The aim of this thesis is to contribute in solving problems related to the on-line
identification and control of unknown dynamic systems using feedforward neural
networks. In this sense, this thesis presents new on-line learning algorithms for
feedforward neural networks based upon the theory of variable structure system
design, along with mathematical proofs regarding the convergence of solutions given
by the algorithms; the boundedness of these solutions; and robustness features of
the algorithms with respect to external perturbations affecting the neural networks'
signals.
In the thesis, the problems of on-line identification of the forward transfer
operator, and the inverse transfer operator of unknown dynamic systems are also
analysed, and neural networks-based identification schemes are proposed. These
identification schemes are tested by computer simulations on linear and nonlinear
unknown plants using both continuous-time and discrete-time versions of the proposed
learning algorithms.
The thesis reports about the direct inverse dynamics control problems using
neural networks, and contributes towards solving these problems by proposing a
direct inverse dynamics neural network-based control scheme with on-line learning
capabilities of the inverse dynamics of the plant, and the addition of a feedback
path that enables the resulting control scheme to exhibit robustness characteristics
with respect to external disturbances affecting the output of the system. Computer
simulation results on the performance of the mentioned control scheme in controlling
linear and nonlinear plants are also included.
The thesis also formulates a neural network-based internal model control scheme
with on-line estimation capabilities of the forward transfer operator and the inverse
transfer operator of unknown dynamic systems. The performance of this internal
model control scheme is tested by computer simulations using a stable open-loop
unknown plant with output signal corrupted by white noise.
Finally, the thesis proposes a neural network-based adaptive control scheme
where identification and control are simultaneously carried out
