Robotic and mechatronic systems, autonomous vehicles, electric power systems, smart grids, and industrial production systems often display complex nonlinear or spatio-temporal dynamics that must be controlled to ensure optimal performance.
This comprehensive reference presents innovative control and estimation techniques for dynamical nonlinear and partial differential equation systems. The authors classify their results into five main areas: approximate (local) linearization, exact (global) linearization, Lyapunov stability methods, control and estimation of distributed parameter systems, and stochastic estimation and fault diagnosis.
Control and Estimation of Dynamical Nonlinear and Partial Differential Equation Systems: Theory and Applications is intended for researchers and professionals in electrical engineering, physics, computer science, robotics, and mechatronics who work on control, condition monitoring, estimation, and fault diagnosis. It is also valuable for technical experts involved in robotics, energy conversion, transportation, and manufacturing applications.
