Nonlinear Modeling and Control of Driving Interfaces and Continuum Robots for System Performance Gains

With the rise of (semi)autonomous vehicles and continuum robotics technology and applications, there has been an increasing interest in controller and haptic interface designs. The presence of nonlinearities in the vehicle dynamics is the main challenge in the selection of control algorithms for real-time regulation and tracking of (semi)autonomous vehicles. Moreover, control of continuum structures with infinite dimensions proves to be difficult due to their complex dynamics plus the soft and flexible nature of the manipulator body. The trajectory tracking and control of automobile and robotic systems requires control algorithms that can effectively deal with the nonlinearities of the system without the need for approximation, modeling uncertainties, and input disturbances. Control strategies based on a linearized model are often inadequate in meeting precise performance requirements. To cope with these challenges, one must consider nonlinear techniques. Nonlinear control systems provide tools and methodologies for enabling the design and realization of (semi)autonomous vehicle and continuum robots with extended specifications based on the operational mission profiles. This dissertation provides an insight into various nonlinear controllers developed for (semi)autonomous vehicles and continuum robots as a guideline for future applications in the automobile and soft robotics field. A comprehensive assessment of the approaches and control strategies, as well as insight into the future areas of research in this field, are presented.First, two vehicle haptic interfaces, including a robotic grip and a joystick, both of which are accompanied by nonlinear sliding mode control, have been developed and studied on a steer-by-wire platform integrated with a virtual reality driving environment. An operator-in-the-loop evaluation that included 30 human test subjects was used to investigate these haptic steering interfaces over a prescribed series of driving maneuvers through real time data logging and post-test questionnaires. A conventional steering wheel with a robust sliding mode controller was used for all the driving events for comparison. Test subjects operated these interfaces for a given track comprised of a double lane-change maneuver and a country road driving event. Subjective and objective results demonstrate that the driver’s experience can be enhanced up to 75.3% with a robotic steering input when compared to the traditional steering wheel during extreme maneuvers such as high-speed driving and sharp turn (e.g., hairpin turn) passing. Second, a cellphone-inspired portable human-machine-interface (HMI) that incorporated the directional control of the vehicle as well as the brake and throttle functionality into a single holistic device will be presented. A nonlinear adaptive control technique and an optimal control approach based on driver intent were also proposed to accompany the mechatronic system for combined longitudinal and lateral vehicle guidance. Assisting the disabled drivers by excluding extensive arm and leg movements ergonomically, the device has been tested in a driving simulator platform. Human test subjects evaluated the mechatronic system with various control configurations through obstacle avoidance and city road driving test, and a conventional set of steering wheel and pedals were also utilized for comparison. Subjective and objective results from the tests demonstrate that the mobile driving interface with the proposed control scheme can enhance the driver’s performance by up to 55.8% when compared to the traditional driving system during aggressive maneuvers. The system’s superior performance during certain vehicle maneuvers and approval received from the participants demonstrated its potential as an alternative driving adaptation for disabled drivers. Third, a novel strategy is designed for trajectory control of a multi-section continuum robot in three-dimensional space to achieve accurate orientation, curvature, and section length tracking. The formulation connects the continuum manipulator dynamic behavior to a virtual discrete-jointed robot whose degrees of freedom are directly mapped to those of a continuum robot section under the hypothesis of constant curvature. Based on this connection, a computed torque control architecture is developed for the virtual robot, for which inverse kinematics and dynamic equations are constructed and exploited, with appropriate transformations developed for implementation on the continuum robot. The control algorithm is validated in a realistic simulation and implemented on a six degree-of-freedom two-section OctArm continuum manipulator. Both simulation and experimental results show that the proposed method could manage simultaneous extension/contraction, bending, and torsion actions on multi-section continuum robots with decent tracking performance (e.g. steady state arc length and curvature tracking error of 3.3mm and 130mm-1, respectively). Last, semi-autonomous vehicles equipped with assistive control systems may experience degraded lateral behaviors when aggressive driver steering commands compete with high levels of autonomy. This challenge can be mitigated with effective operator intent recognition, which can configure automated systems in context-specific situations where the driver intends to perform a steering maneuver. In this article, an ensemble learning-based driver intent recognition strategy has been developed. A nonlinear model predictive control algorithm has been designed and implemented to generate haptic feedback for lateral vehicle guidance, assisting the drivers in accomplishing their intended action. To validate the framework, operator-in-the-loop testing with 30 human subjects was conducted on a steer-by-wire platform with a virtual reality driving environment. The roadway scenarios included lane change, obstacle avoidance, intersection turns, and highway exit. The automated system with learning-based driver intent recognition was compared to both the automated system with a finite state machine-based driver intent estimator and the automated system without any driver intent prediction for all driving events. Test results demonstrate that semi-autonomous vehicle performance can be enhanced by up to 74.1% with a learning-based intent predictor. The proposed holistic framework that integrates human intelligence, machine learning algorithms, and vehicle control can help solve the driver-system conflict problem leading to safer vehicle operations

Risk analisys for cooperation between the driver and the control system of an autonomous vehicle

Orientador: Janito Vaqueiro FerreiraTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: Este trabalho tem como objetivo principal estudar estratégias para a cooperação entre o condutor e o sistema de controle de trajetórias de veículos autônomos por uma análise de risco. Inicialmente apresenta-se um estudo das arquiteturas dos veículos autônomos baseadas nas camadas de percepção, planejamento e controle. Baseado neste estudo definiu-se uma arquitetura de software e de hardware para a plataforma de testes VILMA01 (Veículo Inteligente do Laboratório de Mobilidade Autônoma) que possibilita a interação com o condutor. Depois é apresentada a camada do controle de trajetórias que consiste em manipular os graus de liberdade do veículo (direção, freio e acelerador) para leva-lo a uma posição desejada para cada instante de tempo. Para isso, este trabalho utiliza uma técnica de controle preditivo baseado nos modelos dinâmicos do veículo e da direção. Na sequência é apresentado o planejamento de trajetórias que consiste em saber para onde deve ir o veículo de acordo com a percepção e a missão. Nesta camada é apresentada a parte reativa também conhecida como planejamento local de caminho, onde a rota desejada representada em um espaço curvilíneo é selecionada a partir de indicadores de risco intrínseco e extrínseco de cada caminho. Com as camadas de planejamento e controle definidas é proposto um método pelo qual pode-se estimar durante o controle cooperativo a trajetória desejada do condutor, possibilitando uma decisão a ser tomada com base numa análise de risco para condicionamento do controle cooperativo ou do planejamento cooperativo. Finalmente, diferentes testes experimentais foram realizados os quais permitiram validar a automação, a instrumentação da plataforma VILMA01, e os conceitos de controle e planejamento cooperativosAbstract: This work aims to study strategies of cooperation between the driver and path control system of an autonomous vehicle through a risk analysis. First, it is presented the study of architectures of autonomous vehicles based on the layers of perception, planning and control. An architecture that includes interaction with the driver is proposed to VILMA01 (First Intelligent Vehicle of the Autonomous Mobility Laboratory), as well as their hardware and software architectures. Second, it is presented the path control layer consisting of manipulating the degrees of freedom of the car (steering, braking and acceleration) for bringing it to a desired position for each instant of time. In order to achieve that, the dynamic models of the vehicle and the steering system are used to apply the predictive control technique. Then, it is presented the path planning layer which consists of determining where the vehicle should go according to the perception and the mission. This work shows the reactive part, also known as local path planning, where the desired path represented in a curvilinear space is selected based on intrinsic and extrinsic risk indicators of each path. With the layers of planning and control already set, a method is proposed to estimate the trajectory desired by the driver during the cooperative control, allowing a decision to be made based on a risk analysis for conditioning cooperative planning or the cooperative control. Finally, different tests on VILMA01 are performed to validate the automation, the instrumentation in the vehicle and the concepts of cooperative control and cooperative planningDoutoradoMecanica dos Sólidos e Projeto MecanicoDoutor em Engenharia Mecânica14864126141276/2012-6CAPESCNP