All wheel drive electric motorcycle modelling and control.

Conventional motorcycles are powered through a chain or shaft linking the engine to the rear wheel. However, motorcycle riders are now facing riding conditions and obstacles where having only rear wheel drive can lead to vehicle damage, loss of control and an unstable front wheel during cornering and off-road riding in general. Traction and climbing ability are severely limited in extreme mountain conditions by only having the rear wheel to provide power. Accordingly, there is a need in the industry for a two-wheel drive motorcycle that efficiently and safely transfers power from the motor to the front wheel, because it provides the rider with increased ability to safely negotiate rough terrain. In this background, the design of an optimal torque distribution strategy implemented by two separate electric motors in an all-wheel-drive electric motorcycle has many potentialities not fully explored and deeply understood for two wheel vehicles, that makes this study interesting from a scientific point of view. With this in mind, the research project aims to design control systems for improving rider’s safety and vehicle performance at low as well as high speeds, especially in critical situations and rough terrains, taking into account the presence of the front wheel torque generated by a hub-mounted electric motor. At low speed the research investigates whether and how the front wheel torque helps the stabilization of the vehicle around the upright position, without any rider action required. The study is developed by deriving a simplified analytical model of the vehicle, which captures its lateral motion and a model-based control system, employing the sliding mode control technique. As further requirement, the motorcycle should be balanced in a small bounded area, by means of Multi Input control system. At medium and high speeds the study explores how and how much the traction torque repartition can improve continuously the vehicle performances in combined longitudinal and lateral acceleration situations, such as the exit of a curve, especially in those conditions where a traditional motorcycle falls down because it overcomes tyre adherence limits. Last purpose is achieved deriving a dynamical optimal traction strategy which does not require the a priori knowledge of the friction coefficient. Steady state analysis indicates outperformances of the all wheel drive motorcycle over the classical rear wheel drive one. Then, dynamical simulations of selected manoeuvres, in both flat and uneven road, corroborate the result

Minimum-time trajectory generation for quadrotors in constrained environments

In this paper, we present a novel strategy to compute minimum-time trajectories for quadrotors in constrained environments. In particular, we consider the motion in a given flying region with obstacles and take into account the physical limitations of the vehicle. Instead of approaching the optimization problem in its standard time-parameterized formulation, the proposed strategy is based on an appealing re-formulation. Transverse coordinates, expressing the distance from a frame path, are used to parameterise the vehicle position and a spatial parameter is used as independent variable. This re-formulation allows us to (i) obtain a fixed horizon problem and (ii) easily formulate (fairly complex) position constraints. The effectiveness of the proposed strategy is proven by numerical computations on two different illustrative scenarios. Moreover, the optimal trajectory generated in the second scenario is experimentally executed with a real nano-quadrotor in order to show its feasibility.Comment: arXiv admin note: text overlap with arXiv:1702.0427

A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers

In the study of vehicle dynamics and controls, modeling ultra-high performance maneuvers (i.e., minimum-time vehicle maneuvering) is a fascinating problem that explores the boundaries of capabilities for a human controlling a machine. Professional human drivers are still considered the benchmark for controlling a vehicle during these limit handling maneuvers. Different drivers possess unique driving styles, i.e. preferences and tendencies in their local decisions and corresponding inputs to the vehicle. These differences in the driving style among professional drivers or sets of drivers are duly considered in the vehicle development process for component selection and system tuning to push the limits of achievable lap times. This work aims to provide a mathematical framework for modeling driving styles of professional drivers that can then be embedded in the vehicle design and development process. This research is conducted in three separate phases. The first part of this work introduces a cascaded optimization structure that is capable of modeling driving style. Model Predictive Control (MPC) provides a natural framework for modeling the human decision process. In this work, the inner loop of the cascaded structure uses an MPC receding horizon control strategy which is tasked with finding the optimal control inputs (steering, brake, throttle, etc.) over each horizon while minimizing a local cost function. Therein, we extend the typical fixed-cost function to be a blended cost capable of optimizing different objectives. Then, an outer loop finds the objective weights used in each MPC control horizon. It is shown that by varying the driver\u27s objective between key horizons, some of the sub-optimality inherent to the MPC process can be alleviated. In the second phase of this work, we explore existing onboard measurements of professional drivers to compare different driving styles. We outline a novel racing line reconstruction technique rooted in optimal control theory to reconstruct the driving lines for different drivers from a limited set of measurements. It is demonstrated that different drivers can achieve nearly identical lap times while adopting different racing lines. In the final phase of this work, we use our racing line technique and our cascaded optimization framework to fit computable models for different drivers. For this, the outer loop of the cascaded optimization finds the set of objective weights used in each local MPC horizon that best matches simulation to onboard measurements. These driver models will then be used to optimize vehicle design parameters to suit each driving style. It will be shown that different driving styles will yield different parameters that optimize the driver/vehicle system

Minimum-lap-time optimisation and simulation

The paper begins with a survey of advances in state-of-the-art minimum-time simulation for road vehicles. The techniques covered include both quasi-steady-state and transient vehicle models, which are combined with trajectories that are either pre-assigned or free to be optimised. The fundamentals of nonlinear optimal control are summarised. These fundamentals are the basis of most of the vehicular optimal control methodologies and solution procedures reported in the literature. The key features of three-dimensional road modelling, vehicle positioning and vehicle modelling are also summarised with a focus on recent developments. Both cars and motorcycles are considered