Identification of Evolving Rule-based Models.

An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System

Cakar ayam shaping machine

Cakar ayam (Figure 7.1) is one of the Malay traditional cookies that are made from sliced sweet potatoes deep-fried in the coconut candy. In current practice of moulding the cookies, the fried sweet potatoes are molded using traditional manual tools, which are inefficient and less productive for the mass production purposes. “Kuih cakar ayam” associated with the meaning of the idiom means less messy handwriting has a somewhat negative connotation .This cookies may just seem less attractive in shape but still likeable . In fact, this cookie is considered a popular snack even outside the holiday season. The choice of the name of this cookie is more to shape actually resembles former chicken scratches made by the paw the ground while foraging. The value of wisdom, beauty and creativity of the Malays is clearly evident through the Malay cookie. Although it is attacked by the invention of modern cakes that look far more interesting, these cakes will be able to survive a long time until now

Development of a Fuzzy Logic Model-Less Aircraft Controller

The Modeling and Control for Agile Aircraft Development (MCAAD) group at NASA Langley Research Center(LaRC) is developing techniques for Real-Time Global Modeling (RTGM) and Robust Learning Control (RLC) for NASA’s Transformational Tools and Technologies Project. This project seeks to develop a systematic approach to reduce the iterative nature of aircraft design by introducing a model-less control law and enabling inflight aerodynamic modeling and controller design. The development of the flight control system without prior knowledge of the aircraft aerodynamic model makes use of TakagiSugeno-Kang fuzzy logic inference systems for pitch and roll controllers and are tested in various simulations and wind tunnel platforms. These fuzzy logic controllers are not based on a mathematical model but rather on a rule base of generic flight control laws generated from the designer’s knowledge of aircraft flight mechanics. The controller architecture uses two channels to provide absolute and incremental controller commands as needed. The absolute channel is designed to reject disturbances and decrease rise time, while the incremental channel provides tracking and reduced steady state error. To provide controllers with acceptable performance without the need for tuning, a general method for selecting input and output scaling gains for the fuzzy inference systems is proposed. A performance and robustness comparison of similarly configured Type-1 and Interval Type-2 fuzzy logic controllers is made. The fuzzy logic controllers were implemented on an aircraft model in the NASA Langley 12-Foot low speed tunnel mounted on a free-to-pitch and free-to-roll rig. The development of the controller architectures and wind tunnel results are presented

An empirical learning-based validation procedure for simulation workflow

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models

Adaptive neurofuzzy ANFIS modeling of laser surface treatments

This paper introduces a new ANFIS adaptive neurofuzzy inference model for laser surface heat treatments based on the Green’s function. Due to its high versatility, efficiency and low simulation time, this model is suitable not only for the analysis and design of control systems, but also for the development of an expert real time supervision system that would allow detecting and preventing any failure during the treatment

Power Quality Enhancement in Hybrid Photovoltaic-Battery System based on three–Level Inverter associated with DC bus Voltage Control

This modest paper presents a study on the energy quality produced by a hybrid system consisting of a Photovoltaic (PV) power source connected to a battery. A three-level inverter was used in the system studied for the purpose of improving the quality of energy injected into the grid and decreasing the Total Harmonic Distortion (THD). A Maximum Power Point Tracking (MPPT) algorithm based on a Fuzzy Logic Controller (FLC) is used for the purpose of ensuring optimal production of photovoltaic energy. In addition, another FLC controller is used to ensure DC bus stabilization. The considered system was implemented in the Matlab /SimPowerSystems environment. The results show the effectiveness of the proposed inverter at three levels in improving the quality of energy injected from the system into the grid.Peer reviewedFinal Published versio

Elasto-geometrical modeling and calibration of robot manipulators: Application to machining and forming applications

International audienceThis paper proposes an original elasto-geometrical calibration method to improve the static pose accuracy of industrial robots involved in machining, forming or assembly applications. Two approaches are presented respectively based on an analytical parametric modeling and a Takagi-Sugeno fuzzy inference system. These are described and then discussed. This allows to list the main drawbacks and advantages of each of them with respect to the task and the user requirements. The Fuzzy Logic model is used in a model-based compensation scheme to increase significantly the robot static pose accuracy in a context of incremental forming application. Experimental results show the efficiency of the Fuzzy Logic model while minimizing development and computational resources

Fuzzy-enhanced Dual-loop Control Strategy for Precise Nanopositioning

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