Fuzzy Control for Equilibrium and Roll-Angle Tracking of an Unmanned Bicycle

Chen, Chih-Keng; Dao, Thanh–Son

doi:10.1007/s11044-006-9013-7

Cited by 33 publications

(32 citation statements)

References 9 publications

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“…In this report, the bicycle model with nine generalised co-ordinates and two algebraic variables presented in [15] and [16] is used. Figure 1 The dynamics of bicycles is described by the motion of the reference point c. Six coordinates are used to designate the positions and orientations of c at point c. The other three co-ordinate variables are the steering angle δ, and the rotating angles φ r and φ f of the rear and front wheels.…”

Section: Dynamics Of Bicyclementioning

confidence: 99%

“…The complete procedure of development of dynamic equations is presented in [15] and [16]. Furthermore, in this study the steering resistance is considered.…”

Section: Dynamics Of Bicyclementioning

confidence: 99%

“…The FLC is PD-like since it requires two inputs, the error needs to be minimised, and its variation, which are comparable to the proportional and derivative parts of a PD controller. Compared to the previous studies [15,16], the controller structure has been simplified so that only one FLC is used to generate the torque τ directly from the roll-angle error e θ , as shown in Linguistic quantification used to specify a set of rules for this controller is characterised by the following three typical situations:…”

Section: Controller Structurementioning

confidence: 99%

“…In previous studies [15,16], a diversity of works have been introduced relative to the dynamics and control of an unmanned bicycle. When changing direction of a bicycle, the rider always has to control the roll angle of the bicycle.…”

Section: Introductionmentioning

confidence: 99%

“…This implies that the roll-angle control is a preliminary step for developing the turning or path-tracking controllers [16]. The roll-angle-tracking control structure presented in [15], which is based on the fuzzy-logic controllers (FLCs), showed that the bicycle could follow the roll-angle command with small tracking error. However, that control structure revealed that the control parameters were speed-specific.…”

Section: Introductionmentioning

confidence: 99%

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Speed-adaptive roll-angle-tracking control of an unmanned bicycle using fuzzy logic

Chen

Dao

2010

Vehicle System Dynamics

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This paper presents the development of dynamic equations and roll-angle-tracking controller of an unmanned bicycle. First, the equations of motion and constraints of a bicycle with rolling-withoutslipping contact condition between wheels and ground are developed using Lagrange's equations. The equations are then used to implement the simulation of the bicycle dynamics. With the bicycle model, a fuzzy-logic controller, which is adaptive to the speed change is implemented to control the bicycle to follow the roll-angle commands. The controller parameters where fuzzy membership functions are presented by scaling factors and deforming coefficients are optimised using genetic algorithms. Results show that the bicycle can follow the roll-angle command with short time delay and the control structure can adapt to a wide range of speed.

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Section: Dynamics Of Bicyclementioning

confidence: 99%

“…The complete procedure of development of dynamic equations is presented in [15] and [16]. Furthermore, in this study the steering resistance is considered.…”

Section: Dynamics Of Bicyclementioning

confidence: 99%

Section: Controller Structurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Speed-adaptive roll-angle-tracking control of an unmanned bicycle using fuzzy logic

Chen

Dao

2010

Vehicle System Dynamics

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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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State-of-the-art and challenges of railway and road vehicle dynamics with multibody dynamics approaches

et al. 2020

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A review of the current use of multibody dynamics methods in the analysis of the dynamics of vehicles is given. Railway vehicle dynamics as well as road vehicle dynamics are considered, where for the latter the dynamics of cars and trucks and the dynamics of single-track vehicles, in particular motorcycles and bicycles, are reviewed. Commonalities and differences are shown, and open questions and challenges are given as directions for further research in this field.

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Fuzzy Control for Equilibrium and Roll-Angle Tracking of an Unmanned Bicycle

Cited by 33 publications

References 9 publications

Speed-adaptive roll-angle-tracking control of an unmanned bicycle using fuzzy logic

Speed-adaptive roll-angle-tracking control of an unmanned bicycle using fuzzy logic

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

State-of-the-art and challenges of railway and road vehicle dynamics with multibody dynamics approaches

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