Multistage Fuzzy Control Research Articles

Green microgrid’s LFC using recursive step-by-step optimized multi-stage fuzzy controller with separated inference systems

Amidst the growing integration of renewable energy sources (RES), the efficient administration of load-frequency control (LFC) in microgrids (MG) continues to be a significant challenge. In response to the complexities of modern MGs operations, this study introduces a novel two-stage fuzzy controller aimed at enhancing the system’s dynamic responses. The proposed controller consists of two levels, each of which contains a separate and autonomous fuzzy inference system (FIS). The proposed controller includes proportional (P) and derivative (D) control operators in the first level, and in the second level, the combination of proportional and integral (I) operators is used. The suggested fuzzy P-fuzzy D multiplied by 1+(fuzzy P - fuzzy I) which is named FPFD-(1+FPFI) controller parameters are tuned by solving an optimization problem to reduce energy wastage and prevent undesirable dynamic responses. The parameters of the controller and the membership functions (MF) at each level are both optimized. The optimization process utilizes an enhanced particle swarm optimization (PSO) algorithm. The decisive superiority of the FPFD-(1+FPFI) controller has been confirmed by evaluating its performance in an all-renewable MG compared to conventional controllers. Reduction of frequency deviation in the face of disturbances in the demand side or production of renewables, better performance in the presence of uncertainty in the parameters of the system model, better dynamic responses against nonlinear factors such as time delays, and also, robustness against cyberattacks are prominent features of the proposed FPFD-(1+FPFI) controller. In addition, the results of the studies show that the controller, with its fast and accurate performance, reduces the dependence on the energy storage systems to maintain the stability of the system by more than 40%. The efficiency of the proposed controller is also verified through laboratory scale evaluation.

Fuzzy logic-based energy management system for grid-connected residential DC microgrids with multi-stack fuel cell systems: A multi-objective approach

Hybrid energy storage systems (HESS) are considered for use in renewable residential DC microgrids. This architecture is shown as a technically feasible solution to deal with the stochasticity of renewable energy sources, however, the complexity of its design and management increases inexorably. To address this problem, this paper proposes a fuzzy logic-based energy management system (EMS) for use in grid-connected residential DC microgrids with HESS. It is a hydrogen-based HESS, composed of batteries and multi-stack fuel cell system. The proposed EMS is based on a multivariable and multistage fuzzy logic controller, specially designed to cope with a multi-objective problem whose solution increases the microgrid performance in terms of efficiency, operating costs, and lifespan of the HESS. The proposed EMS considers the power balance in the microgrid and its prediction, the performance and degradation of its subsystems, as well as the main electricity grid costs. This article assesses the performance of the developed EMS with respect to three reference EMSs present in the literature: the widely used dual-band hysteresis and two based on multi-objective model predictive control. Simulation results show an increase in the performance of the microgrid from a technical and economic point of view.

Developing a fuzzy-based decision-making procedure for traffic control in expressway congestion management

This paper presents part of a multi-stage fuzzy logic controller (MS-FLC) that is developed for traffic control in congestion management on expressways. The decision-making process of traffic control for expressway congestion management using the MS-FLC consists of three tasks: (1) evaluation of current traffic congestion; (2) prediction of traffic congestion tendency; and (3) recommendation of control strategies and control actions to alleviate the congestion. This paper presents the 3rd stage of the MS-FLC that develops a fuzzy-based decision-making procedure (FDMP) for management of recurring and non-recurring congestion. Using fuzzy rules, the FDMP evaluates the current and anticipated traffic data and incident information to recommend control strategies at the strategic level, and control actions at the operational level. Results from this research show that: (i) the FDMP offers a comprehensive procedure in deriving control strategies and actions; (ii) FDMP control actions are derived from a systematic decision-making logic where the design of control rules is consistently oriented toward achieving desirable control objectives; (iii) the FDMP targets a proper balance in congestion management between the mainline and the ramp using compromise rule design; (iv) the FDMP facilitates using various forms of available traffic and incident data on an extended expressway segment to derive at control actions, making the system-wide gains possible; and (v) the FDMP could be applied for management of both recurring and non-recurring congestion.

Multimachine power system stabilizer based on optimal multistage fuzzy PID attendant honey bee mating optimization

This article presents a new strategy based on multistage fuzzy PID controller for damping power system stabilizer in multimachine environment using Honey Bee Mating Optimization (HBMO). The proposed technique is a new metaheuristic algorithm which is inspired by mating procedure of the honey bee. Actually, the mentioned algorithm is used recently in power systems which demonstrate the good reflex of this algorithm. Also, finding the parameters of PID controller in power system has direct effect for damping oscillation. Hence, to reduce the design effort and find a better fuzzy system control, the parameters of proposed controller is obtained by HBMO that leads to design controller with simple structure that is easy to implement. The effectiveness of the proposed technique is applied to single machine connected to infinite bus and IEEE 3–9 bus power system. The proposed technique is compared with other techniques through integral of the time multiplied absolute value of the error and figure of demerit. © 2015 Wiley Periodicals, Inc. Complexity 21: 234–245, 2016

The Research of the Intelligent Given and Control for the Dissolved Oxygen in the Aeration Process of Sewage Treatment

Based on these characteristics of dissolved oxygen control in sewage treatment aeration process, such as nonlinearity, large time delay in a timely manner and difficult to model accurately, this paper puts forward an intelligent control strategy of dissolved oxygen. First, according to feed water quality parameters and environmental factors, we build a rule-based multistage fuzzy controller to make intelligent dynamic dissolved oxygen value setting. Then the feedforward tracking and constant value control are proceed, according to error source and threshold we use fuzzy tracking, fuzzy PID separation control and compound control of the major and minor loop cascade control to stabilize the given value of dissolved oxygen. The system is fast and steady which is proved by simulation and experiment results.

Driving/regeneration and stability enhancement of 4WD hybrid vehicles, using multi-stage fuzzy controller

In front wheels driven vehicles, fuel economy can be obtained by summing torques applied on rear wheels. On the other hand, unequal torques applied to rear wheels provides enhanced safety. In this paper, a model with seven degrees of freedom is considered for vehicle body. Thereafter, powertrain subsystems are modelled. Considering an electrical machine on each rear wheel, a fuzzy controller is designed for each driving, braking and stability conditions. Another fuzzy controller recognises the vehicle requirements between the driving/regeneration and stability modes. Simulations performed in MATLAB/Simulink environment show that the proposed structure can enhance the vehicle performance in different modes effectively.

Load frequency control using multi-stage fuzzy logic controller for wind-micro hydro-diesel hybrid power system

This paper presents an analysis of multi-stage fuzzy logic control (FLC) application for load frequency control (LFC) of an isolated wind-micro hydro-diesel hybrid power system. Due to the sudden load changes and intermittent wind power, large frequency fluctuation problems can occur. The LFC deviates the frequency deviation and maintains dynamic performance of the system. In this paper, a multi-stage fuzzy logic proportional integral derivative (PID) controller is proposed for LFC of an isolated wind-micro hydro-diesel hybrid power system. Simulations are performed for this hybrid system with the proposed multi-stage fuzzy logic PID controller, conventional PI controller and FLC with different load disturbances and wind input disturbances. The performance of the proposed approach is verified from simulations and comparisons. Simulation results explicitly show that the performance of the proposed multi-stage fuzzy logic PID controller is superior to the conventional PI controller and FLC in terms of overshoot, settling time and steady state error against various load changes and variations of wind inputs.

Multi-stage fuzzy load frequency control using PSO

In this paper, a particle swarm optimization (PSO) based multi-stage fuzzy (PSOMSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operate under deregulation based on the bilateral policy scheme. In this strategy the control is tuned on line from the knowledge base and fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by PSO algorithm, that has a strong ability to find the most optimistic results. The motivation for using the PSO technique is to reduce fuzzy system effort and take large parametric uncertainties into account. This newly developed control strategy combines the advantage of PSO and fuzzy system control techniques and leads to a flexible controller with simple stricture that is easy to implement. The proposed PSO based MSF (PSOMSF) controller is tested on a three-area restructured power system under different operating conditions and contract variations. The results of the proposed PSOMSF controller are compared with genetic algorithm based multi-stage fuzzy (GAMSF) control through some performance indices to illustrate its robust performance for a wide range of system parameters and load changes.

Robust modified GA based multi-stage fuzzy LFC

Abstract In this paper, a robust genetic algorithm (GA) based multi-stage fuzzy (MSF) controller is proposed for solution of the load frequency control (LFC) problem in a restructured power system that operates under deregulation based on the bilateral policy scheme. In this strategy, the control signal is tuned online from the knowledge base and the fuzzy inference, which request fewer sources and has two rule base sets. In the proposed method, for achieving the desired level of robust performance, exact tuning of the membership functions is very important. Thus, to reduce the design effort and find a better fuzzy system control, membership functions are designed automatically by modified genetic algorithms. The classical genetic algorithms are powerful search techniques to find the global optimal area. However, the global optimum value is not guaranteed using this method, and the speed of the algorithm’s convergence is extremely reduced too. To overcome this drawback, a modified genetic algorithm is being used to tune the membership functions of the proposed MSF controller. The effectiveness of the proposed method is demonstrated on a three area restructured power system with possible contracted scenarios under large load demand and area disturbances in comparison with the multi-stage fuzzy and classical fuzzy PID controllers through FD and ITAE performance indices. The results evaluation shows that the proposed control strategy achieves good robust performance for a wide range of system parameters and load changes in the presence of system nonlinearities and is superior to the other controllers. Moreover, this newly developed control strategy has a simple structure, does not require an accurate model of the plant and is fairly easy to implement, which can be useful for the real world complex power systems.

A systematic approach for designing multistage fuzzy control systems

This paper proposes a systematic approach for designing a multistage fuzzy logic controller (MFLC) for large scale nonlinear systems. In designing such a controller, the major tasks are to derive fuzzy rule bases, determine membership functions of input/output variables, and design input/output scaling factors. In this work, the fuzzy rule bases are generated by rule-generated functions which are based on the negative gradient of a system performance index. The membership functions of isosceles triangle of input/output variables are fixed in the same cardinality, and only the input/output scaling factors are optimized from a genetic algorithm based on a fitness function. As a result, the search space of the parameters is narrowed down to a small space so that the MFLC can be quickly constructed and the fuzzy rules and scaling factors can easily be determined. The performance of the proposed approach is examined by computer simulations on an inverted pendulum system. The performance of single stage structure, binary tree structure and skew-binary tree structure are compared. The binary tree structure has better performance and use fewer fuzzy rules in the illustrative example.

Fuzzy State Controller for the Soft Process Drying of Natural Material Foil

AbstractThis contribution describes the development of a fuzzy‐based process control system for the soft process drying of natural material foils. The system should maintain constant product moisture to facilitate automatic treatment of the foils in subsequent processing steps. A pilot plant was constructed to support the development of the control system. The core of the experimental setup is an optical measuring device, that provides for the simultaneous in‐line determination of drying foil water content and thickness at different places in the drying plant. The sensor possesses a measuring range of 10 %–80 % for water content (X*) and 50 μm–500 μm for thickness. Compared to a reference method, the system shows an accuracy of ± 3 % for the water concentration and 3.5 % for the foil thickness. No significant drift in the output signal could be detected for 5 weeks. These quantities, together with other process information, were used as inputs for a multistage fuzzy control system, which adjusts the outputs temperature and velocity of drying air and belt velocity for optimal soft process drying. In normal operation without the controller, foil moisture variations of 7 % over a period of 2 hours were obtained, while applying the fuzzy logic controller decreased the variations to 4 %. After disturbances of up to 17.5 % in initial viscosity, 6.8 % in concentration and 20 % in foil thickness variations, outlet moisture content variations of only 6.2 % were attained.

Fuzzy learning and applications

From the Book: The resurgence of interest in fuzzy logic over the past few decades has opened many new avenues in its applications. The most notable examples include autofocus cameras, automobile transmissions, air conditioners, aerospace, subway systems, robots, business, and medicine. Fuzzy logic leads to greater generality and better rapport with reality. It is driven by the need for methods of analysis and design which can come to grips with the pervasive imprecision of the real world and exploit the tolerance for imprecision to achieve tractability, robustness, and low cost solution. It also provides effective capability of processing uncertainties to model human thinking. Artificial neural networks and genetic algorithms can be fused with fuzzy logic to provide the learning abilities in machines. This book presents a sample of research on fuzzy learning and their applications by notable researchers. It contains 11 chapters. The first chapter, by Russo, is on evolutionary fuzzy learning. It uses the fusion of neural networks and genetic algorithms for fuzzy supervised learning. It is demonstrated that this approach is superior for tasks such as classification, function approximation, and time series prediction. In chapter 2, Vidal-Verdu, Navas, Delgado-Restituto, and RodriguezVazquez present a stored-programmable mixed-signal fuzzy controller chip with supervised learning capabilities. The proposed architecture consists of a programmable analog core which implement the active rule, a RAM memory for storing global rule set, and a selection circuitry for the identification of subregions. The third chapter, by Botfa, Barbers, and Skasmeta, is on fuzzy modeling in a multi-agent framework for learning in autonomous systems. A number of learning techniques are proposed in the intelligent agents environment. The fourth chapter, by Masulli and Sperduti, is on learning techniques for supervised fuzzy classifiers. A family of learning machines for supervised classification is developed using the neuro-fuzzy systems. The fifth chapter, by Yeh and Chen, is on multistage fuzzy control. A general method to generate fuzzy rules for a multistage fuzzy controller from the performance index of the control system is presented. The simulation results using three-stage fuzzy inference showed that good performance is achieved using this approach. The sixth chapter, by Lotfi, is on learning fuzzy systems. This chapter presents a review of techniques available for updating/ learning the parameters of a fuzzy system. In the seventh chapter, Tachibana, Furuhashi, Shimoda, Kawakami, and Fukunaga present an application of fuzzy modeling to the analysis of rowing boat speed. The fuzzy modeling is applied to reveal the relationships between the supplied power and the boat speed. The eighth chapter, by Cavalieri and Russo, is on a novel fuzzy approach to Hopfield coefficients determination. It is shown that the proposed method features a search for the set of coefficients which guarantees valid solutions very close to the global minima of the energy function, or ones which guarantee robust solution. The ninth chapter, by Fortuna, Muscato, Caponetto, and Xibilia is on fuzzy control of a CD player focusing system. The fuzzy controller is a classical Sugeno type Fuzzy-FD. A practical implementation on a real system using a fuzzy microcontroller validates the superiority of this approach. The tenth chapter, by Ali, Ghafoor, and Lee, is on a neuro-fuzzy scheduler for a multimedia web server. The problem of multimedia synchronization in a web environment is considered. The results are validated and found satisfactory. The last chapter, by Leski and Henzel, is on a neuro-fuzzy system based on logical interpretation of if-then rules. The application of the proposed artificial neural network on standard pattern recognition problems is demonstrated. We believe that this book will be of great value to researchers, scientists and engineers alike. The research students will receive an in-depth tutorial on the topics covered. The seasoned researcher will appreciate the applications and the gold mine of other possibilities for novel research topics. We are grateful to the authors for their contributions. We also thank reviewers for their expertise and time, and CRC Press for their excellent editorial assistance. M. Russo, Italy L.C. Jain, Australia

Control sequence generation in multistage fuzzy control systems for design process

The purpose of this study is to investigate and optimize the dynamics of state transitions during a process of multistage fuzzy control systems. A variation of the methodology for solving the Vehicle Routing Problem, known as the Savings Method, is applied to finding a sequence of controls to maximize the overall performance of multistage control processes. The Savings Method has been modified into a heuristic to traverse state precedence when transitioning from one known system state to another. In addition, the procedure allows for imposing of two different types of precedences (mandatory and optional) among transition states, as well as a pair of fuzzy constraints and goals throughout each stage of the process. As the control of the system progresses, fuzzy goals may be preset or introduced interactively.

Involving objective and subjective aspects in multistage decision making and control under fuzziness: Dynamic programming and neural networks

We extend the basic Bellman and Zadeh's [Manage. Sci., 17, 141–164 (1970)] model of multistage decision making (control) in a fuzzy environment to include both objective and subjective evaluations of how well fuzzy constraints on decisions (controls) applied and fuzzy goals on states (outputs) attained are satisfied. We discuss the solution by an extended fuzzy dynamic programming model. We present Francelin and Gomide's [Proc. of Second IEEE International Conference on Fuzzy Systems—FUZZ-IEEE'93, San Francisco, CA, Vol. 1, 1993, pp. 655–660] [cf. also Francelin, Gomide, and Kacprzyk [Proc. of Sixth IFSA World Congress, Saõ Paolo, Brazil, Vol. II, 1995, pp. 221–224] and Kacprzyk, Romero, and Gomide [Neuro-Fuzzy Techniques for Information Processing, Physica-Verlag (Springer-Verlag), Heidelberg–New York, forthcoming]] neural network implementing fuzzy dynamic programming, and then show its extension to cover both the objective and subjective evaluations involved in the proposed extension of the basic Bellman and Zadeh's [Manage. Sci., 17, 141–164 (1970)] model. We show its use for solving a socioeconomic regional planning problem proposed in Kacprzyk and Straszak [Applied Systems and Cybernetics, Pergamon, New York, 1982, Vol. 6, pp. 2997–3004; Recent Developments in Fuzzy Sets and Possibility Theory, Pergamon, New York, 1982, pp. 531–541; IEEE Trans. Syst. Man, Cybern., SMC-14, 310–313 (1984)] [cf. Kacprzyk's [Multistage Fuzzy Control, Wiley, Chichester, 1997] book], extending Kacprzyk, Romero, and Gomide [Neuro-Fuzzy Techniques for Information Processing, Physica-Verlag (Springer-Verlag), Heidelberg–New York, forthcoming]. ©1999 John Wiley & Sons, Inc.

Multistage fuzzy control—A model-based approach to fuzzy control and decision making

Multistage fuzzy control—A model-based approach to fuzzy control and decision making

Multistage fuzzy control: a model-based approach to fuzzy control and decision making, Kacprzyk, Janusz, John Wiley & Sons Ltd., Chichester, 1997, 327p.

Multistage fuzzy control: a model-based approach to fuzzy control and decision making, Kacprzyk, Janusz, John Wiley & Sons Ltd., Chichester, 1997, 327p.

A novel approach for multistage inference fuzzy control

This paper presents a methodology to the design of a multistage inference fuzzy controller in which the consequence in an inference stage is passed to the next stage as fact, and so forth. A new general method which is based on a performance index of the control system is used to generate fuzzy rule bases for the multistage inference. This proposed method can reduce the design cycle time. In order to reduce the computation time, a method for precomputing the match-degrees of fuzzy values is adopted. Thus, the number of operations that must be carried out at execution time can be significantly reduced. The new method has been applied to two applications, a two-trailer-and-truck system and a three-trailer-and-truck system. The simulation studies showed that the proposed method is feasible.