A self-tuning robust fuzzy controller

We apply the fuzzy control method to the temperature control system with moisture analyzer because of its uncertainty and variation in the parameters and nonlinear lag. Fuzzy control has fast response speed, short regulation time and strong robustness. But it has a steady-state error mainly because the fuzzy control rules are based on the designers' experience in the operation process and fuzzy information. The non-linearity and time-variation of the controlled process result in imperfectness of the fuzzy control rules and these will affect the result of the control from varying degrees. To make up the imperfectness, we designed the self-adjusting parameter fuzzy controller composed of basic fuzzy controller and self-adjusting parameter models. The task of self-adjusting parameter model is to regulate the quantification factor and scale factor on-line and optimize the basic fuzzy controller in order to largely perfect the response performance of the system. We have successfully controlled the temperature control system with infrared moisture analyzer with controller using this algorithm.

This paper deals with the serious problems of a ventilation system in a large road tunnel. Higher visibility and lower concentration of carbon monoxide are the key issues concerning the ventilation system. Prior to designing the fuzzy control model, a configuration layout of the ventilation system including sensing, control and traffic prediction as well is conceptually constructed. Based on the layout that offers assignments of sensors and control elements, a fuzzy logic control model is developed. Membership functions of sensor errors and control increments are physically submitted in order to set up the fuzzy logic rules. Timing and spacing filtering in terms of weighting approaches is employed in the fuzzy logic rules. A dynamic equation describing the concentration of air pollution is also given so as to cooperate with the fuzzy logic rules and to play roles in the computer simulation. The result of computer simulation reveals that the structure can be an observer to work off line with real road tunnels.

In this paper mathematical models of Mamdani type fuzzy PI or PD controllers via Center of Gravity (CoG) defuzzification are obtained. These controllers are called the simplest fuzzy PI or PD controllers as minimal number of fuzzy sets (two fuzzy sets on each of the two-input variables and three fuzzy sets on the output variable) are employed. The basic components of these models are L-type and Y-type membership functions on each input variable, trapezoidal or triangular membership functions on output variable, minimum / algebraic product t-norm, maximum OR operator, three linear fuzzy control rules, minimum / algebraic product inference and CoG defuzzification. Mathematical models of fuzzy controller via CoG defuzzification were not reported in the literature and are therefore presented for the first time in this paper.

Over the past two decades, there has been a rapidly growing interest in approximating a nonlinear system by a Takagi-Sugeno (T-S) fuzzy model (Takagi & Sugeno, 1985). In general, this model is represented by using a set of fuzzy rules to describe a global nonlinear system in terms of a set of local linear models which are smoothly connected by fuzzy membership functions. Based on the T-S fuzzy model, recently, various fuzzy controllers have been developed under the so-called parallel-distributed compensation (PDC) scheme (in which each control rule is distributively designed for the corresponding rule of a T-S fuzzy model) and have been widely and successfully applied in fields ranging from aerospace to process control. The reason is because the fuzzy model-based control method provides a natural, simple and effective design approach to complement other nonlinear control techniques that require special and rather involved knowledge. Below are listed the main features of T-S model-based fuzzy control method: 1. It does not require severe structural assumptions on the plant model. 2. It preserves well-understood linear intuition. 3. It is naturally compatible with decompositions of the overall control problem. The decompositions are typically not hierarchical, and the interactions of sub-problems are captured by physical variables that are typically state variables in a more complete model of the overall system. 4. It enables control systems to respond rapidly to changing operating conditions. For this reason, it is important that the selected physical variables reflect changes in plant dynamics as operating conditions change. In fact, the T-S model-based fuzzy control method (of divide and conquer type) constructs a nonlinear controller, with certain required dynamic properties, by combining, in some sense, the members of appropriate family of linear time-invariant controllers. Here, nonlinear control design task is broken into a number of linear sub-problems, which enable linear design methods to be applied to nonlinear problems. Within the general framework of the T-S fuzzy model-based control method, a flurry of research activities have quickly yielded many important results on the design of fuzzy control systems by means of the following Lyapunov function approaches: 1. Common quadratic Lyapunov function approach (Tanaka & Sugeno, 1992; Tanaka et al, 1996; Wang et al, 1996; Cao & Frank, 2000; Assawinchaichote, 2004).

An energy-saving fuzzy control system for highway tunnel lighting

This book presents a systematic framework targeting at fuzzy modeling and fuzzy control of nonlinear systems with uncertainties. The book is organized into three major parts incorporating 13 chapters. The first part contains four chapters focusing on the modeling of nonlinear dynamical systems by using fuzzy logic. The second part includes five chapters in which fuzzy inference and control techniques are addressed. The final part consists of four chapters, which covers several advanced topics in fuzzy control ranging from controller and filter design to chaotification of fuzzy systems and feedforward fuzzy control of nonlinear systems via Fourier integrals. One of the major contributions of this book is that it presents the latest original research developments in the field. It provides rich examples and references, too. It is a valuable resource for those researchers and practitioners interested in expanding their knowledge from fuzzy logic and applications to nonlinear dynamical systems.

This paper presents the stability analysis of fuzzy-model-based control systems. Stepwise membership functions are introduced to facilitate the stability analysis. Through the stepwise membership functions approximating those of the fuzzy model and fuzzy controller, the information of the membership functions can be brought into the stability analysis. Based on the Lyapunov stability theory, stability conditions in terms of linear matrix inequalities are derived in a simple and easy-to-understand manner to guarantee the system stability. The proposed stability analysis approach offers a nice property to include the membership functions of both fuzzy model and fuzzy controller in the LMI-based stability conditions for a dedicated fuzzy-model-based control system. Furthermore, the proposed stability analysis approach can be applied to the fuzzy-model-based control systems of which the membership functions of both fuzzy model and fuzzy controller are not necessarily the same. Greater design flexibility is allowed by choosing the membership functions during the design of fuzzy controllers. By employing membership functions with simple structure, it is possible to lower the structural complexity and the implementation cost. Simulation example is given to illustrate the merits of the proposed approach.

Type-2 fuzzy model based controller design for neutralization processes

The traditional PID control method has the disadvantages of slow response speed, low precision and poor anti-interference ability when controlling the brushless DC motor system. In view of these shortcomings, a fuzzy PID control based brushless DC motor control system was proposed. The principle and characteristics of fuzzy control were introduced; The control principle of PID control based on fuzzy rules was analyzed; The fuzzy subsets were established and fuzzy control rules were established; The simulation of brushless DC motor control system was simulated by Matlab/Simulink. The results showed that the PID control algorithm based on fuzzy logic control improved the dynamic performance and stronger adaptive ability of the DC brushless motor.

Interpretability improvements to find the balance interpretability-accuracy in fuzzy modeling: an overview.- Regaining comprehensibility of approximative fuzzy models via the use of linguistic hedges.- Identifying flexible structured premises for mining concise fuzzy knowledge.- A multiobjective genetic learning process for joint feature selection and granularity and contexts learning in fuzzy rule-based classification systems.- Extracting linguistic fuzzy models from numerical data-AFRELI algorithm.- Constrained optimization of fuzzy decision trees.- A new method for inducing a set of interpretable fuzzy partitions and fuzzy inference systems from data.- A Feature Ranking Algorithm for Fuzzy Modelling Problems.- Interpretability in multidimensional classification.- Interpretable semi-mechanistic fuzzy models by clustering, OLS and FIS model reduction.- Trade-off between approximation accuracy and complexity: TS controller design via HOSVD based complexity minimization.- Simplification and reduction of fuzzy rules.- Effect of rule representation in rule base reduction.- Singular value-based fuzzy reduction with relaxed normalization condition.- Interpretability, complexity, and modular structure of fuzzy systems.- Hierarchical genetic fuzzy systems: accuracy, interpretability and design autonomy.- About the trade-off between accuracy and interpretability of Takagi-Sugeno models in the context of nonlinear time series forecasting.- Accurate, transparent and compact fuzzy models by multi-objective evolutionary algorithms.- Transparent fuzzy systems in modeling and control.- Uniform fuzzy partitions with cardinal splines and wavelets: getting interpretable linguistic fuzzy models.- Relating the theory of partitions in MV-logic to the design of interpretable fuzzy systems.- A formal model of interpretability of linguistic variables.- Expressing relevance and interpretability of rule-based systems.- Conciseness of fuzzy models.- Exact trade-off between approximation accuracy and interpretability: solving the saturation problem for certain FRBSs.- Interpretability improvement of RBF-based neurofuzzy systems using regularized learning.- Extracting fuzzy classification rules from fuzzy clusters on the basis of separating hyperplanes.

This paper is concerned with an event-triggered communication scheme for a class of networked Takagi-Sugeno (T-S) fuzzy systems with time delay. By introducing the free weighting matrices to deal with the integral items and converting the coupling time-varying matrix inequalities into a group of decoupling matrix inequalities, an innovative delay-dependent stabilization criterion is first presented. The event triggered conditions are mentioned and checked at the discrete sampling instant. Secondly, a networked T-S fuzzy model is delicately constructed, which not only considers time delay in the networked T-S fuzzy model and the parallel distributed compensation (PDC) fuzzy control rules but includes the aforementioned state error as well. Based on the model, some novel criteria for the asymptotic stability analysis and control synthesis of event-triggered networked T-S fuzzy systems with time delay are established in terms of linear matrix inequalities (LMIs). Finally, a numerical example is given to show the effectiveness of the proposed method.

The article is devoted to the process of adaptation of the control system of a mobile robot for its movement along a line. To this end, the article proposes a fuzzy logic and structural model for robot control, combining two methods of adaptation. The first method consists in variation of the parameters of the triangular membership functions and structure of the fuzzy rules, which are used to describe the input and output variables of a fuzzy robot control system. In order to improve the reliability of the solutions proposed in the structure of the fuzzy inference, hard and soft arithmetic operations are used. Evaluation of the system is based on calculation of a standard deviation (RMSE - root mean square error). The best solution for the fuzzy system is the option, in which the RMSE is minimal. The second method envisages a change in the structure of the control code of a mobile robot. The results of the experimental studies presented in the article show that without the use of the methods of the structural and parametric adaptation the accuracy of the soft fuzzy model is 2,7 times higher than the accuracy of a rigid fuzzy model, which has a dead zone, reducing its stability. However, a combination of the above methods makes the accuracy of the rigid model higher than that of the soft model. At that, a deadband is ruled out and computational complexity is reduced. In order to explain the proposed methods of adaptation the article presents the results of the experimental studies evaluating the movement of a mobile robot along a line on an oval track.

Oxygen excess ratio control is closely related to the performance and safety of a Proton Exchange Membrane fuel cell system. Some control strategies should be used to regulate the oxygen excess ratio at the suitable value in order to avoid stack starvation and damage. And in this paper, a simple fuzzy double model control has been proposed to adjust the oxygen excess ratio under variation load currents. The double model controller combines a PID controller and a fuzzy logic controller which can be switched based on the fuzzy inference rules during the regulation process. The simulation results demonstrate that the fuzzy double model control can adjust the oxygen excess ratio at the setting point when the current is changed, and improve the dynamic performance of oxygen excess ratio than fuzzy PID control.KeywordsPEM fuel celloxygen excess ratiofuzzy controlPID controlsoft switching

In this paper, a robust fuzzy digital PID control strategy, via multiobjective genetic algorithm, based on the gain and phase margins specifications, with applications to uncertain dynamic systems with time delay, is proposed. A mathematical formulation based on the gain and phase margins, the fuzzy model and PID digital controller structures and the time delay of the uncertain dinamic system, is deduced. A multiobjective genetic strategy is defined to tune the fuzzy controller parameters so the gain and phase margins of the fuzzy control system are close to the specified ones. Computational results show the efficiency of the proposed methodology through the accuracy in the gain and phase margins of the PID control system compared to the specified ones and tracking of the reference trajectory.

Wind energy is becoming one of the most important and promising areas of renewable energy. During the past few years, wind energy generation underwent strong improvements in several fields including power electronics, mechanics, wind dynamics, etc. However, there is a high need to develop more intelligent control mechanisms that can handle the various sources of uncertainties encountered in wind turbines and allow maximum power to be obtained from wind. The recent years have witnessed the use of type-2 fuzzy logic systems to generate controllers which are able to provide robust control performances in the face of high levels of uncertainty. This paper presents a method to generate a type-2 fuzzy logic model entirely from data to provide a dynamic footprint of uncertainty for the generated fuzzy set. The fuzzy model will be used to predict the wind speed experienced by a wind turbine without the use of sensors. This estimated wind speed is then passed for another fuzzy controller that changes the pitch angles of the wind turbine blades in order to track the maximum power available.