Adaptive backstepping sliding mode control for hydraulic system without overparametrisation

Benayache, R.; Chrifi‐Alaoui, Larbi; Bussy, P.

doi:10.1504/ijmic.2012.046696

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…, θ , and D L x ∈ ∞ , and we can get  z L ∈ ∞ based on equations (18) and (22). According to Barbalat's lemma, 27 .…”

Section: Controller Designmentioning

confidence: 99%

“…Therefore, the adaptive control method and sliding mode control method, which have better robustness against lumped uncertainty, are extended to the backstepping method to deal with lumped uncertainty. [19][20][21][22] For the standard adaptive backstepping sliding mode control, one of the key problems is chattering of sliding mode which can cause system instability and damage to both actuators and plants, and the chattering increases with increasing the gain of discontinuous switching of the control law, which is theoretically larger than the unknown bound of lumped uncertainty.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive backstepping sliding mode control for wheel slip tracking of vehicle with uncertainty observer

Zhang

2018

Measurement and Control

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The wheel slip tracking control is the basis of automatic braking control systems, and the accurate tracking for the desired wheel slip in the presence of lumped uncertainty is a vital guarantee of automatic braking control systems reliable operation. Therefore, an adaptive backstepping sliding mode control approach with radial basis function neural network is proposed to design the nonlinear robust wheel slip controller based on a quarter-vehicle model with lumped uncertainty. The radial basis function neural network as the uncertainty observer can effectively reduce the chattering of sliding mode by estimating the lumped uncertainty, and the adaptive law for the unknown weight vector of radial basis function neural network is derived by Lyapunov-based method. The influence of changes in tire sideslip angle and camber angle on the tire -road friction coefficient acts as an unknown scaling factor, and the adaptive law for the unknown scaling factor is derived via Lyapunov-based method. Then, the performance of the proposed controller is verified through simulations of various maneuvers on a full-vehicle dynamics simulation software.

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“…, θ , and D L x ∈ ∞ , and we can get  z L ∈ ∞ based on equations (18) and (22). According to Barbalat's lemma, 27 .…”

Section: Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping sliding mode control for wheel slip tracking of vehicle with uncertainty observer

Zhang

2018

Measurement and Control

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“…Nonlinear phenomenon is widespread in nature and nonlinear system is the most general but linear system is just one special case. Nonlinear adaptive method is applied in many fields, for example, gain-scheduled control [13][14][15], feedback linearization control [16], sliding mode control [17][18][19], nonlinear adaptive control [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Improved Adaptive Backstepping Sliding Mode Control of Static Var Compensator

Dong

Shen

2018

Energies

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The stability of a single machine infinite bus system with a static var compensator is proposed by an improved adaptive backstepping algorithm, which includes error compensation, sliding mode control and a κ -class function. First, storage functions of the control system are constructed based on modified adaptive backstepping sliding mode control and Lyapunov methods. Then, adaptive backstepping method is used to obtain nonlinear controller and parameter adaptation rate for static var compensator system. The results of simulation show that the improved adaptive backstepping sliding mode variable control based on error compensation is effective. Finally, we get a conclusion that the improved method differs from the traditional adaptive backstepping method. The improved adaptive backstepping sliding mode variable control based on error compensation method preserves effective non-linearities and real-time estimation of parameters, and this method provides effective stability and convergence.

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Medium Density Fiberboard Continuous Hot-Pressing Process Based on Intelligent DSC

Zhou

Zhu

Wang

2018

2018 10th International Conference on Modelling, Identification and Control (ICMIC)

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Adaptive backstepping sliding mode control for hydraulic system without overparametrisation

Cited by 9 publications

References 23 publications

Adaptive backstepping sliding mode control for wheel slip tracking of vehicle with uncertainty observer

Adaptive backstepping sliding mode control for wheel slip tracking of vehicle with uncertainty observer

Improved Adaptive Backstepping Sliding Mode Control of Static Var Compensator

Medium Density Fiberboard Continuous Hot-Pressing Process Based on Intelligent DSC

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