The last
few decades have witnessed tremendous developments in nonlinear control
theory. One of the most important of these is the model-based method of
feedback linearisation in which a nonlinear system is transformed into
a linear system by means of state feedback and nonlinear
transformations. After feedback linearisation, a system can be dealt
with by linear controller design. The extension of these techniques to
include MIMO systems allows for the further simplification of
controller design by decoupling the system.
Strategies for Feedback
Linearisation demonstrates this powerful technique in the light
of research on neural networks which allow the identification of
nonlinear models without the complicated and costly development of
models based on physical laws. Dynamic or recurrent neural networks
have
inherent properties that allow them to approximate nonlinear dynamic
systems. Strategies for the identification of nonlinear systems using
such neural networks are presented in this monograph together with
the use of such models for the design and application of input-output
linearisation and decoupling methods.
Strategies for Feedback Linearisation is written
to serve academic and industrial researchers in nonlinear control and
system identification and practising control engineers interested in
their application to real-world industrial systems. The reader will
gain a balanced view of theoretical and practical issues: relevant
mathematical proofs are provided as are case studies illustrating
design and application issues.
