Catálogo de publicaciones - libros

This paper presents a multiagent-based simulation system for the decision-making research of ship collision avoidance. The system has the characteristics of flexible agent, variable topology, isomorphic function structure, distributed knowledge storage, and integrated control method. The architecture is proposed with four kinds of agent models, that is Control_Agent, Union_Agent, Ship_Agent and VTS_Agent. We developed these agent models for modeling the behaviors for human, ship and VTS using a BDI (Beliefs, Desires, and Intentions) agent framework. The agent communication mechanism based on AIS (Automatic Identification System) message is also established and discussed. The proposed multiagent-based simulation system provides a useful platform for studying multi-target encountering problems and different decision-making methods for collision avoidance.

Electrical power consuming is the largest part cost for the operation of Nodding Donkey oil pump. So there is urgent requirement of reducing the power loss in the system, consequently, reducing the cost. After detailed investigation on nodding donkey machines in oil field, it has been known that the unbalance operation of the oil pumps will produce extra energy consuming when inverters are used. Compared with improving structures of prime-mover and oil-pumping units, regulating the output frequency of inverters is much more cost-effective and simple. Based on the research on the operation of beam pumping units and on the fuzzy control theory, to achieve the goal of energy saving, the voltage across the DC-link of inverter is taken as the control object and an adaptive Fuzzy Proportional Derivative controller is put forth to adjust the inverter output frequency. As result, the electrical energy absorbed from the power grid can be saved up to ten percent.

Intelligent pipeline inspection gauges (PIGs) are inspection vehicles that move along within a gas (or oil) pipeline and acquire signals from their surrounding rings of sensors. By analyzing the signals captured by intelligent PIGs, we can detect pipeline defects, such as holes, curvatures and other potential causes of gas explosions. We notice that the size of collected data using a PIG is usually in GB range. Thus, analyzer software must handle such scalable data and provide various kinds of visualization tools so that analysts can easily detect any defects in the pipeline. In this paper, we propose a scalable pipeline data processing framework using database and visualization techniques. Specifically, we analyze requirements for our system, giving its overall architecture of our system. Second, we describe several important subsystems in our system: such as a scalable pipeline data store, integrated multiple visualization, and automatic summary report generator. Third, by performing experiments with GB-range real data, we show that our system is scalable to handle such large pipeline data. Experimental results show that our system outperforms a relational database management system (RDBMS) based repository by up to 31.9 times.

An adaptive sliding mode fuzzy control approach is proposed for a class of underactuated mechanical systems that have one control input and two generalized position variables. The approach combines SMC’s robustness and FLC’s independence of system model. According to the influences on system dynamic performance, both the slope of sliding mode surface and the coordination between the two subsystems are automatically tuned by real time fuzzy inference respectively. A prototype overhead crane is built, the system stability is analyzed and the effectiveness of the proposed control algorithm is demonstrated by experiment results.

For the uncertain chaotic systems, a synchronization design scheme based on a fuzzy observer is proposed. The T-S fuzzy models for uncertain chaotic systems are exactly derived. Based on the fuzzy chaotic models, an observer for synchronization of the uncertain chaotic systems is designed via the adaptive technique. For the unknown parameters of uncertain chaotic systems, the adaptive law is derived to estimate them and the stability is guaranteed by Lyapunov stability theory. The simulation examples are given to demonstrate the validity of the proposed approach.

Air fuel ratio is a key index affecting the emission of gasoline engine, and its accurate control is the foundation of enhancing the three-way catalytic converting efficiency and improving the emission. In order to overcome the existed transmission delay of air fuel ratio signal, which affects the control accuracy of air fuel ratio if using directive air fuel ratio sensor signal., and a multi-step predictive control method of air fuel ratio based on neural network was provided in the paper. A multi-step predictive model of air fuel ratio based on back propagation neural network was set up firstly, and then a fuzzy controller was designed using the error of predictive values and expected values and its derivative. The simulation was accomplished using experiment data of HL495 gasoline engine, and the results show the air fuel ratio error is less than 3% in the faster throttle movement and it is less than 1.5% in the slower throttle movement.

The focus of situation-aware ubiquitous computing has increased lately. An example of situation-aware applications is a multimedia education system. The development of multimedia computers and communication techniques has made it possible for a mind to be transmitted from a teacher to a student in distance environment. This paper proposed a new model of dynamic window binding by analyzing the window and attributes of the attributes of the object, and based on this, a mechanism that offers a seamless view without interfering with concurrency control is also suggested. As the result of this system’s experimental implementation and analysis of the increase of delay factors accordant with the size of collaboration increase, the simulation results clearly showed that the seam increases seriously in that case, and that the dynamic window binding mechanism proposed in this paper is worth implementation and effective when a large scale of collaboration is required.

This study develops an adaptive speed controller from the adaptive neuro-fuzzy inference system (ANFIS) for an indirect field-oriented (IFO) induction motor drive. First, a two-degree-of-freedom controller (2DOFC) is designed quantitatively to meet the prescribed speed command tracking and load regulation responses at the nominal case. When system parameters and operating conditions vary, the prescribed control specifications cannot be satisfied. To improve this, an adaptive mechanism combining on-line system identification and ANFIS is developed for tuning the parameters of the 2DOFC to reduce control performance degradation. With the adaptive mechanism, the desired drive specifications can be achieved under wide operating ranges. Effectiveness of the proposed controller and the performance of the resulting drive system are confirmed by simulation and experimental results.

A new non model-related algorithm that can perform the auto-piloting of the aircraft under all conditions is presented. For improving the precision of the loosely coupled GPS/SINS integrated navigation system, fusing data from a SINS and GPS hardware utilizes wavelet multi-resolution analysis (WMRA) and Radial Basis Function Neural Networks (RBFNN). The WMRA is used to compare the SINS and GPS position outputs at different resolution levels. These differences represent, in general, the SINS errors, which are used to correct for the SINS outputs during GPS outages. The RBFNN model is then trained to predict the SINS position errors in real time and provide accurate positioning of the moving aircraft. The simulations show that good results in SINS/GPS positioning accuracy can be obtained by applying the new method based on WMRA and RBFNN.

Brake energy regeneration is an electrical current management technology that ensures intelligent generation of electric power by restricting production to the engine overrun phases and the application of the brakes. Compressed air energy storage is a technically feasible and economically attractive method for load management. This work proposed a brake energy regeneration system based on electric-controlled compressed air energy storage technology. In the proposed system, we designed a control strategy based on system capacity estimation and driver purpose identifying. The real road driving was adopted to test the system and the control strategy. The experimental results suggest that the brake energy regeneration system enables fuel economy increase of bus more than 10%. About the system initial cost, the return on investment time is about three years.