Type-2 Fuzzy Logic Control System Research Articles

Interval type-2 fuzzy sliding mode control for a cable-driven parallel robot with elastic cables using metaheuristic optimization methods

In this paper, a supervisory interval type-2 fuzzy adaptive sliding mode control scheme is addressed for cable robots. In the proposed control scheme, intelligent methods are combined with conventional sliding mode control to achieve optimal adjustment of control parameters. This approach ensures accurate tracking performance, despite the structural constraints of cable robots. These constraints include the production of purely tensile forces and the effects due to inherent elasticity, which will make the design of the controller challenging. For this purpose, the internal force concept is utilized to ensure the tension style of cables. Additionally, considering a compensator part in the proposed controller and applying the singular perturbation theorem, the vibration effects of elastic cables are handled, and stability is proved through the second Lyapunov method. Therefore, the desired performance of the control system will be guaranteed for the movements that stimulate the vibration modes of cables. An interval type-2 fuzzy logic controller is proposed to adjust the control gain, effectively reducing the chattering level. Moreover, a supervisory interval type-2 fuzzy logic control system is introduced to regulate the gains within the sliding surface. The Grasshopper Optimization Algorithm is employed to select the optimal parameters for the membership functions of the fuzzy system. The results of the conducted simulations show that the desired tracking performance is provided, despite different uncertain parameters and the structural constraints of the considered cable robot.

Design of Bacterial Foraging Interval Fuzzy Logic Controller on Hybrid Solar Tracker-Ocean Wave Energy Converter

The location of Indonesia which, is crossed by the equator, makes it rich in sunlight. Photovoltaic (PV) can convert solar energy into electrical energy. The Solar Tracker system can maximize the absorption of solar energy that enters the PV. The condition of the Indonesian archipelago also has the potential for ocean wave energy. The Hydrostatic transmission-based Wave Energy Converter system can convert ocean wave energy into electrical energy. This research combines two energies, namely solar energy and ocean waves. The importance of combining these two renewable energies is due to the non-continuous nature of solar energy, therefore combined with the ocean wave energy to maximize the energy produced. A type-2 fuzzy logic control system based on Bacterial Foraging Optimization (BFO) is applied to each converter, summing each converter that has been optimizing. Optimize error and delta error on the solar tracker system and fuzzy logic waveform-based sea-wave type-2 system. The fuzzy boundaries are then optimized using the BFO optimization method. Fuzzy type-2 based on BFO in solar tracking system can increase energy by 67.9% with the best performance at FOU ±0.1. BFO-based type-2 fuzzy control can stabilize the output of the ocean wave conversion system and produce average energy of 34.48 Wh. This research can increase the energy in the system after being optimized using BFO by 19.3%.

A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation

In this paper a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The proposed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller (GT2-FLC) and compensator controller. In this structure, the main controller is the GT2-FLC which is optimized via the Biogeography-based Optimization (BBO) algorithm such that to minimize a cost function in a fixed prediction horizon. The compensator controller is designed to guarantee the closed-loop asymptotic stability. The performance of proposed control strategy is examined on the modified Bergman’s model of some patients with time-varying parameters, external noise perturbation and meal disturbances. The effectiveness of the proposed control scheme is verified and is compared with the other T2 fuzzy and well-known model predictive controllers. The results of the paper clearly show the superiority of the proposed T2 fuzzy logic control system.

Lyapunov approach based design of a gain adaptive interval type-2 fuzzy controller for servo systems

This paper focuses on the development of a stable Mamdani type-2 fuzzy logic based controller for automatic control of servo systems. The stability analysis of the fuzzy controller has been done by employing the concept of Lyapunov. The Lyapunov approach results in the derivation of an original stability analysis that can be used for designing the rule base of our proposed online gain adaptive Interval Type-2 Fuzzy Proportional Derivative controller (IT2-GFPD) for servo systems with assured stability. In this approach a quadratic positive definite Lyapunov function is used and sufficient stability conditions are satisfied by the adaptive type-2 fuzzy logic control system. Illustrative simulation studies with linear and nonlinear models as well as experimental results on a real-time servo system validate the stability and robustness of the developed intelligent IT2-GFPD. A comparative performance study of IT2-GFPD with other controllers in presence of noise and disturbance also proves the superiority of the proposed controller.

Analysis and Control of Fault Ride-Through Capability Improvement for Wind Turbine Based on a Permanent Magnet Synchronous Generator Using an Interval Type-2 Fuzzy Logic System

Recently, wind energy conversion systems in renewable energy sources have attracted attention due to their effective application. Wind turbine systems have a complex structure; however, traditional control systems are inadequate in answering the demands of complex systems. Therefore, expert control systems are applied to wind turbines, such as type-1 and interval type-2 fuzzy logic control (IT-2 FLC) systems. An IT-2 FLC system is used to solve the complexity of the wind turbine system and increases the efficiency of the wind turbine. This paper proposes a new control approach using the IT-2 FLC method applied to a wind turbine based on a permanent magnet synchronous generator (PMSG) to improve the transient stability during grid faults. An IT-2 FLC was designed to enhance the fault ride-through performance of a wind turbine and was implemented to control the machine side converter and grid side converter of a wind turbine. The proposed algorithm performance of a wind turbine based on a PMSG was investigated for different types of grid fault. The analysis results verify that the interval type-2 fuzzy logic control system is robustly utilized under different operational conditions.

Type-2 Fuzzy Logic Control [Book Review

This book presents the first unified and thorough discussion of Type-2 FLCs and Type-2 fuzzy logic control systems, providing the necessary tools for the design of the control of complex nonlinear systems. The analysis includes 1) the mathematical structure of some Type-2 FLCs, 2) stability of Type-2 fuzzy logic control systems, and 3) robustness of the Type-2 fuzzy logic control systems. This book, the first one entirely on Type-2 FLCs, shows the reader how to design Type-2 fuzzy logic control systems based on a variety of choices for the Type-2 FLC components and also demonstrates how to apply Type-2 fuzzy logic control theory to applications. It is written by five of the leading experts on Type-2 fuzzy sets, systems, and control. This book consists of seven chapters. This book has been written with an educational focus rather than a pure research focus. Each chapter includes worked examples, and most refer to computer programs accessible through the book’s Web site, outlining how to use them at a high level. Therefore, this book is appropriate for classroom use in graduate courses in electrical engineering, computer engineering, and computer science. This book also is a self-contained reference suitable for engineers, researchers, and graduate students who want to gain deep insights about Type-2 FLCs.

Nonlinear fuzzy control of a fed-batch reactor for penicillin production

The process of penicillin production is characterized by nonlinearities and parameter uncertainties that make it difficult to control. In the paper the development and testing of a multivariable fuzzy control system that makes use of type-2 fuzzy sets for the control of pH and temperature are described. The performance of the type-2 fuzzy logic control system (T2FLCS) is compared by simulation with that of a type-1 fuzzy logic control system (T1FLCS) and that of a control system with traditional proportional-integral-derivative (PID) controllers proposed in the literature. The fuzzy controllers are optimized using an ANFIS algorithm. The best results are obtained with the T2FLCS particularly when uncertainties are present in the control system.

Systematic design of a stable type-2 fuzzy logic controller

Stability is one of the more important aspects in the traditional knowledge of automatic control. Type-2 fuzzy logic is an emerging and promising area for achieving intelligent control (in this case, fuzzy control). In this work we use the fuzzy Lyapunov synthesis as proposed by Margaliot and Langholz [M. Margaliot, G. Langholz, New Approaches to Fuzzy Modeling and Control: Design and Analysis, World Scientific, Singapore, 2000] to build a Lyapunov stable type-1 fuzzy logic control system, and then we make an extension from a type-1 to a type-2 fuzzy logic control system, ensuring the stability on the control system and proving the robustness of the corresponding fuzzy controller.