In this paper a new approach to a neural network-based model reference adaptive intelligent controller is proposed. In this scheme, the intelligent supervisory loop is incorporated into the conventional model reference adaptive controller framework by utilizing an online growing multilayer back propagation neural network structure in parallel with it. The idea is to control the plant by convent...

Conventional quaternion based methods have been extensively employed for spacecraft attitude control where the aerodynamic forces can be neglected. In the presence of aerodynamic forces, the flight attitude control is more complicated due to aerodynamic moments and inertia uncertainties. In this paper, a robust nero-adaptive quat...

Induction motors are the workhorses of industries. Indirect vector control scheme has been preferred due to its superior dynamic performance. Since the conventional PI controller has bounded operating limits and poor transient response, a search for an alternative controller arises. Recently, Artificial Neural Network (ANN) is gaining momentum as a controller for non linear systems. Herein an a...

In this paper, an intelligent adaptive control system (IACS) for induction motor (IM) servo drive to achieve high dynamic performance is proposed. The proposed IACS comprises a recurrent functional-linkbased Petri fuzzy-neural-network (RFLPFNN) controller and a robust controller so that the developed adaptive control scheme has more robustness against parameters uncertainties and approximation ...

The evolvability of a neural network controller for a hexapod agent encoded directly and symmetrically is examined. The symmetric encoding imposes a structural regularity on the neural network and decreases the size of genotype space relative to the direct encoding. The symmetrically encoded neural networks are found to be more evolvable than the directly encoded neural networks, but it is unkn...

Selection of neural network learning rates to obtain satisfactory performance from neural network controllers is a challenging problem. To assist in the selection of learning rates, this paper investigates robotic system sensitivity to neural network (NN) learning rate. The work reported here consists of experimental and simulation results. A neural network controller module, developed for the ...

A bionic neural network for fish-robot locomotion is presented. The bionic neural network inspired from fish neural network consists of one high level controller and one chain of central pattern generators (CPGs). Each CPG contains a nonlinear neural Zhang oscillator which shows properties similar to sine-cosine model. Simulation results show that the bionic neural network presents a good perfo...

1. Abstract The objective of this paper is to report the results from the research being conducted in reconfigurable flight controls at NASA Ames. A study was conducted with three NASA Dryden test pilots to evaluate two approaches of reconfiguring an aircraft’s control system when failures occur in the control surfaces and engine. NASA Ames is investigating both a Neural Generalized Predictive ...

this work proposes a neural-fuzzy sliding mode control scheme for a hydro-turbine speed governor system. considering the assumption of elastic water hammer, a nonlinear mode of the hydro-turbine governor system is established. by linearizing this mode, a sliding mode controller is designed. the linearized mode is subject to uncertainties. the uncertainties are generated in the process of linear...

This work addresses an autonomous underwater vehicle (AUV) for applying nonlinear control which is capable of disturbance rejection via intelligent estimation of uncertainties. Adaptive radial basis function neural network (RBF NN) controller is proposed to approximate unknown nonlinear dynamics. The problem of designing an adaptive RBF NN controller was augmented with sliding mode robust term ...