Mamdani-type Fuzzy Inference Research Articles

Dynamic Morphing of Smart Trusses and Mechanisms Using Fuzzy and Neuro-Fuzzy Techniques

In the present investigation, the principles of dynamic morphing of smart truss structures and mechanisms are discussed. A possible way in order to find the optimal geometry of the structure for the enhancement of structural performance in terms of vibration control is sought. The vibrations of the host dynamic structures are monitored by controllers which are based on the principles of Mamdani-type fuzzy inference and Sugeno-type adaptive neuro-fuzzy inference. More specifically, the objective of the present study is a design, tuning and an application of robust intelligent control mechanisms by means of the suppression of structural vibrations for several types of excitation forces. The proposed models are discretized by using a finite element method. For the time integration of the equations of motion, the Newmark-β method is used. The calculations and the analysis are conducted within the Matlab environment by using the Adaptive Neuro Fuzzy Inference System (ANFIS) tool, which is included in the fuzzy toolbox. The controllers are tested with different excitation forces applied on a truss-shaped structure. The control outputs are applied on each time of the simulation in order to achieve the lowest possible deformation and to prevent potential damage or corruption of the structure. The same principles are used for the dynamic morphing of structures and mechanisms. The proposed formulation can be applied, among many others, on smart irrigation systems such as spray booms, on radio-telescope bases, on the spars of smart wings, on aircraft wings etc.

Improvement in Two Adjacent Microgrids Frequency Using the AC-to-AC Converter Based on Sugeno Fuzzy Control Scheme

Microgrid and multi-microgrid are solution for integrating DGs into a system and are components of future power systems. Exploitation and frequency control in islanding mode is of special importance due to the lack of sufficient spinning reserve. Almost, DGs are connected to the microgrid through inverters and frequency deviation from an allowed threshold makes their removal from the main grid which is not acceptable for consumers and producers of the electrical power. One way for frequency control and optimized exploitation of the entire system is to connect the adjacent microgrids. Indeed, the microgrids cooperate together through exchanging their power surplus and power shortage. In this paper, a new hybrid control method based on Sugeno- and Mamdani-type fuzzy inference system to control an AC-to-AC converter connector of two adjacent microgrids has been proposed. An analytical method for controlling the converter was developed using the Sugeno. The proposed method is simple and practical and can be easily implemented. Moreover, to eliminate the steady-state error and to modify the performance of the control system, it is proposed to use a PI fuzzy controller in its outer loop. Simulation results show that the AC-to-AC converter with the proposed controlling strategy has prevented the intensive frequency deviations and has improved the frequency control in both microgrids.

Hydro-morphological modeling to characterize the adequacy of jetties and subsidiary alternatives in sedimentary stock rationalization within tidal inlets of marine lagoons

Alterations to the tidal inlets shoals, whether natural or anthropogenic, can dramatically influence the hydro-morphological balance of the inlet system in the short and long-term. Jettied inlets, despite typically supported by former technical design reports in curtailing the sedimentology stock, sometimes they require amendments. This paper provides an expository epitome for understanding the role of jetties in concerning the extent of their adequacy to economically and ecologically rationalize the internal siltation within both the western and the eastern tidal inlets of the Bardawil Lagoon, which is situated along the Mediterranean coastline of the Sinai Peninsula, Egypt. The main tool to characterize that role is the hydro-morphological modeling of the inlets system. We used a two-dimensional hydrodynamic circulation model consisting of two steering modules, CMS-Flow and CMS-Wave. In a five-year simulation process, this study emphasized that the sole use of jetties was noticeably insufficient to fulfill the satisfactory stabilization of the sedimentation processes inside the inlets. Consequently, the lake's ecosystem might entirely collapse in the short run due to the progressive deposits fashioning in the form of ebb/flood-tidal deltas. Accordingly, we proposed alternative solutions based on structural modulation scenarios in the vicinity of inlets domain encompassing jetties extension, flow-diverted wall (FDW), sediment trap (ST), random radial channels (RC) and lateral wide channel (LWC). Based upon the results of an optimization model using the Mamdani-type fuzzy inference system, the addition of a lined LWC alongside with jetties proved to be an effective solution over a longer period that rather meets the needs of the majority of stakeholders satisfactorily.

Accelerating fuzzy cellular automata for modeling crowd dynamics

Pedestrian and crowd dynamics are physical phenomena that are fundamentally characterized by non-linear complexity. In the same time, the need of modern way of living seeks for such dynamics real time modeling also enabling computationally efficient and affordable solutions for sake of safety and easiness of people located in gathering places all over the world. Towards this direction, Cellular Automata (CAs), a parallel computational model combining macro- and microscopic inherent attributes that could severely help with adequate modeling of the aforementioned dynamics, are one of the best compromises among different competing computational techniques. In order to overcome CAs deterministic nature, in this paper the incorporation of fuzzy logic principles in a CA model that simulates crowd dynamics and crowd evacuation processes, with the usage of a Mamdani type fuzzy inference system, is proposed. More specifically, basic concepts of fuzzy logic such as linguistic variables and if-then rules are attributed to the proposed CA model to preserve fuzzy consequents and fuzzy antecedents thus resulting in a realistic and rather efficient modeling approach. Furthermore, in the paper the implementation of fuzziness in CA dynamics is tackled with the acceleration of the proposed model through fully parallel execution on Graphics Processing Units (GPU). The GPU implementation of the fuzzy CA model is analyzed in full detail and stressed against CPU corresponding implementation resulting to an important speed up of fuzzy CA execution. This is further explored through the GPU applications of the fuzzy CA model in a real building, namely the museum `CONSTANTIN XENAKIS', in Serres, Greece.

Robot navigation with obstacle avoidance in unknown environment

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.

Implementation of fuzzy logic approach to estimate the degree of expulsion and spattering index and weld strength in projection welding

A new Mamdani-type fuzzy inference system with four input variables such as weld current, electrode force, a transformed variable of weld time + sheet thickness, and projection height was proposed for both DC 04 and DP 600 steel sheets to predict the degree of expulsion and spattering (conjugate) index (E) and the maximum push-out load of weld (weld strength) (Fk) in projection welding. In the computational analysis, trapezoidal membership functions were constructed for the fuzzy subsets of each model variable. The product (prod) and the center of gravity (centroid) methods were implemented as the built-in AND method and defuzzification method, respectively. Fuzzy logic outputs were compared with the predictions of multiple regression analysis-based models derived within the scope of this work. Model performances were quantified by means of various statistical performance parameters. Linear regressions between the outputs of the fuzzy logic model and the experimentally measured values yielded very high determination coefficients (R2 = 0.967 − 0.994). The well-correlated results reveal applicability of the fuzzy logic model for predicting the expulsion and spattering (conjugate) index, and weld strength, as well as determining the optimal combination of the process parameters to conduct the projection welding operations resulted in desired weld quality.

Numerical simulation of shoreline responses in the vicinity of the western artificial inlet of the Bardawil Lagoon, Sinai Peninsula, Egypt

This paper introduces an appropriate visualization of the tidal inlets including the characterization of internal siltation and the remediation of associated non-affirmative consequences due to the existence of its protective jetties, such as down-side erosion and up-side accretion. Accordingly, the 2D hydrodynamic circulation models CMS-Flow and CMS-Wave have been initially set up to describe the morphodynamics situation inside the artificial western inlet (inlet (1)) of the Bardawil Lagoon, which is situated along the Mediterranean coastline of the Sinai Peninsula, in the presence and absence of the jetties. Simultaneously, the Littoral Processes and Coastline Kinetics (DHI-LITPACK) numerical model is essentially applied to predict the shoreline responses in the vicinity of inlet (1) to numerous different scenarios considering both hard (i.e., groins and detached breakwaters) and soft (i.e., sand nourishment and sand bypassing systems) measures during the period from 2010 to 2030. The study has revealed that the use of hard measures to the east of the inlet would induce exceedingly high recession rates of the shoreline. Nevertheless, the use of a one-time nourishment of 250,000 m3 in the areas between short groins (with lengths of 90 m and an interval of 400 m) during their construction would optimally reduce the expected area of erosion by approximately 91.85%. Meanwhile, a single groin with a length of 200 m and a westward drift distance of 450 m to the west of the inlet would be superior with regard to optimizing the sedimentary stock compared to an annual dredging rate of 100,000 m3/year. Finally, an optimization model has been constructed using a Mamdani-type fuzzy inference system to determine the cheapest and most efficient solution with the minimum impact on the sandy barrier zone of inlet (1).

Performance analysis of fuzzy systems based on quintuple implications method

To improve the quality of approximate reasoning in fuzzy system, quintuple implication principle (QIP) to resolve fuzzy modus ponens (FMP) and fuzzy modus tollens (FMT) problems has been proposed by Zhou et al. [32]. The same approximation as Mamdani-type fuzzy inference can be reached by fuzzy reasoning with QIP method for Gödel implication. It therefore is essential to establish some fundamental properties of fuzzy inference system with QIP method. This paper mainly investigates the robustness and universal approximation capability of fuzzy inference system with QIP method. Firstly, we present the QIP solutions of FMP and FMT for R-, S-, QL-, f- and g-implications. And then the robustness of fuzzy inference system with QIP method is discussed. Finally, we study the universal approximation properties of multiple-input and single-output (MISO) fuzzy systems with QIP method for R-, S- and QL-implications. These results reveal that the QIP method possesses better performance in fuzzy rule-based system.

Intelligent Systems for Structural Damage Assessment

Abstract This research provides a comparative study of intelligent systems in structural damage assessment after the occurrence of an earthquake. Seismic response data of a reinforced concrete structure subjected to 100 different levels of seismic excitation are utilized to study the structural damage pattern described by a well-known damage index, the maximum inter-story drift ratio (MISDR). Through a time-frequency analysis of the accelerograms, a set of seismic features is extracted. The aim of this study is to analyze the performance of three different techniques for the set of the proposed seismic features: an artificial neural network (ANN), a Mamdani-type fuzzy inference system (FIS), and a Sugeno-type FIS. The performance of the models is evaluated in terms of the mean square error (MSE) between the actual calculated and estimated MISDR values derived from the proposed models. All models provide small MSE values. Yet, the ANN model reveals a slightly better performance.

Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry

Comprehensive energy modeling and saving potential analysis play a key role in sustainable development of complex petrochemical industry. However, it is difficult to make effective energy modeling and saving potential analysis due to the characteristics of uncertainty, high nonlinearity, and with noise of the data collected from the practical production. To deal with this problem, an energy modeling and saving potential analysis method using a novel extreme learning fuzzy logic network is proposed. In the proposed method, Mamdani type fuzzy inference system and multi-layer feedforward artificial neural network are integrated. First, the original ethylene production data is fused into a comprehensive energy consumption index. Then the index is fuzzified as outputs instead of precise values. Finally, an extreme learning algorithm based on Moore-Penrose Inverse is utilized to train the network efficiently. Three levels of energy efficiency of “low efficiency, median efficiency and high efficiency” can be effectively classified using the proposed method. For “low efficiency”, valid slack variables are predicted for finding the direction of improving energy efficiency and then analyzing the energy saving potential. The performance and the practicality of the proposed method are confirmed through an application of China ethylene industry. Simulation results show that low-efficiency samples can be effectively improved to be high-efficiency samples and the energy saving potential in terms of the crude oil reduction amount is indicted as 8.82%.

New algorithm for distribution system reconstruction planning based on fuzzy inference and multicriteria decision making

This paper presents a new algorithm for distribution system reconstruction planning based on Mamdani type fuzzy inference and BellmanZadeh multi criteria decision making method. The proposed algorithm takes system attributes as inputs (number of customers served by renewed infrastructure, energy losses, power demand and cost of investment) and returns crisp output values which are used as planning criteria. The aim of this paper is to provide a logical decision making framework which can be used to model, evaluate, and rank projects according to required criteria. The proposed model is flexible and can be extended to include additional planning criteria. The proposed method is tested on a realistic distribution system to demonstrate its relevance. It is expected that this paper will make a contribution toward more effective management of power distribution network planning process and that it will be used by planning engineers in practical problems.

Fuzzy batch controller for granular materials

The paper focuses on batch control of granular materials in production of building materials from fluorine anhydrite. Batching equipment is intended for smooth operation and timely feeding of supply hoppers at a required level. Level sensors and a controller of an asynchronous screw drive motor are used to control filling of the hopper with industrial anhydrite binders. The controller generates a required frequency and ensures required productivity of a feed conveyor. Mamdani-type fuzzy inference is proposed for controlling the speed of the screw that feeds mixture components. As related to production of building materials based on fluoride anhydrite, this method is used for the first time. A fuzzy controller is proven to be effective in controlling the filling level of the supply hopper. In addition, the authors determined optimal parameters of the batching process to ensure smooth operation and production of fluorine anhydrite materials of specified properties that can compete with gypsum-based products.

Control of Robot Manipulators Using Time-Delay Estimation and Fuzzy Logic Systems

A highly accurate model-free controller is proposed for trajectory tracking control of robot manipulators. The proposed controller incorporates time-delay estimation (TDE) to estimate and cancel continuous nonlinearities of robot dynamics, and exploits fuzzy logic systems to suppress the effect of the TDE error, which is due to discontinuous nonlinearities such as friction. To this end, integral sliding mode is defined using desired error dynamics, and a Mamdani-type fuzzy inference system is constructed. As a result, the proposed controller achieves the desired error dynamics well. Implementation of the proposed controller is easy because the design of the controller is intuitive and straightforward, and calculations of the complex robot dynamics are not required. The tracking performance of the proposed controller is verified experimentally using a 3-degree of freedom PUMAtype robot manipulator.

Control of a dc motor using fuzzy logic control algorithm

ABSTRACT This study sought to establish the impact of a fuzzy logic controller (FLC) and a Proportional - Integral - Derivative (PID) controller in the control performance of an industrial type DC motor using MATLAB. The fuzzy logic controller was developed on the basis of Mamdani type fuzzy inference system (FIS). The centroid method of defuzzification was also adopted. A choice of seven membership functions was designed for the error and change in error inputs alongside the output, hence a resultant of 49 rules was achieved for the fuzzy controller. Simulations for the PID controller and with the fuzzy controller were obtained at no load and for a selected reference speed of 1200 rpm. The simulations were further repeated for a load torque of 7Nm with the same reference speed. The simulated results were compared. Based on the findings, it was observed that the fuzzy speed controlled DC motors perform better than the PID speed controlled DC motors in terms of its faster settling time, absence of an overshoot and its sensitivity to applied load. Sequel to the foregoing findings, the researchers concluded that fuzzy logic controllers should be readily implemented in our local industries to enhance precision and improve performance in operations involving DC motors. http://dx.doi.org/10.4314/njt.v36i2.35

A novel routing strategy for cognitive radio ad hoc network based on Sugeno fuzzy logic

In cognitive radio networks, a secondary user (SU) is dependent upon the spectrum, of varied frequency, temporarily vacated by primary users (PU) thereby making it quite a challenging task to design a protocol for finding a reliable and efficient routing path. To address the issue many routing schemes have been proposed based upon Mamdani type fuzzy inference system (FIS) to create a human intelligence analogy. The Mamdani type fuzzy inference mechanism is unable to simultaneously optimise multiple quality of service (QoS) parameters. This paper proposes a routing scheme based upon the Sugeno type fuzzy inference system (SFIS) that handles the continuously changing input parameters in quite effective manner and provides an effective performance with respect to multiple QoS parameters. The proposed routing scheme has been implemented using Matlab-9.0 and simulation results showed that the paths selected using this scheme are much more efficient than the paths selected by other routing schemes.

Process optimization for extraction of bioactive compounds from taro (Colocasia esculenta), using RSM and ANFIS modeling

Response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS) modeling was applied for optimization of process parameters for extraction of bioactive compounds from taro flour. A Face-centered central composite design (FCCD) was adopted to study the effects of process parameters on Total phenolic content (TPC), Total flavonoid content (TFC) and their antioxidant activity in terms of 1,1-Diphenyl-2-picryl-hydrazyl (DPPH), Ferric reducing antioxidant power (FRAP) and Total antioxidant activity (TAA) of taro flour. Three process parameters, solvent concentration, extraction temperature and extraction time were selected in range of 40–80% v/v, 30–60 °C and 1–5 h respectively. The optimum conditions for extraction of antioxidant from taro flour were obtained as solvent concentration 60% v/v, extraction temperature 48 °C and extraction time 4.0 h. Under these optimum conditions, the value of TPC, TFC, DPPH, FRAP and TAA were 14.17 mg GAE/100 g, 10.78 mg RE/100 g, 21.80%, 63.78 mg BHT Eq/100 g and 63.47 mg AAE/100 g with the desirability of 0.91. ANFIS model has been developed in terms of process parameters through Mamdani type fuzzy inference with triangular membership function for the prediction of responses using training data set. The results indicate that it rendered competitive results as obtained with RSM. Under the optimum conditions, experimental values were found close to the predicted values, obtained through RSM and ANFIS modeling. Liquid chromatography mass spectrometry (LCMS) analysis revealed that the caffeic acid, gallic acid, chlorogenic acid, catechin, epicatechin 3, 5 DiCQ acid and flavonols were the major compounds present in taro flour extract.

Weight on drill bit prediction models: Sugeno-type and Mamdani-type fuzzy inference systems compared

Drilling optimization aims to optimize controllable variables during drilling operations, such as weight on bit (WOB), in order to improve drilling rate of penetration and reduce well costs. Prediction models of Weight on Bit (WOB) are developed using two widely-applied fuzzy inference systems (FIS), Mamdani-type and Sugeno-type, for the Ahwaz oil field and Marun gas field formations; two large producing fields in Iran. The FIS are constructed based on field data involving multiple wells; six wells from Ahwaz field, and two wells from Marun field. The controllable input variable data for the FIS includes: rate of penetration, mud weight, formation type, pump pressure, pump flow rate and rotation rate. The key difference between these two FIS techniques is the manner in which they calculate their crisp output values. The Mamdani-type FIS requires defuzzification, whereas the Sugeno-type FIS applies a constant weighted-average technique avoiding defuzzification. The results for the two field cases evaluated convincingly demonstrate that the Sugeno-type FIS is superior to the Mamdani-type FIS for WOB prediction using the same input data and membership functions. There is scope for further refinement of FIS models for WOB prediction (e.g., by adding bit type information) and the Sugeno-type FIS methods should reduce the time and cost associated with the conventional rate of penetration methods for predicting WOB.

Effectiveness of Fuzzy Logic-based FACTS Controller and Classical Controller in Enhancement of Power System Dynamic Stability – A Comparative Study

Performances of Fuzzy Logic based impedance types FACTS controllers and classical controllers in damping power system oscillations have been studied and their effectiveness have been compared. Different FACTS devices are considered, namely (i) Thyristor Controlled Series Compensator (TCSC), (ii) Thyristor Controlled Phase Shifter (TCPS) (iii) Static VAR Compensators (SVC), and their combinations. Mamdani type fuzzy inference system is used. PSS, Lead-Lag and proportional type classical controllers have been considered. Performances of the controller have been demonstrated through time domain simulation studies. Simulation results have been compared and conclusions have been drawn. Results show that the Fuzzy Logic controllers have excellent and comparatively better potential in damping power system oscillations.

Adaptable Fuzzy Expert System for Ship Lock Control Support

A ship lock zone represents a specific area on waterway, and control of the ship lockage process requires a comprehensive approach. This research is a practical application of a Mamdani-type fuzzy inference system and particle swarm optimisation to control this process. It presents an optimisation process that adapts control logic to the desired criteria. The initially proposed Fuzzy Expert System (FES) was developed using suggestions from lockmasters (ship lock operators) with extensive experience. Further optimisation of the membership function parameters of the input variables was performed to achieve better results in the local distribution of ship arrivals. The presented fuzzy logic-based expert system was designed as part of a Programmable Logic Controller (PLC) and Supervisory Control And Data Acquisition (SCADA) system to support decision making and control. The developed fuzzy algorithm is a rare application of artificial intelligence in navigable canals and significantly improves performance of the ship lockage process. This adaptable FES is designed to be used as a support in decision-making processes or for the direct control of ship lock operations.

An Automatic Frequency Controller for Kotmale Hydro Power Station using Fuzzy Logic

During supply demand mismatches, the system frequency can either fall or rise compared with its previous steady state operating value. In such instances, the generator governors either increase or decrease the generation (in accordance with the governor droop setting) to restore the supply demand balance and stop the ensuing frequency change (fall or rise). However, even after the supply demand balance has been restored and the frequency has reached a new steady state value, there can be a frequency error. The governor action assists to minimize the steady state frequency error. However, zeroing the frequency error and bringing the system frequency back to the rated value need adjustment to the “speed reference” of governors. Although this is normally done by Automatic Generation Controllers (AGC) that are on secondary load-frequency control, in Sri Lanka, the frequency controlling act is performed entirely manually through operator intervention. Such manual frequency control has a number of draw backs, including possible operator errors and longer response times. One of the four power stations designated to perform the frequency control function in the Sri Lankan power system is at Kotmale. This paper presents an automatic frequency controller using Fuzzy Logic to carry out frequency controlling at Kotmale power station. The Fuzzy Logic based controller is designed to replace the action of the operator, who presently controls the frequency manually. The paper presents the modeling and the simulation of the existing frequency control system using relevant parameters of the Kotmale governor and the chronological load curve of the Sri Lankan power system. MATLAB/SimulinkTM 2013 software package is used to calculate the steady state frequency error. Subsequently, the parameters are fine-tuned and the Fuzzy Logic Controller (FLC) is introduced as a secondary loop frequency controller and the study is repeated. Mamdani type fuzzy inference system is employed here to implement this project because of its simplicity, robustness, reliability and intuitive characteristics. Finally the simulation results using the FLC are compared with the existing frequency controlling system of Kotmale power station to prove that the FLC yields improved control performance.