Bicycles have seen relatively few safety features added since their invention, especially in comparison to the numerous advancements in automotive safety. This thesis investigates the use of simulation to model bicycle dynamics, progressing from a no-slip model to a more complex model incorporating slip, and finally to a model that simulates the interaction of a bicycle with an unexpected patch of ice. Tire slip on ice was estimated using car tire slip data combined with bicycle measurements taken on dry asphalt. The study evaluates the effectiveness of two control strategies, Proportional-Derivative (PD) control, and Linear Quadratic Regulator (LQR) control, through these simulations. The PD controller was tested with gain values ranging from 0 to 20 at regular intervals, while the LQR controller’s weightings were selected through a process of trial and error. The analysis focuses on how variations in PD gains and LQR weightings affect the bicycle’s stability and ability to traverse icy terrain. Results indicate that the LQR controller consistently stabilized the bicycle at an 8° lean angle across all tested speeds and maintained stability at a 12.5° lean angle at higher speeds when crossing ice. Ultimately, the selected LQR controller used diagonal values of 50 and 5 in the state weighting matrix Q, and a value of 1 in the control weighting matrix R.These findings suggest that advanced control strategies, particularly LQR, can enhance bicycle stability in adverse conditions