Temat: Adaptive control - Katalog OPAC zbiorów

Skocz do pozycji: 1.

Tytuł:: Pole - Placement Adaptive Control for a Plant with Unknown Structure and Parameters - a Simulation Study
Autorzy:: Horla, D.
Powiązania:: https://bibliotekanauki.pl/articles/384688.pdf
Data publikacji:: 2012
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: adaptive control
identification
Opis:: Adaptive pole-placement control for the plant with unknown orders and coefficients of its model is presented in the paper, in an on-line approach. In order to adapt to the plant, the considered controller changes its structure and parameters, along with the identification process. In order to combine structural and parametric identification, the approach presented in [5] has been used, with the simulation runs performed for continuous plant and a discrete-time controller and identification algorithms.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2012, 6, 1; 28-32
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 2.

Tytuł:: Robust Performance of Sampled-data Adaptive Control. From Simulation to Experimental Results
Autorzy:: Horla, D.
Powiązania:: https://bibliotekanauki.pl/articles/950947.pdf
Data publikacji:: 2015
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: adaptive control
robustness
sampled-data systems
servo
Opis:: The paper considers robustness aspects of adaptive control in application to sampled-data systems with poleplacement controller subject to plant-model mismatch, in the sense of mistuning to model parameters with respect to their „true” values. The paper extends the results presented in author’s previous work respecting comparison of sampled-data and discrete-time control systems to experimental results obtained from the control system of a servo drive. Firstly, the adaptive sampleddata controller is introduced and is applied by means of simulation, secondly, it is implemented in real-time control system to extend robust performance issues to realworld control systems. Finally, the regions of robust performance are shown on parameter surface, i.e. visualisation of parameters? span for which there is no severe performance degradation in comparison to the best plantmodel matching, as a function of sampling interval.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2015, 9, 2; 3-8
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 3.

Tytuł:: Adaptive control of autonomous underwater vehicle based on fuzzy neural network
Autorzy:: Qin, Z.
Gu, J.
Powiązania:: https://bibliotekanauki.pl/articles/384507.pdf
Data publikacji:: 2010
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: autonomous underwater vehicle
fuzzy neural network
adaptive control
stability
Opis:: This paper presents an adaptive control method based on fuzzy neural network for Autonomous Underwater Vehicle (AUV). The Fuzzy Neural Network (FNN) could build the inverse model of AUV through on-line learning algorithm, which is free of fuzzy neural network structure knowledge and prior fuzzy inference rules. The adaptive controller for AUV based on FNN is proposed, and then the stability of the resulting AUV closed-loop control system is analyzed by Lyaponov stability theory. The validity of the proposed control method has been verified through computer simulation experiments.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 1; 104-111
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 4.

Tytuł:: Fuzzy switching for multiple model adaptive control in manipulator robot
Autorzy:: Kharabian, B.
Bolandi, H.
Smailzadeh, S. M.
Mousavi Mashhadi, S. K.
Powiązania:: https://bibliotekanauki.pl/articles/384940.pdf
Data publikacji:: 2017
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: manipulator robot
fuzzy logic
multiple model
adaptive control
switching
Opis:: In this paper, fuzzy logic is used to perform switching controllers for Multiple Model Adaptive Control (MMAC) in manipulator robot. In the cases which uncertainty bounds of system’s parameters are large, the performance and stability issue of system are considerable concerns. Multiple Model Adaptive Control approach can be useful method to stabilize these kinds of systems. In this control method, the uncertainty bound is divided into several smaller bounds. As a result, the process of stabilization would be streamlined. In this regard, one estimation is obtained for uncertain parameter in every minor bound, and based on estimation errors designed controller can alter. In order to avoid switching controllers and pertinent challenges a summation of controllers with coefficient tuned by fuzzy logic is considered. Simulation results substantiate the efficacy of this method.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2017, 11, 1; 53-56
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 5.

Tytuł:: Design of a linear quadratic regulator based on genetic model reference adaptive control
Autorzy:: Abdullah, Abdullah I.
Mahmood, Ali.
Thanoon, Mohammad A.
Powiązania:: https://bibliotekanauki.pl/articles/27314263.pdf
Data publikacji:: 2022
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: model reference adaptive control
gradient approach
Linear Quadratic Regulator
genetic algorithm
Opis:: The conventional control system is a controller that controls or regulates the dynamics of any other process. From time to time, a conventional control system may not behave appropriately online; this is because of many factors like a variation in the dynamics of the process itself, unexpected changes in the environment, or even undefined parameters of the system model. To overcome this problem, we have designed and implemented an adaptive controller. This paper discusses the design of a controller for a ball and beam system with Genetic Model Reference Adaptive Control (GMRAC) for an adaptive mechanism with the MIT rule. Parameter adjustment (selection) should occur using optimization methods to obtain an optimal performance, so the genetic algorithm (GA) will be used as an optimization method to obtain the optimum values for these parameters. The Linear Quadratic Regulator (LQR) controller will be used as it is one of the most popular controllers. The performance of the proposed controller with the ball and beam system will be carried out with MATLAB Simulink in order to evaluate its effectiveness. The results show satisfactory performance where the position of the ball tracks the desired model reference.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 3; 75--81
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 6.

Tytuł:: Recurrent neural identification and control of a continuous bioprocess via first and second order learning
Autorzy:: Baruch, I.
Mariaca-Gaspar, C. R.
Powiązania:: https://bibliotekanauki.pl/articles/385133.pdf
Data publikacji:: 2010
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: backpropagation learning
direct adaptive neural control
indirect adaptive sliding mode control
Kalman filter recurrent neural network identifier
Levenberg-Marquardt learning
Opis:: This paper applies a new Kalman Filter Recurrent Neural Network (KFRNN) topology and a recursive Levenberg-Mar quardt (L-M) learning algorithm capable to estimate para meters and states of highly nonlinear unknown plant in noisy environment. The proposed KFRNN identifier, learned by the Backpropagation and L-M learning algorithm, was incorporated in a direct and indirect adaptive neural con trol schemes. The proposed control schemes were applied for real-time recurrent neural identification and control of a continuous stirred tank bioreactor model, where fast convergence, noise filtering and low mean squared error of reference tracking were achieved.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2010, 4, 4; 37-52
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 7.

Tytuł:: Vision-Based Mobile Robot Navigation
Autorzy:: Berrabah, S. A.
Colon, E.
Powiązania:: https://bibliotekanauki.pl/articles/384895.pdf
Data publikacji:: 2008
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: robot navigation
vision-based SLAM
local and global mapping
adaptive fuzzy control
Opis:: This paper presents a vision-based navigation system for mobile robots. It enables the robot to build a map of its environment, localize efficiently itself without use of any artificial markers or other modifications, and navigate without colliding with obstacles. The Simultaneous Localization And Mapping (SLAM) procedure builds a global representation of the environment based on several size limited local maps built using the approach introduced by Davison [1]. Two methods for global map are presented; the first method consists in transforming each local map into a global frame before to start building a new local map. While in the second method, the global map consists only in a set of robot positions where new local maps are started (i.e. the base references of the local maps). In both methods, the base frame for the global map is the robot position at instant . Based on the estimated map and its global position, the robot can find a path and navigate without colliding with obstacles to reach a goal defined the user. The moving objects in the scene are detected and their motion is estimated using a combination of Gaussian Mixture Model (GMM) background subtraction approach and a Maximum a Posteriori Probability Markov Random Field (MAP-MRF) framework [2]. Experimental results in real scenes are presented to illustrate the effectiveness of the proposed method.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2008, 2, 4; 7-13
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 8.

Tytuł:: Comparative Study of Optimised Artificial Intelligence Based First Order Sliding Mode Controllers for Position Control of a DC Motor Actuator
Autorzy:: Nyong-Bassey, B. E.
Akinloye, B.
Powiązania:: https://bibliotekanauki.pl/articles/385114.pdf
Data publikacji:: 2016
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: adaptive fuzzy control
DC motor position control
genetic algorithm
particle swarm optimization (PSO)
sliding mode control
Opis:: This paper aims at critically reviewing various sliding mode control measures applied to Permanent Magnet DC Motor actuator for position control. At first, a hybrid sliding mode controller was examined with its advantages and disadvantages. Then, the smooth sliding mode controller in the same manner. The shortcomings of the two methods were overcome by proper switch design and also using tanh-sinh hyperbolic function. The sliding mode controller switches on when either disturbance or noise is detected. Genetic Algorithm Computational tuning technique is employed to optimize the gains of the controllers for optimal response.The performance of the proposed controller architecture, as well as the reviewed controllers, have been compared for performance evaluation with respect to several operating conditions. This includes load torque disturbance injection, noise injection in a feedback loop, motor nonlinearity exhibited by parameters variation, and a step change in reference input demand.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2016, 10, 3; 58-71
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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Skocz do pozycji: 9.

Tytuł:: Adaptive fuzzy-sliding mode controller for trajectory tracking control of quad-rotor
Autorzy:: Simoud, Lahcen
Kadri, Boufeldja
Bousserhane, Ismail Khalil
Powiązania:: https://bibliotekanauki.pl/articles/384825.pdf
Data publikacji:: 2020
Wydawca:: Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
Tematy:: quadrotor UAV
sliding mode control
adaptive PD-Slidng mode controller
fuzzy PD-sliding mode
Opis:: This paper deals with the design of an adaptive-fuzzy-PD-Sliding mode controller to achieve stabilization of a quadrotor aircraft in the presence of wind disturbance. Firstly, the dynamic system modeling is carried out using Euler-Lagrange formalism. Then, an adaptive PD-sliding mode control system with an integral-operation switching surface is investigated for quadrotor desired trajectory tracking. Finally, an adaptive fuzzy-PD-sliding mode controller is proposed to achieve control objectives and system stabilization where the fuzzy logic system used to dynamically control parameters settings of the PD-sliding mode equivalent control law. Effectiveness and robustness of the proposed control scheme is verified through simulation results taking into account external disturbances. The simulation results of a quadrotor aircraft control with the proposed controller demonstrate the high performance during flight such as null tracking error and robustness in the presence of external disturbances.
Źródło:: Journal of Automation Mobile Robotics and Intelligent Systems; 2020, 14, 2; 15-24
1897-8649
2080-2145
Pojawia się w:: Journal of Automation Mobile Robotics and Intelligent Systems
Dostawca treści:: Biblioteka Nauki

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