G.N. Marichal

Ping-pong player prototype

In this paper, a low-cost robot capable of playing ping-pong against human opponent using a vision system to detect the ball is presented. In the subsequent sections, the three main subsystems of the robot, i.e., the vision system, mechanical structure, and the control systems, are described. A prototype has been designed with lightweight and resistant materials to increase the response time and accurateness of the shot. One of the important features of this system is that it uses only one camera to detect the ball, thus reducing the computational time and hardware requirements. To detect the location of the ball, the robot combines the information about the ball and about the shadow it casts on the table. The expert module control defines the game strategy. Orienting the bat in order to return the ball to the desired position on the table does this. In these experiments, the success rate in returning balls was greater than 80%.

Obstacle avoidance for a mobile robot: A neuro-fuzzy approach

In this paper, a neuro-fuzzy approach is presented in order to guide a mobile robot. This task could be carried out specifying a set of fuzzy rules taking into account the different situations found by the mobile robot. This set of fuzzy rules could be optimised according to different criteria. However, the approach shown in this paper, is able to extract a set of fuzzy rules set from a set of trajectories provided by a human. These trajectories guide the mobile robot towards the target in different cases. Thus, it has been possible to obtain the rules and membership functions automatically, whereas other approaches need a previous definition of the rules and membership functions. In order to verify that the obtained behaviour is satisfactory, the neuro-fuzzy approach has been implemented in two mobile robots.

A self-tuning neuromorphic controller: application to the crane problem

Abstract This paper is concerned with the design and application of a self-tuning controller, aided by means of neural network s (NN). The structure of the controller is based on the use of neural networks as an implicit self-tuner for the controller. The aim o f this approach is to take advantage of the learning properties of the neural networks to increase the performance of the self-tuning. The a pplication of this technique is performed on an overhead crane. The control objective is to suppress undesirable oscillations during op eration of the crane. First, some simulations were carried out, as well as a comparison with a standard self-tuning method, that demon strate the advantages of this method. After this, a real-time implementation on a scale prototype of a crane was done to verify th e applicability of the method.

An Application of a Neural Self-Tuning Controller to an Overhead Crane

A neural network-based self-tuning controller is presented. The scheme of the controller is based on using a multilayer perceptron, or a set of them, as a self-tuner for a controller. The method proposed has the advantage that it is not necessary to use a combined structure of identification and decision, common in a standard self-tuning controller. The paper explains the algorithm for a general case, and then a specific application on a nonlinear plant is presented. The plant is an overhead crane which involves an interesting control problem related to the oscillations of the load mass. The method proposed is tested by simulation in different conditions. A comparison was made with a conventional controller to evaluate the efficiency of the algorithm.

A robotic system based on neural network controllers

Abstract In this paper, a control algorithm based on neural networks is presented. This control algorithm has been applied to a robot arm which has a highly nonlinear structure. The model based approaches for robot control (such as the computed torque technique) require high computational time and can result in a poor control performance, if the specific model-structure selected does not properly reflect all the dynamics. The control technique proposed here has provided satisfactory results. A decentralised model has been assumed here where a controller is associated with each joint and a separate neural network is used to adjust the parameters of each controller. Neural networks have been used to adjust the parameters of the controllers, being the outputs of the neural networks, the control parameters.

Design of a neural network based self-tuning controller for an overhead crane

In the process industry, the use of overhead crane systems for the transportation of material is very common. These are nonlinear systems that present undesirable oscillations during the motion, especially at arrival. The paper presents a self-tuning controller based on neural networks for the anti-swing control problem of the crane. The scheme of the controller is based on using neural networks as self-tuners for the parameters of a state feedback controller. The aim of this approach is to take advantage of the ability to learn of the neural networks and to use them in place of an identifier in the conventional self-tuner scheme. One of the main advantages of this method is that the training of the networks is done online using a backpropagation algorithm. The algorithm was implemented and tested by means of different simulations carried out with the crane.

On the Design and Implementation of a Neuromorphic Self-Tuning Controller

This paper deals with the design and implementation of a neural network-based self-tuning controller. The structure of the controller is based on using a neural network, or a set of them, as a self-tuner for a controller. The intention of this approach is to take advantage of the ability to learn of the neural networks and to use them in place of an identifier in the conventional self-tuner scheme. The work is divided into two main parts. The first one is dedicated to the design of the self-controller. And the second is an application of the algorithm on a nonlinear system: an overhead crane. Some simulations were carried out to verify the efficiency of the self-tuner and then a real-time implementation on a scale prototype was performed.

Obstacle Avoidance Using the Human Operator Experience for a Mobile Robot

In this paper, a neuro-fuzzy technique has been used to steer a mobile robot. The neuro-fuzzy approach provides a good way to capture the information given by a human. In this manner, it has been possible to obtain the rules and membership functions automatically whereas a fuzzy approach needs to make a prior definition of the rules and membership functions. In order to apply the neuro-fuzzy strategy, two mobile robots have been developed. However, in this paper only the smallest one has been considered. Similar results are obtained for the biggest one. The results of the approach are satisfactory, avoiding the obstacles when the mobile robot is steered to the target.

Decoupling of the 10 m GranTeCan telescope's primary mirror dynamics and design of a controller for noise rejection

The goal of the paper is to present a method for the decoupling of a multivariable system and to show how this procedure allows the problem of vibrations rejection to be faced. The system we have worked with represents the dynamic behaviour of the GTCs (Gran Telescopio Canarias) 10 m diameter primary mirror. This telescope will be built in the island of La Palma (Spain). The big size of this mirror has caused its segmentation into 36 hexagonal pieces called segments. The system has 108 inputs and the same number of outputs coupled among them. With the decoupling method we present in this work, the problem of designing a controller for a MIMO system has been converted into the calculus of 108 SISO controllers. One of these controllers is presented as an example of how the decoupling process facilitates the noise rejection.

RETRACTED: Hopf bifurcation stability in Hopfield neural networks

In this paper we consider a simple discrete Hopfield neural network model and analyze local stability using the associated characteristic model. In order to study the dynamic behavior of the quasi-periodic orbit, the Hopf bifurcation must be determined. For the case of two neurons, we find one necessary condition that yields the Hopf bifurcation. In addition, we determine the stability and direction of the Hopf bifurcation by applying normal form theory and the center manifold theorem. An example is given and a numerical simulation is performed to illustrate the results. We analyze the influence of bias weights on the stability of the quasi-periodic orbit and study the phase-locking phenomena for certain experimental results with Arnold Tongues in a particular weight configuration.