Relevant bibliographies by topics / Classical adaptive controllers

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Academic literature on the topic 'Classical adaptive controllers'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Classical adaptive controllers"

1

Costa, Victor, and Wesley Beccaro. "Benefits of Intelligent Fuzzy Controllers in Comparison to Classical Methods for Adaptive Optics." Photonics 10, no. 9 (2023): 988. http://dx.doi.org/10.3390/photonics10090988.

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Adaptive Optics (AO) systems have been developed throughout recent decades as a strategy to compensate for the effects of atmospheric turbulence, primarily caused by poor astronomical seeing. These systems reduce the wavefront distortions using deformable mirrors. Several AO simulation tools have been developed, such as the Object-Oriented, MATLAB, and Adaptive Optics Toolbox (OOMAO), to assist in the project of AO. However, the main AO simulators focus on AO models, not prioritizing the different control techniques. Moreover, the commonly applied control strategies in ground-based telescopes are based on Integral (I) or Proportional-Integral (PI) controllers. This work proposes the integration of OOMAO models to Simulink to support the development of advanced controllers and compares traditional controllers with intelligent systems based on fuzzy logic. The controllers were compared in three scenarios of different turbulence and atmosphere conditions. The simulations were performed using the characteristics/parameters of the Southern Astrophysical Research (SOAR) telescope and assessed with the Full Width at Half Maximum (FWHM), Half Light Radius (HLR), and Strehl ratio metrics to compare the performance of the controllers. The results demonstrate that adaptive optics can be satisfactorily simulated in OOMAO adapted to Simulink and thus further increase the number of control strategies available to OOMAO. The comparative results between the MATLAB script and the Simulink blocks designed showed a maximum relative error of 3% in the Strehl ratio and 1.59% in the FWHM measurement. In the assessment of the control algorithms, the fuzzy PI controller reported a 25% increase in the FWHM metrics in the critical scenario when compared with open-loop metrics. Furthermore, the fuzzy PI controller outperformed the results when compared with the I and PI controllers. The findings underscore the constraints of conventional control methods, whereas the implementation of fuzzy-based controllers showcases the promise of intelligent approaches in enhancing control performance under challenging atmospheric conditions.
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2

Intidam, Abdessamad, Hassan El Fadil, Halima Housny, et al. "Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles." Energies 16, no. 11 (2023): 4395. http://dx.doi.org/10.3390/en16114395.

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This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling time.
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3

Kamal, Tariq, Murat Karabacak, Vedran S. Perić, Syed Zulqadar Hassan, and Luis M. Fernández-Ramírez. "Novel Improved Adaptive Neuro-Fuzzy Control of Inverter and Supervisory Energy Management System of a Microgrid." Energies 13, no. 18 (2020): 4721. http://dx.doi.org/10.3390/en13184721.

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In this paper, energy management and control of a microgrid is developed through supervisor and adaptive neuro-fuzzy wavelet-based control controllers considering real weather patterns and load variations. The supervisory control is applied to the entire microgrid using lower–top level arrangements. The top-level generates the control signals considering the weather data patterns and load conditions, while the lower level controls the energy sources and power converters. The adaptive neuro-fuzzy wavelet-based controller is applied to the inverter. The new proposed wavelet-based controller improves the operation of the proposed microgrid as a result of the excellent localized characteristics of the wavelets. Simulations and comparison with other existing intelligent controllers, such as neuro-fuzzy controllers and fuzzy logic controllers, and classical PID controllers are used to present the improvements of the microgrid in terms of the power transfer, inverter output efficiency, load voltage frequency, and dynamic response.
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4

Zhang, Chao, Sheng Xiu Zhang, and Yi Nan Liu. "Invariant Manifolds Based Modular Adaptive Control for a Class of Nonlinear Systems with Application to Flight Control." Applied Mechanics and Materials 373-375 (August 2013): 1488–92. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.1488.

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In this paper a novel modular framework for adaptive control for a class of nonlinear system is developed and applied to flight controller design. The framework is based on the invariant manifolds approach with a new type of reduced-order estimator which allows for stable dynamics to be assigned to the estimation error. We show that this method can be applied to systems with unknown parameters, leading to a new class of modular adaptive controllers which is easier to tune compared to controllers obtained using the classical adaptive approaches and does not suffer from unpredictable dynamical behavior of the parameter update laws.
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5

Humaidi, Amjad J., Ibraheem Kasim Ibraheem, Ahmad Taher Azar, and Musaab E. Sadiq. "A New Adaptive Synergetic Control Design for Single Link Robot Arm Actuated by Pneumatic Muscles." Entropy 22, no. 7 (2020): 723. http://dx.doi.org/10.3390/e22070723.

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This paper suggests a new control design based on the concept of Synergetic Control theory for controlling a one-link robot arm actuated by Pneumatic artificial muscles (PAMs) in opposing bicep/tricep positions. The synergetic control design is first established based on known system parameters. However, in real PAM-actuated systems, the uncertainties are inherited features in their parameters and hence an adaptive synergetic control algorithm is proposed and synthesized for a PAM-actuated robot arm subjected to perturbation in its parameters. The adaptive synergetic laws are developed to estimate the uncertainties and to guarantee the asymptotic stability of the adaptive synergetic controlled PAM-actuated system. The work has also presented an improvement in the performance of proposed synergetic controllers (classical and adaptive) by applying a modern optimization technique based on Particle Swarm Optimization (PSO) to tune their design parameters towards optimal dynamic performance. The effectiveness of the proposed classical and adaptive synergetic controllers has been verified via computer simulation and it has been shown that the adaptive controller could cope with uncertainties and keep the controlled system stable. The proposed optimal Adaptive Synergetic Controller (ASC) has been validated with a previous adaptive controller with the same robot structure and actuation, and it has been shown that the optimal ASC outperforms its opponent in terms of tracking speed and error.
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6

Noordin, Aminurrashid, Mohd Ariffanan Mohd Basri, and Zaharuddin Mohamed. "Real-Time Implementation of an Adaptive PID Controller for the Quadrotor MAV Embedded Flight Control System." Aerospace 10, no. 1 (2023): 59. http://dx.doi.org/10.3390/aerospace10010059.

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This paper presents the real-time implementation of an altitude-embedded flight controller using proportional, integral, and derivative (PID) control, adaptive PID (APID) control, and adaptive PID control with a fuzzy compensator (APIDFC) for a micro air vehicle (MAV), specifically, for a Parrot Mambo Minidrone. In order to obtain robustness against disturbance, the adaptive mechanism, which was centered on the second-order sliding mode control, was applied to tune the classical parameters of the PID controller of the altitude controller. Additionally, a fuzzy compensator was introduced to diminish the existence of the chattering phenomena triggered by the application of the sliding mode control. Four simulation and experimental scenarios were conducted, which included hovering, as well as sine, square, and trapezium tracking. Moreover, the controller’s resilience was tested at 1.1 m above the ground by adding a mass of about 12.5 g, 15 s after the flight launch. The results demonstrated that all controllers were able to follow the reference altitude, with some spike or overshoot. Although there were slight overshoots in the control effort, the fuzzy compensator reduced the chattering phenomenon by about 6%. Moreover, it was found that in the experiment, the APID and APIDFC controllers consumed 2% and 4% less power, respectively, when compared to the PID controller used to hover the MAV.
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7

Braz-César, Manuel, and Rui Barros. "Optimization of a Fuzzy Logic Controller for MR Dampers Using an Adaptive Neuro-Fuzzy Procedure." International Journal of Structural Stability and Dynamics 17, no. 05 (2016): 1740007. http://dx.doi.org/10.1142/s0219455417400077.

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Intelligent and adaptive control systems are naturally suitable to deal with dynamic uncertain systems with non-smooth nonlinearities; they constitute an important advantage over conventional control approaches. This control technology can be used to design powerful and robust controllers for complex vibration engineering problems such as vibration control of civil structures. Fuzzy logic based controllers are simple and robust systems that are rapidly becoming a viable alternative for classical controllers. Furthermore, new control devices such as magnetorheological (MR) dampers have been widely studied for structural control applications. In this paper, we design a semi-active fuzzy controller for MR dampers using an adaptive neuro-fuzzy inference system (ANFIS). The objective is to verify the effectiveness of a neuro-fuzzy controller in reducing the response of a building structure equipped with a MR damper operating in passive and semi-active control modes. The uncontrolled and controlled responses are compared to assess the performance of the fuzzy logic based controller.
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8

Zhu, Gao Ke, Xiao Gang Duan, and Hua Deng. "Adaptive Fuzzy PID Force Control for a Prosthetic Hand." Applied Mechanics and Materials 433-435 (October 2013): 93–101. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.93.

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An adaptive fuzzy proportional-integral-derivative (PID) force control strategy for a prosthetic hand is presented. The classical PID controller is also applied on the prosthetic hand as comparison. The parameters of PID controller are firstly tuned by Cut and Try method. Then a fuzzy logic system is used to adjust those parameters on line. Real-time force control experiments are realized on LabVIEW and PXI (PCI eXtensions for Instrumentation) real-time (RT) platforms. A rigid object and a compliant object are grasped by the prosthesis respectively to test the performance of controllers. Experimental results indicate that the adaptive fuzzy PID force controller is more effective than PID controller.
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9

Mahdi, Shaymaa M., Noor Q. Yousif, Ahmed A. Oglah, Musaab E. Sadiq, Amjad J. Humaidi, and Ahmad Taher Azar. "Adaptive Synergetic Motion Control for Wearable Knee-Assistive System: A Rehabilitation of Disabled Patients." Actuators 11, no. 7 (2022): 176. http://dx.doi.org/10.3390/act11070176.

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In this study, synergetic-based adaptive control design is developed for trajectory tracking control of joint position in knee-rehabilitation system. This system is often utilized for rehabilitation of patients with lower-limb disabilities. However, this knee-assistive system is subject to uncertainties when applied to different persons undertaking exercises. This is due to the different masses and inertias of different persons. In order to cope with these uncertainties, an adaptive scheme has been proposed. In this study, an adaptive synergetic control scheme is established, and control laws are developed to ensure stable knee exoskeleton system subjected to uncertainties in parameters. Based on Lyapunov stability analysis, the developed adaptive synergetic laws are used to estimate the potential uncertainties in the coefficients of the knee-assistive system. These developed control laws guarantee the stability of the knee rehabilitation system controlled by the adaptive synergetic controller. In this study, particle swarm optimization (PSO) algorithm is introduced to tune the design parameters of adaptive and non-adaptive synergetic controllers, in order to optimize their tracking performances by minimizing an error-cost function. Numerical simulations are conducted to show the effectiveness of the proposed synergetic controllers for tracking control of the exoskeleton knee system. The results show that compared to classical synergetic controllers, the adaptive synergetic controller can guarantee the boundedness of the estimated parameters and hence avoid drifting, which in turn ensures the stability of the controlled system in the presence of parameter uncertainties.
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10

Uçak, Kemal, and Beyza Nur Arslantürk. "Adaptive MIMO fuzzy PID controller based on peak observer." An International Journal of Optimization and Control: Theories & Applications (IJOCTA) 13, no. 2 (2023): 139–50. http://dx.doi.org/10.11121/ijocta.2023.1247.

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In this paper, a novel peak observer based adaptive multi-input multi-output (MIMO) fuzzy proportional-integral-derivative (PID) controller has been introduced for MIMO time delay systems. The adaptation mechanism proposed by Qiao and Mizumoto [1] for single-input single-output (SISO) systems has been enhanced for MIMO system adaptive control. The tracking, stabilization and disturbance rejection performances of the proposed adaptation mechanism have been evaluated for MIMO systems by comparing with non-adaptive fuzzy PID and classical PID controllers. The obtained results indicate that the introduced adjustment mechanism for MIMO fuzzy PID controller can be successfully deployed for MIMO time delay systems.
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