Chuanjiang Li – författare
Visar alla böcker från författaren Chuanjiang Li. Handla med fri frakt och snabb leverans.
3 produkter
3 produkter
Häftad, Engelska, 2023
1 353 kr
Skickas inom 10-15 vardagar
State Space Systems with Time-Delays Analysis, Identification and Applications covers the modeling, identification and control of industrial applications, including system identification, parameter estimation, dynamic simulation, nonlinear control, and other emerging techniques. The book introduces basic time-delay systems, architectures and control methods. Emphasis is placed on the mathematical analysis of these systems, identifying them, and applying them to practical engineering problems such as three independent water tank systems and distillation systems. This book contains a wide range of time-delay system identification methods that can help readers master the system controllers' design methods.
Presents the basic concepts of time delay systems stability analysis and classical time delay system identification methods Discusses the stability analysis of complex time delay systems Presents the identification of uncertain and unknown time delay systems Provides examples of industrial application
1 923 kr
Läs direkt efter köp
State Space Systems with Time-Delays Analysis, Identification and Applications covers the modeling, identification and control of industrial applications, including system identification, parameter estimation, dynamic simulation, nonlinear control, and other emerging techniques. The book introduces basic time-delay systems, architectures and control methods. Emphasis is placed on the mathematical analysis of these systems, identifying them, and applying them to practical engineering problems such as three independent water tank systems and distillation systems. This book contains a wide range of time-delay system identification methods that can help readers master the system controllers'' design methods.- Presents the basic concepts of time delay systems stability analysis and classical time delay system identification methods- Discusses the stability analysis of complex time delay systems- Presents the identification of uncertain and unknown time delay systems- Provides examples of industrial application
Häftad, Engelska, 2027
1 814 kr
Kommande
As low-altitude economy initiatives accelerate the deployment of UAVs in logistics, inspection, and emergency response, the need for safe and autonomous UAV operation has become increasingly critical. Data-Efficient Intelligent Fault Detection and Diagnosis for Unmanned Aerial Vehicles presents a comprehensive approach to intelligent fault detection and diagnosis in UAV systems under data-scarce and complex flying conditions. Focusing on the flight control system - the core of UAV autonomy - it addresses key challenges such as limited fault samples, class imbalance, distribution shifts, and data privacy. The book explores data-efficient learning techniques, including generative adversarial models, meta-learning, and federated learning to enable accurate and robust diagnosis of sensor, actuator, and control surface faults. Additionally, it introduces a data-knowledge hybrid driven framework that maps quantitative results to a structured fault ontology, enhancing interpretability and maintenance efficiency. By combining theory with real-world cases, this book provides researchers, engineers, and graduate students with practical tools and insights for developing reliable and intelligent UAV health monitoring systems to ensure the safety of low-altitude economy.Presents advanced learning techniques tailored for small data and domain bias scenarios, addressing the data scarcity challenges common in UAV fault diagnosis tasksIncludes practical case studies using real UAV flight data and industrial fault experiments are provided to validate the effectiveness and generalizability of the proposed methodsA novel data-knowledge hybrid driven framework is included, which combines quantitative results with qualitative knowledge, enabling interpretable and explainable diagnostic outcomesA dedicated chapter explores a personalized federated meta-learning approach for UAV fault diagnosis, enabling collaborative learning across distributed UAV systems while protecting sensitive flight data and supporting decentralized deployment in real-world applications