Emerging techniques of intelligent or learning control seem attractive for
applications in manufacturing and robotics. It is however important to understand the
capabilities of such control systems. In the past the inverted pendulum has been used as a
The thesis begins with an examination of whether the inverted pendulum or polecart
balancing problem is a representative problem for experimentation for learning
controllers for complex nonlinear systems. Results of previous research concerning the
inverted pendulum problem are presented to show that this problem is not sufficiently
This thesis therefore concentrates on the control of the inverted pendulum with an
additional degree of freedom as a testing demonstrator problem for learning control
system experimentation. A flexible pole is used in place of a rigid one. The transverse
displacement of the flexible pole adds a degree of freedom to the system. The dynamics of
this new system are more complex as the system needs additional parameters to be
defIned due to the pole's elastic deflection. This problem also has many of the signifIcant
features associated with flexible robots with lightweight links as applied in manufacturing.
Novel neural network and fuzzy control systems are presented that control such a
system both in simulation and real time. A fuzzy-genetic approach is also demonstrated
that allows the creation of fuzzy control systems without the use of extensive knowledge.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.