Less conservative robust stabilization conditions for the uncertain polynomial fuzzy system under perfect and imperfect premise matching

Kim, Han Sol; Park, Jin Bae; Joo, Young Hoon

doi:10.1007/s12555-015-0380-9

Cited by 6 publications

(5 citation statements)

References 20 publications

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“…There are many membership functions such as the trapezoidal, triangular, Gaussian, and bell function [34]- [37]. In the research, the bell function was adopted to fuzzify each node, as shown in (3).…”

Section: B Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

“…These rules should have the form P → Q. Since each pre-processed SP k ' has 15 kinds of different values, the condition attributes have been divided into 15 3 equivalence classes. Then each equivalence class C j (j = 1, 2, .…”

Section: Obtaining Driving Knowledge By Rough Setmentioning

confidence: 99%

“…Recently, both industry and academia have made great efforts to improve the safety, efficiency, energy consumption and mobility of autonomous vehicle on road [2]. The objective of the autonomous driving is to select an optimal path to get to the destination without colliding across any obstacles [3], [4]. The randomization process is employed to explore the large state spaces for satisfying the vehicle's running requirements [5], [6].…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Controller for Autonomous Vehicle Based on Rough Sets

Xue

Zhang

et al. 2019

IEEE Access

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This article presents a fuzzy controller for autonomous vehicle to intelligently recognize running environment and avoid an obstacle, which is constructed by rough sets (RSs) and an adaptive neurofuzzy inference system (ANFIS). Firstly, RSs are considered to propose a pyramid normalization (PN) method for normalizing state parameters (SPs) which are defined to recognize relative position such as distance and angle among a vehicle, an obstacle and target pathway, to improve the adaptability of complex environment and optimize the database of driving knowledge. Secondly, ANFIS is employed to design a controller with self-position azimuth correction (SPAC) for performing trajectory tracking and obstacle avoidance. Finally, the proposed methods have been implemented on the model vehicle called ''RoboCar'', and compared with various fuzzy control approaches such as the initial SPs with ANFIS, the normalized SPs with fuzzy neural network (FNN), and the initial SPs with FNN. Time, maximum tracking error and mean tracking error are calculated to evaluate the performance. The experimental results with four kinds of target pathways have shown that the PN-ANFIS-based controller has saved time (7.6%), reduced maximum tracking error (8.1%) and mean tracking error (8.5%).

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Section: B Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Section: Obtaining Driving Knowledge By Rough Setmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy Controller for Autonomous Vehicle Based on Rough Sets

Xue

Zhang

et al. 2019

IEEE Access

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“…Design of stabilizer polynomial fuzzy controller using Lyapunov stability analysis method is one of the problems that have been considered by researchers [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Tracker polynomial fuzzy controller design is another problem where researchers have used the concept of H ∞ control when solving the equation [20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

“…In previous research [15][16][17][27][28][29][30], the effect of disturbance and uncertainty in the controller design has been considered.…”

Section: Introductionmentioning

confidence: 99%

Design of output feedback proportional–integral controller for polynomial fuzzy systems

Radmanesh

Moarefianpour

Jahed‐Motlagh

2022

Asian Journal of Control

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In this paper, the design of dynamic output feedback control law for polynomial fuzzy systems is studied. The main purpose is to design a tracker control law for fuzzy polynomial systems that are subject to external bounded disturbances. The structure of the control law is considered as fuzzy proportional–integral control. The conditions for deriving the control law are formulated in the form of a sum of square (SOS) feasibility problem. The designed control law will be able to force the system state vector to follow the state vector of a stable reference model in addition to guaranteeing the H∞ performance measure. The simulation results show the efficiency of the proposed method exposed to external disturbance.

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