Robust LQR Based ANFIS Control of x-z Inverted Pendulum

Chawla, Ishan; Chopra, Vikram; Singla, Ashish

doi:10.1109/aicai.2019.8701333

Cited by 9 publications

(5 citation statements)

References 10 publications

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“…The FLQR controller is a combination of the optimal control approach (LQR) and the fuzzy logic controller (FLC) method. [11][12][13][14][15][16][17][18][19][20][21] The multiple variables are transformed into error (e) and error derivative ( _ e) which simplifies the FLC controller. e and _ e are the summing of positions and velocities of state variables multiplied by their LQR gains, respectively.…”

Section: Flqrmentioning

confidence: 99%

“…In the step of data collection, the data must be in the form of multiple inputs and a single-output column vector. 20 The input data vectors are obtained from e and _ e of the FLQR. The output data vector is obtained from u FLQR .…”

Section: Anfis-lqr Controllermentioning

confidence: 99%

“…[16][17][18][19] In recent works, the use of the linear quadratic regulator (LQR)-based neuro-fuzzy model has been suggested to improve the performance of the controller. 20 In this article, a novel radial basis neuro-fuzzy linear quadratic regulator (RBNFLQR) controller is developed for an anti-swing control of a double pendulum system. The objective of this work is to study the RBNFLQR controller and to compare it with FLQR and LQR controllers.…”

Section: Introductionmentioning

confidence: 99%

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Anti-swing radial basis neuro-fuzzy linear quadratic regulator control of double link rotary pendulum

Hazem

Fotuhi

Bingül

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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In this article, a radial basis neuro-fuzzy linear quadratic regulator controller is developed for the anti-swing control of a double link rotary pendulum system. The objective of this work is to study the radial basis neuro-fuzzy linear quadratic regulator controller and to compare it with a fuzzy linear quadratic regulator and the linear quadratic regulator controllers. In the proposed radial basis neuro-fuzzy linear quadratic regulator controllers, the positions and velocities of state variables multiplied by their linear quadratic regulator gains are trained using two radial basis neural networks architecture. The output of the two radial basis neural networks is used as the input variables of the fuzzy controller. The novel architecture of the radial basis neuro-fuzzy controller is developed in order to obtain better control performance than the classical adaptive neuro-fuzzy controller. To determine the control performance of the anti-swing controllers, different control parameters are computed. According to the comparative results, the anti-swing radial basis neuro-fuzzy linear quadratic regulator controller yields improved results than fuzzy linear quadratic regulator and linear quadratic regulator. Furthermore, the performance of the three controllers developed was compared based on robustness analysis under external force disturbance. The results obtained here indicate that the anti-swing radial basis neuro-fuzzy linear quadratic regulator controller product has better performance than other controllers in terms of vibration suppression ability.

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Section: Flqrmentioning

confidence: 99%

Section: Anfis-lqr Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Anti-swing radial basis neuro-fuzzy linear quadratic regulator control of double link rotary pendulum

Hazem

Fotuhi

Bingül

2021

Proceedings of the Institution of Mechanical Engineers, Part I:

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“…In addition to deterministic methods, stochastic approaches like Fuzzy control have been explored [49]- [51], but these often rely on expert knowledge for constructing membership functions, leading to varying configurations [52]. The Adaptive Neuro-Fuzzy Inference System (ANFIS) was developed to overcome this limitation [53], [54], employing inverse training to model dynamic systems with finite errors [55], [56]. The stochastic methods give a more nonlinear response than the deterministic methods.…”

Section: Introductionmentioning

confidence: 99%

Humanoid Walking Control Using LQR and ANFIS

Auzan,

Lelono,

Dharmawan

2023

Journal of Robotics and Control

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Humanoid robots possess remarkable mobility and adaptability for diverse environments. Nonetheless, accurate walking pattern tracking remains challenging, especially when employing the linear quadratic regulator (LQR) due to delays in high-mobility setpoint tracking. We propose a novel control approach to address this limitation by integrating an artificial neuro-fuzzy inference system (ANFIS) with the LQR to enhance pattern tracking. The research contributes to developing a control system that combines LQR and ANFIS to enable humanoid robots to follow various walking patterns with increased precision and efficiency and also the scheme to incorporate LQR and ANFIS. The study involves four experiments: step response, walking phase, static straight walking, and varied straight walking. Each test runs for 5 seconds with a 100-millisecond sampling rate, repeated five times, and employs the Integral Absolute Value (IAE) metric for evaluation. The LQR-ANFIS method exhibits superior performance, achieving a maximum overshoot of 0%, a rise time of 0.3 seconds, a settling time of 0.3 seconds, and a steady-state error of 0% in the step response experiment. The proposed control system also enables stable walking with step periods ranging from 0.15 to 4 seconds and step ranges of 0.05 to 0.03 meters. In conclusion, the integration of ANFIS with the LQR significantly enhances the mobility of humanoid robots, enabling them to navigate diverse environments and accurately track various walking patterns proficiently.

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“…gibi pek çok kontrol yönteminden bahsetmek mümkündür. (Ashok Kumar & Kanthalakshmi, 2018;Bǎlan, Mǎtieş, & Stan, 2005;Chawla, Chopra, & Singla, 2019;Irfan, Mehmood, Razzaq, & Iqbal, 2018;Roose, Yahya, & Al-Rizzo, 2017;Yu & Jian, 2014) Bu kontrol yöntemlerinden bazıları lineer, bazıları ise lineer olmayan yöntemlerdir. PID ve LQR gibi lineer yöntemlerin performansı, tasarımcının deneyimine bağlı olarak, birtakım kontrol parametrelerinin uygun seçimi ile yakından ilgilidir.…”

Section: Introductionunclassified

Mopso Tabanli LQG Servo Kontrol Yaklaşimi İle Araç Üzeri̇nde Konumlu Ters Sarkacin Kontrolü

UYGUR

2022

Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi

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The Inverted Pendulum, located on the vehicle, is widely used in the academic sense for the application of various control methods and comparison of their performance. An unstable and non-linear Inverted Pendulum is sensitive and fragile to system disturbances and measurement noises. Exposure to disturbances and sensor measurement noise adversely affects the performance of control systems and causes a decrease in control quality. One of the methods used as a solution to the mentioned problem is the control design method known as Linear Quadratic Gaussian (LQG), which is a combination of Kalman Filter and LQR control. In this study, for the Inverted Pendulum located on the vehicle, which is exposed to system disturbances and sensor noise, while the vehicle carrying the pendulum follows a given reference, the pendulum is required to maintain its unstable vertical equilibrium position. System disturbances and sensor noises are chosen as Gaussian White Noise. LQG servo control approach is adopted to provide reference tracking and maintain stability around the balance point. It is necessary to optimize the performance of the controller to be used in order to best meet the control requirements. For this purpose, the performance criterion weight matrices for the LQI block within the LQG servo controller have been optimized with the help of the Multi-Objective Particle Swarm Optimization Algorithm (MOPSO).

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Robust LQR Based ANFIS Control of x-z Inverted Pendulum

Cited by 9 publications

References 10 publications

Anti-swing radial basis neuro-fuzzy linear quadratic regulator control of double link rotary pendulum

Anti-swing radial basis neuro-fuzzy linear quadratic regulator control of double link rotary pendulum

Humanoid Walking Control Using LQR and ANFIS

Mopso Tabanli LQG Servo Kontrol Yaklaşimi İle Araç Üzeri̇nde Konumlu Ters Sarkacin Kontrolü

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