N. Sadegh, A NODAL LINK PERCEPTRON NETWORK WITH APPLICATIONS TO CONTROL OF A NONHOLONOMIC SYSTEM, IEEE transactions on neural networks, 6(6), 1995, pp. 1516-1523
A new perceptron neural network (PNN) for functional approximation and
control of a general class of nonlinear systems is introduced. The ba
sic structure of the network along with the conditions for its exponen
tial convergence under a suitable training law are derived, A novel di
screte-time control strategy is formulated that employs the PNN for di
rect online estimation of the feedforward control input. The developed
controller can be applied to both discrete- and continuous-time plant
s. Unlike most of the existing direct adaptive or learning schemes, th
e nonlinear plant is not assumed to be feedback linearizable. The deve
loped controller is then applied for tracking control of a nonholonomi
c (free-flying) robot. The simulation results of this application demo
nstrate a perfect tracking performance after the network is fully trai
ned.