Design and implementation of the tree-based fuzzy logic controller

In this paper, a tree-based approach is proposed to design the fuzzy logic controller. Based on the proposed methodology, the fuzzy logic controller has the following merits: the fuzzy control rule can be extracted automatically from the input-output data of the system and the extraction process can be done in one-pass; owing to the fuzzy tree inference structure, the search spaces of the fuzzy inference process are largely reduced; the operation of the inference process can be simplified as a one-dimensional matrix operation because of the fuzzy tree approach; and the controller has regular and modular properties, so it is easy to be implemented by hardware. Furthermore, the proposed fuzzy tree approach has been applied to design the color reproduction system for verifying the proposed methodology. The color reproduction system is mainly used to obtain a color image through the printer that is identical to the original one. In addition to the software simulation, an FPGA is used to implement the prototype hardware system for real-time application. Experimental results show that the effect of color correction is quite good and that the prototype hardware system can operate correctly under the condition of 30 MHz clock rate.     

Circuit implementation of linguistic-hedge fuzzy logic controller in current-mode approach

In this paper, a novel fuzzy logic controller called linguistic-hedge fuzzy logic controller in a mixed-signal circuit design is discussed. The linguistic-hedge fuzzy logic controller has the following advantages: 1) it needs only three simple-shape membership functions for characterizing each variable prior to the linguistic-hedge modifications; 2) it is sufficient to adopt nine rules for inference; 3) the rules are developed intuitively without heavy dependence on the endeavors of experts; 4) it performs better than conventional fuzzy logic controllers; and 5) it can be realized with a lower design complexity and a smaller hardware overhead as compared with the controllers that required more than nine rules. In this implementation, a current-mode approach is adopted in designing the signal processing portions to simplify the circuit complexity; digital circuits are adopted to implement the programmable units. This design was fabricated with a TSMC 0.35 /spl mu/m single-polysilicon-quadruple-metal CMOS process. In this chip, the LHFLC processes two input variables and one output variable. Each variable is specified using three membership functions. Nine inference rules, scheduled in a rule table with a dimension of 3 /spl times/ 3, define the relationship implications between these three variables. Under a supply voltage of 3.3 V, the measurement results show that the measured control surface and the control goal are consistent. The speed of inference operation goes up to 0.5M FLIPS that is fast enough for the control application of the cart-pole balance system. The cart-pole balance system experimental results show that this chip works with nine inference rules. Furthermore, by performing some off-chip modifications, such as shifting and scaling on the input signals and output signal of this design, according to the specifications defined by the controlled plants, this design is suitable for many control applications.     

Vague controller: A generalization of fuzzy logic controller

In this research, a vague controller (VC) is synthesized by using the notion of vague sets, which are a generalization of fuzzy sets and characterized by a truth-membership function and a falsity-membership function. The vague sets follow the basic set operations and logic operations defined for fuzzy sets, and are superior to fuzzy sets in that they could deal with the uncertainty encountered in real-world applications in a more natural way. Depending on the vague sets, the VC is developed as a generalization of fuzzy logic controller (FLC). The design procedures of the VC, which allow an arbitrary number of input variables, and each variable could have a distinct number of linguistic values, are outlined in this paper. In order to compensate the effort in constructing two series of membership functions for vague sets and to ease the difficulties in designing VCs, a new means of designating membership functions for VCs is also presented in this article. This method constructs a set of membership functions systematically by using only two parameters: number of linguistic values of a linguistic variable and shrinking factor. The membership functions generated by this method, shrinking-span membership functions (SSMFs), have different spans over the universe of discourse and, therefore, are more rational and more practical from the human expert's point of view.     

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.     

A PWM fuzzy logic controller

Researchers usually implement fuzzy inference systems in software on digital computers or microprocessors. This approach copes with most problems, however real-time systems often require very short time responses. In this case, a hardware implementation becomes the only solution. This 1.5-μm CMOS implementation uses a current mode circuit to generate input membership functions and processes inferences using pulse width modulation     

A fuzzy logic based multi-agents controller

This paper presents a fuzzy logic based controller (Multi-Agents System Controller (MASC)) which regulates the number of agents released to the network on a Multi-Agents Systems (MASs). A fuzzy logic (FL) model for the controller is as presented. The controller is a two-inputs-one-output system. The controllability is based on the network size (NTZ) and the available bandwidth (ABD) which are the inputs to the controller, the controller’s output is number of agents (ANG). The model was simulated using SIMULINK software. The simulation result is presented and it shows that ABD is the major constraint for the number of agents released to the network.     

Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle

Helicopters have generated considerable interest in both the control community due to their complex dynamics, and in military community because of their advantages over regular aerial vehicles. In this paper, we present the modeling and control of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as quadrotor aircraft. This model has been generated using Newton-Euler equations. In order to control the helicopter, classical PD (proportional derivative) and Hybrid Fuzzy PD controllers have been designed. Although fuzzy control of various dynamical systems has been presented in literature, application of this technology to quadrotor helicopter control is quite new. A quadrotor helicopter has nonlinear characteristics where classical control methods are not adequate especially when there are time delays, disturbances and nonlinear vehicle dynamics. On the other hand, Fuzzy control is nonlinear and it is thus suitable for nonlinear system control. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations. For the evaluation of the autonomous flight controllers, some experiments were also performed. For this purpose, an experimental test stand has been designed and manufactured. This study showed that although, both of the classical PD and the Fuzzy PD controllers can control the system properly, the Fuzzy PD controllers performed slightly better than the classical PD controllers, and have benefits such as better disturbance rejection, ease of building the controllers.     

Simplifying the automatic design of a fuzzy logic controller using evolutionary programming

With the availability of a wide range of Evolutionary Algorithms such as Genetic Algorithms, Evolutionary Programming, Evolutionary Strategies and Differential Evolution, every conceivable aspect of the design of a fuzzy logic controller has been optimized and automated. Although there is no doubt that these automated techniques can produce an optimal fuzzy logic controller, the structure of such a controller is often obscure and in many cases these optimizations are simply not needed. We believe that the automatic design of a fuzzy logic controller can be simplified by using a generic rule base such as the MacVicar-Whelan rule base and using an evolutionary algorithm to optimize only the membership functions of the fuzzy sets. Furthermore, by restricting the overlapping of fuzzy sets, using triangular membership functions and singletons, and reducing the number of parameters to represent the membership functions, the design can be further simplified. This paper describes this method of simplifying the design and some experiments performed to ascertain its validity.     

Mixed analog-digital fuzzy logic controller withcontinuous-amplitude fuzzy inferences and defuzzification

A fuzzy logic controller has been realized using mixed analog-digital CMOS very large scale integration (VLSI) circuits for application in cases where the input and output variables are in analog form. It employs a new architecture where time sweeping of variables allows continuous-amplitude evaluation of fuzzy inferences and defuzzification during each evaluation cycle without having to discretize input and output variables. Direct processing of the analog input signal is used to obtain the corresponding crisp value; the digital portion is used only for programmability. No A/D and D/A converters are needed. The controller can handle three inputs, one output, and 25 programmable fuzzy rules. The test IC chips were fabricated using 0.7-μm CMOS technology. A control problem of stabilizing a ping-pong ball in a tube with a controllable air flow has been successfully demonstrated     

A simplified version of mamdani's fuzzy controller: the natural logic controller

This paper proposes the natural logic controller (NLC) that it comes through a very important simplification of the Mamdani's fuzzy controller (MFC) allowing easy-design for single-input-single-output (SISO) regulation problems. Usually, fuzzy controllers are built with two classical signals of process: The error and its rate of change. They use a moderate number of fuzzy subsets and fuzzy rules. The main features of the NLC approach are that use the minimal fuzzy partition (only two fuzzy subsets per variable) and it use the minimal fuzzy rule base (only two rules). The nonlinear resulting fuzzy controller is the simplest one with an analytically well-defined, input-output mapping and accepting a linear approximation at origin. It allows easy extension to more than two signals of process. Some properties of nonlinear mapping of NLC are analyzed and some results are also presents on testing stability when NLC is used on a linear process. A special attention is addressed to the two inputs NLC case, where stability can be tested using the circle criterion. Finally, two application examples are discussed in details.     

SCADA system of DC motor with implementation of fuzzy logic controller on neural network

In this paper, a supervisory control and data acquisition system of DC motor with implementation of fuzzy logic controller (FLC) on neural network (NN) is presented. We successfully avoid complex data processing of fuzzy logic in the proposed scheme. After designed a FLC for controlling the motor speed, a NN is trained to learn the input–output relationship of FLC. The tasks of sampling and acquiring the input signals, process of the input data, and output of the voltage are commanded by using LabVIEW. Finally, the experimental results are provided to confirm the performance and effectiveness of the proposed control approach.     

Design and analysis of a fuzzy logic controller

The fuzzy logic controller (FLC) presented by Siler and Ying (1989) is discussed here and is proved to be equivalent to a non-fuzzy, nonlinear, proportional-integral (PI) controller. Some characteristic properties of this fuzzy logic controller are then investigated. The achievable performance of such a specific fuzzy controller is examined and found to be not necessarily better than that of the conventional, linear, non-fuzzy PI controller. Various extended designs of the basic FLC, including the FLC with dual control laws and the three-piece FLC, are then presented to enhance control performance. These extensions can provide servo-control performance. These extensions can provide servo-control performance superior to that of the basic FLC design, as illustrated by simulation results. Finally a highly nonlinear neutralization process is advanced to demonstrate the applicability of the various FLCs to industrial process control.     

A high-speed, reconfigurable fuzzy logic controller

The modular architecture and reconfigurable inference engine of this analog fuzzy controller offer more flexibility than existing implementations. Its high inference speed and small size make the controller suitable for embedded system applications. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

一种改进的区间二型模糊控制器设计

针对二型模糊控制器设计中出现的降型计算方法损失不确定性信息的问题,提出一种改进的区间二型模糊控制器．该控制器在充分利用二型模糊推理结果的前提下,对区间模糊输出进行再次优化,其优化指标可直接与被控系统性能相关,由此可得到更有利于提高系统整体性能的准确输出量．最后,将改进的控制器用于汽车非线性悬架系统的控制,仿真结果验证了所提出方法的有效性．     

Single-chip fuzzy logic controller design and an application on a permanent magnet dc motor

This paper describes a low-cost single-chip PI-type fuzzy logic controller design and an application on a permanent magnet dc motor drive. The presented controller application calculates the duty cycle of the PWM chopper drive and can be used to dc–dc converters as well. The self-tuning capability makes the controller robust and all the tasks are carried out by a single chip reducing the cost of the system and so program code optimization is achieved. A simple, but effective algorithm is developed to calculate numerical values instead of linguistic rules. In this way, external memory usage is eliminated. The contribution of this paper is to present the feasibility of a high-performance non-linear fuzzy logic controller which can be implemented by using a general purpose microcontroller without modified fuzzy methods. The developed fuzzy logic controller was simulated in MATLAB/SIMULINK. The theoretical and experimental results indicate that the implemented fuzzy logic controller has a high performance for real-time control over a wide range of operating conditions.     

Design and application of an analog fuzzy logic controller

In this paper, we present an analog fuzzy logic hardware implementation and its application to an autonomous mobile system. With a simple structure the fabricated fuzzy controller shows good performance in processing speed and area consumption. Accomplished with 13 reconfigurable rules, a speed of up to 6 MFLIPS has been achieved. To stress the advantages of the new architecture, speed and flexibility, the same control strategy is implemented on the new analog fuzzy controller and on a digital multipurpose microcontroller in software. The results of the two implementations show that the analog approach is not only faster but also flexible enough to compete with digital fuzzy approaches     

Theoretical and linguistic aspects of the fuzzy logic controller

Successful applications of the fuzzy logic controller by various researchers to a variety of ill-defined processes motivated this theoretical study of the fuzzy logic controller. Initially the controller is analysed by traditional (nonlinear) algebraic methods which are particularly useful in stability studies, provided the process is algebraically modelled. Despite the success of this technique, it suffers from a major limitation in that the algebraic model of the controller cannot directly deal with the linguistic aspects of the fuzzy logic controller. This observation leads to the introduction of a more concise, and hence more powerful, notation for representing the linguistic rules that describe the fuzzy logic controller. The so-called linguistic models that arise from this notation are shown to be extremely useful for modelling highly nonlinear low-order systems and for determining, explicitly, the rules of ‘optimal’ fuzzy logic controllers.     

Architecture of a testable analog fuzzy logic controller

The authors discuss problems of testability of analog fuzzy logic controllers implemented as VLSI chips. Enhancements to standard architecture of fuzzy logic controllers which facilitate testing are proposed. To improve controllability and observability of internal nodes, analog switching blocks are introduced together with some additional circuitry. These blocks allow one to test each basic cell of a fuzzy logic controller (e.g., membership function cell, MINIMAX cell, etc.) separately. The analog switching blocks do not contribute to the power consumption in a working chip end therefore can be used in low-power analog fuzzy logic controllers     

一种基于模糊逻辑的入口匝道自适应控制器

高速公路的交通流存在很大的不确定性,模糊逻辑是解决其控制问题的有效方法。对传统的ALINEA模型进行了扩展,提出一种新的自适应模糊匝道控制器。当高速公路路段的临界密度不能被预先正确估计或者因交通环境的实时变化而难以估计时,提出的自适应模糊控制器将显示出其优越性。在仿真试验中,根据交通流的各种性能指标,将新的自适应控制器同传统的ALINEA方法做了详细的对比。     

Design of a PID-like compound fuzzy logic controller

The paper describes a novel method for the design of a fuzzy logic controller (FLC) with near-optimal performance for a variety of operating conditions. The approach is based on the analysis of the system behaviour in the error state-space. The final control structure, in a form of a compound FLC, is arrived at in two stages. The first stage encompasses design and tuning of a PID-like fuzzy controller. The second stage consists of placing an additional fuzzy controller, of a structure similar to that of the first one, in parallel with the PID-like fuzzy controller designed in the first stage. The resulting compound controller is characterised with high performance in the wide range of operating conditions, and with small number of parameters that can be adjusted using simple optimisation methods. The controller is developed and tested for a plant comprising a vector controlled induction motor drive.