Fuzzy optimal control for double inverted pendulum

Al‐Hadithi, Basil Mohammed; Barragán, Antonio Javier; Andújar, José Manuel; Jiménez, Agustín

doi:10.1109/iciea.2012.6360687

Cited by 11 publications

(5 citation statements)

References 12 publications

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“…However, it is also known that the number of rules in TS fuzzy models is increased as a lower approximation error is desired [69]. This implies that the modeling process is very important, both for analysis [70]- [72] and the design of fuzzy control systems [73], [74].…”

Section: A Takagi-sugeno Fuzzy Modelsmentioning

confidence: 99%

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

et al. 2020

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Hydrogen economy is one of the recently opened alternatives in the field of non-polluting energy. Hydrogen fuel cells show high performance, high reliability in stationary applications and minimal environmental impact. To increase the efficiency of the hydrogen fuel cell it is very important to have a good model to predict its dynamic behavior. In addition, this model must be able to adapt iteratively to the changes that occur in its performance due to operating conditions and even to the degradation through its lifespan. This paper presents the application of an iterative fuzzy modeling methodology based on the extended Kalman filter applied to a real hydrogen fuel cell. Two algorithms based on the Kalman filter will be compared with the well-known backpropagation algorithm from three different initializations: by uniform partitioning, subtractive clustering and CMeans clustering. The used data have been collected during the actual operation of a real 3.4 kW proton exchange membrane fuel cell. As the article experimentally shows, the Takagi-Sugeno type fuzzy model allows to create a very accurate nonlinear dynamic model of the fuel cell, which can be very useful to design an efficient fuel cell control system.

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Section: A Takagi-sugeno Fuzzy Modelsmentioning

confidence: 99%

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

et al. 2020

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“…Since the DIP is a nonlinear model with a high level of nonlinearities widely applied in testing new control approaches, different nonlinear and intelligent methods have been used to control it. Al-Hadithi et al [27], for instance, contributed to the stabilization of the DIP by employing a new optimal fuzzy controller. Lo and Lin [28] proposed a new approach that is based on the sliding mode controller to eliminate the chattering-generated phenomenon.…”

Section: Introductionmentioning

confidence: 99%

T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum

Elkinany

Alfidi

Chaibi

et al. 2020

Advances in Fuzzy Systems

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This article provides a representation of the double inverted pendulum system that is shaped and regulated in response to torque application at the top rather than the bottom of the pendulum, given that most researchers have controlled the double inverted pendulum based on the lower part or the base. To achieve this objective, we designed a dynamic Lagrangian conceptualization of the double inverted pendulum and a state feedback representation based on the simple convex polytypic transformation. Finally, we used the fuzzy state feedback approach to linearize the mathematical nonlinear model and to develop a fuzzy controller H ∞ , given its great ability to simplify nonlinear systems in order to reduce the error rate and to increase precision. In our virtual conceptualization of the inverted pendulum, we used MATLAB software to simulate the movement of the system before applying a command on the upper part of the system to check its stability. Concerning the nonlinearities of the system, we have found a state feedback fuzzy control approach. Overall, the simulation results have shown that the fuzzy state feedback model is very efficient and flexible as it can be modified in different positions.

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“…Many control approaches have been proposed in order to stabilizing a double-link inverted pendulum system on two wheels. There are including Neural Network (NN) base controller [10][11], Fuzzy Logic base controller [12][13][14][15], hybrid Linear Quadratic Regulator-Fuzzy controller (LQR-Fuzzy) [16][17] and swing-up scheme control [22]. Moreover, the existing research also worked on Proportional Derivative (PD) controller [24], cascading Proportional Integral Derivative (PID) controller [25] and group of Sliding Mode Controller (SMC) [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Two-Wheeled Wheelchair Stabilization Using Interval Type-2 Fuzzy Logic Controller

Jamin

Ghani

Ibrahim

et al. 2019

International Journal of Simulation: Systems, Science &Amp; Technology

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In this paper, an Interval Type-2 Fuzzy Logic Control (IT2FLC) is proposed to control a two-wheeled wheelchair system which mimics double-links inverted pendulum and known as highly nonlinear, unstable and complex system. The control structures of the two-wheeled wheelchair is based on IT2FLC for balancing and maintaining stability of two-wheeled wheelchair system in the upright position. This paper is aimed to develop a 3-Dimensional (3D) model of two-wheeled wheelchair using a SimWise 4D (SW4D) software, which replace a complex mathematical representation that is obtained using long equation and derivation. The movement of the system is visualized using the SW4D as it is integrated with Matlab Simulink. Simulation results show that the IT2FLC give a good performance in term of tilt angle at zero degree in the upright position.

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Fuzzy optimal control for double inverted pendulum

Cited by 11 publications

References 12 publications

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

Iterative Fuzzy Modeling of Hydrogen Fuel Cells by the Extended Kalman Filter

T-S Fuzzy System Controller for Stabilizing the Double Inverted Pendulum

Two-Wheeled Wheelchair Stabilization Using Interval Type-2 Fuzzy Logic Controller

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