A nodal link perceptron network with applications to control of a nonholonomic system

A new perceptron neural network (PNN) for functional approximation and control of a general class of nonlinear systems is introduced. The basic structure of the network along with the conditions for its exponential convergence under a suitable training law are derived. A novel discrete-time control strategy is formulated that employs the PNN for direct online estimation of the feedforward control input. The developed controller can be applied to both discrete- and continuous-time plants. Unlike most of the existing direct adaptive or learning schemes, the nonlinear plant is not assumed to be feedback linearizable. The developed controller is then applied for tracking control of a nonholonomic (free-flying) robot. The simulation results of this application demonstrate a perfect tracking performance after the network is fully trained.