A hierarchical adaptive nonlinear model predictive control approach for maximizing tire force usage in autonomous vehicles

The ability to reliably maximize tire force usage would improve the safety of autonomous vehicles, especially in challenging edge cases. However, vehicle control near the limits of handling has many challenges, including robustly contending with tire force saturation, balancing model fidelity and computational efficiency, and coordinating inputs with the lower level chassis control system. This work studies Nonlinear Model Predictive Control for limit handling, specifically adapting to changing tire-road conditions and maximally allocating tire force utilization. We present a novel hierarchical framework that combines a single-track model with longitudinal weight transfer dynamics in the predictive control layer, with lateral brake distribution occurring at the chassis control layer. This vehicle model is simultaneously used in an Unscented Kalman Filter for online friction estimation. Comparative experiments on a full-scale vehicle operating on a race track at up to 95% of maximum tire force usage demonstrate the overall practical effectiveness of this approach.Comment: Preprint of accepted paper in Field Robotic

Modelling and Controlling the Kinetic and Dynamic of a Bicycle

En este trabajo, se presenta el modelado y control posterior de una mini-bicicleta autónoma, que se utilizará para la enseñanza de sistemas de control. El modelo mecánico se construye inicialmente a partir de un diseño CAD y posteriormente se integra en Simulink, conjuntamente con los módulos de control. Se lleva a cabo el modelado del sistema completo, incluyendo las partes mecánicas, sensores, actuadores y la fricción de las ruedas con el suelo, consiguiéndose el mismo comportamiento que con la bicicleta física. A partir de las ecuaciones matemáticas que definen el comportamiento del sistema se diseña un controlador PID y un controlador LQG en el espacio de estados. Para verificar el modelo, los controladores diseñados se prueban también con los mismos parámetros en la mini-bicicleta física, obteniéndose un resultado idéntico.In this work, the modeling and subsequent control of an autonomous mini-bicycle is presented, which will be used for the teaching of control systems. The mechanical model is initially built from a CAD design and then integrated into Simulink, together with the control modules. The modeling of the complete system is carried out, including the mechanical parts, sensors, actuators and the friction of the wheels with the ground, achieving the same behavior as with the physical bicycle. From the mathematical equations that define the behavior of the system, a PID controller and an LQG controller are designed in the state space. To verify the model, the designed controllers are also tested with the same parameters in the physical mini-bicycle, obtaining an identical result.Universidad de Granada: Departamento de Arquitectura y Tecnología de Computadore

Modelling and Control of Narrow Tilting Vehicle for Future Transportation System

The increasing number of cars leads traffic congestion and parking problems in urban area. Small electric four-wheeled narrow tilting vehicles (NTV) have the potential to become the next generation of city cars. However, due to its narrow width, the NTV has to lean into corners like two-wheeled vehicles during a turn. It is a challenge to maintain its roll stability to protect it from falling down. This chapter aims to describe the development of NTV and drive assistance technologies in helping to improve the stability of an NTV in turning. The modelling of an NTV considers the dynamics of the tyres and power train of the vehicle. A nonlinear tilting controller for the direct tilting control mechanism is designed to reduce the nonlinear behaviour of an NTV operating at different vehicle velocities. In addition, two torque vectoring based torque controllers are designed to reduce the counter-steering process and improve the stability of the NTV when it turns into a corner. The results indicate that the designed controllers have the ability to reduce the yaw rate tracking error and maximum roll rate. Then riders can drive an NTV easily with the drive assistance system

Mobility in China: a conceptual take on a personal vehicle for China in 2020 that enhances maneuverability

China has the largest projected automobile market in the world, expected to surpass the United States as the largest car market in the world by 2025. The combination of large population, a mass movement of citizens to cities, and a pollution crisis creates unique opportunities in China for automobile design. The first generations of Chinese to embrace the automobile have been attracted to them by the same values that have been embraced by the West such as prestige, a reflection of personal success, and a sense of freedom of movement. This attraction has given rise to traditional brands such as Buick, Audi and Mercedes Benz. However, as a new generation matures aware of China\u27s problems presented by a growing number of automobiles, a shift is happening. Awareness of ecological issues, as well as an acute sense of forthcoming issues with traffic density inside and surrounding China\u27s vast metropolises, suggests future generations are more willing to embrace alternative solutions. China has a young automotive identity, currently relating to aesthetic qualities of certain brands. Without the same historical narrative that has informed the rise of the car in the West, China is poised to create one that can respond more acutely to its needs. With fossil fuels the source of many potential problems in both pollution and cost of use, alternative energy vehicles will likely form the backbone of future growth of the automobile in China. Currently Toyota, GM, BMW, and Audi, to name a few, are actively pursuing alternative energy power plant designs. By 2020, alternative energy vehicles will make up a significant percentage of new vehicle sales in the Western world. Potential solutions come in the form of gas and diesel hybrids, all electric, hydrogen fuel cell and Hydrogen internal combustion engines. For a car to successfully meet the needs of Chinese consumers, it will need to be both ecologically friendly and highly maneuverable to maximize use of the limited space available on congested streets. The simple act of making a U-turn on a narrow street in a conventional four-wheeled vehicle can cause traffic jams. Additionally, automation in future thoroughfares can reduce the space between individual automobiles, effectively placing more vehicles in less space. This thesis establishes the need for rethinking the physical footprint of the automobile in the context of the Chinese market and provides a framework for a new vehicle design

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

Test platform design and control of a bicycle-type two-wheeled autonomous vehicle

Bicycle dynamics and behaviors have been vastly studied through modeling and simulation. Due to the complexity, software models are often assumed subjecting to di erent nonholonomic constraints in order to simplify the models and control algorithms. A real life autonomous bicycle faces perturbances from the road, wind, tire deformation, slipping among other external forces. Limitations of simulations will not always allow these to apply. All these issues make the autonomous bicycle research very challenging. To study the bicycle control problems a few research results from the literature are reviewed. A nonlinear bicycle model was used to conduct control simulations. Model based nonlinear controllers were applied to simulate the balance and path tracking control. A PID controller is more practical to replace the non-linear controller for the balance control. Simulation results of the di erent controllers are compared in order to decide the proper control strategies on the hardware platform. The controller design of the platform complies with practicality based on the hardware con guration. Two control schemes are implemented on the test platform; both are developed with PID algorithms. The rst scheme is a single PID control loop in which the controller takes the roll angle feedback and balances the running platform by means of steering. If the desired roll angle is zero the controller will try to hold the platform at the upright position. If the desired roll angle is non-zero the platform will be balanced at an equilibrium roll angle. A xed roll angle will lead to a xed steering angle as the result of balance control. The second scheme is directional control with balance consisting of two cascaded PID loops. Steering is the only means to control balance and direction. To do so the desired roll angle must be controlled to achieve the desired steering angle. The platform tilts to the desired side and steering follows to the same side of the tilt; the platform can then be lifted up by the centrifugal force and eventually balanced at an equilibrium roll angle. The direction can be controlled using a controlled roll angle. Many implementation issues have to be dealt with in order for the control algorithm to be functional. Dynamic roll angle measurement is implemented with complementary internal sensors (accelerometer and gyroscope). Directional information is obtained through a yaw rate gyroscope which operates on the principle of resonance. To monitor the speed of the platform, a rotational sensor was formed by using a hard drive stepper motor attached to the axis of the vehicle's driving motor. The optoelectronic circuit plays the vital role to ensure the system functionality by isolating the electromagnetic noise from the motors. Finally, in order to collect runtime data, the wireless communication is implemented through Bluetooth/RS232 serial interface. The data is then plotted and analyzed with Matlab. Controller gains are tuned through numerous road tests. Field test results show that the research has successfully achieved the goal of testing the low level control of autonomous bicycle. The developed algorithms are able to balance the platform on semi-smooth surfaces

Estimating the potential for shared autonomous scooters

Recent technological developments have shown significant potential for transforming urban mobility. Considering first- and last-mile travel and short trips, the rapid adoption of dockless bike-share systems showed the possibility of disruptive change, while simultaneously presenting new challenges, such as fleet management or the use of public spaces. In this paper, we evaluate the operational characteristics of a new class of shared vehicles that are being actively developed in the industry: scooters with self-repositioning capabilities. We do this by adapting the methodology of shareability networks to a large-scale dataset of dockless bike-share usage, giving us estimates of ideal fleet size under varying assumptions of fleet operations. We show that the availability of self-repositioning capabilities can help achieve up to 10 times higher utilization of vehicles than possible in current bike-share systems. We show that actual benefits will highly depend on the availability of dedicated infrastructure, a key issue for scooter and bicycle use. Based on our results, we envision that technological advances can present an opportunity to rethink urban infrastructures and how transportation can be effectively organized in cities