Control of dynamic systems using fuzzy logic and neural networks

The use of artificial neural network is proposed for high-speed processing of rules in fuzzy logic controller (FLC). the logic element of an FLC is replaced by a single hidden layer feedforward network. the input and output fuzzy subsets are expressed it of numerical patterns. the network is trained using the back-propagation algori to establish fuzzy associations between the input and output fuzzy subsets. the inference mechanism of the network is compared with that of compositional law of inference. In the proposed implementation of FLC, all the rules are processed in paralle. This implementation has potential for high-speed processing of rules if the network is realized in hardware. the use of neural networks in fuzzy logic self-organizing is also ivestigated. © 1993 John Wiley & Sons, Inc.     

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     

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.     

Design of adaptive fuzzy logic controller based on linguistic-hedgeconcepts and genetic algorithms

In this paper, we propose a novel fuzzy logic controller, called linguistic hedge fuzzy logic controller, to simplify the membership function constructions and the rule developments. The design methodology of linguistic hedge fuzzy logic controller is a hybrid model based on the concepts of the linguistic hedges and the genetic algorithms. The linguistic hedge operators are used to adjust the shape of the system membership functions dynamically, and ran speed up the control result to fit the system demand. The genetic algorithms are adopted to search the optimal linguistic hedge combination in the linguistic hedge module, According to the proposed methodology, the linguistic hedge fuzzy logic controller has the following advantages: 1) it needs only the simple-shape membership functions rather than the carefully designed ones for characterizing the related variables; 2) it is sufficient to adopt a fewer number of rules for inference; 3) the rules are developed intuitionally without heavily depending on the endeavor of experts; 4) the linguistic hedge module associated with the genetic algorithm enables it to be adaptive; 5) it performs better than the conventional fuzzy logic controllers do; and 6) it can be realized with low design complexity and small hardware overhead. Furthermore, the proposed approach has been applied to design three well-known nonlinear systems. The simulation and experimental results demonstrate the effectiveness of this design.     

Digital fuzzy logic controller: design and implementation

In this paper, various aspects of digital fuzzy logic controller (FLC) design and implementation are discussed, Classic and improved models of the single-input single-output (SISO), multiple-input single-output (MISC), and multiple-input multiple-output (MIMO) FLCs are analyzed in terms of hardware cost and performance. A set of universal parameters to characterize any hardware realization of digital FLCs is defined. The comparative study of classic and alternative MIMO FLCs is presented as a generalization of other controller configurations. A processing element for the parallel FLC architecture realizing improved inferencing of MIMO system is designed, characterized, and tested. Finally, as a case feasibility study, a direct data stream architecture for complete digital fuzzy controller is shown as an improved solution for high-speed, cost-effective, real-time control applications     

A self-learning fuzzy logic controller using genetic algorithmswith reinforcements

This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of “success” or “failure” signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations     

A systematic method for design of multivariable fuzzy logic controlsystems

This paper proposes a systematic method to design a multivariable fuzzy logic controller for large-scale nonlinear systems. In designing a fuzzy logic controller, the major task is to determine fuzzy rule bases, membership functions of input/output variables, and input/output scaling factors. In this work, the fuzzy rule base is generated by a rule-generated function, which is 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 generated from a genetic algorithm based on a fitness function. As a result, the searching space of parameters is narrowed down to a small space, the multivariable fuzzy logic controller can quickly constructed, and the fuzzy rules and the scaling factors can easily be determined. The performance of the proposed method is examined by computer simulations on a Puma 560 system and a two-inverted pendulum system     

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     

一种自调整模糊控制器的设计与仿真研究

提出了一种基于规则，参数自整定的模糊控制器的设计方法，它通过引入误差加权因子和误差变化率加权因子，以系统阶跃响应的上升时间和超调量为性能评价指标，并根据评价结果建立自适应算法，对模糊控制器的模糊推理规则及参数进行自调整。仿真结果进行表明，误差变化率加权因子以明显改善模糊控制器的稳定性，性能评价指标函数，模糊推理规则和参数调整算法简单实用，便于实现，对被控对象的参数变化具有较强的适应能力，控制效果良好。     

A self-organizing fuzzy logic controller for the active control of flexible structures using piezoelectric actuators

This paper proposes an on-line self-organizing fuzzy logic controller (FLC) design applied to the control of vibrations in flexible structures containing distributed piezoelectric actuator patches. In this methodology, the fuzzy rules are generated using the history of input/output (I/O) pairs without using any plant model. The generated rules are stored in the fuzzy rule space and updated on-line by a self-organizing procedure. The validity of the proposed fuzzy logic control has been demonstrated experimentally in a steel cantilever test beam and a set of experimental tests are made in the system to verify the efficiency of the on-line self-organizing fuzzy controller.     

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.     

An 8-bit systolic AES architecture for moderate data rate applications

The complexity involved in mapping an algorithm to hardware is a function of the controller logic and data path. Minimizing data path size can lead to significant savings in hardware area and power dissipation. This paper presents an implementation of a novel architectural transformation technique for mapping a word bit wide algorithm to byte vector serial architecture. The technique divides the input word to several bytes and then traces each byte for extracting architectural transformation. The technique is applied on Advanced Encryption Standard (AES) algorithm which is non-linear in nature. Using this technique, the 32-bit AES algorithm is transformed into a byte-systolic architecture. The novelty of the technique is more pronounced around the mix column design which is the most complex part of the AES algorithm. The complex matrix multiplication component and standard transformations of the 32-bit AES algorithm are transformed to support 8-bit operations. The resulted AES architectures reuse same logic resources for key expansion and encryption/decryption. The proposed design offers moderate data rates in the range of 41 Mbps for encryption and 37 Mbps for decryption while utilizing 236 and 280 slices, respectively, on Xilinx Virtex II xc2v1000-6 FPGA. Comparison results show significant gain in throughput when compared with other 8-bit designs. This makes it a viable data/communication security solution for a variety of embedded and consumer electronics.     

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.     

三级倒立摆的加权变量模糊神经网络控制

为了提高三级倒立摆系统控制的响应速度和稳定性,在设计Mamdani型摸糊推理规则控制器控制倒立摆系统稳定的基础上,设计了一种更有效率的基于Sugeno型模糊推理规则的模糊神经网络控制器。该控制器使用BP神经网络和最小二乘法的混合算法进行参数训练,能够准确归纳输入输出量的模糊隶属度函数和模糊逻辑规则。通过与Mamdani型控制器的仿真对比,表明该Sugeno型模糊神经网络控制器对三级倒立摆系统的控制具有良好的稳定性和快速性,以及较高的控制精度。     

An ART-based fuzzy adaptive learning control network

This paper addresses the structure and an associated online learning algorithm of a feedforward multilayer neural net for realizing the basic elements and functions of a fuzzy controller. The proposed fuzzy adaptive learning control network (FALCON) can be contrasted with traditional fuzzy control systems in network structure and learning ability. An online structure/parameter learning algorithm, FALCON-ART, is proposed for constructing FALCON dynamically. It combines backpropagation for parameter learning and fuzzy ART for structure learning. FALCON-ART partitions the input state space and output control space using irregular fuzzy hyperboxes according to the data distribution. In many existing fuzzy or neural fuzzy control systems, the input and output spaces are always partitioned into “grids”. As the number of variables increases, the number of partitioned grids grows combinatorially. To avoid this problem in some complex systems, FALCON-ART partitions the I/O spaces flexibly based on data distribution. It can create and train FALCON in a highly autonomous way. In its initial form, there is no membership function, fuzzy partition, and fuzzy logic rule. They are created and begin to grow as the first training pattern arrives. Thus, the users need not give it any a priori knowledge or initial information. FALCON-ART can online partition the I/O spaces, tune membership functions, find proper fuzzy logic rules, and annihilate redundant rules dynamically upon receiving online data     

车辆主动悬架模糊控制器的设计及其仿真分析

该文通过车辆悬架系统和路面输入模型的建立，应用模糊逻辑控制理论，进行了车辆主动悬架模糊控制器的设计，并在Matlab/Sirmulink环境中对此模糊控制器进行了仿真，把它与传统的被动悬架和用LQG控制器控制的主动悬架作了比较及性能分析，仿真结果证明了相对于其它两种悬架来说，具有模糊控制器作用的主动悬架在降低车身加速度、改善车辆的行驶平顺性和乘坐舒适性上有显著的效果。     

A self-organizing fuzzy logic controller for dynamic systems usinga fuzzy auto-regressive moving average (FARMA) model

The paper proposes a complete design method for an online self-organizing fuzzy logic controller without using any plant model. By mimicking the human learning process, the control algorithm finds control rules of a system for which little knowledge has been known. In a conventional fuzzy logic control, knowledge on the system supplied by an expert is required in developing control rules, however, the proposed new fuzzy logic controller needs no expert in making control rules, Instead, rules are generated using the history of input-output pairs, and new inference and defuzzification methods are developed. The generated rules are stored in the fuzzy rule space and updated online by a self-organizing procedure. The validity of the proposed fuzzy logic control method has been demonstrated numerically in controlling an inverted pendulum     

压力（位移）自动检测控制器检测单元接口设计

主要研究电液控制放大器模拟量输入通道中,位移传感器检测单元的测量放大电路和各输入信号与智能控制器的接口电路,使控制器的输入与各传感器的输出信息相匹配。     

基于模糊控制的智能感控视力保护仪

针对青少年长时间使用电子产品导致视力下降的问题,提出一种基于模糊控制的智能感控视力保护仪;该保护仪的控制系统综合运用了传感器技术、数字信息处理技术、模拟数据处理技术以及智能算法,实现了软硬件的结合;针对检测过程中距离和光照强度难调节的问题,引入了模糊控制算法,通过模糊控制规则来调节距离和光照,并进行了仿真实验,将采集到的光照强度和检测距离作为输入量,定时时间作为输出量,达到提醒学生的目的 ;对比实验结果表明,模糊控制比PID控制的定时时间增加了10.6 min,相对时间增大了18.563％,能够为学生创造更良好的学习环境.     

A High-Performance FPGA-Based Fuzzy Processor Architecture for Medical Diagnosis

An auto-decision-making system for medical diagnosis could help make up for the lack of physicians in rural areas of many third-world countries. This high-performance, low-power, pipelined parallel fuzzy processor based on a dedicated single-chip architecture performs high-speed fuzzy inferences with processing speed up to 5.0 Mflips at a clock frequency of 40 MHz using 256 rules having one consequent each, 16 input variables, and 16-bit resolution. The processor operates in real time producing results within an interval of 1.92μs. The processor implemented on board consumes as low as 70 milliwatt power. The processor performs medical diagnosis with 97.5 percent accuracy.