GA-based neural fuzzy control of flexible-link manipulators

The limitations of conventional model-based control mechanisms for flexible manipulator systems have stimulated the development of intelligent control mechanisms incorporating fuzzy logic and neural networks. Problems have been encountered in applying the traditional PD-, PI-, and PID-type fuzzy controllers to flexible-link manipulators. A PD-PI-type fuzzy controller has been developed where the membership functions are adjusted by tuning the scaling factors using a neural network. Such a network needs a sufficient number of neurons in the hidden layer to approximate the nonlinearity of the system. A simple realisable network is desirable and hence a single neuron network with a nonlinear activation function is used. It has been demonstrated that the sigmoidal function and its shape can represent the nonlinearity of the system. A genetic algorithm is used to learn the weights, biases and shape of the sigmoidal function of the neural network

Local model and controller network design for a single-link flexible manipulator

This paper describes a new genetic learning approach to the construction of a local model network (LMN) and design of a local controller network (LCN) with application to a single-link flexible manipulator. A highly nonlinear flexible manipulator system is modelled using an LMN comprising Autoregressive–moving-average model with exogenous inputs (ARMAX) type local models (LMs) whereas linear Proportional-integral-derivative (PID) type local controllers (LCs) are used to design an LCN. In addition to allowing the simultaneous optimisation of the number of LMs and LCs, model parameters and interpolation function parameters, the approach provides a flexible framework for targeting transparency and generalisation. Simulation results confirm the excellent nonlinear modelling properties of an LM network and illustrate the potential benefits of the proposed LM control scheme