Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

Al-Mekhlafi, Mohammed A. A.; Wahid, Herman; Aziz, Azian Abd.

doi:10.11591/ijece.v8i5.pp3657-3665

Cited by 7 publications

(5 citation statements)

References 18 publications

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“…A main objective of feedback system design is to achieve a nominal performance specification for a given design model of the plant, and to maintain this performance over a range of expected errors between the design model and the true plant [18][19][20][21]. In this section, three control methods are proposed and described in brief: LQI controller, LQG and LTR based on optimal control theory, the proposed approach in this paper is detailed in the scheme presented in Figure 4.…”

Section: Lqi/lqr/ltr Optimal Controls Methodsmentioning

confidence: 99%

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

Djaballah¹,

Mohammed²,

Boughanmi³

2019

IJECE

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This paper investigates a new strategy for geostationary satellite attitude control using Linear Quadratic Gaussian (LQG), Loop Transfer Recovery (LTR), and Linear Quadratic Integral (LQI) control techniques. The sub-system satellite attitude determination and control of a geostationary satellite in the presence of external disturbances, the dynamic model of sub-satellite motion is firstly established by Euler equations. During the flight mission at 35000 Km attitude, the stability characteristics of attitude motion are analyzed with a large margin error of pointing, then a height performance-order LQI, LQG and LTR attitude controller are proposed to achieve stable control of the sub-satellite attitude, which dynamic model is linearized by using feedback linearization method. Finally, validity of the LTR order controller and the advantages over an integer order controller are examined by numerical simulation. Comparing with the corresponding integer order controller (LQI, LQG), numerical simulation results indicate that the proposed sub-satellite attitude controller based on LTR order can not only stabilize the sub-satellite attitude, but also respond faster with smaller overshoot.

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Section: Lqi/lqr/ltr Optimal Controls Methodsmentioning

confidence: 99%

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

Djaballah¹,

Mohammed²,

Boughanmi³

2019

IJECE

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“…Pregled primene upravljanja induktivnih motora AN-FIS-om dat je u [33]. U literaturi se mogu naći radovi gde ANFIS zamenjuje neki konvencionalni upravljački sistem u upravljanju obrnutog rotacionog klatna, za translatorno (gde su dobijene performanse nadmašile standardni fazi US Sugeno tipa) i za rotaciono (gde je ANFIS upravljanje nadmašilo standardni LQR) [34]. Kao multifunkcionalne programabilne mašine koje su u stanju da obavljaju čitav spektar kompleksnih zadataka, inteligentni roboti i njihovo kretanje česta su tema radova.…”

Section: Identifikacija I Upravljanjeunclassified

Adaptive neuro fuzzy Inference systems in identification, modeling and control: The state-of-the-art

Vesović¹,

Jovanović²

2022

Tehnika

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Adaptive Neural Fuzzy Inference Systems ANFIS have an increasing tendency to be used in scientific research and practical applications. The digitization of production and the emergence of Industry 4.0 enabled the development of this trend, primarily due to the ability to adapt to the task by integrating artificial neural networks and fuzzy logic, which can potentially use the advantages of both techniques in unique frameworks. This approach facilitated the modeling, data analysis, classification and control processes. The advantage of the ANFIS, compared to conventional methods, is reflected in the ability to predict the output based on a set of inputs and on the rule base. Also, these systems are suitable, because they provide the possibility to adjust the parameters of the control system. This paper presents the structure of the ANFIS system and gives a detailed review of the achievements so far, through a comparative analysis, where some possible spheres of interdisciplinary application are highlighted. Possibilities for variations, improvements and innovations of the algorithm, as well as reducing the complexity of the network architecture itself, are discussed. Proposals for some new, as yet unused combinations with metaheuristic optimization methods are presented. Finally, important guidelines are provided on when and where it is useful to apply ANFIS systems.

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“…A classical implementation example is the cart inverted pendulum shown in Figure 1, in which a DC motor drives a cart supporting a pendant-type pendulum. Numerous techniques have been proposed to control the cart inverted pendulum, including sliding mode control (SMC) [1][2][3], backstepping SMC [4,5], super-twisting control [6,7], higher-order SMC [8,9], adaptive control [10], passivity-based control [11], energy-based control [12], linear quadratic regulator (LQR)-based control [13,14], fuzzy logic-, and neural network-based control [15][16][17], model-predictive control [18], optimal control [19] and others. A vibrational control strategy is presented in [20], where external vibrations are applied to optimize the working conditions in an experimental electro-mechanical system with uncertain dynamics.…”

Section: Motivationmentioning

confidence: 99%

Finite-Time State Estimation for an Inverted Pendulum under Input-Multiplicative Uncertainty

et al. 2020

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A sliding mode observer is presented, which is rigorously proven to achieve finite-time state estimation of a dual-parallel underactuated (i.e., single-input multi-output) cart inverted pendulum system in the presence of parametric uncertainty. A salient feature of the proposed sliding mode observer design is that a rigorous analysis is provided, which proves finite-time estimation of the complete system state in the presence of input-multiplicative parametric uncertainty. The performance of the proposed observer design is demonstrated through numerical case studies using both sliding mode control (SMC)- and linear quadratic regulator (LQR)-based closed-loop control systems. The main contribution presented here is the rigorous analysis of the finite-time state estimator under input-multiplicative parametric uncertainty in addition to a comparative numerical study that quantifies the performance improvement that is achieved by formally incorporating the proposed compensator for input-multiplicative parametric uncertainty in the observer. In summary, our results show performance improvements when applied to both SMC- and LQR-based control systems, with results that include a reduction in the root-mean square error of up to 39% in translational regulation control and a reduction of up to 29% in pendulum angular control.

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Adaptive Neuro-Fuzzy Control Approach for a Single Inverted Pendulum System

Cited by 7 publications

References 18 publications

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

An implementation of optimal control methods (LQI, LQG, LTR) for geostationary satellite attitude control

Adaptive neuro fuzzy Inference systems in identification, modeling and control: The state-of-the-art

Finite-Time State Estimation for an Inverted Pendulum under Input-Multiplicative Uncertainty

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