Control theory of nonlinear systems, in which either the linear part is known but the relevant nonlinearities in place, kind or parameters are unknown, or both the linear and the nonlinear parts are partially or even most unknown, is a new, demanding and highly interesting field. This book treats the problem by focussing on the role of learning. Intelligent learning techniques are able to determine the unknown components of nonlinear systems. These processes are always stable and convergent. The methods presented can be used both on-line and off-line. They have applications in mechatronics, hydraulics and combustion engines.

Control theory of nonlinear systems, in which either the linear part is known but the relevant nonlinearities in place, kind or parameters are unknown, or both the linear and the nonlinear parts are partially or even most unknown, is a new, demanding and highly interesting field. This book treats the problem by focussing on the role of learning. Intelligent learning techniques are able to determine the unknown components of nonlinear systems. These processes are always stable and convergent. The methods presented can be used both on-line and off-line. They have applications in mechatronics, hydraulics and combustion engines.

Control theory of nonlinear systems, in which either the linear part is known but the relevant nonlinearities in place, kind or parameters are unknown, or both the linear and the nonlinear parts are partially or even most unknown, is a new, demanding and highly interesting field. This book treats the problem by focussing on the role of learning. Intelligent learning techniques are able to determine the unknown components of nonlinear systems. These processes are always stable and convergent. The methods presented can be used both on-line and off-line. They have applications in mechatronics, hydraulics and combustion engines.