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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pastrana, M.A. Oliveira, L.H. Bautista, J. Pinto-Lopera, Jesus Mendoza-Penaloza, Jose Munoz, Daniel M. University of the Amazonia Faculty of Engineering Systems Undergraduate Program Florencia Caquetá Colombia University of Brasilia Mechatronics Graduate Program Department of Mechanical Engineering Brasilia Brazil University of Brasilia Faculty of Gama Electronics Engineering Undergraduate Program Brasilia Brazil

ISBN: (纸本)9798350374575

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-The-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%. © 2024 IEEE.

关键词： MATLAB

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

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2024 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2024

作者： Pastrana, M.A. Filho, Jose Oniram De A. Limaverde Oliveira, Luiz Henrique Bautista, Jaider Fortaleza, Eugenio Munoz, Daniel M. Department of Mechanical Engineering Brasília Brazil University of Brasília Department of Electrical Engineering Brasília Brazil University of the Amazonia Faculty of Engineering Systems Undergraduate Program Florencia Colombia

ISBN: (纸本)9798350374575

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A modified gravitational search algorithm for continuous optimization 14

A modified gravitational search algorithm for continuous opt...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： De, Emerson Hochsteiner Segundo, Vasconcelos Neto, Gabriel Fiori Da Silva, Andre Mendes Mariani, Viviana Cocco Dos Santos Coelho, Leandro Mechanical Eng. Grad. Program Pontifical Catholic University of Parana Curitiba PR Brazil Curitiba PR Brazil Mechatronics Engineering Undergraduate Program Pontifical Catholic University of Parana Curitiba PR Brazil Mechanical Engineering Graduate Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil Industrial and Systems Eng. Graduate Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil

ISBN: (纸本)9781450328814

The gravitational search algorithm (GSA) is a stochastic population-based metaheuristic inspired by the interaction of masses via Newtonian gravity law. In this paper, we propose a modified GSA (MGSA) based on logarithm and Gaussian signals for enhancing the performance of standard GSA. To evaluate the performance of the proposed MGSA, well-known benchmark functions in the literature are optimized using the proposed MGSA, and provides comparisons with the standard GSA.

关键词： Optimization

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End-to-End Reinforcement Learning for Torque Based Variable Height Hopping

arXiv

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arXiv 2023年

作者： Soni, Raghav Harnack, Daniel Isermann, Hauke Fushimi, Sotaro Kumar, Shivesh Kirchner, Frank Department of Electronics Engineering Indian Institute of Technology Banaras Hindu University Varanasi India DFKI GmbH Robotics Innovation Center Bremen Germany Undergraduate Course Program of Mechanical and Systems Engineering Kyoto University Kyoto Japan

Legged locomotion is arguably the most suited and versatile mode to deal with natural or unstructured terrains. Intensive research into dynamic walking and running controllers has recently yielded great advances, both in the optimal control and reinforcement learning (RL) literature. Hopping is a challenging dynamic task involving a flight phase and has the potential to increase the traversability of legged robots. Model based control for hopping typically relies on accurate detection of different jump phases, such as lift-off or touch down, and using different controllers for each phase. In this paper, we present a end-to-end RL based torque controller that learns to implicitly detect the relevant jump phases, removing the need to provide manual heuristics for state detection. We also extend a method for simulation to reality transfer of the learned controller to contact rich dynamic tasks, resulting in successful deployment on the robot after training without parameter tuning. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

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Sub-optimal Perceptron Controller Using WOA Algorithm for Farming Watering Mobile Robot

Sub-optimal Perceptron Controller Using WOA Algorithm for Fa...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana L. H. Oliveira J. Bautista Jesus Pinto-Lopera Jose Mendoza-Peñaloza Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

Artificial neural networks (ANNs) are intricate mathematical models, drawing inspiration from the biological nervous system, and offering intelligence alongside nonparametric capabilities. The efficacy of ANNs heavily relies on their learning process. Heuristic search algorithms have been proposed in literature as reliable options for optimizing neural networks utilizing the Perceptron architecture. This paper presents an suboptimal neural network Perceptron training method employing the Whale Optimization Algorithm for the application of farming watering mobile robots. To achieve this, a fitness function based on system responses such as overshoot, settling Time, and steady-state Error was developed. The sub-optimal Perceptron controller was then implemented using MATLAB, CoppeliaSim software and the EVA robot, resulting in a 66.17% overshoot, a 4.7% steady-state error, and a settling time of 3.05 seconds. Moreover, the sub-optimal Perceptron controller was mapped onto a System on Chip FPGA AMD-Xilinx Zynq 7020, resulting in a hardware resources consumption of 1,870 (3.52%) Lookup table, 2 (0.9%) Digital Signal Processing blocks, 1,019 (0.96%) Flip-Flops and a power consumtion of 0.005 W. Finally, Hardware-in-the-Loop validation was performed using CoppeliaSim, Python, Universal Direct Memory Access, and the EVA robot. The results showed an overshoot of 94.83%, a settling time of 2 seconds, and a steady-state error of 4.5%.

关键词： Software algorithms Signal processing algorithms Software Hardware Whale optimization algorithms Steady-state Mobile robots MATLAB Farming Python

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Teaching Control Theory using Mobile Robot Obstacle Following/Avoidance with CoppeliaSim and MFO Algorithm.

Teaching Control Theory using Mobile Robot Obstacle Followin...

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IEEE Latin American Robotics Symposium, LARS

作者： M. A. Pastrana J. Bautista Jose Mendoza-Peñaloza L. H. Oliveira D. A. Mendes Daniel M. Muñoz Department of Mechanical Engineering Mechatronics Graduate Program University of Brasilia Brasilia DF Brazil Faculty of Engineering Systems Undergraduate Program Florencia University of the Amazonia Caquetá Colombia Faculty of Gama Electronics Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

The advancements in information and communication technologies have had a significant impact on the engineering educational system. Virtual laboratories are progressively being adopted to improve the way in which students interact with simulations for control systems. The enhancement of visualization and interaction offered by modern computers presents an opportunity to teach the theoretical foundation with a more organic approach. In addition, there are optimization algorithms that can be employed to designing controllers in an optimal way without having extensive knowledge in the area of control theory. This paper delineates the utilization of CoppeliaSim software, the Moth Flame Optimization (MFO) algorithm, and the EVA mobile robot for teaching control theory with Single-Input, Single-Output systems (SISO), for mobile robot obstacle following/avoidance application. The approach employs an online multi-language (Spanish and Portuguese) methodology for students without knowledge of control theory.

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End-to-End Reinforcement Learning for Torque Based Variable Height Hopping

End-to-End Reinforcement Learning for Torque Based Variable ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Raghav Soni Daniel Harnack Hauke Isermann Sotaro Fushimi Shivesh Kumar Frank Kirchner Department of Electronics Engineering Indian Institute of Technology (Banaras Hindu University) Varanasi India DFKI GmbH Robotic-s Innovation Center Bremen Germany Undergraduate Course Program of Mechanical and Systems Engineering Kyoto University Kyoto Japan

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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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Biogeography-Based Optimization Combined with Predator-Prey Approach Applied to Economic Load Dispatch

Biogeography-Based Optimization Combined with Predator-Prey ...

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Brazilian Symposium on Neural Networks

作者： Marsil de Athayde Costa e Silva Leandro dos Santos Coelho Undergraduate Course in Mechatronics Engineering Pontifical Catholic University of Paraná (PUCPR) Curitiba Paraná Brazil Industrial and Systems Engineering Graduate Program Pontifical Catholic University of Paraná (PUCPR) Curitiba Paraná Brazil

Economic Load Dispatch (ELD) problems are nonlinear constrained problems which occupy an important role in the economic operation of power system. Recently, as an alternative to the conventional mathematical approaches, evolutionary algorithms have been given much attention by researchers due to their ability to find good solutions in ELD problems. In this paper, a biogeography based-optimization approach is validated. Biogeography is the study of the geographical distribution of biological organisms. Furthermore, a new approach of the biogeography based-optimization methodology combined with predator-prey approach were proposed and validated for two test systems consisting of 15 and 20 generation units (thermal generators), respectively. Simulation results are compared with those of other studies reported in the literature.

关键词： Optimization Biogeography Mathematical model Generators Economics Convergence Equations

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Bat-inspired optimization approach applied to Jiles-Atherton hysteresis parameters tuning 14

Bat-inspired optimization approach applied to Jiles-Atherton...

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16th Genetic and Evolutionary Computation Conference Companion, GECCO 2014 Companion

作者： Dos Santos Coelho, Leandro Da Silva, Andre Mendes Mariani, Viviana Cocco Batistela, Nelson Jhoe Ayala, Helon Vicente Hultmann Leite, Jean Vianei Industrial and Systems Eng. Grad. Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil Mechatronics Eng. Undergraduate Program Pontifical Catholic University of Parana Curitiba PR Brazil Mechanical Eng. Grad. Program Pontifical Catholic University of Parana and Electrical Engineering Federal University of Parana Curitiba PR Brazil Florianopolis SC Brazil Curitiba PR Brazil

ISBN: (纸本)9781450328814

Recently, it was proposed a new metaheuristic optimization approach namely bat algorithm (BA), which is based on the fascinating capability of microbats in to find their prey and discriminate different types of insects even in complete darkness. In this paper, we introduce a novel enhanced hybrid approach combining BA with a mutation style operator from differential evolution and beta probability distribution (BADEBD) and report its performance on to identification of the 10 hysteresis parameters for the 2-dimensional Jiles-Atherton hysteresis modeling to a material under rotational excitation. Compared with the classical BA, the proposed BADEBD performs better in terms of the solution accuracy and the convergence rate.

关键词： Hysteresis

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