Part of the goal of Europe’s Strategy Vision Zero is to eliminate all severe cycling accidents in Europe by 2030. The majority of cycling accidents are single vehicle accidents; this term indicates the absence of collisions with other road participants and implies a fall of the cyclist. Research has shown that mainly elderly cyclists are victim to those accidents. An untested hypothesis is that their slowed motor responses are one of the main reasons behind this. This study aims to address this hypothesis by studying the influence of neural time delay on the lateral stability of bicycle-rider systems. In this study, the slowed motor responses are represented by single neural time delay values. The influence is quantified by the sizes of the identified basins of attraction of stability (BoA). The BoA contains the set of finite lateral disturbances for which stability is retained and is acquired via numerical integration. Binary threshold criteria are used to determine the stability of the solution. The bicycle-rider system consists of two components: a bicycle model and a rider model. The bicycle is modelled using the Whipple(-Carvallo) bicycle model with the set of non-linear equations derived by Basu-Mandal [2]. The rider is modelled using an implicit experimentally validated model from literature [31]. This model consists of a PID controller with full state feedback, neuro-muscular dynamics and, in this study, is extended to include nonzero time delay. The neural time delay value of a young cyclist has been based on literature [4]. The value is doubled to model an older cyclist. The control strategy of the young cyclist is identified using system identification techniques. The basins of the young and old cyclist are compared to study the detrimental effect of time delay on lateral stability in cycling. It declined over 80% when the time delay was doubled. The human’s ability to adapt its control to circumstances has been considered by repeating the control identification process for the rider which suffers from double the time delay. With respect to the young cyclist, a decline of over 50% was observed. Therefore, the results strongly support the hypothesis. Further research should focus on increasing complexity of the rider model to include preview and prediction. In this way, the influence of slowed motor responses can be mapped more clearly. A secondary objective of this thesis is the preliminary development of a steer assist control model to aid the elderly cyclist balance during cycling. This development builds further on a simple control model from literature [29] which uses roll angle feedback. As a result, a nonlinear velocity dependent roll rate feedback control law was developed. This control law yields a constant basin height over the commonly used velocity range of cyclists. This height indicates the maximum allowable steer rate perturbations the bicycle-steer assist system could handle and is approximately the same height as what was identified for a young cyclist. Future research is required for improving the steer assist. This means adding maximum allowable control torque, sensorial time delays and trajectory tracking

Brake squeal is a well know problem in the bicycle industry. Most of the time brake squeal occurs due to wear of the brake components and/or in certain environmental conditions (e.g. rain and dirt). Bicycle brake squeal is often solved by trial and error. Compared to automotive industry, there is not much research done on the fundamental cause of bicycle brake squeal. As a result, bicycle brands are not able to guarantee that their bicycles will not produce squeal noise over time. The goal of this graduation project is to get more insight into the fundamental cause of bicycle brake squeal and to find the crucial parameters that influence bicycle brake squeal.

The compliance (stiffness) of a human arm varies, depending on the task at hand. Some precise tasks require high stiffness, while others need a level of flexibility to deal with unknown disturbances. To describe this human behaviour a portable device is needed to give small force perturbations (input) while the resulting reaction of the arm (output) is measured. There are multiple state-of-the-art devices to choose from to provide such input. However, the majority of these devices either offer too little versatility or impede the free movement of the user. Implementation of a novel parallel mechanism allows for a lightweight (0.175kg) and compact device that can generate various signals in three degrees-of-freedom. Since the device is designed to be mounted around the wrist, it leaves your arm and hand unobstructed. A full-scale prototype is constructed and the concept is tested using a force sensor. Implementing powerful yet compact servo motors allows for controlled perturbations in the order of 4N, with bandwidths up to 12Hz.

Up to now, not much is known about how humans control bicycles, especially when subject to large perturbations. In order to learn more about the extent to which these perturbations can be handled, a new experimental setup is required, which can deliver large perturbations to cyclists and bring them to fall. This thesis describes the detailed design and evaluation of such a setup. Several requirements are formulated regarding different experiment aspects that the setup must adhere to. The proposed design consists of a treadmill on which a subject rides a bicycle. Ropes are guided from the ends of the bicycle’s handlebar toward the front and back of the setup, where they are attached to four motor units of a robotic rope-pulling system. Based on a feed-forward conversion added with PI force feedback control, a shared controller commands the motors to maintain a tracking force or perturb the cyclist by applying a net torque on the handlebar for a short time. An active safety harness is the main feature that prevents the subject from harm. Meanwhile, motion capture recordings of strategically placed passive markers and data from inertial measurement units on the bicycle are collected. With appropriate processing, the angles and angular velocities that describe the dynamics and control of the bicycle-rider system can be obtained from these measurements. The force data is also collected, by the controller. Interesting results that can be obtained with this setup include the probabilities to fall after perturbations of variable forces and the data required to evaluate the equations of motions of the bicycle-rider system during and shortly after a perturbation. This can be used to provide a baseline against which future bicycles can be compared and it is useful for validating rider models. The experiment shows consistent performance and generates high quality measurements. Neither the 12 pilot participants nor the 26 subjects who participated in follow-up experiments encountered any safety issues. The experiment could even be improved by resolving issues regarding the motor behaviour and CPU overloads. The workload of the experiment operators could be decreased if intervention of the safety harness was trained on cycling and coupled to deactivation of the treadmill. A next step would be to automate the data processing by developing a classifier that distinguishes recoveries from falls.

A lot of research has been done on the behaviour of pneumatic tyres and this has led to various tyre models and a lot of measurement data. However, in the specific field of bicycle tyres, not so much measurement data is available. However, in 2013 Andrew Dressel received the Degree of Doctor of Philosophy in Engineering at the University of Wisconsin-Milwaukee by presenting his research: Measuring and modeling the mechanical properties of bicycle tires. In this research he did a lot of measurements with multiple bicycle tyre brands and models under different conditions. The results from these measurements are interesting to use for modelling purposes. The goal of this research is to find out if it is possible to estimate the bicycle tyre behaviour in terms of vertical stiffness, cornering stiffness and camber stiffness based on known parameters like the inflation pressure, tyre width, rim width, vertical load and the rubber compound using a tyre model. An important part of this thesis are tyre models. The measurement data will be analysed using various tyre models. The first used tyre model is the brush model. This model uses a single material parameter and it turns out that this is too less to be able to extract clear relations between the tyre behaviour and the known parameters like inflation pressure, tyre width and rim width. The second tyre model that is used is the enhanced string model. This model is an extension of the brush model. This model has three material parameters for the vertical direction and also three material parameters for the lateral direction. Investigating this model shows that there is reasonable suspicion to assume that one of the model parameters represents the inflation pressure. In order to find the material parameters for every tyre, inflation pressure and normal load combination a parameter optimisation is required. The results from this parameter optimisation show that the suspected model parameter is actually not related to the inflation pressure. When the obtained model parameters from the optimisation are put back into the tyre model interesting results are acquired, because the model output agrees fairly well with the measurement data. This is unexpected because the model parameters were differing a lot depending on how they were obtained, e.g. obtaining the parameters from cornering stiffness or from camber stiffness. The answer to the research question is no. In order to extract relations between the model parameters and the tyres behaviour depending on their measurable properties, a lot more complete measurements are needed. The whole idea was to be able to estimate tyre behaviour without the need of extensive testing. This still might be possible, but in order to to that a lot more measurements are needed first. These measurements should create a baseline of model parameters which can be used to estimate the model parameters of unmeasured tyres.

Introduction During Bicycle Motocross races (BMX SX), the start has been proven to be crucial for good overall performance [1]. Riders reaching the bottom of the eight meter high starting ramp in front of the pack, have a favorable position to perform the first jump and can pick the most ideal line through the first corner. The chances of getting involved in collisions with other riders are also highly reduced. Since riders start behind a gate, the anticipation and timing with respect to this gate movement are most important [2]. Most scientific research regarding the BMX SX gate start is focused on defining the performance indicators for a fast start [2] [3] [4] or using in-field experiments to evaluate the effects of minor changes to the bicycle design [5] [6]. However, using the results of these studies to actually improve the start performance would require extensive training using these new conditions. Using predictive simulations, these adaptations can be evaluated without practicing and can thus have a huge contribution to enhancing the gate start technique. However, before these simulation models can have any impact, they must be thoroughly analyzed to prove their validity. Objective The main goal for this study was to construct a biomechanical model for the BMX SX gate start which could reproduce experimental data. The model must be able to track kinematic data with an accuracy of less than 5% while also match the main kinetic characteristics without tracking those. The kinetic profiles should show the same peak pattern as is commonly seen in cycling and must not differ more than 10% with experimental data. When these goals are reached, this model could serve as a framework for future applications within BMX SX gate start research or other cycling disciplines. Method A nine degree-of-freedom biomechanical planar model was created within the open-source software package OpenSim [7] [8]. The model consists out of the ground surface, the gate, the BMX SX bicycle, and the rider. The latter two are connected using kinematic constraints on the feet and pedals. The upper body is connected to the frame by a single arm. The model is driven by eight optimal torque actuators located at the hip, knee, ankle, shoulder, and elbow joints. The contact dynamics of the wheels to the ground and the gate are included using the Hunt-Crossley model [9]. Moco [10], a direct collocation package for OpenSim, was used to solve the kinematic tracking optimization problem. The kinematic data was taken from a prior study by Melle van Dilgt [11] who captured three-dimensional kinematics of an elite female BMX SX athlete of the Dutch National team using an Xsens suit (Xsens Technologies, Enschede, The Netherlands). This IMU data was projected on the planar model using OpenSense, a tool within OpenSim that converts experimental IMU data into the model’s generalized coordinates. Simulation outcomes were compared to kinetic data collected by Hylke van Grieken [4], who used a fully instrumented bicycle including special cranks (Axis2D, Swift Performance, Brisbane, Australia) to capture the pedal forces executed during in-field experiments with a sample rate of 100 Hz. These experiments used the same elite participant but were taken on a different day using a different bicycle. Results The optimized tracking simulation showed close agreement with experimental kinematic data, showing an average root mean squared error (RMSE) of 0.337° or 0.52% for the six leg joints. For the tracking of the crank angle and the horizontal displacement of the bicycle similar results were found (RMSEs of 0.079% and 0.6% respectively). Simulated crank torque peak values were off by 4.4%, 7.7%, and 5.1% for the first, second and third torque peak respectively. Overall the crank torque was reproduced with an RMSE of 18%. Conclusion This work shows the suitability of the designed model for future applications in predictive simulation of the BMX SX gate start. The model can be used to study a wide range of "what-if" scenarios and could lead to the improvement of gate start performance. The way the model is constructed, the main building blocks can be adjusted to more accurate, but also more complex, components if desired.