Petri type 2 fuzzy neural networks approximator for adaptive control of uncertain non‐linear systems

Bibi, Youssouf; Bouhali, Omar; Bouktir, Tarek

doi:10.1049/iet-cta.2017.0610

Cited by 28 publications

(20 citation statements)

References 27 publications

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“…The experimental results show the effectiveness of VMC on under actuated systems. Compared with similar studies [23], [38], [49] in the literature, the proposed control system is tested under more difficult conditions (the position of the cart is tested with a reference position of 0.2 m amplitude). According to the results obtained, maximum overshoot, car position error and pendulum angle error are much less than similar studies.…”

Section: Discussionmentioning

confidence: 99%

“…Inverted pendulum mechanism is mostly used as a benchmark system for control algorithms because of its nonlinear and under actuated nature. Wide variety of control techniques have been used to control this mechanism such as PID (Proportional, Integral and Derivative) [1]- [3], [44], [46], LQR (Linear Quadratic Regulator) [4], [5], Sliding Mode Control [6]- [11], Backstepping Control [12], [13], [45], Grey Prediction Control [14], Energy Based Control [15], [16], FLC (Fuzzy Logic Control) [17]- [20], NN (Neural Network) [21] and ANFIS (Adaptive Neuro-Fuzzy Inference System) [22], [23]. Most of these control strategies are based on complex mathematical equations and the success of them is directly proportional to the precise acquisition of the mathematical model of the system [24].…”

Section: Introductionmentioning

confidence: 99%

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On the Implementation of Fuzzy VMC for an Under Actuated System

Bicakcı

2019

IEEE Access

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Most of control strategies are based on complex mathematical equations and the success of them is directly related to the precise acquisition of the mathematical model of the system. Virtual Model Control (VMC) allows the system to be mechanically controlled like a puppet with intuitive approaches instead of complex mathematical equations. In this paper, a novel implementation of fuzzy VMC on inverted pendulum, which is an under actuated mechanism, is presented. The cart of the inverted pendulum is controlled by manipulating the vertical equilibrium point of the pendulum with two cascaded VMCs. A Takagi-Sugeno fuzzy parameter tuner is designed to improve the VMC performance. An LQR controller is also designed to compare the performances of the controllers. The complete control system is implemented on FPGA based embedded platform in real time. The simulation and experimental results indicated that the proposed VMC successfully controls the inverted pendulum. In addition, the VMC with fuzzy parameter tuner has lower rise time, settling time, IAE and ITAE when compared to the LQR which is widely used controller in the literature. INDEX TERMS Fuzzy logic, inverted pendulum, parameter tuner, virtual model control.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Implementation of Fuzzy VMC for an Under Actuated System

Bicakcı

2019

IEEE Access

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“…. n h (49) where n h denotes the number of samples; e xh , e yh , e zh , e wh denote the tracking error for the h th sample. The parameters of the proposed controller consist of the following: σ init = 0.4, ∆σ = 0.05, n i = 3, n j = 4, n k = 1, D g = 0.2, D d = 0.02, and ϑ = 0.04; the sliding surface order is n = 2; the adaptive-learning rates areη w = 0.01,η m = 0.001,η σ = 0.001, andη r = 0.001.…”

Section: Illustrative Examplesmentioning

confidence: 99%

“…Due to the work of Peterson and Looney [44,45], Petri nets (PNs) and fuzzy PNs (FPNs) have been widely investigated in various fields [46][47][48][49][50]. A PN is a directed, weighted, and bipartite graph in which each node is either a place or a transition.…”

Section: Introductionmentioning

confidence: 99%

Chaotic Synchronization Using a Self-Evolving Recurrent Interval Type-2 Petri Cerebellar Model Articulation Controller

Lin

Quynh

et al. 2020

Mathematics

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In this manuscript, the synchronization of four-dimensional (4D) chaotic systems with uncertain parameters using a self-evolving recurrent interval type-2 Petri cerebellar model articulation controller is studied. The design of the synchronization control system is comprised of a recurrent interval type-2 Petri cerebellar model articulation controller and a fuzzy compensation controller. The proposed network structure can automatically generate new rules or delete unnecessary rules based on the self-evolving algorithm. Furthermore, the gradient-descent method is applied to adjust the proposed network parameters. Through Lyapunov stability analysis, bounded system stability is guaranteed. Finally, the effectiveness of the proposed controller is illustrated using numerical simulations of 4D chaotic systems.

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“…In the past two decades, several NN models and neural training schemes were applied to system controller design, and many promising results were presented [7, 8]. However, the NN has the shortcomings of slow learning speed, weak generalisation ability and unsatisfied robust performance [9, 10]. In short, the existing methods cannot fully solve the control problem of the non‐linear uncertain time‐varying system with time‐varying noise disturbance.…”

Section: Introductionmentioning

confidence: 99%