Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control

Han, Seong-Ik; Lee, Kwon Soon

doi:10.1016/j.mechatronics.2010.02.005

Cited by 38 publications

(31 citation statements)

References 21 publications

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“…Assume that the desirable reference signal for the displacement vector is given by x r and the capacity of the control force in i-th DOF is given by F max, i . Then, with the application of nonlinear control force (15) and (16), the update laws (17)- (20), the assistant λ-system (21)- (22) and the error variable (23), the following statements hold: (i) All of the signals are globally bounded and the closed-loop system is stable.…”

Section: Main Theoremmentioning

confidence: 99%

Adaptive block backstepping control for civil structures with unknown parameters subjected to seismic excitation

Ghaderi

Amini

2016

Struct. Control Health Monit.

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This paper presents an active control scheme for structures with unknown parameters subjected to ground acceleration. It is assumed that the mass, stiffness and damping matrices are unknown to the designer, and no measurement of the ground acceleration is available. Instead, the displacement and velocity vectors are accessible for control purposes. Adaptive block backstepping is used to propose a control force vector which makes the response of the structure follow a predefined reference signal as closely as possible. The provided numerical examples in this paper show the admissible performance of the proposed closed-form solution for the control force. The benefits and drawbacks of this approach are discussed as well.

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Section: Main Theoremmentioning

confidence: 99%

Adaptive block backstepping control for civil structures with unknown parameters subjected to seismic excitation

Ghaderi

Amini

2016

Struct. Control Health Monit.

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“…The intensive diversity of the DR found in some wavelet families can be smoothed by setting decimal d j and t j in (13). W in ∈ R N ×M , W d ∈ R N ×N , and W fb ∈ R N ×1 are the input, internal, and feedback connection weight matrices, respectively, and ν is a suitably normalized noise vector.…”

Section: B Fwesn Systemmentioning

confidence: 99%

“…Its first drawback is the requirement that nonlinear dynamic models should be known exactly or be linearly parameterized with respect to known nonlinear functions, and in real applications, this requirement causes a robustness problem since most nonlinear functions are often not available a priori. As an alternative, fuzzy logic [7], [8], neural networks (NNs) [9]- [11], and fuzzy NNs [12], [13] have been combined with adaptive backstepping methods by approximating unknown nonlinear functions. The second drawback is that it inevitably suffers from the problem of "explosion of complexity" caused by repeated differentiations of some virtual nonlinear control functions [14], which leads to drastic complexity in controller terms as the order of the system increases.…”

Section: Introductionmentioning

confidence: 99%

Precise Positioning of Nonsmooth Dynamic Systems Using Fuzzy Wavelet Echo State Networks and Dynamic Surface Sliding Mode Control

Han

Lee

2013

IEEE Trans. Ind. Electron.

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This paper presents a precise positioning robust hybrid intelligent control scheme based on the effective compensation of nonsmooth nonlinearities, such as friction, deadzone, and uncertainty in a dynamic system. A new adaptive fuzzy wavelet echo state network algorithm is proposed to improve performance in terms of approximating unknown uncertainties in conventional neural network algorithms. A strict feedback controller and adaptive laws for estimating the unknown friction and deadzone parameters were developed using the recursive steps of dynamic surface control and a sliding mode control based on Lyapunov stability theory. Lyapunov stability analysis confirmed the boundedness and convergence of the closed-loop system. The performance of the proposed control scheme was validated experimentally by applying it to the precise positional control of a robot manipulator.Index Terms-Deadzone and friction, dynamic surface sliding mode control, fuzzy wavelet echo state networks (FWESNs), robot manipulator.

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“…To attack this problem, a recurrent fuzzy neural network (RFNN) has been attracting great interest [24][25][26][27]. Unlike a FNN, the RFNN, which involves dynamic elements in the form of feedback connections used as internal memories, performs dynamic mapping.…”

Section: Introductionmentioning

confidence: 99%

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

Hsu

2015

Neural Comput & Applic

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The purpose of the antilock braking system (ABS) is to regulate the wheel longitudinal slip at its optimum point in order to generate the maximum braking force; however, the vehicle braking dynamic is highly nonlinear. To relax the requirement of detailed system dynamics, this paper proposes an intelligent exponential sliding-mode control (IESMC) system for an ABS. A functional recurrent fuzzy neural network (FRFNN) uncertainty estimator is designed to approximate the unknown nonlinear term of ABS dynamics, and the parameter adaptation laws are derived in the sense of projection algorithm and Lyapunov stability theorem to ensure the stable control performance. Since the outputs of the functional expansion unit are used as the output weights of the FRFNN uncertainty estimator, the FRFNN can effectively capture the input-output dynamic mapping. In addition, a nonlinear reaching law, which contains an exponential term of sliding surface to smoothly adapt the variations of sliding surface, is designed to reduce the level of the chattering phenomenon. Finally, the simulation results demonstrate that the proposed IESMC system can achieve robustness slip tracking performance in different road conditions.

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Robust friction state observer and recurrent fuzzy neural network design for dynamic friction compensation with backstepping control

Cited by 38 publications

References 21 publications

Adaptive block backstepping control for civil structures with unknown parameters subjected to seismic excitation

Adaptive block backstepping control for civil structures with unknown parameters subjected to seismic excitation

Precise Positioning of Nonsmooth Dynamic Systems Using Fuzzy Wavelet Echo State Networks and Dynamic Surface Sliding Mode Control

Intelligent exponential sliding-mode control with uncertainty estimator for antilock braking systems

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