Search results for "work"

Modeling and control of uncertain nonlinear systems

2018

A survey of the methodologies associated with the modeling and control of uncertain nonlinear systems has been given due importance in this paper. The basic criteria that highlights the work is relied on the various patterns of techniques incorporated for the solutions of fuzzy equations that corresponds to fuzzy controllability subject. The solutions which are generated by these equations are considered to be the controllers. Currently, numerical techniques have come out as superior techniques in order to solve these types of problems. The implementation of neural networks technique is contributed in the complex way of dealing the appropriate coefficients and solutions of the fuzzy systems.

Bio-inspired evolutionary dynamics on complex networks under uncertain cross-inhibitory signals

2019

Given a large population of agents, each agent has three possiblechoices between option 1 or 2 or no option. The two options are equally favorable and the population has to reach consensus on one of the two options quickly and in a distributed way. The more popular an option is, the more likely it is to be chosen by uncommitted agents. Agents committed to one option can be attracted by those committed to the other option through a cross-inhibitory signal. This model originates in the context of honeybee swarms, and we generalize it to duopolistic competition and opinion dynamics. The contributions of this work include (i) the formulation of a model to explain the behavioral traits of the ho…

Metaheuristic procedures for the lexicographic bottleneck assembly line balancing problem

2015

The goal of this work is to develop an improved procedure for the solution of the lexicographic bottleneck variant of the assembly line balancing problem (LB-ALBP). The objective of the LB-ALBP is to minimize the workload of the most heavily loaded workstation, followed by the workload of the second most heavily loaded workstation and so on. This problem-recently introduced to the literature (Pastor, 2011)-has practical relevance to manufacturing facilities. We design, implement and fine-tune GRASP, tabu search (TS) and scatter search (SS) heuristics for the LB-ALBP and show that our procedures are able to obtain solutions of a quality that outperforms previous approaches. We rely on both s…

Interrogating witnesses for geometric constraint solving

2012

International audience; Classically, geometric constraint solvers use graph-based methods to decompose systems of geometric constraints. These methods have intrinsic limitations, which the witness method overcomes; a witness is a solution of a variant of the system. This paper details the computation of a basis of the vector space of free infinitesimal motions of a typical witness, and explains how to use this basis to interrogate the witness for dependence detection. The paper shows that the witness method detects all kinds of dependences: structural dependences already detectable by graph-based methods, but also non-structural dependences, due to known or unknown geometric theorems, which…

An algebraic continuous time parameter estimation for a sum of sinusoidal waveform signals

2016

In this paper, a novel algebraic method is proposed to estimate amplitudes, frequencies, and phases of a biased and noisy sum of complex exponential sinusoidal signals. The resulting parameter estimates are given by original closed formulas, constructed as integrals acting as time-varying filters of the noisy measured signal. The proposed algebraic method provides faster and more robust results, compared with usual procedures. Some computer simulations illustrate the efficiency of our method. Copyright © 2016 John Wiley & Sons, Ltd.

JOINT TOPOLOGY LEARNING AND GRAPH SIGNAL RECOVERY VIA KALMAN FILTER IN CAUSAL DATA PROCESSES

2018

In this paper, a joint graph-signal recovery approach is investigated when we have a set of noisy graph signals generated based on a causal graph process. By leveraging the Kalman filter framework, a three steps iterative algorithm is utilized to predict and update signal estimation as well as graph topology learning, called Topological Kalman Filter or TKF. Similar to the regular Kalman filter, we first predict the a posterior signal state based on the prior available data and then this prediction is updated and corrected based on the recently arrived measurement. But contrary to the conventional Kalman filter algorithm, we have no information of the transition matrix and hence we relate t…

Parallel distributed compensation for voltage controlled active magnetic bearing system using integral fuzzy model

2018

Parallel Distributed Compensation (PDC) for current-controlled Active Magnetic Bearing System (AMBS) has been quite effective in recent years. However, this method does not take into account the dynamics associated with the electromagnet. This limits the method to smaller scale applications where the electromagnet dynamics can be neglected. Voltage-controlled AMBS is used to overcome this limitation but this comes with serious challenges such as complex mathematical modelling and higher order system control. In this work, a PDC with integral part is proposed for position and input tracking control of voltage-controlled AMBS. PDC method is based on nonlinear Takagi-Sugeno (T-S) fuzzy model. …

Clothoid-Based Three-Dimensional Curve for Attitude Planning

2019

Interest in flying robots, also known as unmanned aerial vehicles (UAVs), has grown during last years in both military and civil fields [1, 2]. The same happens to autonomous underwater vehicles (AUVs) [3]. These vehicles, UAVs and AUVs, offer a wide variety of possible applications and challenges, such as control, guidance or navigation [2, 3]. In this sense, heading and attitude control in UAVs is very important [4], particularly relevant in airplanes (fixed-wing flying vehicles), because they are strongly non-linear, coupled, and tend to be underactuated systems with non-holonomic constraints. Hence, designing a good attitude controller is a difficult task [5, 6, 7, 8, 9], where stabilit…

2021

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and w…

Location-Aware MAC Scheduling in Industrial-Like Environment

2018

We consider an environment strongly affected by the presence of metallic objects, that can be considered representative of an indoor industrial environment with metal obstacles. This scenario is a very harsh environment where radio communication has notorious difficulties, as metallic objects create a strong blockage component and surfaces are highly reflective. In this environment, we investigate how to dynamically allocate MAC resources in time to static and mobile users based on context awareness extracted from a legacy WiFi positioning system. In order to address this problem, we integrate our WiFi ranging and positioning system in the WiSHFUL architecture and then define a hypothesis t…