In this article, sensorless speed control of DC motor has been proposed using the extended Kalman filter (EKF) estimator and Takagi–Sugeno-Kang (TSK) fuzzy logic controller (FLC). industry, high-cost measurement systems/sensors are necessary for better controlling monitoring, which can be replaced by a technique to reduce cost, size increase system reliability robustness. EKF used perform estim...

The Knowledge Base of a Fuzzy Logic Controller (FLC) encapsulates expert knowledge and consists of the Data Base (membership functions) and Rule-Base of the controller. Optimization of these Knowledge Base components is critical to the performance of the controller and has traditionally been achieved through a process of trial and error. Such an approach is convenient for FLCs having low number...

A novel reconfigurable membership function circuit (RMFC) for analog fuzzy logic controller (FLC) is presented. The reconfigurable characteristic makes it more suitable in industrial control applications when compared with the MFC proposed in [1]. By proper signal arrangement, the RMFC can re-sample the same input current in different configuration, which reduces the number of MFC in analog FLC...

Hybrid Electric Vehicles (HEVs) are driven by two energy convertors, i.e., an Internal Combustion (IC) engine and an electric machine. To make powertrain of HEV as efficient as possible, proper management of the energy elements is essential. This task is completed by HEV controller, which splits power between the IC engine and Electric Motor (EM). In this paper, a Genetic-Fuzzy control strategy...

This paper presents a fuzzy logic controller (FLC) for the implementation of some behaviour of Sony legged robots. The adaptive heuristic Critic (AHC) reinforcement learning is employed to refine the FLC. The actor part of AHC is a conventional FLC in which the parameters of input membership functions are learned by an immediate internal reinforcement signal. This internal reinforcement signal ...

While many widely accepted methods and techniques exist for validation and verification of traditional controllers, at this time no solutions have been accepted for Fuzzy Logic Controllers (FLCs). Due to the highly nonlinear nature of such systems, and the fact that developing a valid FLC does not require a mathematical model of the system, it is quite difficult to use conventional techniques t...

The paper proposes a new method to automatically extract all fuzzy parameters of a Fuzzy Logic Controller (FLC) in order to control nonlinear industrial processes. The main objective of this paper is the extraction of a FLC from data extracted from a given process while it is being manually controlled. The learning of the FLC is performed by a hierarchical genetic algorithm (HGA), from a set of...

A novel FLC (Fuzzy Logic Controller) for eliminating buffer overflow at the user level is proposed. It tunes the buffer size in a dynamic manner to ensure that the buffer length always covers the queue length by the given safety margin ∆ . The FLC operation is based on the objective function: 2 } , 0 { ∆ . Our timing analysis shows that the FLC requires very short execution time for its control...

A Decision Table looking up algorithm for Fuzzy Logic Controller (FLC) with Genetic Algorithm (GA) optimization for Automatic Generation Control (AGC) system is developed. The single-input single-output (SISO) cascade loop structure of the AGC system is analyzed first. Then the interaction between the fast Area Control Error (ACE) loop and slow inner Boiler-Turbine-Generator (BTG) loop is decou...