Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Multidimensional polynomial powers of sigmoid (PPS) Wavelet neural networks

Wavelet functions have been used as the activation function in feedforward neural networks. An abundance of R&D has been produced on wavelet neural network area. Some successful algorithms and applications in wavelet neural network have been developed and reported in the literature. However, most of the aforementioned reports impose many restrictions in the classical backpropagation algorithm, such as low dimensionality, tensor product of wavelets, parameters initialization, and, in general, the output is one dimensional, etc. In order to remove some of these restrictions, a family of polynomial wavelets generated from powers of sigmoid functions is presented. We described how a multidimensional wavelet neural networks based on these functions can be constructed, trained and applied in pattern recognition tasks. As an example of application for the method proposed, it is studied the exclusive-or (XOR) problem.

Feedforward neural networks based on PPS-wavelet activation functions

Function approximation is a very important task in environments where computation has to be based on extracting information from data samples in real world processes. Neural networks and wavenets have been recently seen as attractive tools for developing efficient solutions for many real world problems in function approximation. In this paper, it is shown how feedforward neural networks can be built using a different type of activation function referred to as the PPS-wavelet. An algorithm is presented to generate a family of PPS-wavelets that can be used to efficiently construct feedforward networks for function approximation.

Proposta de implementação de redes de base radial em tecnologias CMOS e BiCMOS

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Aeroelastic stability analysis using linear matrix inequalities

The present work describes an alternative methodology for identification of aeroelastic stability in a range of varying parameters. Analysis is performed in time domain based on Lyapunov stability and solved by convex optimization algorithms. The theory is outlined and simulations are carried out on a benchmark system to illustrate the method. The classical methodology with the analysis of the system's eigenvalues is presented for comparing the results and validating the approach. The aeroelastic model is represented in state space format and the unsteady aerodynamic forces are written in time domain using rational function approximation. The problem is formulated as a polytopic differential inclusion system and the conceptual idea can be used in two different applications. In the first application the method verifies the aeroelastic stability in a range of air density (or its equivalent altitude range). In the second one, the stability is verified for a rage of velocities. These analyses are in contrast to the classical discrete analysis performed at fixed air density/velocity values. It is shown that this method is efficient to identify stability regions in the flight envelope and it offers promise for robust flutter identification.

Control of Limit Cycle Oscillation in a Three Degrees of Freedom Airfoil Section Using Fuzzy Takagi-Sugeno Modeling

This work presents a strategy to control nonlinear responses of aeroelastic systems with control surface freeplay. The proposed methodology is developed for the three degrees of freedom typical section airfoil considering aerodynamic forces from Theodorsen's theory. The mathematical model is written in the state space representation using rational function approximation to write the aerodynamic forces in time domain. The control system is designed using the fuzzy Takagi-Sugeno modeling to compute a feedback control gain. It useds Lyapunov's stability function and linear matrix inequalities (LMIs) to solve a convex optimization problem. Time simulations with different initial conditions are performed using a modified Runge-Kutta algorithm to compare the system with and without control forces. It is shown that this approach can compute linear control gain able to stabilize aeroelastic systems with discontinuous nonlinearities.

Aeroelastic stability analysis considering a continuous flight envelope

This paper presents a new methodology to analyze aeroelastic stability in a continuous range of flight envelope with varying parameter of velocity and altitude. The focus of the paper is to demonstrate that linear matrix inequalities can be used to evaluate the aeroelastic stability in a region of flight envelope instead of a single point, like classical methods. The proposed methodology can also be used to study if a system remains stable during an arbitrary motion from one point to another in the flight envelope, i.e., when the problem becomes time-variant. The main idea is to represent the system as a polytopic differential inclusion system using rational function approximation to write the model in time domain. The theory is outlined and simulations are carried out on the benchmark AGARD 445.6 wing to demonstrate the method. The classical pk-method is used for comparing results and validating the approach. It is shown that this method is efficient to identify stability regions in the flight envelope. (C) 2014 Elsevier Ltd. All rights reserved.

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use.

A new approach to fuzzy estimation of Takagi-Sugeno model and its applications to optimal control for nonlinear systems

An efficient approach is presented to improve the local and global approximation and modelling capability of Takagi-Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy. The main problem is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the use of the T-S method because this type of membership function has been widely used during the last two decades in the stability, controller design and are popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S method with optimized performance in approximating nonlinear functions. A simple approach with few computational effort, based on the well known parameters' weighting method is suggested for tuning T-S parameters to improve the choice of the performance index and minimize it. A global fuzzy controller (FC) based Linear Quadratic Regulator (LQR) is proposed in order to show the effectiveness of the estimation method developed here in control applications. Illustrative examples of an inverted pendulum and Van der Pol system are chosen to evaluate the robustness and remarkable performance of the proposed method and the high accuracy obtained in approximating nonlinear and unstable systems locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the algorithm.

The ATPase Domain but Not the Acidic Region of Cockayne Syndrome Group B Gene Product Is Essential for DNA Repair

Cockayne syndrome (CS) is a human genetic disorder characterized by UV sensitivity, developmental abnormalities, and premature aging. Two of the genes involved, CSA and CSB, are required for transcription-coupled repair (TCR), a subpathway of nucleotide excision repair that removes certain lesions rapidly and efficiently from the transcribed strand of active genes. CS proteins have also been implicated in the recovery of transcription after certain types of DNA damage such as those lesions induced by UV light. In this study, site-directed mutations have been introduced to the human CSB gene to investigate the functional significance of the conserved ATPase domain and of a highly acidic region of the protein. The CSB mutant alleles were tested for genetic complementation of UV-sensitive phenotypes in the human CS-B homologue of hamster UV61. In addition, the CSB mutant alleles were tested for their ability to complement the sensitivity of UV61 cells to the carcinogen 4-nitroquinoline-1-oxide (4-NQO), which introduces bulky DNA adducts repaired by global genome repair. Point mutation of a highly conserved glutamic acid residue in ATPase motif II abolished the ability of CSB protein to complement the UV-sensitive phenotypes of survival, RNA synthesis recovery, and gene-specific repair. These data indicate that the integrity of the ATPase domain is critical for CSB function in vivo. Likewise, the CSB ATPase point mutant failed to confer cellular resistance to 4-NQO, suggesting that ATP hydrolysis is required for CSB function in a TCR-independent pathway. On the contrary, a large deletion of the acidic region of CSB protein did not impair the genetic function in the processing of either UV- or 4-NQO-induced DNA damage. Thus the acidic region of CSB is likely to be dispensable for DNA repair, whereas the ATPase domain is essential for CSB function in both TCR-dependent and -independent pathways.

Yeast and human genes that affect the Escherichia coli SOS response

The sequencing of the human genome has led to the identification of many genes whose functions remain to be determined. Because of conservation of genetic function, microbial systems have often been used for identification and characterization of human genes. We have investigated the use of the Escherichia coli SOS induction assay as a screen for yeast and human genes that might play a role in DNA metabolism and/or in genome stability. The SOS system has previously been used to analyze bacterial and viral genes that directly modify DNA. An initial screen of meiotically expressed yeast genes revealed several genes associated with chromosome metabolism (e.g., RAD51 and HHT1 as well as others). The SOS induction assay was then extended to the isolation of human genes. Several known human genes involved in DNA metabolism, such as the Ku70 end-binding protein and DNA ligase IV, were identified, as well as a large number of previously unknown genes. Thus, the SOS assay can be used to identify and characterize human genes, many of which may participate in chromosome metabolism.

Subband energy in two-band δ-doped semiconductors

We study electron dynamics in a two-band δ-doped semiconductor within the envelope-function approximation. Using a simple parametrization of the confining potential arising from the ionized donors in the δ -doping layer, we are able to find exact solutions of the Dirac-type equation describing the coupling of host bands. As an application we then consider Si δ -doped GaAs. In particular we find that the ground subband energy scales as a power law of the Si concentration per unit area in a wide range of doping levels. In addition, the coupling of host bands leads to a depression of the subband energy due to nonparabolicity effects.

Terahertz Response of Excitons in Nanorod Heterostructures

Quantum-confined systems are one of the most promising ways to enable us to control a material's interactions with light. Nanorods in particular offer the right dimensions for exploring and manipulating the terahertz region of the spectrum. In this thesis, we model excitons confined inside a nanorod using the envelope function approximation. A region-matching transfer matrix method allows us to simulate excitonic states inside arbitrary heterostructures grown along the length of the rod. We apply the method to colloidal CdSe rods 70 nm in length and under 10 nm in diameter, capped with ligands of DDPA and pyridine. We extend past studies on these types of rods by taking into account their dielectric permittivity mismatch. Compared to previous calculations and experimentally measured terahertz absorption, we predict a higher energy main 1S$z$ to 2P$z$ transition peak. This indicates that the rods are likely larger in diameter than previously thought. We also investigate a nanorod with GaAs/Al$_{0.3}$Ga$_{0.7}$As coupled double dots. The excitonic transitions were found to be manipulable by varying the strength of an applied electric field. We employ quasi-static state population distributions to simulate the effects of exciton relaxation from optically active states to dim ground states. A critical value of the applied field, corresponding to the exciton binding energy of ~18 meV, was found to dramatically alter the terahertz absorption due to state mixing. Above this critical field, more nuanced shifts in transition energies were observed, and gain from radiative relaxation to the ground state is predicted.

Contributions to Physics-Based Aeroservoelastic Uncertainty Analysis

Thesis (Ph.D.)--University of Washington, 2016-06