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Predictive Control of Power Converters and Electrical Drives
<p>The application Model Predictive Control (MPC) controls electrical energy with the use of power converters and offers a highly flexible alternative to the use of modulators and linear controllers. This new approach takes into account the discrete and nonlinear nature of the power converters and drives and promises to have a strong impact on control in power electronics in the coming decades.</p> <p><i>Predictive Control of Power Converters and Electrical Drives</i> provides a comprehensive overview of the general principles and current research into MPC and is ideal for engineers, specialists and researchers needing: </p> <ul> <li>a straightforward explanation of the theory and implementation of predictive control;</li> <li>analysis on classical converter control methods and electrical drives control methods;</li> <li>application examples and case studies demonstrating how control schemes have been implemented;</li> <li>practice in running their own MATLAB<sup>(R)</sup> simulations through the companion website.</li> </ul> <p>With the information provided, power electronics specialists will be able to start applying this new control technique. This book will help electrical, electronics and control engineers, R&D engineers, product development engineers working in power electronics and drives, and industry engineers of power conversions and motor drives. It is also a complete reference for university researchers, graduate and senior-level undergraduate students of electrical and electronics engineering, academic control specialists, and academics in electrical drives.</p> <p>URL: www.wiley.com/go/rodriguez_control</p>
eBook
Modern power electronics and AC drives
\"A clear understanding of power electronics and AC drives is crucially important in a wide range of modern systems, from household appliances to automated factories and it requires cross-disciplinary expertise that many engineers lack. Now, in Modern Power Electronics and AC Drives, one of the world's leading experts covers every aspect of the topic, including crucial innovations such as artificial intelligence, advanced estimation, and sensorless control. This book is not only important as an advanced reference but also covers the material for one senior-level and two graduate-level courses.\"--Jacket.
Book
Chaos in Electric Drive Systems
In <i>Chaos in Electric Drive Systems: Analysis, Control and Application</i> authors Chau and Wang systematically introduce an emerging technology of electrical engineering that bridges abstract chaos theory and practical electric drives. The authors consolidate all important information in this interdisciplinary technology, including the fundamental concepts, mathematical modeling, theoretical analysis, computer simulation, and hardware implementation. The book provides comprehensive coverage of chaos in electric drive systems with three main parts: analysis, control and application. Corresponding drive systems range from the simplest to the latest types: DC, induction, synchronous reluctance, switched reluctance, and permanent magnet brushless drives. <ul> <li>The first book to comprehensively treat chaos in electric drive systems</li> <li>Reviews chaos in various electrical engineering technologies and drive systems</li> <li>Presents innovative approaches to stabilize and stimulate chaos in typical drives</li> <li>Discusses practical application of chaos stabilization, chaotic modulation and chaotic motion</li> <li>Authored by well-known scientists in the field</li> <li>Lecture materials available from the book's companion website</li> </ul> <p>This book is ideal for researchers and graduate students who specialize in electric drives, mechatronics, and electric machinery, as well as those enrolled in classes covering advanced topics in electric drives and control. Engineers and product designers in industrial electronics, consumer electronics, electric appliances and electric vehicles will also find this book helpful in applying these emerging techniques.</p> <p>Lecture materials for instructors available at<br /> <a href=\"http://www.wiley.com/go/chau_chaos\">www.wiley.com/go/chau_chaos</a></p>
eBook
Model Predictive Control of High Power Converters and Industrial Drives
In this original book on model predictive control (MPC) for power electronics, the focus is put on high-power applications with multilevel converters operating at switching frequencies well below 1 kHz, such as medium-voltage drives and modular multi-level converters. Consisting of two main parts, the first offers a detailed review of three-phase power electronics, electrical machines, carrier-based pulse width modulation, optimized pulse patterns, state-of-the art converter control methods and the principle of MPC. The second part is an in-depth treatment of MPC methods that fully exploit the performance potential of high-power converters. These control methods combine the fast control responses of deadbeat control with the optimal steady-state performance of optimized pulse patterns by resolving the antagonism between the two.
eBook
Control of Electrical Drives
Electrical drives play an important part as electromechanical energy converters in transportation, materials handling and most production processes.This book presents a unified treatment of complete electrical drive systems, including the mechanical parts, electrical machines, and power converters and control.
eBook
Detection of Driving Capability Degradation for Human-Machine Cooperative Driving
Due to the limitation of current technologies and product costs, humans are still in the driving loop, especially for public traffic. One key problem of cooperative driving is determining the time when assistance is required by a driver. To overcome the disadvantage of the driver state-based detection algorithm, a new index called the correction ability of the driver is proposed, which is further combined with the driving risk to evaluate the driving capability. Based on this measurement, a degraded domain (DD) is further set up to detect the degradation of the driving capability. The log normal distribution is used to model the boundary of DD according to the bench test data, and an online algorithm is designed to update its parameter interactively to identify individual driving styles. The bench validation results show that the identification algorithm of the DD boundary converges finely and can reflect the individual driving characteristics. The proposed degradation detection algorithm can be used to determine the switching time from manual to automatic driving, and this DD-based cooperative driving system can drive the vehicle in a safe condition.
Journal Article
Analysis of explicit model predictive control for path-following control
In this paper, explicit Model Predictive Control(MPC) is employed for automated lane-keeping systems. MPC has been regarded as the key to handle such constrained systems. However, the massive computational complexity of MPC, which employs online optimization, has been a major drawback that limits the range of its target application to relatively small and/or slow problems. Explicit MPC can reduce this computational burden using a multi-parametric quadratic programming technique(mp-QP). The control objective is to derive an optimal front steering wheel angle at each sampling time so that autonomous vehicles travel along desired paths, including straight, circular, and clothoid parts, at high entry speeds. In terms of the design of the proposed controller, a method of choosing weighting matrices in an optimization problem and the range of horizons for path-following control are described through simulations. For the verification of the proposed controller, simulation results obtained using other control methods such as MPC, Linear-Quadratic Regulator(LQR), and driver model are employed, and CarSim, which reflects the features of a vehicle more realistically than MATLAB/Simulink, is used for reliable demonstration.
Journal Article
Deep Learning-Based Object Detection and Scene Perception under Bad Weather Conditions
Large cities’ expanding populations are causing traffic congestion. The maintenance of the city’s road network necessitates ongoing monitoring, growth, and modernization. An intelligent vehicle detection solution is necessary to address road traffic concerns with the advancement of automatic cars. The identification and tracking vehicles on roads and highways are part of intelligent traffic monitoring while driving. In this paper, we have presented how You Only Look Once (YOLO) v5 model may be used to identify cars, traffic lights, and pedestrians in various weather situations, allowing for real-time identification in a typical vehicular environment. In an ordinary or autonomous environment, object detection may be affected by bad weather conditions. Bad weather may make driving dangerous in various ways, whether due to freezing roadways or the illusion of low fog. In this study, we used YOLOv5 model to recognize objects from street-level recordings for rainy and regular weather scenarios on 11 distinct classes of vehicles (car, truck, bike), pedestrians, and traffic signals (red, green, yellow). We utilized freely available Roboflow datasets to train the proposed system. Furthermore, we used real video sequences of road traffic to evaluate the proposed system’s performance. The study results revealed that the suggested approach could recognize cars, trucks, and other roadside items in various circumstances with acceptable results.
Journal Article
Application of Automatic Longitudinal Control Strategy Scene Extraction of Vehicle Speed in Intelligent Assistant Driving System
The intelligent assistant driving system can ensure driving comfort at a safe driving distance. The longitudinal control system mainly determines the longitudinal movement of the vehicle at the safe driving speed. The purpose of this study is to accelerate the maturity of automatic vehicle driving system, shorten the response time of control strategy and improve its comfort. Based on the automatic speed control strategy, a driving vehicle test system is designed. Scene extraction, noise removal and working condition reorganization are carried out from the longitudinal control strategy. The performance analysis and experimental test under layered test evaluation are conducted from the scenes of constant speed cruise, target vehicle stationary, target vehicle low speed, target vehicle deceleration etc. The results show that the proposed scene extraction algorithm is 42.86% different from the traditional algorithm in acceleration comparison. The initial braking distance of the algorithm is 0.80 m. The safe triggering distance is the minimum (less than 15m). The maximum deceleration of braking and stopping at constant speed is -0.65 g, which has better driving comfort and safety and reduces the risk of collision. The automatic test system can effectively ensure driving safety, driving accuracy and convenience in different driving scenarios.
Journal Article