In recent years, soft computing methods have generated a large research interest. The synthesis of the fuzzy logic and the evolutionary algorithms is one of these methods. A particular evolutionary algorithm (EA) is differential evolution (DE). As for any EA, DE algorithm also requires parameters tuning to achieve desirable performance. In this paper tuning the perturbation factor vector of DE ...

Conventional fuzzy logic controllers (FLC) are frequently being used to wherever the load is non-linear or when the mathematical model of a plant is unknown or to make a system or plant robust to external disturbances. The main working of the fuzzy logic controller depends upon the number of rules present in the rule base. Higher the number of rules better is the performance. However, with the ...

In this paper, a new structure possessing the advantages of low-power consumption, less hardware and high-speed is proposed for fuzzy controller. The maximum output delay for general fuzzy logic controllers (FLC) is about 86 ns corresponding to 11.63 MFLIPS (fuzzy logic inference per second) while this amount of the delay in the designed fuzzy controller becomes 52ns that corresponds to 19.23 M...

Received Oct 18, 2016 Revised May 30, 2017 Accepted Jun 15, 2017 It is important to have an efficient maximum power point tracking (MPPT) technique to increase the photovoltaic (PV) generation system output efficiency. This paper presents a design of MPPT techniques for PV module to increase its efficiency. Perturb and Observe method (P&O), incremental conductance method (IC), and Fuzzy logic c...

In this paper, we present a new fuzzy logic controller (FLC) for discrete-time systems. All the decision rules of FLC are automatically generated by the Lyapunov stability criterion. The proposed control scheme can be easily derived with minimum information of the controlled plant. Furthermore, the fuzzy inference scheme can successfully be applied to stabilize the nonlinear discrete-time syste...

This paper proposes a genetic-based reinforcement learning for fuzzy logic control systems (GR-FLCS) to solve reinforcement learning problems. The proposed GR-FLCS is constructed by integrating a real-coded genetic algorithm with a time accumulator as the fitness evaluator, a success criterion, a fuzzy logic controller (FLC), and a parameter adapter for the FLC. In this simple but powerful arch...

The paper describes a new proposed algorithm to automatically tune a Fuzzy Logic Controller by using motor Speed profile and Genetic Algorithm (FLCSGA algorithm) in controlling a DC Servo Motor. In the new method, the tuning process of the Fuzzy Logic Controller (FLC) is divided into two consecutive stages which are tuning rule base and tuning Membership Functions (MFs). The tuning rule base (F...

To conform to strict environmental safety regulations, pH control is used in many industrial applications. For this purpose modern process industries are increasingly relying on intelligent and adaptive control strategies. On one hand intelligent control strategies try to imitate human way of thinking and decision making using artificial intelligence (AI) based techniques such as fuzzy logic wh...

The ant colony optimization (ACO) algorithm is an evolutionary computational technique based on the behavior of a set of ants that communicate through the deposit of pheromone. It involves a node choice probability which is a function of pheromone strength and inter-node distance to construct a path through a node-arc graph. The algorithm allows fast near optimal solutions to be found compared ...

In this work the auto-tuning procedure proposed by Astrom and Hagglund is extended and developed for tuning the scaling factors of a modified hybrid PID type fuzzy logic controller (MHPID-FLC). This new procedure is based on two steps. First, mathematical expressions to link the scaling factors of the MHPID-FLC with the proportional, integral and derivative actions of its traditional counterpar...