Fuzzy logic controller for UAV with gains optimized via genetic algorithm.

A gains optimizer of a fuzzy controller system for an Unmanned Aerial Vehicle (UAV) based on a metaheuristic algorithm is developed in the present investigation. The contribution of the work is the adjustment by the Genetic Algorithm (GA) to tune the gains at the input of a fuzzy controller. First, a typical fuzzy controller was modeled, designed, and implemented in a mathematical model obtained by Newton-Euler methodology. Subsequently, the control gains were optimized using a metaheuristic algorithm. The control objective is that the UAV consumes the least amount of energy. With this basis, the Genetic Algorithm finds the necessary gains to meet the design parameters. The tests were performed using the Matlab-Simulink environment. The results indicate an improvement, reducing the error in tracking trajectories from 30% in some tasks and following trajectories that could not be completed without a tuned controller in other tasks.

A wireless sensor network for urban traffic characterization and trend monitoring.

Sustainable mobility requires a better management of the available infrastructure resources. To achieve this goal, it is necessary to obtain accurate data about road usage, in particular in urban areas. Although a variety of sensor alternates for urban traffic exist, they usually require extensive investments in the form of construction works for installation, processing means, etc. Wireless Sensor Networks (WSN) are an alternative to acquire urban traffic data, allowing for flexible, easy deployment. Together with the use of the appropriate sensors, like Bluetooth identification, and associate processing, WSN can provide the means to obtain in real time data like the origin-destination matrix, a key tool for trend monitoring which previously required weeks or months to be completed. This paper presents a system based on WSN designed to characterize urban traffic, particularly traffic trend monitoring through the calculation of the origin-destination matrix in real time by using Bluetooth identification. Additional sensors are also available integrated in different types of nodes. Experiments in real conditions have been performed, both for separate sensors (Bluetooth, ultrasound and laser), and for the whole system, showing the feasibility of this approach.

A new robotic endoscope manipulator. A preliminary trial to evaluate the performance of a voice-operated industrial robot and a human assistant in several simulated and real endoscopic operations.

We report our learning experience in simulated and real surgical tasks with a new voice-controlled robotic endoscope manipulator: an industrial robot with the tool-holder arm modified to support the optic and camera. The manipulator control-card programs have been rewritten to meet the needs of endoscopic surgeons. For this preliminary work, systems engineers with an additional monitor monitored, recorded, and compared the percentage effectiveness and precision of the responses of the robotic and human assistant to successive oral commands during the several different experimental surgical tasks. Simultaneously, to help develop this voice-commanded system for future, more precise robotic manipulation of surgical instruments, they measured the cartesian and spherical coordinates of successive positions of the optic. In unexpectedly difficult experimental conditions, the tireless robot proved more precise and effective than the demonstrably fatigable human: the steadier screen images of the robotic manipulations helped the surgeon tie knots in 7-0 sutures.

An input-output based robust stabilization criterion for neural-network control of nonlinear systems.

A stabilization method based on the input-output conicity criterion is presented. Conventional learning algorithms are applied to adjust the controller dynamics, and robust stability of the closed-loop system is guaranteed by modifying the training patterns which yield unstable behavior. The methodology developed expands the class of nonlinear systems to be controlled using neural control schemes, so that the stabilization of a broad class of neural-network-based control systems, even with unknown dynamics, is assured. Straightforwardness in the application of this method is evident in contrast to the Lyapunov function approach.

[Transurethral resection by remote control]. / Resección transuretral mediante control remoto.

OBJECTIVE: Telepresence allows working in distant environments by means of telemanipulators, which consist of two main modules: a master arm handled by the human operator and a slave arm that reproduces its movements. We have found in TUR unique conditions for telemanipulation and our aim is to perform this operation by remote control; in other words, to create a telepresence system for prostatectomy that would permit a surgeon to carry out an operation from a distant location for the first time. METHODS: We have developed a slave arm equipped with a resectoscope at its tip and which is able to perform the necessary movements for this type of surgery with ultramillimetric precision. This device is teloperated by the surgeon by means of a master arm that simulates a working element. RESULTS/CONCLUSION: This system will permit performing an operation from a remote site easily and for the first time, with all the advantages this may entail.