Dynamic modeling and simulation of a bicycle stabilized by LQR control

Owczarkowski, Adam; Horla, Dariusz; Kozierski, Piotr; Sadalla, Talar

doi:10.1109/mmar.2016.7575258

Cited by 10 publications

(11 citation statements)

References 8 publications

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“…LQR, LQI and LMI-based LQR control in [30]. The results turned the attention of the authors to a problem with lesser DoF to be concerned [32]. First, by introducing robustness into LQR/LQI control laws with results based on experiments [31], then introducing feedback linearization to a simulation model [44], with in-depth analysis of impact of initial conditions and robustness parameter [45], and different linearization schemes [17].…”

Section: Introductionmentioning

confidence: 99%

Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel

Owczarkowski¹,

Horla

Ziętkiewicz

2018

Asian Journal of Control

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In this paper, the Jacobian-linearization-and feedback-linearization-based techniques of obtaining linearized model approaches are combined with a family of robust LQR control laws to identify the pairing which results in superior control performance of the bicycle robot, despite uncertainty and constraints, what is the main contribution of the paper. The control performance is analyzed using various indices, related, e.g. to energy consumption of the considered laws, with the experiments conducted on a real bicycle robot. As a result, the easily-implementable controller is obtained, which requires only to perform a set of off-line computations with a single additional parameter in comparison with a standard linear-quadratic controller, to obtain a state-feedback vector, which, when implemented to the control system, ensures proper regulation of the output signal of the plant, despite uncertainty or possible actuator failures, obtaining energy-efficient control law.

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Section: Introductionmentioning

confidence: 99%

Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel

Owczarkowski¹,

Horla

Ziętkiewicz

2018

Asian Journal of Control

Self Cite

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“…as Lagrange, Euler equations or the detailed nonlinear Whipple scientific description [9], [10]; for example, [11] used the Linear-quadratic regulator (LQR algorithm) to analyze the bicycle mathematical model, this method is considered accurate but time-consuming at the same time. For a straightforward and time efficient modeling, the classical single-track model could be an alternative [12].…”

Section: Introductionmentioning

confidence: 99%

Bicycle Simulator Improvement and Validation

Shoman

Imine

2021

IEEE Access

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In this paper, we present the new features implemented on the bicycle simulator developed by the Perceptions, Interactions, Behaviors and Simulations Lab for road and street users (PICS-L) at Gustave Eiffel University. The added features were deemed necessary to study road-bicycle interactions. We equipped the simulator platform with: three actuators to render the road profile vibrations, an asphalt specimen attached to the rear tire to render the road adhesion, and a new virtual reality environment to render a part of the city of Vanves in France. Simultaneously, we developed a mathematical model with 6 degrees of freedom including the three rotational angles (Yaw, Pitch and Roll) and their influence on vertical, lateral and longitudinal modeling. In order to validate the simulator and the developed model physically and subjectively, we conducted an experiment involving 36 participants who rode the simulator for around 600 meters with full control on the handlebar, pedals and brakes. The improved simulator/mathematical model will be employed to further study bicycle dynamics, cyclist behavior and the interaction with the infrastructure and other road users.

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“…Rare papers are for the situations of time-varying forward velocity. On the other hand, it is found that the conventional optimal controller design methods, like linear quadratic regulator (LQR) [20,21], can minimize the tracking error and control input, but cannot consider the transient performances, such as rise value, convergence time, and steady error. It depends on the practical experience to select a proper objective function, which hinders the applications of these methods.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

Zhao

Chen

et al. 2021

IET Control Theory & Appl

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Existing studies for the balance control of unmanned bicycle robots only consider constant forward velocity and a single optimal objective that cannot be applied to the complex motion situation. To balance the bicycle robot with time‐varying forward velocity, only with the steering actuator, the multiple objective optimal balance control issue is studied here. A fuzzy state‐space model under different forward velocities is firstly offered based on the non‐linear Euler–Lagrange model. Based on this, a closed‐loop equation under a fuzzy controller is verified. To regulate the feedback gain of the fuzzy controller, a modified particle swarm optimization (MPSO) algorithm with two stages is proposed. In the MPSO's second stage, a novel objective fitness function, consisting of multiple objectives and combining the conventional Hurwitz stability analysis criterium, is designed. Procedures for the MPSO dynamic programming approach are presented. By two examples, the efficiency of the MPSO algorithm, for time‐varying and time‐constant velocity situations, and faster capacity for iteration convergence, are examined.

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Dynamic modeling and simulation of a bicycle stabilized by LQR control

Cited by 10 publications

References 8 publications

Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel

Introduction of Feedback Linearization to Robust LQR and LQI Control – Analysis of Results from an Unmanned Bicycle Robot with Reaction Wheel

Bicycle Simulator Improvement and Validation

Fuzzy model‐based multi‐objective dynamic programming with modified particle swarm optimization approach for the balance control of bicycle robot

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