The main objectives of the work described are to devise an effective path-based motorcycle simulation capability and to add to understanding of how riders control motorcycles. Optimal linear preview control theory was previously applied to the tracking of a roadway by a car, using a simple car model operating in fixed control. Similar theory is applied to path control of motorcycles. The simple car previously employed is replaced by a much more elaborate motorcycle. The steering angle control used previously is changed into steering torque control. Rider upper body lean torque is also allowed as a control input. The machine speed is considered constant but is a parameter of the motion. The objective of the optimal control is to minimize a weighted sum of tracking errors, rider lean angle and control power. The time-invariant optimal control corresponding to a white noise disturbance and to an infinite optimization horizon is found for many situations, involving variations in machine speed and performance priorities. Tight controls, corresponding to high weightings on performance, and loose controls, corresponding to high weightings on control power, are identified. Results show the expected pattern for preview control, that information well into the future is of limited value in determining the present control inputs. Full system performance is achievable with only finite preview. The extent of the preview necessary for full performance is determined as a function of machine speed and performance priorities. This necessary preview is found to be in accord with conventional wisdom of motorcycle riding and rider training. Optimal path tracking preview controls are shown to represent the inverse dynamics of the motorcycle. New light is shed on the relative effectiveness of steering torque and body lean torque controls. Simulations of an optimally controlled motorcycle and rider combination are conducted. A typical lane change path and an S-shaped path from the literature are used. For a chosen speed, optimal controls are installed on the machine for which they were derived and simulation results showing tracking performance, control inputs, and other responses are included. Transformation of the problem from a global description, in which the optimal control is found, to a local description corresponding to the rider’s view, is described. It is concluded that a motorcycle rider model representing a useful combination of steering control capability and computational economy has been established. The model yields new insights into rider and motorcycle behavior.