Fuzzy control of a two-wheeled mobile pendulum system

Odry, Ákos; Burkus, Ervin; Kecskés, István; Fodor, János; Ódry, Péter

doi:10.1109/saci.2016.7507348

Cited by 18 publications

(22 citation statements)

References 13 publications

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“…The heuristic particle swarm optimization (PSO) algorithm was utilized for the filter tuning problem, as it does not require gradient information, guides the search well even in nonlinear noisy systems, and has demonstrated greater effectiveness and robustness than other optimization methods [79][80][81]. Both the algorithm and applied PSO-based optimization procedure have been presented in detail in our earlier works [1,82,83]; therefore, only key information is described in the following paragraphs.…”

Section: Tuning Of Filter Parametersmentioning

confidence: 99%

“…The optimization could begin running once the parameters were initialized. The PSO parameters were selected based on earlier studies [84,85], whereas the filter parameters (x 0 , P 0 , Q, and R) were initialized by employing the results presented in [1]. As the sampling time in the ROS-based framework was relatively low (T s = 1ms), the adaptive strategy could be executed with bigger window size of L = 400; moreover, the length of the transform was L FFT = 2 9 and the threshold oscillation frequency and amplitude were f thr = 10 Hz and |Ω| thr = 0.26 rad/s, respectively.…”

Section: Tuning Of Filter Parametersmentioning

confidence: 99%

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A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

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This paper proposes a novel fuzzy-adaptive extended Kalman filter (FAEKF) for the real-time attitude estimation of agile mobile platforms equipped with magnetic, angular rate, and gravity (MARG) sensor arrays. The filter structure employs both a quaternion-based EKF and an adaptive extension, in which novel measurement methods are used to calculate the magnitudes of system vibrations, external accelerations, and magnetic distortions. These magnitudes, as external disturbances, are incorporated into a sophisticated fuzzy inference machine, which executes fuzzy IF-THEN rules-based adaption laws to consistently modify the noise covariance matrices of the filter, thereby providing accurate and robust attitude results. A six-degrees of freedom (6 DOF) test bench is designed for filter performance evaluation, which executes various dynamic behaviors and enables measurement of the true attitude angles (ground truth) along with the raw MARG sensor data. The tuning of filter parameters is performed with numerical optimization based on the collected measurements from the test environment. A comprehensive analysis highlights that the proposed adaptive strategy significantly improves the attitude estimation quality. Moreover, the filter structure successfully rejects the effects of both slow and fast external perturbations. The FAEKF can be applied to any mobile system in which attitude estimation is necessary for localization and external disturbances greatly influence the filter accuracy.The MEMS inertial measurement unit (IMU), composed of tri-axis MEMS accelerometer, gyroscope, and magnetometer sensors, also known as the measurement system of magnetic, angular rate, and gravity (MARG) sensor arrays, is the most commonly utilized device to track the real-time orientation of mobile platforms at present. The low-cost, low power consumption, and small size characteristics meet technological requirements, and therefore these devices have been widely utilized in embedded systems, where the filtering algorithm is executed by a microprocessor. As a result, an attitude and heading reference system (AHRS) has been formed, which provides the complete orientation measurement relative to the Earth's gravitational and magnetic fields (global reference system), where the attitude denotes the roll and pitch angles, whereas heading refers to the yaw Euler angle [19]. The role of the aforementioned filtering algorithm is to combine the individual features of each sensor and provide both properly smoothed and robust attitude results with regard to the global reference system, in either Euler angles or quaternions. The most common method applied in sensor fusion techniques synthesizes the short-term accuracy of gyroscope-based attitude realizations and the accelerometer and magnetometer provide rough, low-frequency attitude corrections. This technique cancels the accumulated error (drift), smooths the signals, and produces long-term stable outputs if the IMU is in stationary states. Significant decrease in estimation performance arises when...

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Section: Tuning Of Filter Parametersmentioning

confidence: 99%

Section: Tuning Of Filter Parametersmentioning

confidence: 99%

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Odry

Kecskés

Šarčević

et al. 2020

Sensors

Self Cite

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show abstract

“…The Lagrangian is taken to be the total kinetic energy of the system minus the potential energy (gravity effect) [11]- [13]. On the one hand we have the effect of the two main wheels (see the first line of the following formula) and on the other hand, the wheelchair body including the driver (see the second line of the following formula):…”

Section: A Euler-lagrange Modelmentioning

confidence: 99%

Probabilistic Shared Control for a Smart Wheelchair: A Stochastic Model-Based Framework

Teodorescu

Zhang

Carlson

2019

2019 IEEE International Conference on Systems, Man and Cybernetics (SMC)

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This article presents progress made towards implementing a shared control framework for a smart wheelchair based upon stochastic dynamic programming. First, we describe the mechanical, electrical and software design process of our instrumented wheelchair platform. Then, we detail a deterministic control-oriented model of the wheelchair motion dynamics using Euler-Lagrange equations. This is followed by the development of a stochastic model of the human driver's intention using Markov chain. Finally, we end our contribution with a discussion of the future implementation and evaluation of our stochastic dynamic programming.

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“…Kajtar and Kassai [12][13][14][15] dealt with computerized simulation of the energy consumption. Some further works [16][17][18][19][20][21] dealt with the same topics as in this paper, including mathematical modeling, numerical procedures, heat pump and heat pump heating-cooling system.…”

Section: Introductionmentioning

confidence: 99%

Dynamical Behavior of a Heat Pump Coaxial Evaporator Considering the Phase Border’s Impact on Convergence

2018

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Using a dynamical mathematical model, we investigated transient behavior of a water-water heat pump’s evaporator. The model consists of time and space dependent partial differential equations of water, pipe wall and refrigerant. Mathematically the thermal expansion valve (TEV) and compressor are described with lumped parameters and represent the boundary conditions. During the numerical solution of this system of equations the problem emerged of divergence of solutions. It was determined that the cause of the divergence solution was in the location of phase change of the refrigerant. The aim of this paper is, firstly, to display and propose a new approach to the elimination of divergence. In addition, it examines dynamic behavior of the heat pumps’ coaxial evaporator.

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Fuzzy control of a two-wheeled mobile pendulum system

Cited by 18 publications

References 13 publications

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

A Novel Fuzzy-Adaptive Extended Kalman Filter for Real-Time Attitude Estimation of Mobile Robots

Probabilistic Shared Control for a Smart Wheelchair: A Stochastic Model-Based Framework

Dynamical Behavior of a Heat Pump Coaxial Evaporator Considering the Phase Border’s Impact on Convergence

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