A wobble instability is one of the major problems of a three-wheeled vehicle commonly used in India, and these instabilities are of great interest to industry and academia. In this paper, we studied this instability using a multi-body dynamic model and with experiments conducted on a prototype three-wheeled vehicle on a test track. The multi-body dynamic model of a three-wheeled vehicle is developed using the commercial software ADAMS/Car. In an initial model, all components including main structures such as the frame, the steering column and the rear forks are assumed to be rigid bodies. A linear eigenvalue analysis, which is carried out at different speeds, reveals a mode that has predominantly a steering oscillation, also called a wobble mode, with a frequency of around 5-6 Hz. The analysis results shows that the damping of this mode is low but positive up to the maximum speed of the three-wheeled vehicle. However, the experimental study shows that the mode is unstable at speeds below 8.33 m/s. To predict and study this instability in detail, a more refined model of the three-wheeled vehicle, with flexibilities of three important bodies, was constructed in ADAMS/Car. With flexible bodies, three modes of a steering oscillation were observed. Two of these are well damped and the other is lightly damped with negative damping at lower speeds. Simulation results with flexibility incorporated show a good match with the instability observed in the experimental studies. Further, we investigated the effect of each flexible body and found that the flexibility of the steering column is the major contributor for wobble instability and is similar to the wheel shimmy problem in aircraft.

The increased number of vehicles and poor road conditions in many countries result in slow moving traffic. At low-speeds, riding a motorcycle requires continuous input from a rider to achieve stability, which causes fatigue to the rider. Therefore, in this research, the low-speed stability of a motorcycle is studied using a theoretical and experimental approach to identify the parameters that can reduce the rider’s effort. Initially, a linear mathematical model of the motorcycle and rider system is presented; wherein, the equation of motion for the stability of the system in roll direction is derived. The open-loop and closed-loop poles from the equation are calculated to determine the regions for the low-speed stability. Subsequently, experiments are conducted on the motorcycle instrumented with the required sensors, on a straight path at speeds below 10 km/h. The input and output parameters from the experimental data are analyzed using a statistical method. Steering angle and steering torque are the input parameters; roll and yaw angles and their corresponding velocities are the output parameters selected for the analysis. Correlation and lead time between the input and output parameters are compared to identify the parameters useful for the rider to attain the low-speed stability. The results obtained from the experimental analysis validate the mathematical model. In addition, these findings also validate that the input parameters required to control the motorcycle to achieve low-speed stability can be estimated using the identified output parameters.

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Motorcycles are a primary mode of daily transportation in many developing countries, especially in towns and cities. The increased traffic congestion constrains the average speed of the motorcycle, causing stability and safety concerns for the riders. A controller that assists the riders can improve this scenario. This paper presents a new controller developed using an experimental study that improves the low-speed stability of a motorcycle. The experiments were conducted on a motorcycle with the riders of three experience levels: beginner, intermediate and expert. The input parameters: steering angle and steering torque; the output parameters: roll angle, yaw angle, roll rate and yaw rate were measured. Critical input and output parameters were identified statistically from the experimental measurements and used for the controller modelled in Simulink. The controller model was co-simulated with a multi-body dynamics model of the motorcycle. The co-simulation results showed the controller developed herein stabilises the motorcycle model at low speeds.