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Number of results: 5
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Abstract

This paper proposes an analysis of the effect of vertical position of the pivot point of the inverted pendulum during humanoid walking. We introduce a new feature of the inverted pendulum by taking a pivot point under the ground level allowing a natural trajectory for the center of pressure (CoP), like in human walking. The influence of the vertical position of the pivot point on energy consumption is analyzed here. The evaluation of a 3D Walking gait is based on the energy consumption. A sthenic criterion is used to depict this evaluation. A consequent reduction of joint torques is shown with a pivot point under the ground.

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Authors and Affiliations

Sahab Omran
Sophie Sakka
Yannick Aoustin
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Abstract

The problem of mathematical modelling and indication of properties of a DIP has been investigated in this paper. The aim of this work is to aggregate the knowledge on a DIP modelling using the Euler-Lagrange formalism in the presence of external forces and friction. To indicate the main properties important for simulation, model parameters identification and control system synthesis, analytical and numerical tools have been used. The investigated properties include stability of equilibrium points, a chaos of dynamics and non-minimum phase behaviour around an upper position. The presented results refer to the model of a physical (constructed) DIP system.

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Authors and Affiliations

Kamil Andrzejewski
Mateusz Czyżniewski
Maciej Zielonka
Rafał Łangowski
ORCID: ORCID
Tomasz Zubowicz
ORCID: ORCID
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Abstract

The article presents the possibilities of using popular MEMS inertial sensors in the object tilt angle estimation system and in the system for stabilizing the vertical position of the balancing robot. Two research models were built to conduct the experiment. The models use microcontroller development board of the STM32F3 series with the Cortex-M4 core, equipped with a three-axis accelerometer, magnetometer and gyroscope. To determine the accuracy of the angle estimation, comparative tests with a pulse encoder were performed.
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Bibliography

[1] P. Groves, “Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems”, Norwood, MA: Artech House, 2008.
[2] J. Collin, P. Davidson, M. Kirkko-Jaakkola, H. Leppäkoski “Inertial Sensors and Their Applications” S. Bhattacharyya, E. Deprettere, R. Leupers, J.Takala “Handbook of Signal Processing Systems”. Springer, 2019, pp.51-85. DOI 10.1007/978-3-319-91734-4_2
[3] M. Labowski, P. Kaniewski, P. Serafin, "Inertial Navigation System for Radar Terrain Imaging," Proceedings of IEEE/ION PLANS 2016, Savannah, GA, pp. 942-948, April 2016.
[4] M. Elhoushi, J. Georgy, A. Noureldin and M. J. Korenberg, "A Survey on Approaches of Motion Mode Recognition Using Sensors," in IEEE Transactions on Intelligent Transportation Systems, vol. 18, no. 7, pp. 1662-1686, July 2017, DOI: 10.1109/TITS.2016.2617200.
[5] B. Aguiar, T. Rocha, J. Silva and I. Sousa, "Accelerometer-based fall detection for smartphones," 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Lisboa, 2014, pp. 1-6, DOI: 10.1109/MeMeA.2014.6860110.
[6] J. M. Darmanin et al., "Development of a High-G Shock Sensor Based on MEMS Technology for Mass-Market Applications," 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Naples, FL, USA, 2019, pp. 1-4, DOI: 10.1109/ISISS.2019.8739763.
[7] M. Mansoor, I. Haneef, S. Akhtar, M. A. Rafiq, S. Z. Ali and F. Udrea, "SOI CMOS multi-sensors MEMS chip for aerospace applications," SENSORS, 2014 IEEE, Valencia, Spain, 2014, pp. 1204-1207, DOI: 10.1109/ICSENS.2014.6985225.
[8] A. Mikov, A. Panyov, V. Kosyanchuk and I. Prikhodko, "Sensor Fusion For Land Vehicle Localization Using Inertial MEMS and Odometry," 2019 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Naples, FL, USA, 2019, pp. 1-2, DOI: 10.1109/ISISS.2019.8739427.
[9] C. Acar, "High-performance 6-Axis MEMS inertial sensor based on Through-Silicon Via technology," 2016 IEEE International Symposium on Inertial Sensors and Systems, Laguna Beach, CA, 2016, pp. 62-65, DOI: 10.1109/ISISS.2016.7435545.
[10] I. P. Prikhodko, B. Bearss, C. Merritt, J. Bergeron and C. Blackmer, "Towards self-navigating cars using MEMS IMU: Challenges and opportunities," 2018 IEEE International Symposium on Inertial Sensors and Systems (INERTIAL), Moltrasio, 2018, pp. 1-4, DOI: 10.1109/ISISS.2018.8358141.
[11] R. E. Kalman, "A New Approach to Linear Filtering and Prediction Problems." ASME. J. Basic Eng. March 1960; vol. 82, no1, pp. 35–45. DOI 10.1115/1.3662552
[12] J. Gajda, R. Sroka, M. Stencel, T. Żegleń, “Data fusion applications in the traffic parameters measurement”, Metrology and Measurement Systems, vol. 2, no. 3, pp. 249–262, 2005.
[13] S.Chudzik, “The idea of using artificial neural network in measurement system with hot probe for testing parameters of heat-insulating materials”, Measurement, vol. 42 pp. 764–770, 2009.
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Authors and Affiliations

Stanisław Chudzik
1

  1. Czestochowa University of Technology, Czestochowa, Poland
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Abstract

This paper presents an adaptive particle swarm optimization (APSO) based LQR controller for optimal tuning of state feedback controller gains for a class of under actuated system (Inverted pendulum). Normally, the weights of LQR controller are chosen based on trial and error approach to obtain the optimum controller gains, but it is often cumbersome and tedious to tune the controller gains via trial and error method. To address this problem, an intelligent approach employing adaptive PSO (APSO) for optimum tuning of LQR is proposed. In this approach, an adaptive inertia weight factor (AIWF), which adjusts the inertia weight according to the success rate of the particles, is employed to not only speed up the search process but also to increase the accuracy of the algorithm towards obtaining the optimum controller gain. The performance of the proposed approach is tested on a bench mark inverted pendulum system, and the experimental results of APSO are compared with that of the conventional PSO and GA. Experimental results prove that the proposed algorithm remarkably improves the convergence speed and precision of PSO in obtaining the robust trajectory tracking of inverted pendulum.
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Authors and Affiliations

Jovitha Jerome
Kumar E. Vinodh
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Abstract

As nonlinear optimization techniques are computationally expensive, their usage in the real-time era is constrained. So this is the main challenge for researchers to develop a fast algorithm that is used in real-time computations. This work proposes a fast nonlinear model predictive control approach based on particle swarm optimization for nonlinear optimization with constraints. The suggested algorithm divide and conquer technique improves computing speed and disturbance rejection capability, demonstrating its suitability for real-time applications. The performance of this approach under constraints is validated using a highly nonlinear fast and dynamic real-time inverted pendulum system. The solution presented through work is computationally feasible for smaller sampling times and it gives promising results compared to the state of art PSO algorithm
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Authors and Affiliations

Supriya P. Diwan
1
Shraddha S. Deshpande
2

  1. Government College of Engineering, Karad-415124, Maharashtra, India
  2. Walchand College of Engineering, Sangli-416415, Maharashtra, India

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