Hybrid spiral dynamic bacterial chemotaxis optimisation for hybrid fuzzy logic control of a novel two wheeled robotic vehicle

This paper presents a fuzzy logic control scheme for the navigation of a mobile robot in the presence of obstacles. A fuzzy navigation controller is described which guides the robot from a start position to a goal, or sub-goal, position assuming that no obstacles are in the path. Obstacles affect the navigation controller according to a set of fuzzy inhibitive rules that take into account the vehicle geometry, the distance of the obstacle from the robot and the probability of the object being at the position indicated. To ensure that all possible collision-free paths are considered, each entry in the fuzzy fit vector is distributed, or spread, across the output universe of discourse before using a sliding window defuzzification technique to produce a crisp output value. The use of the sliding window defuzzification technique helps to remove indecision from the controller. Both simulated and laboratory test results are presented.

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Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Jurnal Intake : Jurnal Penelitian Ilmu Teknik dan Terapan ◽

10.32492/jintake.v9i1.720 ◽

2018 ◽

Vol 9 (1) ◽

pp. 1-5

Author(s):

Fachrudin Hunaini ◽

Imam Robandi ◽

Nyoman Sutantra

Keyword(s):

Fuzzy Logic ◽

Particle Swarm Optimization ◽

Control System ◽

Membership Function ◽

Fuzzy Logic Control ◽

Particle Swarm ◽

Optimization Method ◽

Swarm Optimization ◽

Motion Error ◽

Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

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Implementation of Fuzzy Logic Control on Battery Charging System

ACMIT Proceedings ◽

10.33555/acmit.v3i1.34 ◽

2019 ◽

Vol 3 (1) ◽

pp. 118-126 ◽

Cited By ~ 1

Author(s):

Prihangkasa Yudhiyantoro

Keyword(s):

Complex System ◽

Fuzzy Logic ◽

Fuzzy Logic Control ◽

Expert Knowledge ◽

Experimental Result ◽

Rule Base ◽

Battery Charging ◽

Charging System ◽

Logic Control ◽

Charging Control

This paper presents the implementation fuzzy logic control on the battery charging system. To control the charging process is a complex system due to the exponential relationship between the charging voltage, charging current and the charging time. The effective of charging process controller is needed to maintain the charging process. Because if the charging process cannot under control, it can reduce the cycle life of the battery and it can damage the battery as well. In order to get charging control effectively, the Fuzzy Logic Control (FLC) for a Valve Regulated Lead-Acid Battery (VRLA) Charger is being embedded in the charging system unit. One of the advantages of using FLC beside the PID controller is the fact that, we don’t need a mathematical model and several parameters of coefficient charge and discharge to software implementation in this complex system. The research is started by the hardware development where the charging method and the combination of the battery charging system itself to prepare, then the study of the fuzzy logic controller in the relation of the charging control, and the determination of the parameter for the charging unit will be carefully investigated. Through the experimental result and from the expert knowledge, that is very helpful for tuning of the embership function and the rule base of the fuzzy controller.

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