Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

Chen, Ying Jen; Wang, Wen June; Chang, Chung Lung

doi:10.1002/asjc.402

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…In recent years, many control methods have been developed for integer-order systems subjected to input constraints (Azinheira and Moutinho, 2008; Gouta et al, 2015; Li et al, 2014b, 2015, 2016; Wang et al, 2014; Yang et al, 2015). For instance, a bounded fuzzy controller has been developed for the system with model uncertainty and input/state constraints by Chen et al (2011). Chen and Chen (2009) have presented a sliding mode control scheme based on the disturbance observer for a class of nonlinear integer-order systems, and the stability of the closed-loop system is established using the Lyapunov method.…”

Section: Introductionmentioning

confidence: 99%

Adaptive constrained sliding mode control of uncertain nonlinear fractional-order input affine systems

Moghaddam

Nikravesh

Khosravi

2019

Journal of Vibration and Control

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This paper addresses asymptotic stabilization of uncertain nonlinear fractional-order systems with bounded inputs in the presence of model uncertainties and external disturbances. To develop the idea, it is assumed that the upper bound of perturbations is a nonlinear function of the pseudostates norm in which its coefficients are unknown and are obtained via proposed adaptive laws. The main contribution of this paper is to develop a new bounded fractional-order chattering free adaptive sliding mode control in which the system states converge to the sliding surface at a predefined finite time. The stability of the closed-loop system with the proposed control scheme is guaranteed by the Lyapunov theory. Furthermore, for more clarification, a comparison with the classical integer-order case is also presented; finally, some practical simulation results are provided to show the effectiveness of the proposed control algorithm.

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Section: Introductionmentioning

confidence: 99%

Adaptive constrained sliding mode control of uncertain nonlinear fractional-order input affine systems

Moghaddam

Nikravesh

Khosravi

2019

Journal of Vibration and Control

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“…proposed a linear parameter‐varying (LPV) state‐feedback controller to automatically balance a riderless bicycle in the upright position. In addition to linear controllers, nonlinear control approaches such as fuzzy logic control and sliding mode control (SMC) have also gained much attention for stabilizing the motion of two‐wheeled vehicles.…”

Section: Introductionmentioning

confidence: 99%

Continuous‐Action XCSR with Dynamic Reward Assignment Dedicated to Control of Black‐Box Mechanical Systems

Hashemnia

Panahi

Mahjoob

2017

Asian Journal of Control

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A real-value classifier system (CSR) is improved by the introduction of a continuous domain of actions to be employed for control of mechanical systems where there is no information concerning the system's mathematical model. To enable the classifier system to handle real-world control problems where continuous (non-discrete) actions are required, the exploitation of fuzzy membership functions is proposed. To cope with the dynamic system's delayed response due to its mass inertia, a dynamic reward assignment mechanism is incorporated into the proposed CSR. This allows the rapid calculation of the reward and hence enables the controller to be used in such real time applications. To demonstrate the efficiency of the developed enhanced CSR, it is employed as the controller to balance an unmanned bicycle, without using bicycle properties for the design process of the enhanced CSR. Simulation results show that in terms of overshoot and settling time, the proposed classifier system outperforms traditional XCSR as well as some of the more common balance-control strategies reported in the literature, as verified using ADAMS software.

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“…Today, analysis of deviated systems is very important, and many methods have been proposed in this area, such as robust control, adaptive control, predictive control, and fuzzy control . Fuzzy differential equations offer a reasonable method for consideration of the above deviations in the mathematical model, which can lead to better controller design.…”

Section: Introductionmentioning

confidence: 99%

Stability Analysis of Uncertainty Linear Systems Described by Fuzzy Characteristic Equations

Hallaji

Zare

Kamyad

2012

Asian Journal of Control

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This paper uses Kharitonov's theorem to present a new idea for assessment of the stability of linear systems that are described by fuzzy differential equations, while system uncertainty is expressed as fuzzy convex sets for coefficients of the characteristic equation. The paper then deals with determination of stability margins and the design of classical robust controllers of fuzzy type for those systems. In each part, illustrative examples and simulation results are provided.

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Guaranteed‐cost fuzzy controller design for a self‐sustaining bicycle with practical constraints

Cited by 6 publications

References 20 publications

Adaptive constrained sliding mode control of uncertain nonlinear fractional-order input affine systems

Adaptive constrained sliding mode control of uncertain nonlinear fractional-order input affine systems

Continuous‐Action XCSR with Dynamic Reward Assignment Dedicated to Control of Black‐Box Mechanical Systems

Stability Analysis of Uncertainty Linear Systems Described by Fuzzy Characteristic Equations

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