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Southern Conference on programmable Logic

作者： Muñoz, Daniel M. Llanos, Carlos H. Coelho, Leandro Dos S. Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACO University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424488483

Achieving high performance optimization algorithms for embedded applications can be very challenging, particularly when several requirements such as high accuracy computations, short elapsed time, area cost, low power consumption and portability must be accomplished. This paper proposes a hardware implementation of the Particle Swarm Optimization algorithm with passive congregation (HPPSOpc), which was developed using several floating-point arithmetic libraries. The passive congregation is a biological behavior which allows the swarm to preserve its integrity, balancing between global and local search. The HPPSOpc architecture was implemented on a Virtex5 FPGA device and validated using two multimodal benchmark problems. Synthesis, simulation and execution time results demonstrates that the passive congregation approach is a low cost solution for solving embedded optimization problems with a high performance. © 2011 IEEE.

关键词： Digital arithmetic

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Hardware Implementation of a Sliding Detection Algorithm for Robotic Hands Using Force Sensors

Hardware Implementation of a Sliding Detection Algorithm for...

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Symposium on Integrated Circuits and systems Design (SBCCI)

作者： Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Systems Graduate Program Automation and Control Group/GRACO Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

In this work, a sliding detection algorithm was proposed for a previously developed robotic hand through the utilization of force sensors. Previous works have designed several hardware architectures to filter sensor data, implement dynamic control, and machine learning models for controlling the robotic fingers using a single Field programmable Gate Arrays (FPGA) chip. In this regard, the slip detection algorithm was developed to comprise three stages, each with a low computational cost, including a moving average filter, a first-order derivative, and a peak detection algorithm. A reference model was constructed and validated through an experimental protocol employing daily use objects to create a database. Subsequently, the slip detection algorithm was mapped onto hardware utilizing previously developed floating-point arithmetic IP cores and implemented using a Zynq 7020 device. The FPGA implementation was characterized in terms of resource occupation, power consumption, execution time, and numerical error with the reference model.

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Accelerating the Shuffled Frog Leaping algorithm by parallel implementations in FPGAs

Accelerating the Shuffled Frog Leaping algorithm by parallel...

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2010 IEEE 5th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2010

作者： Muñoz, Daniel M. Llanos, Carlos H. Dos S. Coelho, Leandro Ayala-Rincón, Mauricio Department of Mechanical Engineering Automation and Control Group/GRACG University of Brasilia Brasilia D.F. Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Parana Curitiba Parana Brazil Department of Computer Science and Mathematics University of Brasilia Brasilia D.F. Brazil

ISBN: (纸本)9781424464388

Meta-heuristics are efficient techniques for solving large scale optimization problems in which traditional mathematical techniques are impractical or provide sub-optimal solutions. The Shuffled Frog Leaping algorithm (SFLA) is a stochastic iterative method, bio-inspired on the memetic evolution of a group of frogs when seeking for food, which combines the social behavior-based of the particle swarm optimization technique (PSO) and the global information exchange of memetic algorithms. However, the SFLA algorithm suffers on large execution times, being this problem clearly evident when solving complex optimization problems for embedded applications. This drawback can be overcome by exploiting the parallel capabilities of the SFLA. This paper proposes a hardware parallel implementation of the SFLA algorithm (HPSFLA) using FPGAs (Field programmable gate Arrays) and the efficient floating-point arithmetic. The proposed architecture allows the SFLA to improve the functionality of the algorithm as well as to decrease the execution times by implementing parallel frogs and parallel memeplexes. Three well-known benchmark problems have been used to validate the implemented algorithm and simulation results demonstrate that the HPSFLA speeds-up by factors of 362, 727 and 211 a C-code implementation using an embedded microprocessor for the Sphere, Rastrigin and Rosenbrock benchmarks problems, respectively. Synthesis, simulation and execution time results demonstrate the effectiveness of the proposed HPSFLA architecture for embedded optimization systems. © 2010 IEEE.

关键词： Global optimization

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Efficient Hardware Implementation of Nonlinear Moving-horizon State Estimation with Artificial Neural Networks

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IFAC-PapersOnLine 2020年第2期53卷 7813-7818页

作者： Rafael Koji Vatanabe Brunello Renato Coral Sampaio Carlos H Llanos Leandro dos Santos Coelho Helon Vicente Hultmann Ayala Department of Mechanical Engineering UnB 70910-900 DF Brazil Industrial and Systems Engineering Graduate Program PUCPR 80215-910 PR Brazil Department of Electrical Engineering UFPR 81531-980 PR Brazil Department of Mechanical Engineering PUC-Rio 22231-180 RJ Brazil

In this contribution we investigate the application of radial basis functions artificial neural networks embedded in hardware for real-time moving-horizon state estimation. The solution of the optimal moving-horizon state estimation problem may be faced as the mapping from the inputs and outputs of the system to the state estimates according to the system model. This mapping may be solved offline with the optimal formulation and then approximated by any higher order function approximation algorithm, such as the ones from machine learning. An approximate version with radial basis functions neural networks is developed and implemented in a Field programmable Gate Array (FPGA) showing good results in terms of accuracy and computational time. We show that the state estimate using the approximate version of the moving-horizon algorithm can be run using a laboratory scale kit of approximately 500 kHz for an inverted pendulum at a clock rate of about 110 MHz. The latency to provide an estimate can be further reduced when FPGAs with higher clocks are used as the artificial neural network architecture is inherently parallel.

关键词： Estimation filtering Nonlinear observers filter design Nonlinear predictive control Neural networks Embedded computer architectures

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Responsiveness Improvement of Idling Speed Control for Automotive Using SMC

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Journal of Software engineering and Applications 2009年第5期2卷 309-315页

作者： Yang ZHANG Nobuo KURIHARA Hiroyuki YAMAGUCHI Doctor of Science Program in Mechanical Systems Hachinohe Institute of Technology Graduate School of Engineering Hachinohe Institute of Technology Department of System and Information Hachinohe Institute of Technology

To improve the responsiveness of engine speed control to disturbances, robust controls were investigated by simulation. The intake air control system of a gasoline engine is a typical nonlinear system, and the disturbances and parameter perturbations are generally regarded as being the unstable factors with regard to engine control. In this paper, a Mean-Value Engine Model (MVEM) with disturbances and parameter perturbations is investigated using Sliding Mode Control (SMC), which is a form of variable structure control, with a view to address instability in the idle speed control process. The simulation results confirmed that, compared with a conventional PI (Proportional-Integral) controller, the stability of the idle speed for an engine that is being subjected to disturbances, parameter variations and background noise is greatly improved by the application of SMC.

关键词： Gasoline Engine Intake Air Control Sliding Mode Control Simulation

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Cultural differential evolution approach to optimize the economic dispatch of electrical energy using thermal generators

Cultural differential evolution approach to optimize the eco...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Leandro dos Santos Coelho Adriano Del Vigna de Almeida Viviana Cocco Mariani Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná Curitiba Parana Brazil Mechanical Engineering Program Pontifical Catholic University of Paraná Curitiba Parana Brazil

Differential evolution (DE) is a powerful population-based algorithm of evolutionary computation field designed for solving global optimization problems. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. However, the control parameters and learning strategies involved in DE are highly dependent on the problems under consideration. Choosing suitable parameter values requires also previous experience of the user. Despite its crucial importance, there is no consistent methodology for determining the control parameters of a DE. In this paper, different DE approaches combined with a cultural algorithm technique based on normative and situational knowledge are proposed as alternative methods to solving the economic load dispatch problem of thermal generators with valve-point effect. The DE approaches are validated for a test system consisting of 13 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Numerical results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving load dispatch problems with the valve-point effect.

关键词： Cultural differences Optimization Evolution (biology) Generators Economics Mathematical model Evolutionary computation

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Development of a Lightweight Real-Time Application for Dynamic Hand Gesture Recognition

Development of a Lightweight Real-Time Application for Dynam...

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IEEE International Conference on Mechatronics and Automation

作者： Oluwaleke Yusuf Maki Habib Department of Mechanical Engineering Robotics Control and Smart Systems (RCSS) Program The American University in Cairo (AUC) Cairo Egypt

Hand Gesture Recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and network architectures with varying levels of efficacy. Such frameworks maximized performance at the expense of increased hardware and computational requirements. These drawbacks can be tackled by transforming the relatively complex dynamic hand gesture recognition task into a simpler image classification task. This paper presents a skeleton-based HGR framework that implements data-level fusion for encoding spatiotemporal information from dynamic gestures into static representational images. Said static images are subsequently processed by a custom, end-to-end trainable multi-stream CNN architecture for gesture classification. Our framework reduces the hardware and computational requirements of the HGR task while remaining competitive with the state-of-the-art on the CNR, FPHA, LMDHG, SHREC2017, and DHG142S benchmark datasets. We demonstrated the practical utility of our framework by creating a lightweight real-time application that makes use of skeleton data extracted from RGB video streams captured by a standard inbuilt PC webcam. The application operates successfully with minimal CPU and RAM footprint while achieving 93.46% classification accuracy with approximately 2s latency at 15 frames per second.

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Nonlinear model predictive control hardware implementation with custom-precision floating point operations

Nonlinear model predictive control hardware implementation w...

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Mediterranean Conference on Control and Automation (MED)

作者： Helon Ayala Renato Sampaio Daniel M. Muñoz Carlos Llanos Leandro Coelho Ricardo Jacobi Industrial and Systems Graduate Program Pontifical Catholic University of Parana Brazil Department of Mechanical Engineering University of Brasilia Brazil

Model predictive control (MPC) based techniques have found many applications both in academia and in industry. Its reach, however, may not be compared to classical control techniques due to e.g. the difficulty of solving an optimization problem at each sampling interval with real-time requirements. Most of the efforts to make the application of MPC viable address this problem with more efficient solvers. This paper, in contrast, proposes a new approach for a real-time MPC solution by mapping an approximate off-line solution into an artificial neural network in a FPGA (Field programmable Gate Array). We implemented a radial basis function artificial neural network on a low cost FPGA using custom precision floating point operations and tested the control on a single-link robotic manipulator. The amount of time used to calculate the control action at each time instant is in around one microsecond. The comparison between the offline and the approximate solution shows the soundness of the idea. We provide an analysis of hardware usage and execution time in order to achieve the best compromise considering the precision for a given application.

关键词： Hardware Field programmable gate arrays Optimization Real-time systems Neurons Predictive control Neural networks

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Reinforcement Learning Compensator Robust to the Time Constants of First Order Delay Elements

Reinforcement Learning Compensator Robust to the Time Consta...

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IEEE International Conference on systems, Man and Cybernetics

作者： Shoki Kobayashi Takeshi Shibuya Master’s Program in Intelligent and Mechanical Interaction Systems University of Tsukuba Ibaraki Japan Faculty of Engineering Information and Systems University of Tsukuba Ibaraki Japan

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Reinforcement learning is a learning paradigm in which a control is learned automatically based on rewards through trial and error based on rewards. When reinforcement learning is employed for robot control, the action that is output by reinforcement learning and the input of the actuator are often the same. A robot's actuator has a time constant of a first-order delay element between input and output. Delays result in the deterioration of the reinforcement learning performance because the environments that contain them lack the Markov property. Although there have been studies of such environments, they are problematic in that performance deteriorates when the time constant of a first-order time-delay element greater than the control cycle. The principal contribution of this paper is to propose a compensator for reinforcement learning that is more effective than conventional methods for environments with a time constant of a first-order time-delay element greater than the control cycle. The purpose of the compensator is to minimize the difference between actions in delayed environments and those not in delayed environments. Experiments reveal that the compensator increases rewards within wider ranges than conventional methods.

关键词： Actuators Robot control Reinforcement learning Markov processes Manipulators Delays Cybernetics

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A partially permeable mixed-mode crack embedded in a functionally graded magneto electro elastic layer

A partially permeable mixed-mode crack embedded in a functio...

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13th International Conference on Fracture 2013, ICF 2013

作者： Rekik, M. El-Borgi, S. Ounaies, Z. Applied Mechanics and Systems Research Laboratory Tunisia Polytechnic School University of Carthage B.P. 743 La Marsa 2078 Tunisia Texas A and M University at Qatar Engineering Building Mechanical Engineering Program PO. Box 23874 Education City Doha Qatar Department of Mechanical Engineering Pennsylvania State University 157B Hammond Building University Park PA 16802 United States

ISBN: (纸本)9781629933696

This work paper considers the problem of a partially permeable mixed-mode crack embedded in a graded magneto electro elastic layer subjected to magneto electro mechanical loads. The medium is graded in the direction orthogonal to the crack plane and is modeled as a non homogeneous medium with anisotropic constitutive laws. Using Fourier transform, the resulting magneto electro elasticity equations are converted analytically into singular integral equations which are then solved numerically to yield the crack-tip mode I and II stress, electric displacement and magnetic induction intensity factors. The main objective of this work is to study the influence of material non homogeneity, crack position and magneto electric permeabilities on the fields' intensity factors for the purpose of gaining better understanding on the behavior of fractured graded magneto electro elastic layers. Results showed that fields' intensity factors increase with nonhomogeneity, and decrease with magnetic and electric permeabilities, and as the crack become closer to the layer's center.

关键词： Magnetos

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