Danilo Chavez

Towards a Fuzzy Cognitive Map for Opinion Mining

Abstract In this paper, we propose a Fuzzy Cognitive Map (FCM) to opinion mining, with special attention to media influence on public opinion. Particularly, in this paper, we describe the FCM, the concepts and relationships among them. Our opinion mining model is based on a multilevel FCM, to distribute the concepts according to the aspects that describe the elements conforming public opinion, which are: social, technological and biological. We carry out preliminary tests, and the results are very encouraging.

Trajectory tracking for quadcopter's formation with two control strategies

The aim of this paper is to analyze the performance through the ISE index between the two techniques SMC and PD control for quadcopters formation based on a virtual structure by establishing geometrical relationships between the virtual leader (centroid) and quadcopters located in each vertex. The controllers are used for stabilization and trajectory tracking. To analyze the stability, the direct method of Lyapunov is used.

A recursive patterns matching model for the dynamic pattern recognition problem

ABSTRACT This paper defines a new recursive pattern matching model based on the theory of the systemic functioning of the human brain, called pattern recognition theory of mind, in the context of the dynamic pattern recognition problem. Dynamic patterns are characterized by having properties that change in intervals of time, such as a pedestrian walking or a car running (the negation of a dynamic pattern is a static pattern). Novel contributions of this paper include: (1) Formally develop the concepts of dynamic and static pattern, (2) design a recursive pattern matching model, which exploits the idea of recursivity and time series in the recognition process, and the unbundling/integration of pattern to recognize, and (3) develop strategies of pattern matching from two major orientations: recognition of dynamic patterns oriented by characteristic, or oriented by perception. The model is instantiated in several cases, to analyze its performance.

A Sliding-Mode Controller from a Reduced System Model: Ball and Plate System Experimental Application.

The purpose of this work is to design a Sliding-Mode Control from a reduced system model using a PID as sliding surface. The controller is applied to a Ball and Plate system which has extremely non-linear characteristics and therefore does not have a unique solution in terms of ball stabilization control. The results are obtained by simulations and with real experiments in the implemented system. A comparative performance analysis is done between the proposed approach and a PID controller to stabilize the ball at fixed points of the plate.

Different Intelligent Approaches for Modeling the Style of Car Driving.

In this paper, we propose a hierarchical pattern of the style of driving, which is composed of three levels, one to recognize the emotional state, other to recognize the state of the driver, and finally, the last one corresponds to the style of driving. Each level is defined by different types of descriptors, which are perceived in different multi-modal ways (sound, vision, etc.). Additionally, we analyze three techniques to recognize the style of driving, using our hierarchical pattern, one based on fuzzy logic, another based on chronicles (a temporal logic paradigm), and another based on an algorithm that models the functioning of the human neocortex, exploiting the idea of recursivity and learning in the recognition process. We compare the techniques considering the dynamic context where a car driver operates.

Inverse Response Systems Identification using Genetic Programming.

In this paper, we apply genetic programming as a tool for identifying an inverse response system. In previous works, the genetic programming has been used in the context of identification problems, where the goal is to obtain the descriptions of a given system. Identification problems have been studied much from control theory, due to their practical application in industry. In some cases, a description of a system in terms of mathematical equations is not possible, for these cases are necessary new heuristic approaches like the genetic programming. Here, we like to test the quality of the genetic programming to identify inverse response systems, which are systems where the initial response is in a direction opposite to the final outcome. The tool used to develop the model of identification is GPTIPS V2, we use our approach in two cases: in the first one, the equation that describes inverse response system is determined; and in the second case, the transfer function of the system in the frequency domain is found.

A Blended Sliding Mode Control with Linear Quadratic Integral Control based on Reduced Order Model for a VTOL System.

In this paper, a Sliding Mode Control with chattering reduction based on reduced order model using Linear Quadratic Integral Control as sliding surface, is implemented to One Degree of Freedom Vertical Take-Off Landing System (VTOL). The controller performance is measured using Integral of the Square Error index by simulation and real tests. Finally, the Sliding Mode Control with a Linear Quadratic Integral Control as sliding surface performance for reference tracking and, robustness against VTOL system physical parameter uncertainties and external disturbances are verified by experimental results.

Adaptive sliding mode control based on fuzzy logic for variable dead time processes

This paper aims to show an adaptive control scheme of Sliding Mode Control and Fuzzy Logic to adjust the tuning parameters of a Sliding Mode Controller (SMC) when it is used for variable dead time systems. The idea behind this work is to take advantage of the fuzzy adapting part to improve the performance of the Sliding Mode Control approach. The original Sliding Mode Control yields a limited performance when applied to variable dead time processes. A simple identification procedure is used to obtain the characteristic parameters of the process. Later they are used to calculate the original tuning parameters of the Sliding Mode control, that represent the initial tuning values for the controller, then the fuzzy adaptive scheme part acts to adjust these tuning parameters in order to get a better performance.

Two-wheeled inverted pendulum path planning: An experimental validation

This paper presents experimental results of a two-wheeled inverted pendulum path planning. The model of the robot, which has six identified variables, is linearized by least squares estimation. A Linear-quadratic Regulator is designed to maintain the robot stabilized, while it moves over a path. Furthermore, an integral part is added to the controller in order to achieve steady state equal to zero. Thus the result is an optimal PI controller. The path planning is designed using a Rapidly Exploring Random Tree Connect method. In addition, two algorithms are presented to improve the quality of the path, the first algorithm reduces the redundant paths and the second algorithm uses the Bezier curves to smooth the path.