Catálogo de publicaciones - libros

This paper presents a dynamic optimization approach based on the differential evolution (DE) strategy which is applied to the concurrent optimal design of a continuously variable transmission (CVT). The structure-control integration approach is used to state the concurrent optimal design as a dynamic optimization problem which is solved using the Constraint Handling Differential Evolution (CHDE) algorithm. The DE strategy is compared with the sequential approach. The results presented here demonstrate that the DE strategy is less expensive than the sequential approach from the computational implementation point of view.

This paper presents a new approach to improve performance in wireless ad-hoc networks with the DSR protocol using a Behavior-Based Architecture. Four levels of competence based on strategies to improve the cache were implemented for the Behavior-Based Architecture: sort routes, prefer fresher routes, selection, and disperse traffic. A conflict solver was implemented to solve counter level commands. Three different activation criteria for the conflict solver were developed, generating instances of the architecture. Two metrics were used to evaluate the performance of the ad-hoc network: end-to-end delay and dropped packet average for voice and data services, respectively. Six scenarios were analyzed showing the improvement of the network performance with respect to the original DSR protocol.

The main goal of this work is to study the reliability of fuzzy logic based systems. Three different configurations were compared to support this research. The context used was to guide a simulated robot through a virtual world populated with obstacles. In the first configuration, the system controls only the rotation angle of the robot. In the second one, there is an additional output that controls its step. In the third one, improvements were included in the decision process that controls the step and the rotation angle. In order to compare the performance of these approaches, we studied the controller stability based on the removal of rules. We measured two parameters: processing time and the amount of step necessary to reach the goal.

This research shows that simplicity and easiness of the design of fuzzy controllers don’t compromise its efficiency. Our experiments confirm that fuzzy logic based systems can properly perform under adverse conditions.

The features of a novel dynamical discrete-time algorithm for robust adaptive learning in feed-forward neural networks and its application to the neuro-adaptive nonlinear feedback control of systems with uncertain dynamics are presented. The proposed approach makes a direct use of variable structure systems theory. It establishes an inner sliding motion in terms of the neurocontroller parameters, leading the learning error toward zero. The outer sliding motion concerns the controlled nonlinear system, the state tracking error vector of which is simultaneously forced towards the origin of the phase space. It is shown that there exists equivalence between the two sliding motions. The convergence of the proposed algorithm is established and the conditions are given. Results from a simulated neuro-adaptive control of Duffing oscillator are presented. They show that the implemented neurocontroller inherits some of the advantages of the variable structure systems: high speed of learning and robustness.

In this work a stable task space neuro algorithm for set-point control of robot manipulators with uncertain parameters is proposed. A depart from current approaches is the fact that a Wavelet Neural Network with on-line real-time learning seeks to explicitly compensate both the unknown gravity terms and the mismatch between the true and the estimated Jacobian matrix and the fact that it does not need velocity measurements. Linear position filtering is used to estimated the robot joint velocity in the control law and the properties of the Wavelet Neural Network are employed for avoiding velocity measurements in the learning rule. It is shown that all the closed loop signals are uniformly ultimately bounded. Experimental results in a two degrees of freedom robot are presented to evaluate the proposed controller.

Input-Output data modelling using fully tuned radial basis function networks(RBF) for a tilt rotor aircraft experimental platform is presented in this paper. The behavior of the four degree-of-freedom platform exemplifies a high order nonlinear system with significant cross-coupling between longitudinal, latitudinal directional motions, and tilt rotor nacelles rolling movement. This paper develops a practical algorithm coupled with model validity tests for identifying nonlinear autoregressive moving average model with exogenous inputs(NARMAX). It is proved that input-output data modelling using fully tuned algorithm is suitable for modelling novelty configuration air vehicles. A procedure for system modelling was proposed in the beginning of this paper and the subsequent sections provided detailed descriptions on how each stage in the procedure could be realized. The effectiveness of this modelling procedure is demonstrated through the tilt rotor aircraft platform. The estimated model can be utilized for nonlinear flight simulation and control studies.

This paper proposes a fuzzy logic based autonomous flight controller for UAVs (unmanned aerial vehicles). Three fuzzy logic modules are developed for the control of the altitude, the speed, and the roll angle, through which the altitude and the latitude-longitude of the air vehicle are controlled. The implementation framework utilizes MATLAB’s standard configuration and the Aerosim Aeronautical Simulation Block Set which provides a complete set of tools for rapid development of detailed 6 degree-of-freedom nonlinear generic manned/unmanned aerial vehicle models. The Aerosonde UAV model is used in the simulations in order to demonstrate the performance and the potential of the controllers. Additionally, Microsoft Flight Simulator and FlightGear Flight Simulator are deployed in order to get visual outputs that aid the designer in the evaluation of the controllers. Despite the simple design procedure, the simulated test flights indicate the capability of the approach in achieving the desired performance.

Industrial CNC-milling centers demand adaptive control systems for better product quality. Surface roughness of machined parts is a key indicator of product quality, as it is closely related to functional features of parts such as fatigue life, friction, wear, etc. However, on-line control systems for surface roughness are not yet ready for industrial use. One of the main reasons is the absence of sensors that provide measurements reliably and effectively in a hostile machining environment. One potential solution is to combine readings from several different kinds of sensors in an intelligent sensor-fusion monitoring system. We implemented such a system and compared three modelling approaches for sensor-fusion: multiple regression, artificial neural networks (ANNs), and a new probabilistic approach. Probabilistic approaches are desirable because they can be extended beyond simple prediction to provide confidence estimates and diagnostic information as to probable causes. While our early experimental results with aluminum show that the ANN approach has the greatest predictive power over a variety of operating conditions, our probabilistic approach performs well enough to justify continued research given its many additional benefits.

We propose a general affective behavior model integrated to an intelligent tutoring system with the aim of providing the students with a suitable response from a pedagogical and affective point of view. The affective behavior model integrates the information from the student cognitive state, student affective state, and the tutorial situation, to decide the best pedagogical action. The affective model is implemented as a decision network with a utility measure on learning. For the construction of the affective behavior model, we are using personality questionnaires and emotions models. An initial evaluation of the model is presented, based on questionnaires applied to experienced teachers. We present the initial results of this evaluation.

Open learning environments often involve simulation where learners can experiment with different aspects and parameters of a given phenomenon to observe the effects of these changes. These are desirable in virtual laboratories. However, an important limitation of open learning environments is the effectiveness for learning, because it strongly depends on the learner ability to explore adequately. We have developed a semi-open learning environment for a virtual robotics laboratory based on simulation, to learn through free exploration, but with specific performance criteria that guide the learning process. We proposed a generic architecture for this environment, in which the key element is an intelligent tutoring system coupled to a virtual laboratory. The tutor module combines the performance and exploration behaviour of a student in several experiments, to decide the best way to guide his/herWe present an evaluation with an initial group of 20 students. The results show how this semi-open leraning environment can help to accelerate and improve the learning process.