ABSTRACT
Structures and Bridges ...............................................................................4 1.3 Chapter 4: Model-Free Adaptive Dynamic Programming
Algorithms for H-Infinity Control of Complex Linear Systems...........5 1.4 Chapter 5: Optimization and Distributed Control for Fair Data
Gathering in Wireless Sensor Networks ..................................................5 1.5 Chapter 6: Optimization Problems in the Deployment
of Sensor Networks ....................................................................................6 1.6 Chapter 7: Congestion Control in Computer Networks .......................6 1.7 Chapter 8: Persistent Autonomous Formations and Cohesive
Motion Control ............................................................................................7 1.8 Chapter 9: Modeling and Control of Unmanned Aerial Vehicles:
Current Status and Future Directions ......................................................7 1.9 Chapter 10: A Framework for Large-Scale Autonomous
Multi-Robot Teams......................................................................................8 1.10 Chapter 11: Modeling and Control in Cancer Genomics ......................8 1.11 Chapter 12: Modeling and Estimation Problems
in the Visuomotor Pathway .......................................................................9 1.12 Chapter 13: Modeling, Simulation, and Control
of Transportation Systems..........................................................................9 1.13 Chapter 14: Backstepping Controllers for Stabilization
of Turbulent Flow PDEs ...........................................................................10 1.14 Chapter 15: An Approach to Home Automation by means
of MAS Theory...........................................................................................10 1.15 Chapter 16: Multi-Robot Social Group-Based Search
Algorithms ................................................................................................. 11
of Complex
The modeling of complex dynamic systems has always been a challenging research topic due to the fact that mathematical models cannot accurately describe nature. A real system is often nonlinear, with infinite dimensions, noise, and external disturbances, and characteristics that can vary with time. It is impossible to describe these dynamic characteristics with mathematical equations and achieve a high level of accuracy in the sense that for the same inputs the outputs of the model match those of the real system over the whole frequency spectrum. What is possible, however, and useful for all practical purposes is to achieve model/system output matching over the frequency range of interest, which is often the low-frequency range. In this case modeling is even more challenging as decisions have to be made as to which phenomena and dynamics are neglected and which ones are modeled. Modeling is therefore not only a mathematical exercise but involves a good understanding of the system and its functionality. Models can be developed using physical laws as well as experiments and processing of data. Once a model is developed it has to be validated using real data over the frequency spectrum of interest.
