Behavior-based Fuzzy Control For A Mobile Robot With Non-holonomic Constraints

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2005Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2005Bu çalışmada robotik alanında yeni yaklaşımlar olan davranış temelli robotik ve bulanık mantık konuları gerçek zamanda mobil robot uygulamaları bakımından incelenmiş, dört ilerlemeli, dört yönelmeli bir mobil robot için Engelden Sakın , Hedefe Git , Duvarı İzle , Yola Teğet İlerle , Avare Gez davranışları oluşturulmuştur. Bu davranışların içinden Engelden Sakın , Hedefe Git ve Duvarı İzle davranışları için sonar sensör matematik modelleri oluşturulmuş ve bu davranışların yapısında bulanık mantık yaklaşımı kullanılmıştır. Mobil robot, kinetik ve dinamik olarak holonomik olmayan kısıtları kullanılarak modellenmiştir ve simülasyon sırasında mobil robotun pozisyonu, tekerlek ve robot yönelimleri, tekerlek ve robot hızları, tekerlek torkları gibi parametreler izlenebilmektedir. Davranışlar da, simülasyon ortamında kazanımları, bulanık mantık işleme yapıları, gerçek zaman uygulanabilirliği ve davranışların koordine edilmeleri bakımından incelenmiştir. Bu çalışma gerçek bir robotta yapılacak deneyler için temel teşkil etmektedir.In this study, the new approaches to the robotics subject, behavior-based robotics and fuzzy logic control are investigated for the real-time applications of mobile robots, Avoid Obstacle , Move to Goal , Wall Following , Head-on , Wander behaviors are built up for a four-wheel driven and four-wheel steered mobile robot. Sonar sensor mathematical models are formed for Avoid Obstacle , Move to Goal and Wall Following behaviors and fuzzy logic concepts are used in the structure of these behaviors. The mobile robot is modelled kinematically and dynamically considering the non-holonomic constraints. The posture and speed of the robot and the configurations, speeds and torques of the wheels can be obtained from the simulation. The behaviors are investigated regarding their gains, fuzzy inference structures, real-time applicabilities and thein coordination. This study constitutes basis for the experiments on a real mobile robot.Yüksek LisansM.Sc

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

A Systematic Survey of Control Techniques and Applications: From Autonomous Vehicles to Connected and Automated Vehicles

Vehicle control is one of the most critical challenges in autonomous vehicles (AVs) and connected and automated vehicles (CAVs), and it is paramount in vehicle safety, passenger comfort, transportation efficiency, and energy saving. This survey attempts to provide a comprehensive and thorough overview of the current state of vehicle control technology, focusing on the evolution from vehicle state estimation and trajectory tracking control in AVs at the microscopic level to collaborative control in CAVs at the macroscopic level. First, this review starts with vehicle key state estimation, specifically vehicle sideslip angle, which is the most pivotal state for vehicle trajectory control, to discuss representative approaches. Then, we present symbolic vehicle trajectory tracking control approaches for AVs. On top of that, we further review the collaborative control frameworks for CAVs and corresponding applications. Finally, this survey concludes with a discussion of future research directions and the challenges. This survey aims to provide a contextualized and in-depth look at state of the art in vehicle control for AVs and CAVs, identifying critical areas of focus and pointing out the potential areas for further exploration

Active neuro-fuzzy integrated vehicle dynamics controller to improve the vehicle handling adn stability at complicated maneuvers

With the recent advancements in vehicle’s industry, driving safety in passenger vehicles is considered one of the key issues in designing any vehicle. According to other studies Electronic Stability Control (ESC) is considered to be the greatest road safety innovation since the seatbelt. Yet ESC has its drawbacks, that encouraged the development of other stability systems to correct or compensate these draw backs. But to efficiently make up for the ESC problems the integration of various control systems is needed, which is a pretty complicated task on its own. Lately, solving this stability problem became a hot research topic accompanied by the market demands for improving the available stability systems. Therefore, this thesis aims to add an innovative approach to help improve the vehicle stability. This approach consists of an intelligent algorithm that collects data about the vehicle characteristics and behavior. Then it uses an Artificial Neural Network to construct a fuzzy logic control system through learning from the optimum control values that was generated beforehand by the intelligent algorithm. This way, the proposed controller didn’t depend only on experts’ knowledge like the other controllers presented in the literature. This makes the controller more generic and reliable which is a very important aspect in designing a safety critical controller, like the presented one, where any fault in it can lead to a fatal accident. Also using the technique of using an Artificial Neural Network to construct a fuzzy logic control allows benefiting from the learning and autoautoadaption capability of neural networks and the smooth controlling performance that fuzzy logic controllers offers. Simulations results show the effectiveness of the proposed controller for improving the vehicle stability in different driving maneuvers. Where the controller’s results were compared to an uncontrolled vehicle and another vehicle controlled by a controller from the literature. -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Cuando un vehículo entra en una curva a alta velocidad, la aceleración lateral producida hace que el vehículo tienda a ser más inestable y menos controlable desde el punto de vista del conductor. Esta inestabilidad, podría conllevar un comportamiento no deseado del vehículo, como el sub-viraje o el sobre-viraje, que pueden llevar al vehículo a salirse de su curso previsto o que vuelque. Además, las estadísticas concluyen que la inestabilidad lateral del vehículo es causa de accidentes de fatales consecuencias. Para hacer frente a este problema, se han propuesto varios sistemas de control, con el objetivo de generar una acción contraria que lleve de nuevo al vehículo a su curso deseado. Estos sistemas pretenden alterar de una manera u otra las fuerzas centrífugas del neumático con el fin de producir fuerzas de compensación que ayuden a mantener el control lateral del vehículo. Estos controladores presentan estrategias de control diferentes: algunos intentan afectar directamente a los ángulos de dirección de los neumáticos, otros inciden en las fuerzas longitudinales de los neumáticos para crear un momento de guiñaada alrededor del eje vertical del vehículo, y por último, otros intentan afectar a la distribución de la carga vertical entre los neumáticos. Por ello, debido a la diferencia de las características de cada uno de estos sistemas, sus capacidades de controlar también difieren. Sin desmerecer a ninguno de ellos, algunos demuestran mayor eficacia en situaciones de inestabilidad suaves; otros lo son cuando el vehículo llega a sus límites de adhesión, y los hay cuando la aceleración lateral supera un cierto valor. Por esta razón, se recomienda el uso de más de un sistema de control para beneficiarse de las ventajas de sus diferentes conceptos de control. Sin embargo, la combinación de más de un controlador de estabilidad de un vehículo, no es tarea fácil, dado que podrían producirse conflictos entre los diferentes controladores, así como la superposición de los diferentes objetivos de control. Adicionalmente, una simple combinación podría llevar a una mayor complejidad del hardware y el software usados, debido a la posible repetición de sensores y actuadores, y en consecuencia a una complejidad de cables de conexión. Por ello, se han propuesto sistemas de Dinámica de Vehículos de Control Integral (IVDC), para proporcionar una integración cuidadosamente diseñada con el objetivo de coordinar los diferentes sistemas de control del chasis. De esta manera, los conflictos de control podrían ser eliminados, y los resultados podrían reforzarse aún más mediante tal combinación. Igualmente el coste y la complejidad del sistema podrían reducirse debido al posible uso compartido de sensores, actuadores, unidades de control y cables. Recientemente, los sistemas de IVDC han sido un tema de investigación recurrente, existiendo distintos sistemas en la literatura que han intentado controlar varias combinaciones de los citados controladores utilizando una variedad de técnicas de control, muchos de los cuales han mostrado resultados prometedores en la mejora del manejo del vehículo a través de los resultados de simulaciones. No obstante, estos sistemas eran manualmente diseñados y probados en un número limitado de maniobras y condiciones. Además, han sido testados en las mismas maniobras utilizadas para su dise˜no y, por tanto, su fiabilidad y previsibilidad son cuestionables. Por otra parte, los sistemas de control de estabilidad del vehículo son considerados como sistemas de seguridad crítica, donde cualquier error podría causar un accidente fatal. De este modo, como consecuencia de la imprecisión humana, un controlador diseñado manualmente que ha sido desarrollado a través de pruebas de situación limitada, es propenso a errores que generan deficiencias en ciertas zonas de control o a inexactitudes en las decisiones de los valores de control. Por otra parte, la selección manual del margen de control dedicado a cada sub-sistema integrado no asegura la optimización de las capacidades de los controladores. Además, dado que estos controladores son diseñados por el hombre, cualquier variación de las características del modelo del vehículo, como por ejemplo algo tan sencillo como el cambio en la rigidez de la suspensión, necesitaría de intervención humana para volver a calibrar o volver a ajustar manualmente el sistema con el objetivo de adaptarse a la variación realizada. Por lo tanto, en esta tesis se intentará reemplazar el conocimiento humano y los sistemas diseñados manualmente, por un sistema automatizado e inteligente, que autoconstruye el sistema de control sin intervención humana. Este método utilizará una red neuronal inteligente que aprende los valores óptimos de control a través de un algoritmo extenso de minería de datos. En consecuencia, se autoconstruye un controlador de lógica difusa que corrige la estabilidad del vehículo a través de un sistema activo de corrección de la entrada al volante y un sistema de control de ángulo de guiñada mediante los frenos. Las entradas de control de estos sistemas serán la velocidad del ángulo de guiñada y el ángulo de deslizamiento lateral, siendo los controladores más eficaces presentados en la literatura

Enhanced active front steering control using sliding mode control under varying road surface condition

In vehicle lateral dynamic control, the handling quality or steering ability of the vehicle is determined by the yaw rate response performances. The uncertainty of tire cornering stiffness due to varying tire-road adhesion coefficient, u caused by road surfaces perturbation during cornering manoeuvre may influence the transient performances of yaw rate response. Therefore, in this research, the enhanced control law of robust yaw rate tracking controller using the Sliding Mode Control (SMC) algorithm is proposed for active front steering (AFS) control strategy to improve the yaw rate response as desired. The vehicle lateral dynamics behaviors are described using the linear and nonlinear vehicle models. The linear 2 degree-of-freedom (DOF) single track model is used for controller design while the nonlinear 7 DOF two-track model is used for simulation and controller evaluations. The sliding surface of SMC is design based on yaw rate tracking error information. The control law equation is enhanced by integrating the uncertainty of cornering stiffness at the front wheels and to ensure the controller stability, the Lyapunov stability theory is applied. The transient performances and performance indices of AFS control responses are evaluated using the step steer and single lane change cornering manoeuvres test for varying values of u at dry, wet and snow or icy road surfaces. The simulations results demonstrated that the proposed enhanced control law using SMC is able to track the reference yaw rate with similar transient response performances. The proposed enhanced control law also provided low performance indices of ITAE and IAE compared to the conventional control law using SMC and robust CNF control for lower value of u at wet and snow or icy road surface. In terms of percentage of differential performance indices, the proposed control law has a better tracking ability of up to 58.45% compared to two other control laws. Therefore, this research concluded that the proposed enhanced control law using SMC has overcome the cornering stiffness uncertainty in AFS control strategy for different road surfaces during cornering manoeuvre and this enhancement is expected as a knowledge contribution to vehicle lateral dynamic study

Robust Model Predictive Control for Linear Parameter Varying Systems along with Exploration of its Application in Medical Mobile Robots

This thesis seeks to develop a robust model predictive controller (MPC) for Linear Parameter Varying (LPV) systems. LPV models based on input-output display are employed. We aim to improve robust MPC methods for LPV systems with an input-output display. This improvement will be examined from two perspectives. First, the system must be stable in conditions of uncertainty (in signal scheduling or due to disturbance) and perform well in both tracking and regulation problems. Secondly, the proposed method should be practical, i.e., it should have a reasonable computational load and not be conservative. Firstly, an interpolation approach is utilized to minimize the conservativeness of the MPC. The controller is calculated as a linear combination of a set of offline predefined control laws. The coefficients of these offline controllers are derived from a real-time optimization problem. The control gains are determined to ensure stability and increase the terminal set. Secondly, in order to test the system's robustness to external disturbances, a free control move was added to the control law. Also, a Recurrent Neural Network (RNN) algorithm is applied for online optimization, showing that this optimization method has better speed and accuracy than traditional algorithms. The proposed controller was compared with two methods (robust MPC and MPC with LPV model based on input-output) in reference tracking and disturbance rejection scenarios. It was shown that the proposed method works well in both parts. However, two other methods could not deal with the disturbance. Thirdly, a support vector machine was introduced to identify the input-output LPV model to estimate the output. The estimated model was compared with the actual nonlinear system outputs, and the identification was shown to be effective. As a consequence, the controller can accurately follow the reference. Finally, an interpolation-based MPC with free control moves is implemented for a wheeled mobile robot in a hospital setting, where an RNN solves the online optimization problem. The controller was compared with a robust MPC and MPC-LPV in reference tracking, disturbance rejection, online computational load, and region of attraction. The results indicate that our proposed method surpasses and can navigate quickly and reliably while avoiding obstacles