Embedded intelligent adaptive PI controller for an electromechanical system

El-Nagar, Ahmad M.

doi:10.1016/j.isatra.2016.06.006

Cited by 16 publications

(7 citation statements)

References 52 publications

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“…The comparisons are done based on the measuring of some performance indices such as the mean absolute error (MAE) and the root mean square of error (RMSE). These are defined as (El-Bardini and El-Nagar; 2014; El-Nagar 2016)…”

Section: Simulation Studymentioning

confidence: 99%

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

Elkenawy

El-Nagar

El-Bardini

et al. 2020

Transactions of the Institute of Measurement and Control

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This paper proposes an observer-based adaptive control for unknown nonlinear systems using an adaptive dynamic programming (ADP) algorithm. First, a diagonal recurrent neural network (DRNN) observer is proposed to estimate the unknown dynamics of the nonlinear system states. The proposed neural network offers a simpler structure with deeper memory and guarantees the faster convergence. Second, a neural controller is constructed via ADP method using the observed states to get the optimal control. The optimal control law is determined based on the new structure of the critic network, which is performed using the DRNN. The learning algorithm for the proposed DRNN observer-based adaptive control is developed based on the Lyapunov stability theory. Simulation results and hardware-in-the-loop results indicate the robustness of the proposed ADP to respond the system uncertainties and external disturbances compared with other existing schemes.

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Section: Simulation Studymentioning

confidence: 99%

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

Elkenawy

El-Nagar

El-Bardini

et al. 2020

Transactions of the Institute of Measurement and Control

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“…The voltage of the converter DC bus is regulated by a proportional and integral (PI) controller, which provides the value of the duty-cycle D to be applied to the isolated DC/DC converter according to the error verified between the DC bus voltage and its reference value. The mathematical expression for the PI controller is given in [36] by:…”

Section: Proportional Integral (Pi) Controlmentioning

confidence: 99%

Maximum Power Point Tracking for a Point Absorber Device with a Tubular Linear Switched Reluctance Generator

2018

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This paper addresses the control of a Tubular Linear Switched Reluctance Generator (TLSRG) with application in a point absorber device. A maximum power point tracking (MPPT) strategy is proposed to maximize the power extraction from ocean waves. The generator is characterized by average maximum force of 120 kN and a maximum velocity of 1.3 m/s. The proposed MPPT is achieved by changing the generator damping load according to the excitation force induced by a regular wave. A hysteresis controller is applied to regulate the phase current intensity which allows the control of the linear force provided by the generator. The conversion system direct current (DC) bus voltage is adjusted by an isolated DC/DC converter with a proportional integral controller to define the appropriate duty-cycle.

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“…Fuzzy logic (FL) is one type of intelligent control method that has been widely adopted in many feedback control systems due to the nonlinear property of its control gain. A number of control structures have been designed within the fuzzy framework that aim to both attenuate the effects from the nonlinearities and guarantee set point tracking [12], and they are usually referred to as fuzzy logic control (FLC). FLCs can be constructed in different ways, depending on the control objectives and performance specifications.…”

Section: Introductionmentioning

confidence: 99%

Robust ℋ∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

Ahmad

2020

Sensors

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Motion control involving DC motors requires a closed-loop system with a suitable compensator if tracking performance with high precision is desired. In the case where structural model errors of the motors are more dominating than the effects from noise disturbances, accurate system modelling will be a considerable aid in synthesizing the compensator. The focus of this paper is on enhancing the tracking performance of a wheeled mobile robot (WMR), which is driven by two DC motors that are subject to model parametric uncertainties and uncertain deadzones. For the system at hand, the uncertain nonlinear perturbations are greatly induced by the time-varying power supply, followed by behaviour of motion and speed. In this work, the system is firstly modelled, where correlations between the model parameters and different input datasets as well as voltage supply are obtained via polynomial regressions. A robust H ∞ -fuzzy logic approach is then proposed to treat the issues due to the aforementioned perturbations. Via the proposed strategy, the H ∞ controller and the fuzzy logic (FL) compensator work in tandem to ensure the control law is robust against the model uncertainties. The proposed technique was validated via several real-time experiments, which showed that the speed and path tracking performance can be considerably enhanced when compared with the results via the H ∞ controller alone, and the H ∞ with the FL compensator, but without the presence of the robust control law.

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Embedded intelligent adaptive PI controller for an electromechanical system

Cited by 16 publications

References 52 publications

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

Maximum Power Point Tracking for a Point Absorber Device with a Tubular Linear Switched Reluctance Generator

Robust ℋ∞-Fuzzy Logic Control for Enhanced Tracking Performance of a Wheeled Mobile Robot in the Presence of Uncertain Nonlinear Perturbations

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