Reinforcement Interval Type-2 Fuzzy Controller Design by Online Rule Generation and Q-Value-Aided Ant Colony Optimization

2009 ◽  
Vol 39 (6) ◽  
pp. 1528-1542 ◽  
Author(s):  
Chia-Feng Juang ◽  
Chia-Hung Hsu
Keyword(s):  
Ant Colony Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Ant Colony ◽  
Rule Generation ◽  
Q Value ◽  
Reinforcement Interval ◽  
Interval Type

scholarly journals Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Processes ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 823
Author(s):  
Wen-Jer Chang ◽  
Yu-Wei Lin ◽  
Yann-Horng Lin ◽  
Chin-Lin Pen ◽  
Ming-Hsuan Tsai
Keyword(s):  
Nonlinear Systems ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Actuator Saturation ◽  
Design Method ◽  
Fuzzy Model ◽  
The Stability ◽  
Interval Type ◽  
Takagi Sugeno

In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

scholarly journals Multilayer Interval Type-2 Fuzzy Controller Design for Quadcopter Unmanned Aerial Vehicles Using Jaya Algorithm

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 181246-181257
Author(s):  
Tien-Loc Le ◽  
Nguyen Vu Quynh ◽  
Ngo Kim Long ◽  
Sung Kyung Hong
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Jaya Algorithm ◽  
Aerial Vehicles ◽  
Interval Type

Fuzzy Controller Design by Clustering-Aided Ant Colony Optimization

2008 ◽  
Vol 41 (2) ◽  
pp. 12297-12302
Author(s):  
Chia-Feng Juang ◽  
Yu-Ping Kang ◽  
Chiang Lo
Keyword(s):  
Ant Colony Optimization ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Ant Colony

scholarly journals Ant Colony Optimization Based Type-2 Fuzzy Force-Position Control for Backhoe Excavator Robot

2018 ◽  
Vol 21 (1) ◽  
pp. 1 ◽  
Author(s):  
Mohammed Y. Hassan ◽  
Athraa Faraj Sugban
Keyword(s):  
Fuzzy Logic ◽  
Ant Colony Optimization ◽  
Position Control ◽  
Friction Model ◽  
Ant Colony ◽  
Hydraulic Actuator ◽  
Lugre Friction Model ◽  
Actuator System ◽  
Interval Type

This paper proposes the design and simulation of Interval Type-2 Fuzzy Logic Control using MATLAB/Simulink to control the position of the bucket of the backhoe excavator robot during digging operations. In order to reach accurate position responses with minimum overshoot and minimum steady state error, Ant Colony Optimization (ACO) algorithm is used to tune the gains of the position and force parts for the force-position controllers to obtain the best position responses. The joints are actuated by the electro-hydraulic actuators. The force-position control incorporating two-Mamdani type-Proportional-Derivative-Interval Type-2 Fuzzy Logic Controllers for position control and 3-Proportional-Derivative Controllers for force control. The nonlinearity and uncertainty in the model that inherit in the electro hydraulic actuator system are also studied. The nonlinearity includes oil leakage and frictions in the joints. The friction model is represented as a Modified LuGre friction model in actuators. The excavator robot joints are subjected to Coulomb, viscous and stribeck friction. The uncertainty is represented by the variation of bulk modulus. It can be shown from the results that the ACO obtain the best gains of the controllers which enhances the position responses within the range of (19, 23 %) compared with the controllers tuned manually.

Design and application of Type-2 fuzzy logic system based on improved ant colony algorithm

2018 ◽  
Vol 40 (16) ◽  
pp. 4444-4454
Author(s):  
Zhifeng Zhang ◽  
Tao Wang ◽  
Yang Chen ◽  
Jie Lan
Keyword(s):  
Fuzzy Logic ◽  
Ant Colony Optimization ◽  
Ant Colony Algorithm ◽  
Fuzzy Logic System ◽  
Ant Colony ◽  
Original Algorithm ◽  
Interval Type ◽  
Logic System

In this paper, an improved ant colony optimization (IACO) with global pheromone update is proposed based on ant colony optimization (ACO), and it is used to design interval Type-2 TSK fuzzy logic system (FLS), including parameters adjustment and rules selection. The performance of the system can be improved by obtaining the optimal parameters and reducing the redundant rules. In order to verify the feasibility of the proposed method, the intelligent FLS is applied to predict the international petroleum price and the Zhongyuan environmental protection shares price. It is proved that the IACO can improve the efficiency of the original algorithm and accelerate the convergence speed. The simulations show that both IACO and ACO are feasible and have a high performance for the design of FLS. The simulation results compared with back-propagation design (BP algorithm) show that intelligent algorithms have an advantage over the classical algorithm, the simulation result compared with without rule-selection shows that reduced redundant rules can improve the performance, and the result compared with the Type-1 FLS shows that interval Type-2 TSK FLS has a better performance than the Type-1 TSK FLS.

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