Variable structure control with chattering reduction of a generalized T-S model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the state variables. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi-Sugeno (T-S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T-S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved

Curvature-based sparse rule base generation for fuzzy rule interpolation

Fuzzy logic has been successfully widely utilised in many real-world applications. The most common application of fuzzy logic is the rule-based fuzzy inference system, which is composed of mainly two parts including an inference engine and a fuzzy rule base. Conventional fuzzy inference systems always require a rule base that fully covers the entire problem domain (i.e., a dense rule base). Fuzzy rule interpolation (FRI) makes inference possible with sparse rule bases which may not cover some parts of the problem domain (i.e., a sparse rule base). In addition to extending the applicability of fuzzy inference systems, fuzzy interpolation can also be used to reduce system complexity for over-complex fuzzy inference systems. There are typically two methods to generate fuzzy rule bases, i.e., the knowledge driven and data-driven approaches. Almost all of these approaches only target dense rule bases for conventional fuzzy inference systems. The knowledge-driven methods may be negatively affected by the limited availability of expert knowledge and expert knowledge may be subjective, whilst redundancy often exists in fuzzy rule-based models that are acquired from numerical data. Note that various rule base reduction approaches have been proposed, but they are all based on certain similarity measures and are likely to cause performance deterioration along with the size reduction. This project, for the first time, innovatively applies curvature values to distinguish important features and instances in a dataset, to support the construction of a neat and concise sparse rule base for fuzzy rule interpolation. In addition to working in a three-dimensional problem space, the work also extends the natural three-dimensional curvature calculation to problems with high dimensions, which greatly broadens the applicability of the proposed approach. As a result, the proposed approach alleviates the ‘curse of dimensionality’ and helps to reduce the computational cost for fuzzy inference systems. The proposed approach has been validated and evaluated by three real-world applications. The experimental results demonstrate that the proposed approach is able to generate sparse rule bases with less rules but resulting in better performance, which confirms the power of the proposed system. In addition to fuzzy rule interpolation, the proposed curvature-based approach can also be readily used as a general feature selection tool to work with other machine learning approaches, such as classifiers

Industrial Robotics

This book covers a wide range of topics relating to advanced industrial robotics, sensors and automation technologies. Although being highly technical and complex in nature, the papers presented in this book represent some of the latest cutting edge technologies and advancements in industrial robotics technology. This book covers topics such as networking, properties of manipulators, forward and inverse robot arm kinematics, motion path-planning, machine vision and many other practical topics too numerous to list here. The authors and editor of this book wish to inspire people, especially young ones, to get involved with robotic and mechatronic engineering technology and to develop new and exciting practical applications, perhaps using the ideas and concepts presented herein

Neuro-fuzzy modelling and control of robotic manipulators

The work reported in this thesis aims to design and develop a new neuro-fuzzy control system for robotic manipulators using Machine Learning Techniques, Fuzzy Logic Controllers, and Fuzzy Neural Networks. The main idea is to integrate these intelligent techniques to develop an adaptive position controller for robotic manipulators. This will finally lead to utilising one or two coordinated manipulators to perform upper-limb rehabilitation. The main target is to benefit from these intelligent techniques in a systematic way that leads to an efficient control and coordination system. The suggested control system possesses self-learning features so that it can maintain acceptable performance in the presence of uncertain loads. Simulation and modelling stages were performed using dynamical virtual reality programs to demonstrate the ideas of the control and coordination techniques. The first part of the thesis focuses on the development of neuro-fuzzy models that meet the above requirement of mimicking both kinematics and dynamics behaviour of the manipulator. For this purpose, an initial stage for data collection from the motion of the manipulator along random trajectories was performed. These data were then compacted with the help of inductive learning techniques into two sets of if-then rules that form approximation for both of the inverse kinematics and inverse dynamics of the manipulator. These rules were then used in fuzzy neural networks with differentiation characteristics to achieve online tuning of the network adjustable parameters. The second part of the thesis introduces the proposed adaptive neuro-fuzzy joint-based controller. To achieve this target, a feedback Fuzzy-Proportional-Integral-Derivative incremental controller was developed. This controller was then applied as a joint servo-controller for each robot link in addition to the main neuro-fuzzy feedforward controller used to compensate for the dynamics interactions between robot links. A feedback error learning scheme was applied to tune the feedforward neuro-fuzzy controller online using the error back-propagation algorithm. The third part of the thesis presents a neuro-fuzzy Cartesian internal model control system for robotic manipulators. The neuro-fuzzy inverse kinematics model of the manipulator was used in addition to the joint-based controller proposed and the forward mathematical model of the manipulator in an adaptive internal model controller structure. Feedback-error learning scheme was extended to tune both of the joint-based neuro-fuzzy controller and the neuro-fuzzy internal model controller online. The fourth part of the thesis suggests a simple fuzzy hysteresis coordination scheme for two position-controlled robot manipulators. The coordination scheme is based on maintaining certain kinematic relationships between the two manipulators using reference motion synchronisation without explicitly involving the hybrid position/force control or modifying the existing controller structure for either of the manipulators. The key to the success of the new method is to ensure that each manipulator is capable of tracking its own desired trajectory using its own position controller, while synchronizing its motion with the other manipulator motion so that the differential position error between the two manipulators is reduced to zero or kept within acceptable limits. A simplified test-bench emulating upper-limb rehabilitation was used to test the proposed coordination technique experimentally

Modelo fuzzy genético para a estimação de forças em correntes a partir da medição das frequências naturais

Orientador: Milton Dias JuniorDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: As instalações em alto mar possuem linhas de ancoragem, chamadas de amarras, para proporcionar estabilidade, suporte e sustentação às estruturas. Essas linhas de ancoragem são geralmente compostas por cabos, correntes e cordas de fibra sintética. Quando a solicitação de carga é alta, as linhas de ancoragem devem ser constituídas por corrente. O monitoramento da força atuando nestas correntes é vital para a confiabilidade e segurança da produção de energia. Os métodos atuais para supervisionar as cargas nas amarras são caros e têm muitas incertezas envolvidas. Nesse contexto, propõe-se uma nova metodologia para a estimativa de força em correntes através da medição de suas frequências naturais. Um sistema de inferência difuso e otimizado por um algoritmo genético foi desenvolvido para estimar da carga nas correntes. As entradas dos modelos difusos são as frequências naturais das correntes e a saída é a força estimada. As metodologias Mamdani e Sugeno foram implementadas e comparadas. Funções de pertinência triangular e gaussiana foram usadas para modelar as entradas e a saída. As regras foram definidas de acordo com as relações entre as frequências naturais e a força na corrente. Para otimizar o sistema, o algoritmo genético pode usar como dados de treinamento os resultados fornecidos por um modelo matemático ou por um conjunto de medições. O modelo matemático desenvolvido apresenta boa concordância com os dados experimentais. O modelo genético difuso foi simulado e testado, fornecendo boa precisão na estimativa da força. Finalmente, demonstrou-se que a fuzzificação não singleton pode ser uma ferramenta útil quando as entradas são ruidosasAbstract: Offshore facilities have mooring lines to provide stability, support and holding to the structures. These mooring lines are commonly made up of synthetic fiber ropes, cables and chains. When the load solicitation is high, the mooring lines must be made up of chain. The monitoring of the strength of these chains is vital for the reliability and security of the production of energy. The current methods for supervising the loads on the chains are expensive and have many uncertainties involved. In this context, it is proposed a new methodology for the force estimation in chains through the measurements of their natural frequencies. The present dissertation arises as an improvement of this approach. A fuzzy inference system optimized by a genetic algorithm is introduced to enhance the estimation of the load on the chains. The inputs of the fuzzy models are the natural frequencies of the chains and the output is the estimated force. The Mamdani and Sugeno methodologies were implemented and compared. Triangular and Gaussian membership functions were used to model the inputs and the output. The rules were set according to the relations between the natural frequencies and the force on the chain. To optimize the system, the genetic algorithm can use the results provided by a mathematical model or by a set of measurements as training data. The mathematical model has good agreement with the experimental data. The fuzzy genetic model was simulated and tested providing good accuracy in estimating the force. In addition, the non-singleton fuzzification demonstrated that can be a helpful tool when the entries are noisyMestradoMecanica de Solidos e Projeto MecanicoMestre em Engenharia Mecânica33003017CAPE

Advanced Control of Piezoelectric Actuators.

168 p.A lo largo de las últimas décadas, la ingeniería de precisión ha tenido un papel importante como tecnología puntera donde la tendencia a la reducción de tamaño de las herramientas industriales ha sido clave. Los procesos industriales comenzaron a demandar precisión en el rango de nanómetros a micrómetros. Pese a que los actuadores convencionales no pueden reducirse lo suficiente ni lograr tal exactitud, los actuadores piezoeléctricos son una tecnología innovadora en este campo y su rendimiento aún está en estudio en la comunidad científica. Los actuadores piezoeléctricos se usan comúnmente en micro y nanomecatrónica para aplicaciones de posicionamiento debido a su alta resolución y fuerza de actuación (pueden llegar a soportar fuerzas de hasta 100 Newtons) en comparación con su tamaño. Todas estas características también se pueden combinar con una actuación rápida y rigidez, según los requisitos de la aplicación. Por lo tanto, con estas características, los actuadores piezoeléctricos pueden ser utilizados en una amplia variedad de aplicaciones industriales. Los efectos negativos, como la fluencia, vibraciones y la histéresis, se estudian comúnmente para mejorar el rendimiento cuando se requiere una alta precisión. Uno de los efectos que más reduce el rendimiento de los PEA es la histéresis. Esto se produce especialmente cuando el actuador está en una aplicación de guiado, por lo que la histéresis puede inducir errores que pueden alcanzar un valor de hasta 22%. Este fenómeno no lineal se puede definir como un efecto generado por la combinación de acciones mecánicas y eléctricas que depende de estados previos. La histéresis se puede reducir principalmente mediante dos estrategias: rediseño de materiales o algoritmos de control tipo feedback. El rediseño de material comprende varias desventajas por lo que el motivo principal de esta tesis está enfocado al diseño de algoritmos de control para reducir la histéresis. El objetivo principal de esta tesis es el desarrollo de estrategias de control avanzadas que puedan mejorar la precisión de seguimiento de los actuadores piezoeléctricos comerciale

Systems modelling and ethical decision algorithms for autonomous vehicle collisions

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.There has been an increasing interest in autonomous vehicles (AVs) in recent years. Through the use of advanced safety systems (ASS), it is expected that driverless AVs will result in a reduced number of road traffic accidents (RTAs) and fatalities on the roads. However, until the technology matures, collisions involving AVs will inevitably take place. Herein lies the hub of the problem: if AVs are to be programmed to deal with a collision scenario, which set of ethically acceptable rules should be applied? The two main philosophical doctrines are the utilitarian and deontological approaches of Bentham and Kant, with the two competing societal actions being altruistic and selfish as defined by Hamilton. It is shown in simulation, that the utilitarian approach is likely to be the most favourable candidate to succeed as a serious contender for developments in the programming and decision making for control of AV technologies in the future. At the heart of the proposed approach is the development of an ethical decision-maker (EDM), with this forming part of a model-to-decision (M2D) approach. Lumped parameter models (LPMs) are developed that capture the key features of AV collisions into an immovable rigid wall (IRW) or another AV, i.e. peak deformation and peak acceleration. The peak acceleration of the AV is then related to the accelerations experienced by the occupant(s) on-board the AV, e.g. peak head acceleration. Such information allows the M2D approach to decide on the collision target depending on the selected algorithm, e.g. utilitarian or altruistic. Alongside the EDM is an active collision system (ACS) which is able to change the AV structural stiffness properties. The ACS is able to compensate for situations when AVs are predicted to experience potentially severe and fatal injury severity levels