The field of intelligent control has recently emerged as a response to
the challenge of controlling highly complex and uncertain nonlinear
systems. It attempts to endow the controller with the key properties of
adaptation, learn- ing and autonomy. The field is still immature and
there exists a wide scope for the development of new methods that
enhance the key properties of in- telligent systems and improve the
performance in the face of increasingly complex or uncertain conditions.
The work reported in this book represents a step in this direction. A
num- ber of original neural network-based adaptive control designs are
introduced for dealing with plants characterized by unknown functions,
nonlinearity, multimodal behaviour, randomness and disturbances. The
proposed schemes achieve high levels of performance by enhancing the
controller's capability for adaptation, stabilization, management of
uncertainty, and learning. Both deterministic and stochastic plants are
considered. In the deterministic case, implementation, stability and
convergence is- sues are addressed from the perspective of Lyapunov
theory. When compared with other schemes, the methods presented lead to
more efficient use of com- putational storage and improved adaptation
for continuous-time systems, and more global stability results with less
prior knowledge in discrete-time sys- tems.