Agent Based Ground Flight Control Using Type-2 Fuzzy Logic and Hybrid Ant Colony Optimization to a Dynamic Environment

2018 ◽

Vol 21 (1) ◽

pp. 1 ◽

Cited By ~ 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.

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

Transactions of the Institute of Measurement and Control ◽

10.1177/0142331217750221 ◽

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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