Using FPGA Technique for Design and Implementation of a fuzzy Inference System

Fuzzy inference engine, as one of the most important components of fuzzy systems, can obtain some meaningful outputs from fuzzy sets on input space and fuzzy rule base using fuzzy logic inference methods. In multi-input–single-output (MISO) fuzzy systems, in order to enhance the computational efficiency of fuzzy inference engine, this paper aims mainly to investigate three MISO fuzzy hierarchical inference engines employed fuzzy implications satisfying the law of importation with aggregation functions (LIA). We firstly find some aggregation functions for well-known fuzzy implications such that they satisfy LIA. The fuzzy implication satisfying LIA with respect to a given aggregation function is then characterized. Finally, three fuzzy hierarchical inference engines in MISO fuzzy systems are constructed according to aforementioned theoretical developments. Three examples are also provided to illustrate our theoretical arguments.

Fast incremental best estimate directed Search (fast IBEDS) is a fast and effective search method for Takagi-Sugeno (TS) type fuzzy logic controller (FLC) optimization. Fast IBEDS only optimizes an FLC's parameters in the rule output functions. The rule antecedents remain fixed during optimization. The paper proposes to combine the adaptive-network-based fuzzy inference system (ANFIS) and fast IBEDS to gain full range optimization of an FLC. The role of ANFIS is to optimize the rule antecedents so that an FLC can be optimized even further. Simulation results on a 4D inverted pendulum show that by combining ANFIS and fast IBEDS together, an FLC can be further optimized to have better global performance.

In this tutorial, the utility of a fuzzy system is demonstrated by providing a broad overview, emphasizing analog mode hardware, along with a discussion of the author's original work. First, the difference between deterministic words and fuzzy words is explained as well as fuzzy logic. The description of the system using mathematical equations, linguistic rules, or parameter distributions (e.g., neural networks) is discussed. Fuzzy inference and defuzzification algorithms are presented, and their hardware implementation is discussed. The fuzzy logic controller was used to stabilize a glass with wine balanced on a finger and a mouse moving around a plate on the tip of an inverted pendulum.

This paper addresses the use of fuzzy logic to synthesize multiple numerical attributes on urban street networks, allowing conventional Geographic Information Systems ­ GIS to deal with the concept of a fuzzy "best path" instead of the classical "shortest path". A new methodology to use fuzzy inference in GIS, implementing fuzzy path planning inside an urban network model is proposed. A computational tool was developed to perform data acquisition, fuzzy inference and GIS database updating in real-time. Practical results of fuzzy path planning are presented and discussed.

Wood is a natural derivative, it possesses randomly distributed inherent defects all over its mass. This complicates the cost estimation process; data collected showed that even planks with similar defect pattern would have different percentage of material loss. Such uncertain loss was caused by changes in cutting parameters. In this study, a Fuzzy Inference (FI) method was employed to predict wood loss in a rubber wooden toy manufacturing cutting process. Notable variables are: length of cut, and area of cut. Prediction accuracy from the FI method was compared with that of other alternative methods. Common practice assumes a constant defective proportion, resulting in inaccurate cost estimation. A regression equation allows the loss to be varied by the cutting parameters; but only one parameter was found to be significant. Experimental results show that the FI method greatly outperformed regression and conventional methods. These findings emphasize the influence of cutting parameters on product cost. Accurate cost estimation enables better planning for efficient pricing strategies and enhances business competitiveness.

The most commonly used controller in the industry field is the proportional-plus-integral (PI) controller, which requires a mathematical model of the system. Fuzzy logic controller (FLC) provides an alternative to conventional PI controller, especially when the available system models are inexact or unavailable. Also, rapid advances in digital technologies have given designers the option of implementing controllers using field programmable gate array (FPGA) which depends on parallel programming. This method has many advantages over classical microprocessors. In this research work, a FLC, which is fabricated on modern FPGA card (Spartan-3A, Xilinx Company), is proposed to implement a prototype of a speed controller for three-phase induction motor (squirrel cage type). The FLC and the PWM inverter strategies which have been built in FPGA appeared fast speed response and good stability in controlling the three-phase induction motor. For comparison purpose, a conventional PI controller has been implemented in the same FPGA card to examine the performance of the FLC. These controllers have been tested using MATLAB/Simulink program under various reference speeds. The designed FPGA-based closed-loop FLC’s performance is weighed against with that of a conventional PI controller. The system has been simulated in MATLAB/Simulink, and the results have been attached. The simulation and experimental results obtained using a FPGA-based conventional PI controller and the FPGA-based FLC have been compared in terms of settling time, and it has been found out that the proposed FPGA-based FLC shows a better performance than the conventional PI controller.KeywordsField programmable gate array (FPGA)Sinusoidal PWM (SPWM)60 degrees PWMRandom frequency PWM (RFPWM)Third harmonic injection (THI) PWMInverterTotal harmonic distortion (THD)Harmonic spread factor (HSF)VHSIC hardware description language (VHDL)

Abstract. Natural ventilation as part of a greenhouse environment control system can save energy, reduce pollution, and cut the costs of production. Many studies have used the energy balance method to model greenhouse environments and calculate the rates of ventilation needed to control them. However, the efficacy of this method is influenced by many factors. There are often many parameters in the energy balance model, related to the greenhouse structure, crop growth, and climate. Thus, the effectiveness and applicability of greenhouse ventilation systems based on energy balance can be compromised. Here, we study a hierarchical fuzzy control method and use simple fuzzy logic controllers to control the coordination and opening angles of a new energy-saving solar greenhouse roof and sidewall ventilation. This design reduces the complexity of the fuzzy rule base and the fuzzy subsystem related to the physical model, therefore making it easy to design and build. The system uses the fuzzy tool in the Matlab environment, enabling a quick design, and the fuzzy inference engine fis.c file (Fuzzy Inference System) to load the design results into the fuzzy control system, thus making the modification and maintenance of the system easier. The experimental data showed that the new hierarchical fuzzy control reduced temperature fluctuations and maintained temperature closer to desired temperature than a non-fuzzy control method. Moreover, this method can also be easily used to control other equipment in the greenhouse. Keywords: Hierarchical fuzzy model, Natural ventilation, Matlab, Solar greenhouse.

This paper introduced modeling and simulation results of an isolated solar photovoltaic system with lead-acid battery. The system is operated at maximum power point tracking (MPPT) using MATLAB/Simulink environment. The proposed controllers used in this paper are proportional – integral (PI) controller, fuzzy logic controller (FLC) and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. The proposed controllers are used for controlling dc-dc converter. All simulation results are recorded and compared with each other using the conventional and intelligent controllers.

Fuzzy inference systems and inventory allocation decisions: Exploring the impact of priority rules on total costs and service levels

On February 22, 2010, Prof. Ebrahim H. Mamdani who devised Mamdani fuzzy inference has passed away. His work in fuzzy inference, which rapidly paved the way to its practical use, helped disseminate Prof. Lotfi Zadehfs fuzzy logic and the development of fuzzy research. Prof. Mamdanifs two papers on fuzzy inference ? gApplication of fuzzy algorithms for control of simple dynamic planth (Proc. IEE, Vol.121, No. 12, pp. 1585-1588, 1974) and gAn experiment in linguistic synthesis with a fuzzy logic controllerh (Int. J. of Man-Machine Studies, Vol.7, No.1, pp. 1-13, 1975) with S. Assilian ? enabled fuzzy inference technology to develop dramatically both indicatively and indirectly to where it has been applied, including fuzzy control systems. This special issue honors Prof. Mamdani for his invaluable efforts in these and many other fields. We have asked for submissions by researchers influenced by Prof. Mamdanifs achievements, including his work in fuzzy inference, and have narrowed down to one review and seven full papers. The review by Hirosato Seki and Kai Meng Tay provides an incisive overview on the many aspects of fuzzy inference that Prof. Mamdani brought to light. Prof. Mamdanifs fuzzy inference has become a deterministic technology that can be chosen naturally and that will continue to be influential and survivable well into the future.

The fuzzy logic inference system was developed to identify driving intention. The membership functions and rules of the fuzzy logic inference system were built by using mathematical statistics and neural network. The vehicle model was built based on a series–parallel hybrid vehicle using Cruise software. The driving intention inference system was designed in Simulink. The simulation is done based on Cruise and Simulink. The simulation results prove that the fuzzy inference system can identify driving intentions excellently and the control strategy based on driving intentions can help to reduce more fuel consumption.KeywordsDriving intentionFuzzy inferenceHybrid vehiclesNeural networkSimulation

In this paper, design and simulation of a current-mode CMOS Fuzzy Logic Controller (FLC) is presented. The proposed controller is composed of Membership Function Circuit (MFC), fuzzy inference engine with min-product method and current-mode defuzzification circuit. MFC has a new architecture that generates basic four membership functions (trapezoidal, triangle, Z-shape and S-shape). These membership functions are simply tuneable by adjusted two voltages via setting some analogue switches in a shape/label select circuit block which is called programming unit. Also, a novelty designed current-mode defuzzification circuit is based on current-mode four-quadrant multipliers that have been constructed simple current squarer and substractor circuits. The proposed current-mode CMOS FLC has been simulated and verified for two inputs, one output and nine tuneable fuzzy rules by using PSPICE program with MIETEC 1.2 µm design parameters. The proposed architecture has an operation speed of 6.25 MFLIPS (6.25 × 106 Fuzzy Logic Inference per Second).

In this paper, a robot navigation model is constructed in MATLAB-Simulink. This robot navigation model make the robot capable for the obstacles avoidance in unknown environment. The navigation model uses two types of controllers: pure pursuit controller and fuzzy logic controller. The role of the pure pursuit controller is to generate linear and angular velocities to drive the robot from its current position to the given goal position. The obstacle avoidance is achieved through the fuzzy logic controller. For the fuzzy controller, two novel fuzzy inference systems (FISs) are developed. Initially, a Mamdani-type fuzzy inference system (FIS) is generated. Using this Mamdani-type FIS in the fuzzy controller, the training data of input and output mapping, is collected. This training data is supplied to the adaptive neuro-fuzzy inference system (ANFIS) to obtain the second FIS as of Sugeno-type. The navigation model, using the proposed FISs, is implemented on the simulated as well as real robots.

This paper presents a novel implementation of a Takagi-Sugeno-type fuzzy logic controller (FLC) on a two-wheeled mobile robot (2WMR), which consists of two wheels in parallel and an inverse pendulum. The control objective of the 2WMR is to achieve position control of the wheels while keeping the pendulum around the upright position that is an unstable equilibrium. The novelties of this work lie in three aspects. First, the FLC is a synthesized design which utilizes both heuristic knowledge and model information of the 2WMR system. The FLC structure, including the fuzzy labels, membership functions, and inference, is chosen based on heuristic knowledge about the 2WMR. The output parameters of the FLC are determined by comparing the output of the FLC with that of a linear controller at certain operating points, which avoids the difficulty and tediousness in manual tuning. The linear controller is designed based on a linearized model of the 2WMR system. Second, the proposed FLC is a simple and realizable design for real implementation. Only two fuzzy labels are adopted for each fuzzy variable. Sixteen fuzzy rules are used with eight output parameters and four range parameters for the membership functions to be determined. Third, the proposed FLC is successfully implemented on a real-time 2WMR for regulation and setpoint control tasks. Satisfactory responses are achieved when the 2WMR travels not only on a flat surface but also on an inclined surface. Through comprehensive experiment-based investigations, the effectiveness of the proposed FLC is validated, and the FLC shows superior performance than the existing methods.

ABSTRACTThe tremendous advancement in very large scale integration technology encourages chip designers to rely on commercial electronic design automation tools, offshore fabrication services, and outsourced intellectual property cores. As third-party involvement are more in chip design cycle, integrated circuits (IC) such as programmable logic devices (PLD) and application specific ICs (ASIC) are highly vulnerable to hardware Trojan (HT) attacks. HTs are malicious additions or modifications to existing circuit elements, which can be inserted at any stages of IC life cycle to modify/destroy the functionality, reduce reliability, leak sensitive information, etc. Besides, HT can be designed either as always on or triggered only under certain operating conditions. For example, HT attacks in safety critical applications such as nuclear power plant, space and defence will jeopardise the safety and security of the unit and cause serious consequences. Hence, it is most important to ensure that the chip being in use performs only the intended function. In this paper, an in-depth analysis of feasible HT attacks in PLD and ASIC life cycles are presented. Based on the extensive literature survey on HTs and their countermeasure techniques, the state of the art HT prevention, detection, and diagnosis techniques are recommended to the valid stages of PLD and ASIC life cycles. Therefore, incorporating possible HT defence solutions in respective phases of PLD/ASIC-based safety systems thwart them against HT attacks.