Adaptive Feedforward Neural Network Control with an Optimized Hidden Node Distribution

TL;DR

This paper introduces an adaptive feedforward neural network controller with optimized hidden node distribution, improving approximation accuracy and reducing complexity for control tasks, especially in manipulators with unknown dynamics.

Contribution

It proposes a novel hidden node distribution method using K-means, satisfying PE conditions and converging weights, enhancing neural network control performance over traditional lattice-based approaches.

Findings

Reduces the number of hidden nodes needed.

Achieves better approximation and control performance.

Demonstrates effectiveness through simulation results.

Abstract

Composite adaptive radial basis function neural network (RBFNN) control with a lattice distribution of hidden nodes has three inherent demerits: 1) the approximation domain of adaptive RBFNNs is difficult to be determined a priori; 2) only a partial persistence of excitation (PE) condition can be guaranteed; and 3) in general, the required number of hidden nodes of RBFNNs is enormous. This paper proposes an adaptive feedforward RBFNN controller with an optimized distribution of hidden nodes to suitably address the above demerits. The distribution of the hidden nodes calculated by a K-means algorithm is optimally distributed along the desired state trajectory. The adaptive RBFNN satisfies the PE condition for the periodic reference trajectory. The weights of all hidden nodes will converge to the optimal values. This proposed method considerably reduces the number of hidden nodes, while achieving a better approximation ability. The proposed control scheme shares a similar rationality to that of the classical PID control in two special cases, which can thus be seen as an enhanced PID scheme with a better approximation ability. For the controller implemented by digital devices,the proposed method, for a manipulator with unknown dynamics, potentially achieves better control performance than model-based schemes with accurate dynamics.Simulation results demonstrate the effectiveness of the proposed scheme. This result provides a deeper insight into the coordination of the adaptive neural network control and the deterministic learning theory.