Multiplexing architecture for mixed-signal CMOS fuzzy controllers

Limited precision imposes limits on the complexity of analogue circuits, and hence fuzzy analogue controllers are usually oriented to fast low-power systems with low-medium complexity. A strategy to preserve most of the advantages of an analogue implementation, while allowing a marked increment in system complexity, is presented.Comisión Interministerial de Ciencia y Tecnología TIC96-1392-C02-0

A multiplexing architecture for mixed-signal CMOS fuzzy controllers

Limits to precision impose limits to the complexity of analog circuits, hence fuzzy analog controllers are usually oriented to fast low-power systems with low-medium complexity. This paper presents a strategy to preserve most of the advantages of an analog implementation, while allowing a marked increment in system complexity.The works in this papaer has been partially funded by the spanish C.I.C.Y.T. under contract TIC96-1392-C02-02 (SIVA

A Modular Programmable CMOS Analog Fuzzy Controller Chip

We present a highly modular fuzzy inference analog CMOS chip architecture with on-chip digital programmability. This chip consists of the interconnection of parameterized instances of two different kind of blocks, namely label blocks and rule blocks. The architecture realizes a lattice partition of the universe of discourse, which at the hardware level means that the fuzzy labels associated to every input (realized by the label blocks) are shared among the rule blocks. This reduces the area and power consumption and is the key point for chip modularity. The proposed architecture is demonstrated through a 16-rule two input CMOS 1-μm prototype which features an operation speed of 2.5 Mflips (2.5×10^6 fuzzy inferences per second) with 8.6 mW power consumption. Core area occupation of this prototype is of only 1.6 mm 2 including the digital control and memory circuitry used for programmability. Because of the architecture modularity the number of inputs and rules can be increased with any hardly design effort.This work was supported in part by the Spanish C.I.C.Y.T under Contract TIC96-1392-C02- 02 (SIVA)

ON A DEFUZZIFICATION PROCESS OF FUZZY CONTROLLERS

In this paper, innovations in the field of automatic control systems with fuzzy controllers have been considered. After a short introduction on fuzzy controllers, four different ways of a defuzzification process were introduced, and verified on the simulation of nuclear reactor fuzzy controller. The default Matlab fuzzy toolbox solution is timely most demanding, while two solutions based on the defuzzification on trapezoidal fuzzy numbers have the advantage in the process of crisp numbers calculation. Also, a solution based on the determination of the line dividing the obtained polygon into two parts of equal areas is presented.

Development of FPGA based Standalone Tunable Fuzzy Logic Controllers

Soft computing techniques differ from conventional (hard) computing, in that unlike hard computing, it is tolerant of imprecision, uncertainty, partial truth, and approximation. In effect, the role model for soft computing is the human mind and its ability to address day-to-day problems. The principal constituents of Soft Computing (SC) are Fuzzy Logic (FL), Evolutionary Computation (EC), Machine Learning (ML) and Artificial Neural Networks (ANNs). This thesis presents a generic hardware architecture for type-I and type-II standalone tunable Fuzzy Logic Controllers (FLCs) in Field Programmable Gate Array (FPGA). The designed FLC system can be remotely configured or tuned according to expert operated knowledge and deployed in different applications to replace traditional Proportional Integral Derivative (PID) controllers. This re-configurability is added as a feature to existing FLCs in literature. The FLC parameters which are needed for tuning purpose are mainly input range, output range, number of inputs, number of outputs, the parameters of the membership functions like slope and center points, and an If-Else rule base for the fuzzy inference process. Online tuning enables users to change these FLC parameters in real-time and eliminate repeated hardware programming whenever there is a need to change. Realization of these systems in real-time is difficult as the computational complexity increases exponentially with an increase in the number of inputs. Hence, the challenge lies in reducing the rule base significantly such that the inference time and the throughput time is perceivable for real-time applications. To achieve these objectives, Modified Rule Active 2 Overlap Membership Function (MRA2-OMF), Modified Rule Active 3 Overlap Membership Function (MRA3-OMF), Modified Rule Active 4 Overlap Membership Function (MRA4-OMF), and Genetic Algorithm (GA) base rule optimization methods are proposed and implemented. These methods reduce the effective rules without compromising system accuracy and improve the cycle time in terms of Fuzzy Logic Inferences Per Second (FLIPS). In the proposed system architecture, the FLC is segmented into three independent modules, fuzzifier, inference engine with rule base, and defuzzifier. Fuzzy systems employ fuzzifier to convert the real world crisp input into the fuzzy output. In type 2 fuzzy systems there are two fuzzifications happen simultaneously from upper and lower membership functions (UMF and LMF) with subtractions and divisions. Non-restoring, very high radix, and newton raphson approximation are most widely used division algorithms in hardware implementations. However, these prevalent methods have a cost of more latency. In order to overcome this problem, a successive approximation division algorithm based type 2 fuzzifier is introduced. It has been observed that successive approximation based fuzzifier computation is faster than the other type 2 fuzzifier. A hardware-software co-design is established on Virtex 5 LX110T FPGA board. The MATLAB Graphical User Interface (GUI) acquires the fuzzy (type 1 or type 2) parameters from users and a Universal Asynchronous Receiver/Transmitter (UART) is dedicated to data communication between the hardware and the fuzzy toolbox. This GUI is provided to initiate control, input, rule transfer, and then to observe the crisp output on the computer. A proposed method which can support canonical fuzzy IF-THEN rules, which includes special cases of the fuzzy rule base is included in Digital Fuzzy Logic Controller (DFLC) architecture. For this purpose, a mealy state machine is incorporated into the design. The proposed FLCs are implemented on Xilinx Virtex-5 LX110T. DFLC peripheral integration with Micro-Blaze (MB) processor through Processor Logic Bus (PLB) is established for Intellectual Property (IP) core validation. The performance of the proposed systems are compared to Fuzzy Toolbox of MATLAB. Analysis of these designs is carried out by using Hardware-In-Loop (HIL) test to control various plant models in MATLAB/Simulink environments

Design, analysis and fabrication of an articulated mobile manipulator

The process involved in designing, fabricating and analysing a mobile robotic manipulator to carry out pick and place task in a dynamic and unknown environment has been explained here. The manipulator designed and fabricated has a 5 – axis articulated arm for pick and place application but also can be reconfigured to do other tasks. The manipulator is built with its driving or power means fitted at the bottom to distribute the load effectively and also make handling easier. The mobile platform employs a novel suspension system which helps in relatively distributing the load equally to all wheels regardless of the wheels position giving the mobile platform better control and stability. With reference to many available manipulators and mobile platforms in the market, a practical design is perceived using designing tools and a fully functional prototype is fabricated. The kinematic model determining the end effector’s position and orientation is analysed systematically and presented. Navigational controls are built using fuzzy logic and genetic algorithm with the help of the sensors’ information so that the robot can negotiate obstacle while carrying out various tasks in an unknown environment. The path tracking for pick-and-place application is the overall target of this industrial manipulator

Desarrollo de modelo diferencial neurodifuso de hidrogenerador de la Central Agoyán para la sintonización de controlador PID lineal invariante en el tiempo y PID neurodifuso.

En esta investigación se desarrolló el modelo diferencial lineal e invariante en el tiempo (LTI) y neurodifuso de la unidad hidrogeneradora de la Central Agoyán, ubicada en la localidad de Baños de Agua Santa, para su posterior sintonización de los controladores PID LTI y neurodifuso, con ese propósito se parametrizaron los modelos LTI de la turbina, el generador y el servomotor, haciendo uso del método de optimización del gradiente decreciente, lográndose el ajuste de registro de la potencia eléctrica del generador como respuesta a la variación de la señal de control. Una vez obtenido el modelo LTI, se estructuró el modelo neurodifuso y con el método del gradiente decreciente, se ajustaron los singletones o pesos del modelo lo que permitió una reproducción del comportamiento lineal ya obtenido, permitiendo una igualdad de desempeño en el punto de partida de comparación entre ambos modelos, LTI y neurodifuso. Finalmente, se planteó una función de costo que minimizó el estrés mecánico medido a nivel de servomotores del modelo, permitiendo el ajuste del controlador PID tanto neurodifuso como LTI. Como resultado, se verificó la hipótesis, demostrándose que un controlador PID neurodifuso, al presentar más parámetros para su sintonización, partiendo de una respuesta inicial igual al controlador PID LTI, fue capaz de presentar mejor desempeño al contrastar su respuesta de manera comparativa con una señal de desempeño patrón, y empleando como criterio de valoración la raíz del error cuadrático medio y el factor de correlación cuadrático multivariable de Pearson, ante una función de costo, como criterio de valoración de desempeño durante la optimización empleando método del gradiente decreciente, que considera al error cuadrático y el cuadrado de la velocidad del servomotor de manera global.In this investigation, the differential linear model and the invariant of time (LTI) was developed and the neuro-fuzzy of the hydro-generation plant of the Central Agoyan, located in Banos de Agua Santa, for the subsequent tuning of the PID LTI controllers and the neuro-fuzzy; for this reason, the LTI models of the turbine, the generator and the servo-motor were divided and studied, making use of the optimization method of the decreasing gradient, accomplishing the adjustment of registration of electric power from the generator in response to the variation of the control signal. After obtaining the model LTI, the neuro-fuzzy model was put together, and with the decreasing gradient method, the single-tones were adjusted, or the weights of the model that allowed reproduction of the already obtained linear behavior, allowing for equality of performance at the point of comparison of records between both models, LTI and neuro-fuzzy. Finally, a cost function was planted that minimized the mechanical stress calculated by the level of servomotors of the model, allowing the adjustment of the PID controller, both the neuro-fuzzy and the LTI. As a result, the hypothesis was verified, demonstrating that a PID neuro-fuzzy controller, when presenting more parameters for its tuning, based on the initial response, equal to the controller PID LTI, was capable of presenting better performance when contrasting its response in a comparative way with a sign of performance pattern, and employing the origin of the average quadratic error and the correlation factor Pearson quadratic multivariable as criteria for valuation, compared to a cost function, as criteria for valuation of performance during the optimization employing the decreasing gradient method, which considers the quadratic error and the square of the velocity of the global servo-motor

Navigational Path Analysis of Mobile Robot in Various Environments

This dissertation describes work in the area of an autonomous mobile robot. The objective is navigation of mobile robot in a real world dynamic environment avoiding structured and unstructured obstacles either they are static or dynamic. The shapes and position of obstacles are not known to robot prior to navigation. The mobile robot has sensory recognition of specific objects in the environments. This sensory-information provides local information of robots immediate surroundings to its controllers. The information is dealt intelligently by the robot to reach the global objective (the target). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimisation problem and thus can be analyzed and solved using AI techniques. The optimisation of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A successful way of structuring the navigation task deals with the issues of individual behaviour design and action coordination of the behaviours. The navigation objective is addressed using fuzzy logic, neural network, adaptive neuro-fuzzy inference system and different other AI technique.The research also addresses distributed autonomous systems using multiple robot

Samoorganiziranje neizrazitog analitičkog regulatora pri vođenju mobilnog robota

U ovoj doktorskoj disertaciji predlaže se novi analitički oblik funkcije pripadnosti ulaznih neizrazitih skupova i novi oblik analitičke funkcije za preslikavanje ulaznih neizrazitih skupova na pozicije centara izlaznih neizrazitih skupova. Promjenom osnovne strukture, povećava se neizrazito područje djelovanja neizrazitog analitičkog regulatora, čime su mogućnosti u procesu inferencije poboljšane. U faznoj ravnini stanja provedena je analiza utjecaja slobodnih parametara na formiranje prostora neizrazitog djelovanja, i utemeljen je prijedlog po kojemu »Dovoljan broj slobodnih parametara neizrazitog logičkog regulatora može aproksimirati bilo koju nepoznatu analitičku funkciju«. Predložen je postupak početnog podešavanja neizrazitog regulatora bez baze pravila regulacije koji je pokazao dobru emulaciju klasičnog PD i PI regulatora. S obzirom na gradijent pogreške slijeđenja nominalne trajektorije u prostoru unutarnjih koordinata predložen je u OFF-line modu rada postupak samoorganiziranja slobodnih parametara suvremenog neizrazitog regulatora. S obzirom na brzinu konvergencije i stabilnost nelinearnog algoritma samoorganiziranja postavljena su ograničenja na koeficijente obučavanja, kao i ograničenja koja prema Lyapunovu trebaju biti zadovoljena da bi se osigurala stabilnost algoritma upravljanja. Poboljšanim neizrazitim analitičkim regulatorom realizirane su nelinearne diskretne strukture sa integralnim djelovanjem u sklopu kojih je predložen i algoritam podešavanja integralnog pojačanja. Da bi se osiguralo ON-line vođenje autonomnog mobilnog robota u realnom vremenu, predložena je zamjena strukture samoorganizirajućeg neizrazitog regulatora sa diskretnim podacima zapisanim u memorijskoj kartici neizrazitog upravljačkog djelovanja. Na osnovu pogreške pozicije u uvjetima stacionarne i nestacionarne mjerne smetnje tipa »bijelog šuma« predložen je neizraziti mehanizam podešavanja slobodnih parametara poboljšanog neizrazitog analitičkog regulatora i pojačanja klasičnog PI-regulatora i kako bi se učinio kompromis između smanjenja utjecaja mjerne smetnje i povećanja brzine estimacije upravljanih varijabli. Ispravnost svih predloženih postupaka, provjerena je simulacijom vođenja autonomnog mobilnog robota zadanom nominalnom trajektorijom složenijeg oblika