Motorcycles.

Esboza las principales características y los usos apropiados de las motocicletas, como forma de transporte contestando preguntas como qué es una motocicleta, qué aspecto tiene, cómo eran las motocicletas hace tiempo. Diseñado para ayudar a concentrarse y aprendera formular las propias preguntas sobre el mundo que nos rodea. Tiene un glosario.

Motorcycles. Fatal accident reporting system. Special report on motorcycles.

Mode of access: Internet.

Motorcycles: a bibliography.

Mode of access: Internet.

Development of a low cost anti-lock brake system for small motorcycles. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Towards a low cost highly reliable anti-lock brake system for small motorcycles. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

Census of manufactures: 1914. Motorcycles, bicycles and parts.

Running title: Motorcycles, bicycles, and parts.

Stability issues in racing motorcycles: an in-depth analysis of the chatter vibration

Racing motorcycles are prone to an unstable oscillatory motion of the swingarm and rear wheel, commonly known as ‘chatter’. This vibration mode typically has a frequency of 17 Hz to 22 Hz and typically occurs during heavy braking manoeuvres. The appearance of chatter can cause reduced rider confidence, and therefore lead to longer lap times during races and the increased risk of crashing. This thesis looks to further the understanding of this mode. It includes the development of a simplified model to explore the effects roll angle and lateral dynamics have on the chatter mode using linear analysis. The mechanisms of instability and parameter sensitivities are also examined. The effects of the nonlinearities present in the minimal model equations of motion are examined, including the identification of limit cycles and their stability, inspecting individual nonlinear terms and their effects, and introducing tyre relaxation and determining the effect it has on the dynamics. Finally, an exploratory study of the mid-corner region of a typical racing manoeuvre is performed in hopes to better understand if any high frequency tyre induced instabilities like chatter can occur.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.