Adaptive fuzzy control design for the molten steel level in a strip casting process

This paper studies the adaptive fuzzy control problem of the molten steel level for a class of twin roll strip casting systems. Based on fuzzy logic systems (FLSs) and the mean value theorem, a novel adaptive tracking controller with parameter updated laws is effectively designed. It is proved that all the closed-loop signals are uniformly bounded and the system tracking errors can asymptotically converge to zero by using the Lyapunov stability analysis. Simulation results of semi-experimental system dynamic model and parameters are provided to demonstrate the validity of the proposed adaptive fuzzy design approach

Intelligent STATCOM Voltage Regulation using Fuzzy Logic Control

Reactive power compensation is a very important and challenging task in electrical power systems today. Future trends foreseen in power systems such as high interconnectivity and the integration of renewable energy resources produce even more issues related to power system control and stability. Flexible AC transmission systems are vastly used in power systems in order to mitigate several performance aspects found in typical power systems. One shunt connected device in particular, STATCOM, is very powerful and commonly used in voltage regulation at the power transmission level. STATCOM uses voltage sourced converters to inject or absorb reactive power from the power grid as commanded to stabilize the transmission line voltage at the point of connection. The control of STATCOM has relied historically on using traditional PI controllers, however, since the dynamic response of STATCOM highly affects its ability to perform its task, improving the capabilities of STATCOM using more advanced control approaches has become vital for both manufacturers and power systems operators. Fuzzy logic control, as one area of artificial intelligence techniques, has been emerging in recent years as a complement to the conventional methods in various areas of power systems control. The most significant advantage of fuzzy controller as an intelligent controller is that it doesn’t require mathematical modelling. It is robust and nonlinear in its nature, and expert’s knowledge can be utilized in generating control rules. The main contribution is to use fuzzy logic control theory to design a pure fuzzy logic control and another fuzzy adaptive PI control strategies for STATCOM that are superior in performance to traditional PI control approach. This will increase STATCOM’s ability to seamlessly perform their task in voltage regulation. This work investigates the performance of classical PI controlled STATCOM then compares it with fuzzy logic based STATCOM and fuzzy adaptive PI controlled STATCOM. Simulations done using MATLAB on a three generator test system show that adaptive fuzzy PI control technique is faster in responding to voltage variations and better in tracking the reactive current reference. Results also show that a direct control using fuzzy logic provides even faster voltage regulation and acts almost as a perfect tracker for reference reactive current

A reconfigurable hybrid intelligent system for robot navigation

Soft computing has come of age to o er us a wide array of powerful and e cient algorithms that independently matured and in uenced our approach to solving problems in robotics, search and optimisation. The steady progress of technology, however, induced a ux of new real-world applications that demand for more robust and adaptive computational paradigms, tailored speci cally for the problem domain. This gave rise to hybrid intelligent systems, and to name a few of the successful ones, we have the integration of fuzzy logic, genetic algorithms and neural networks. As noted in the literature, they are signi cantly more powerful than individual algorithms, and therefore have been the subject of research activities in the past decades. There are problems, however, that have not succumbed to traditional hybridisation approaches, pushing the limits of current intelligent systems design, questioning their solutions of a guarantee of optimality, real-time execution and self-calibration. This work presents an improved hybrid solution to the problem of integrated dynamic target pursuit and obstacle avoidance, comprising of a cascade of fuzzy logic systems, genetic algorithm, the A* search algorithm and the Voronoi diagram generation algorithm

Prescribed Performance Fuzzy Adaptive Output-Feedback Control for Nonlinear Stochastic Systems

A prescribed performance fuzzy adaptive output-feedback control approach is proposed for a class of single-input and single-output nonlinear stochastic systems with unmeasured states. Fuzzy logic systems are used to identify the unknown nonlinear system, and a fuzzy state observer is designed for estimating the unmeasured states. Based on the backstepping recursive design technique and the predefined performance technique, a new fuzzy adaptive output-feedback control method is developed. It is shown that all the signals of the resulting closed-loop system are bounded in probability and the tracking error remains an adjustable neighborhood of the origin with the prescribed performance bounds. A simulation example is provided to show the effectiveness of the proposed approach

Design of Adaptive Sliding Mode Fuzzy Control for Robot Manipulator Based on Extended Kalman Filter

In this work, a new adaptive motion control scheme for robust performance control of robot manipulators is presented. The proposed scheme is designed by combining the fuzzy logic control with the sliding mode control based on extended Kalman filter. Fuzzy logic controllers have been used successfully in many applications and were shown to be superior to the classical controllers for some nonlinear systems. Sliding mode control is a powerful approach for controlling nonlinear and uncertain systems. It is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances, provided that the bounds of these uncertainties and disturbances are known. We have designed a new adaptive Sliding Mode Fuzzy Control (SMFC) method that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full nonlinear robot model as well as the joint positions and velocities. These estimates are used by the SMFC to generate the input torques. The combination of the EKF and the SMFC is shown to result in a stable adaptive control scheme called trajectory-tracking adaptive robot with extended Kalman (TAREK) method. The theory behind TAREK method provides clear guidelines on the selection of the design parameters for the controller. The proposed controller is applied to a two-link robot manipulator. Computer simulations show the robust performance of the proposed scheme

RANCANG BANGUN PENGENDALI KECEPATAN MOTOR DC PADA KONVEYOR BERBASIS PENERAPAN ADAPTIF LOGIKA FUZZY

ABSTRAK Kebutuhan sistem kendali yang efisien akan mampu meningkatkan kualitas dari hasil industri. Sistem kendali cerdas dapat diaplikasikan untuk mengatur kecepatan kedalam kontrol motor DC. Sistem cerdas yang banyak digunakan adalah menggunakan logika fuzzy. Namun dengan menggunakan pendekatan adaptif logika fuzzy dapat memberikan alternatif lain dalam sistem kendali cerdas. Pada penelitian ini membahas tentang sistem pengendalian kecepatan motor DC pada konveyor dengan menggunakan adaptif logika fuzzy. Dalam sistem ini menggunakan modulasi lebar pulsa atau PWM sebagai output keputusan dari sistem pengaturan kecepatan motor.Variabel input yang digunakan yaitu jumlah benda yang melewati konveyor dan kecepatan yang dibaca oleh sensor kecepatan, kemudian outputnya berupa besaran perintah PWM kepada motor DC untuk mencapai kecepatan sesuai dengan perancangan pada software Matlab. Hasil dari temuan pengujian pada penelitian ini adalah sistem kendali dengan menggunakan pendekatan adaptif logika fuzzy dapat bekerja untuk menstabilkan putaran motor yang menggerakkan konveyor pada keadaan pengaruh beban benda yang berbeda.; The need for an efficient control system will be able to improve the quality of industrial output. Intelligent control system can be applied to regulate speed into the DC motor control. A smart system that is widely used is using fuzzy logic. However, using an adaptive approach to fuzzy logic can provide other alternatives in intelligent control systems. In this study discusses the DC motor speed control system on conveyors using adaptive fuzzy logic. In this system uses pulse width modulation or PWM as the decision output of the motor speed regulation system. Input variables used are the number of objects passing through the conveyor and the speed read by the speed sensor, then the output is in the form of PWM commands to the DC motor to reach the speed in accordance with design of Matlab software. The results of the test findings in this study are the control system using an adaptive approach to fuzzy logic can work to stabilize the rotation of the motor that drives the conveyor under different load conditions

An Interval Type-II Robust Fuzzy Logic Controller for a Static Compensator in a Multimachine Power System

This paper presents a novel fuzzy logic based controller for a Static Compensator (STATCOM) connected to a power system. Type-II fuzzy systems are selected that enable the controller to deal with design uncertainties and the noise associated with the measurements in the power system. Interval type-II fuzzy is computationally more effective than the ordinary type-II fuzzy systems and is more suitable for the power network with fast changing dynamics. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system in order to provide extra voltage support and improve the system dynamic performance. Simulation results are provided to show that the proposed controller outperforms a conventional PI controller during large scale faults as well as small disturbances. The type-II fuzzy membership functions provide a robust performance for the controller and eliminate the need for a model based adaptive control scheme

Control of nonlinear systems using Sugeno fuzzy approximators

This thesis deals with the issue of controlling nonlinear systems by integrating available classical as well as modern tools such as fuzzy logic and neural networks. The proposed approaches throughout this thesis are based on the well known first-order Sugeno fuzzy system. To achieve a better understanding of the approximation and interpolation capabilities of Sugeno fuzzy system, the influence of the fuzzy set parameters and the reasoning method on the interpolation function of the fuzzy system is investigated. Control of nonlinear system based on known dynamic is considered first. A fuzzy gain scheduling approach is developed. The proposed approach is based on quasi-linear dynamic models of the plant. Classical optimal controllers for each set of operating conditions were developed. These controllers are used to construct a single fuzzy-logic gain scheduling-like controller. Adaptive-neuro-fuzzy inference system was used to construct the rules for the fuzzy gain schedule. This will guarantee the continuous change in the gains as the system parameters change in time or space. This procedure is systematic and can be used to design controllers for many nonlinear systems. Also, a modeling approach of some known types of static nonlinearities is proposed. Control of nonlinear system with unknown dynamic is also considered. An adaptive feedback control scheme for the tracking of a class of continuous-time plants is presented. A parameterized Sugeno fuzzy approximator is used to adaptively compensate for the plant nonlinearities. All parameters in the fuzzy approximator are tuned using a Luapunov-based design. In the fuzzy approximator, a first-order Sugeno consequent is used in the IF-THEN rules of the fuzzy system, which has a better approximation capability compared with that of a constant consequent. Global boundedness of the adaptive system is established. Finally, simulation and experimentation are used to demonstrate the effectiveness of the proposed controllers

Adaptive Critic Design Based Neuro-Fuzzy Controller for a Static Compensator in a Multimachine Power System

This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming, a member of the adaptive Critic designs family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system and a 45-bus network (a section of the Brazilian power system). Simulation results are provided to show that the proposed controller outperforms a conventional PI controller in large scale faults as well as small disturbance

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany