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Due to the increasing computational power, significant progress has been made over the past decades when it comes to CAD, multibody and simulation software. The application of this software allows to develop products from scratch, or to investigate the static and dynamic behavior of multibody models with remarkable precision. In order to keep the development costs low for highly sophisticated products, more precisely motorcycle rider assistance systems, it is necessary to focus extensively on the virtual prototyping using different software tools. In general, the interconnection of different tools is rather difficult, especially when considering the coupling of a detailed multibody model with a simulation software like MATLAB Simulink. The aim of this paper is to demonstrate the performance of a motorcycle rider assistance algorithm using a cosimulation approach between the free multibody software called FreeDyn and Simulink based on a sophisticated multibody motorcycle model.

This paper presents a study on the state estimation of out-of-plane dynamics of motorcycles based on the Sharp 71 model. The Sharp 71 model is a linear time-variant system that describes the out-of-plane dynamics of a motorcycle. Comparisons with multi-body simulations and measurement data show that this relatively simple model is capable of principally representing the lateral dynamics of the motorcycle. Two relevant variables of out-of-plane dynamics are the roll angle and the tire lateral forces. The structure of the Sharp 71 model offers the possibility of estimating these two variables model-based with the aid of corresponding measured output variables. The input variable is the steering torque, which obviously cannot be measured with reasonable effort. Therefore, an unknown-input observer is used to estimate the states. This state estimator allows a systematic consideration of the unknown input variable.

In this study a preliminary investigation regarding closed-loop acceleration control for motorcycles is presented. Comprehensive considerations for the implementation of such a controller are discussed. Challenges, which are addressed, are a stable and sufficiently accurate measurement with the help of low-cost sensors and the consideration of the varying available maximum acceleration for set point calculation. In case of torque control, the maximum available torque is scaled by the throttle and thus automatically meets the limitation. Using acceleration as control variable, the varying set point limitation must be considered. According to current hypothesis, a precise closed loop control of the motorcycle longitudinal dynamics can be realized on the basis of the reference variable acceleration, yielding new possibilities in drive train control. The current control of the longitudinal dynamics is done by specifying a target output torque.

In this study we focus on systematic disturbances caused by the motorcycle pitch dynamic when measuring longitudinal acceleration on motorcycles using low-cost acceleration sensors. Major systematic influences in the sensor measurement like gravitational acceleration, suspension dynamics and the road slope are addressed. During acceleration phases the motorcycle pitch angle changes according to the suspension setting. As a result the longitudinal sensing axis of the accelerometer includes parts of the gravitational acceleration and lags parts of the longitudinal acceleration. Gravitational acceleration has also significant influence on inclined roads. To obtain correct values of the effective longitudinal acceleration, the disturbances in the measured signal are analyzed and in further consequence compensated. For this purpose a linearized in-plane-dynamics model of the motorcycle is derived from a comprehensive multibody simulation.

Vehicle dynamics control (VDC) for motorcycles had a fast growth during the last 10 years. The available technologies comprise curve-safe ABS and traction control (TC) systems, anti-wheelie control, right up to comprehensive motorcycle stability systems including even more control functions. VDC systems rely on real-time information about the current motorcycle dynamic state. Thus motorcycles are equipped with additional sensor units, namely MEMS inertial measurement devices, capable of gathering accelerations and angular rates. The application of model-based estimation theory enables the determination of the necessary information about the in-plane and out-of-plane motion, e.g. the motorcycle lean angle. Since VDC systems include safety critical control functions, the validation within simulations including sensor characteristics is mandatory.