Juan A. Méndez

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.

Implementing Motivational Features in Reactive Blended Learning: Application to an Introductory Control Engineering Course

This paper presents a significant advance in a reactive blended learning methodology applied to an introductory control engineering course. This proposal was based on the inclusion of a reactive element (a fuzzy-logic-based controller) designed to regulate the workload for each student according to his/her activity and performance. The contribution of this proposal stands on the inclusion of elements related to motivational factors in the students. Student motivation has been widely identified as a key factor for the academic success of every teaching-learning activity.

Adaptive computer control of anesthesia in humans

This paper presents an efficient computer control technique for regulation of anesthesia in humans. The anesthetic used is propofol and the objective is to control the degree of hypnosis of the patient. The paper describes the basic hardware/software setup of the system and the closed-loop methodologies. The bispectral index (BIS) is considered as the feedback signal. The control methods proposed here are based in the use of proportional integral controllers with dead-time compensation to avoid undesirable oscillations in the BIS signal during the process. The compensation is based on the Smith predictor. To guarantee the applicability of the method to different patients, an adaptive module to tune the compensator is developed. Some real and simulated results are presented in this work to attest the efficiency of the methods used.

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 reactive blended learning proposal for an introductory control engineering course

As it happens in other fields of engineering, blended learning is widely used to teach process control topics. In this paper, the inclusion of a reactive element - a Fuzzy Logic based controller - is proposed for a blended learning approach in an introductory control engineering course. This controller has been designed in order to regulate the workload for each student, according to his activity and performance. The proposed course is based on a web tool called ControlWeb, which includes a complete vision of control topics and is used intensively along the course. The results of the evaluation of the methodology attest its efficiency in terms of learning degree and performance of the students.

State-space approach to interpolation in MPC

Interpolation between unconstrained optimal input trajectories and feasible trajectories forms the basis for a computationally efficient predictive control algorithm but lacks robustness in that uncertainty can destroy the guarantee of feasibility. To overcome this problem it is possible to introduce into the interpolation process a further input trajectory which is referred as ‘mean level’. This has been accomplished in an input–output setting and the purpose of the present paper is to show that it is possible to get a considerably simpler algorithm by recasting the problem into state-space form. Copyright

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.