Virtual Rider Research Articles

Real-time Learning-based Nonlinear Model Predictive Control of a virtual motorcycle employing grey-box modeling through Gaussian processes

Virtual prototyping tools simulations are nowadays largely adopted methods in the automotive industry. However, to achieve effective simulative tests for two-wheeled vehicles, a virtual rider, i.e. a controller for virtual motorcycles, is typically needed due to the inherent instability of the system. Different control strategies have been employed to deal with this control task, and promising results have been obtained using Nonlinear Model Predictive Control (NMPC). Yet, performance of NMPC highly relies on the plant characterization, that can be highly complex to obtain analytically for 2-wheel vehicles. To improve it, learning dynamics approaches can be used within a Learning-based Nonlinear Model Predictive Control (LbNMPC) framework, exploiting the combination of data-driven techniques and NMPC strategy. In this manuscript, we present a tailored real-time capable LbNMPC for a virtual motorcycle that relies on a continuous grey-box model based on Gaussian Processes (GPs), method that has proven to be effective for 4-wheel vehicle applications. To cope with the real-time requirement, a feature selection procedure and sparse GP approximations have been adopted. A comparison with a physics-based model NMPC implementation taken from the literature has been carried out, analyzing the impact of different sparse GP representations. Remarkable improvements in both model accuracy and tracking performance have been obtained, and the generalization properties of the grey-box model have been assessed.

Real-Time Nonlinear Model Predictive Control of a Virtual Motorcycle

Virtual prototyping tools are widely used in the development of new motorcycles, but the instability of two-wheel vehicles leads to the necessity of implementing a vehicle driver. In this brief, the development of an effective virtual rider (VR) is described that is capable of controlling the motorcycle during complex, high performance maneuvers. The control inputs are the steering angle, the lateral rider movement, and the throttle/braking effort. The proposed motorcycle model consists of a plane that can roll and slide (both in x- and y-directions), on which a moving point mass with lateral degree of freedom (DoF) is attached, representing the rider. The controller is designed within the nonlinear model predictive control (MPC) framework. The implementation is developed in MATMPC, an open-source toolbox, and tested in co-simulation with the commercial software VI-BikeRealTime, specifically designed to reproduce vehicles behavior with high fidelity. A chicane and a lap on a challenging track have been used to both show flexibility and evaluate performance, demonstrating the capability of the proposed VR of handling complex maneuvers.

A Computer Model for Simulating the Bicycle Rider's Behavior in a Virtual Riding System

People are increasingly demanding to experience realistic behavior of virtual characters in computer games. In this article, we build a computer model for simulating a bicycle rider’s behavior in a virtual riding system. A method to calculate the speed of a bicycle rider is proposed to improve the reality in a virtual riding system. In this method, the property of physical energy is introduced, and the bicycle-riding speed is calculated in real time according to the relationship between the rider’s physical energy and bicycle-riding speed. Then based on the analysis of the behavior of a cyclist in a real competition, various behaviors of the virtual rider are designed and a behavior-tree for the virtual bicycle rider is constructed accordingly. On the basis of these, a virtual riding system is developed. The experiments results show that our system can simulate the behavior of a virtual bicycle rider, and thus encourage exercise on a stationary bicycle.

Validating power management strategies for hybrid sport motorcycles: a virtual prototyping approach

In this paper, we consider the problem of assessing the performance of different power management strategies for a hybrid, 125cc sport motorbike. The electrical machine is used to obtain a torque boost during accelerations, with reduced emissions. Being a sport motorcycle, impact of the hybridisation on performance in terms of handling has to be carefully evaluated. To this aim, a virtual environment has been realised, that allows to describe in detail the vehicle dynamics, as well as the characteristics of the hybrid power train. The simulation environment is completed by a virtual rider and a tool for building test circuits. The virtual environment is used to evaluate two different power management strategies, based on a standard heuristic and on torque-split optimal control strategies, respectively. Results of simulations on a virtual track and of field tests are included.

An advanced multibody model for evaluating rider’s influence on motorcycle dynamics

The dynamics of a high-performance motorcycle are greatly influenced by the rider’s weight and movements especially when the power-to-weight ratio is very high. Generally in motor vehicles, the driver’s/rider’s weight is a significant fraction of the entire system. This work is about ADAMS/View multibody modelling of a motorcycle and virtual rider who simulates handlebar interaction and saddle sliding. In the literature, the rider’s influence is unrealistic being limited to considering him as a concentrated mass or in other cases as a fixed passive system. Even vehicle modelling is often inaccurate, referring at best to simplified models of rigid bodies. In this work, the vehicle and rider have been accurately modelled to most realistically reproduce the dynamic behaviour of the system. The motorcycle was modelled with 12 bodies incorporating concentrated flexibility for the two suspension units and considering the chassis as a flexible body using modal synthesis. The virtual rider is made up of 15 rigid bodies and has 28 degrees of freedom. To study the effects on the motorcycle of the rider’s movements as well as the motorcycle’s dynamics and performance, a monitoring system similar to that in the literature was used to read handlebar torque and engine and braking torque. Furthermore, in the literature there are simulations of standard manoeuvres whereas in this work an entire lap of Monza was simulated. There were simulations of a fixed and mobile rider validating the model in advance and thereafter monitoring the most significant dynamic parameters. The multibody model provides useful results at the design phase and insights into the whole vehicle/rider dynamic to setup all the reference parameters for immediately evaluating system effects.

A Virtual Rider for Motorcycles: Maneuver Regulation of a Multi-Body Vehicle Model

This work develops a virtual rider that can be used to make a multi-body two-wheeled vehicle follow a specified ground path with a prescribed velocity profile. The virtual rider system is based on a simplified motorcycle model, the sliding plane motorcycle, which is composed of a single rigid body with two ground contact points. This reduced order nonlinear system was presented in an earlier work, together with a dynamic inversion procedure for computing a state-control trajectory corresponding to the desired task. This dynamic inversion procedure is combined in this work with a maneuver regulation controller to yield a nonlinear feedback control strategy. A transverse coordinate system that is consistent with the mechanical symmetries of ground vehicles is constructed and used in the development of the maneuver regulation controller. An inverse optimal control strategy, which also exploits the mechanical symmetries, is developed to shape the dynamic response of the closed loop system. Numerical results with the virtual rider driving a multi-body vehicle through a demanding maneuver with lateral accelerations reaching 1 g are presented.

A virtual motorcycle driver to simulate real manoeuvres from experimental data

The control of single-track vehicles represents a challenging task. The stability is characterized by vibration modes which change with the vehicle speed and acceleration and may possibly become unstable under certain motion conditions. This paper presents a virtual rider who accounts for these effects by updating the control action at each instant, based on the information of the approaching road section and the current vehicle state. This approach mimics the behaviour of a real rider, who looks ahead, learning a portion of the track and continuously using this information to decide when and how to steer and accelerate or decelerate. As an example of application, the virtual rider is employed to simulate a real manoeuvre using the experimental signals (namely the speed and the roll angle) of the real vehicle as the target motion for the virtual vehicle. A comparison between the virtual rider inputs and the real rider inputs is included. The essential properties that make the behaviour of the virtual rider consistent with that of the real human rider are addressed.

A nonlinear virtual rider for motorcycles

This work presents a virtual rider for the guidance of a nonlinear motorcycle model. The target motion is defined in terms of roll angle and speed. The virtual rider inputs are the steering torque, the rear-wheel driving/braking torque and front-wheel braking torque. The virtual rider capability is assessed by guiding the nonlinear motorcycle model in demanding manoeuvres with roll angles of 50° and longitudinal accelerations up to 0.8 g. Considerations on the effective preview distance used by the virtual rider are included.

Advanced motorcycle virtual rider

The target of this work is the development of a motorcycle virtual rider model that plans a trajectory and follows it. The reference speed and trajectory are obtained by applying the optimal manoeuvre method (i.e. a nonlinear optimal control technique) to a basic model of the motorcycle. Then, the vehicle control and guidance are obtained using a PID architecture and the gains vary with speed. Separated loops for speed and lateral motion controls have been implemented, the lateral motion control is complex because of the motorcycle instability, whereas the longitudinal control is simpler. The characteristics of the PID control are illustrated and discussed in detail, some examples are given for a cornering manoeuvre first and then for a run in the Mugello circuit.

A virtual motorcycle driver for closed-loop simulation

The development of a motorcycle driver for virtual prototyping applications is discussed. The driver is delivered with a commercial multibody code as a tool for performing closed-loop maneuvers with virtual motorcycle models. The closed-loop controller is developed with a qualitative analysis of how a human rider controls a motorcycle. The analysis concerns handling and maneuverability, which are relevant for real and virtual vehicle performance evaluation. A motorcycle model for control design and a controller structure are developed. The model is based on a mathematical representation of common-sense rules of motorcycle riding. The virtual rider is then tested in various operating conditions to assess whether the control requirements are achieved. Criteria for evaluating driver models are briefly discussed