This book presents two nonlinear control strategies for complex dynamical networks. First, sliding-mode control is used, and then the inverse optimal control approach is employed. For both cases, model-based is considered in Chapter 3 and Chapter...Lasīt vairāk
The book presents two nonlinear control strategies for complex dynamical networks. First, sliding-mode control is used, and then the inverse optimal control approach is employed. This book contains mathematical analysis, simulation examples, and r...Lasīt vairāk
Multi-Agent Systems: Platoon Control and Non-Fragile Quantized Consensus aims to present recent research results in designing platoon control and non-fragile quantized consensus for multi-agent systems. The main feature of this book is that distri...Lasīt vairāk
The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. The simulation results that appear in each chapter include rigorous mathematical analyses, based on th...Lasīt vairāk
Using real-life examples to illustrate the performance of learning algorithms and instructing readers how to apply them to practical applications, this work offers a comprehensive treatment of subspace learning algorithms for neural networks. The au...Lasīt vairāk
Deterministic Learning Theory for Identification, Recognition, and Control presents a unified conceptual framework for knowledge acquisition, representation and knowledge utilization in uncertain dynamic environments. It provides systematic design ap...Lasīt vairāk
The increasing complexity of aerospace engineering, automotive technology, military, and industrial systems have rendered traditional feedback control systems increasingly less able to meet desired performance requirements, thus sparking interest in...Lasīt vairāk
