

Wang

et al. 2020

IET Control Theory & Applications

View full text Add to dashboard Cite

Section: Back-stepping Control Law Design Augmented With Esomentioning

confidence: 99%

Section: Co-simulationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Disturbance observer‐based output feedback control of hydraulic servo system considering mismatched uncertainties and internal pressure dynamics stability

Wang

Zhang

Wang

et al. 2020

IET Control Theory & Applications

View full text Add to dashboard Cite

“…However, some design parameters are needed further determination. Some scholars have also studied various approaches of control and tracking for systems with PI hysteresis, such as robust adaptive back‐stepping control and adaptive robust output feedback control, and so on 10‐12 …”

Section: Introductionmentioning

confidence: 99%

Anti‐disturbance adaptive sampled‐data observers for a class of nonlinear systems with unknown hysteresis

Shen

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, based on radial basis function neural network (RBFNN) and disturbance estimator (DE), an adaptive sampled‐data observer design scheme is proposed for a class of nonlinear systems with unknown Prandtl–Ishlinskii (PI) hysteresis and unknown multiple disturbances. To begin with, we investigate a class of sampled‐data nonlinear systems and present corresponding sufficient conditions ensuring ultimate uniform boundedness (UUB). Subsequently, a sampled‐data observer and a DE are designed to estimate the unknown states and compounded disturbances, respectively. Additionally, the unknown hysteresis and the unknown unmatched disturbances are approximated by RBFNNs. Meanwhile, we also give the learning laws of the weights of RBFNNs. The estimation errors of the states and the weights are verified to be UUB in the light of the obtained sufficient conditions and a special constructing Lyapunov–Krasovskii function. Finally, the effectiveness of the proposed design method is verified by numerical simulations.

show abstract

“…Due to the instability and performance degradation effects of stochastic disturbances on a system, the control of stochastic nonlinear systems has received much attention in recent years . Therefore, several nonlinear stochastic control techniques including sliding mode, Takagi‐Sugeno (T‐S) fuzzy, and backstepping have been presented. Among the existing methods, the capabilities of the backstepping controller in integrating with nonlinear approximators and stochastic theories to estimate unknown terms and uncertainties and handling the stochastic disturbance input provide an interesting platform for nonlinear control of the stochastic systems.…”

Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

Homayoun

Arefi

Vafamand

2020

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

This paper studies the problem of adaptive observer-based radial basis function neural network tracking control for a class of strict-feedback stochastic nonlinear systems comprising an unknown input saturation, uncertainties, and unknown disturbances. To handle the issue of a non-smooth saturation input signal, a smooth function is chosen to approximate the saturation function and the state observer is used to estimate unmeasured states. By the so-called command filter method in the controller design procedure, the implementation complexity is reduced in the proposed backstepping method. Moreover, a radial basis function neural network is deployed to reconstruct the unknown nonlinear functions. In addition, the gains of all radial basis function neural networks are updated through one updating law leading to a minimal learning parameter which is independent of the number of neural nodes and the order of the system.Comparing with the existing results, the proposed approach can stabilize a constrained stochastic system more effectively and with less computational burden. Finally, a practical example shows the performance of the proposed controller design. K E Y W O R D Sadaptive neural network, command filter, input saturation, minimal learning parameter, observer, stochastic nonlinear systems 1 3296

show abstract

Robust adaptive neural control for pure-feedback stochastic nonlinear systems with Prandtl–Ishlinskii hysteresis

Cited by 21 publications

References 57 publications

Disturbance observer‐based output feedback control of hydraulic servo system considering mismatched uncertainties and internal pressure dynamics stability

Disturbance observer‐based output feedback control of hydraulic servo system considering mismatched uncertainties and internal pressure dynamics stability

Anti‐disturbance adaptive sampled‐data observers for a class of nonlinear systems with unknown hysteresis

Robust adaptive backstepping tracking control of stochastic nonlinear systems with unknown input saturation: A command filter approach

Contact Info

Product

Resources

About