1. Introduction

The continuous material weighing conveyor is an automatic weighing machinery, used to weigh the particulate materials in the form of a continuous stream. These weighing conveyors are used for continuous transport of the solid particulate materials in industries, such as: mining and quarrying, building materials, beverage and food processing, power generation, agricultural, pharmaceutical. These weighing conveyors may be incorporated into the conveying line to obtain a mass feed rate in a continuous process or totalized mass material delivered. The mass flow rate of material is deduced from two distinct measurements: the velocity of the conveyor and the average linear density of the material (mass per length) contained within the weigh length (the length of a conveyor section installed weigh frame with load cell sensor [1]). Consequently, the weighing conveyor performance depends not only on the conveyor velocity control loop, but also on the material mass flow control loop.

The weight feeder system is presented in [2] with a dedicated controller to control material at a specified rate, and then it is applied in many fields, such as processing industry [3], cement industry [4] and so on. Sato and Kameoka [5] proposed the generalized minimum variance control method to minimize the variance of discharged material. Then, Sato [6] applied the generalized predictive control to improve the quality of the weight control conveyor. Recent studies [7-9] focus on applying modern control techniques, such as combining PID with PSO algorithm to achieve optimal PID parameters, close to industrial weighing process.  The studies [10,11] present fuzzy logic-based control method that mimics PID control rules or use fuzzy logic to adjust PID parameter to overcome some nonlinear characteristic of weighing conveyor.

The majority of these studies [2-11] only mention the conveyor velocity control loop with acceptable control quality but have not consider to the fact that output mass flow control of the conveyor must be attached according to the production technology trajectory (for material mass flow control problem). Moreover, the only-velocity-control-loop can cause large mass difference when conveyor runs at high velocity, or when hopper’s material varies greatly (source of input materials).

This article presents a novel cascade control strategy for the continuous weighing conveyor based on PID control and fuzzy logic with two-loop-control-structure: inner-velocity-control-loop and outer-mass-flowrate-control-loop. The PID parameters are synthesized based on the identified velocity control object. Then, the fuzzy logic block was developed to adjust the PID parameters of the material mass flowrate control loop according to the input reference, based on the output error and the output error differential. The parameters of this controller can be assigned directly to PLC instructions to stabilize the material mass flowrate according to the input reference. The rest of this article is organized as follow. Section 2 introduces the structure and operation principle of the weighing conveyor. Section 3 deals with the design of controllers for material weighing conveyor. Section 4 presents the experimental setup of the real-time weight system. And then, the experimental results are discussed in section 5. Finally, section 6 presents conclusions.

2. Structure and operation principle of the weighing conveyor

2.1.  Description of continuous weighing conveyor

Figure 1 describes the basic components of a continuous weighing conveyor, are built to test the research result in this article, including: (1)-material hopper, (2)-weighing table, (3) loadcell, (4)-conveyor motor, (5)- loadcell signal amplifier, (6)- PLC controller, (7,8)-power converter, (9)-DC source, (10)-control panel, (11)-PLC cable, (12)-PC for monitoring.

Figure 1. Structure of continuous weighing conveyor

This weighing conveyor system has the follows basic parameters: conveyor length 1000[mm], conveyor velocity 0.2[m/s], material’s mass flowrate 1.0[kg/s], weighing table length 100[mm], roller diameter 26[mm]. Conveyor drive by EXCEM DC motor with capacity 400[W], power require 24[V], maximum velocity 3000[rpm]. Measure the density of material on conveyor using a weighing table, putting on OBU loadcell with power supply 10[V], capacity 10[kg], rated output 2±10%[mV/V]. The velocity of conveyor is measured by LPD3806 rotary encoder with a DC operating voltage of 5-24[V], resolution 100[pulse/rotation].

2.2.  Weighing conveyor block diagram

Material mass flowrate on weighing conveyor is determined by simultaneously measuring two parameters: conveyor velocity (v [m/s]) and material density (g [kg/m]). The control of material mass flowrate on conveyor following on reference input is done by adjusting conveyor velocity, as shown in Figure 2.

Types of solid grain materials (grain coal, small rocks, cement concrete, corn grain, rice and so on) contain in hopper, are spread evenly on weighing conveyor to ensure uniformity in the density of materials on weighing table.

Where: EN- conveyor velocity measuring encoder, LC- material density on weighing table weight measuring loadcell, KD- weight measuring signal amplifier, HS- gearbox, DC- electric motor, BD- power converter for motor, PLC- controller (ADC- analog/digital converter, HSC- high velocity counter, PWM- pulse width modulation, ALQ- algorithm of material mass flowrate control).

Figure 2. Schematic view of the weighing conveyor

2.3.  Calculation of the material mass flowrate on conveyor

      Conveyor velocity is determined by counting the numbers of pulses of the encoder attached to conveyor spindle (the conveyor always rotates follow the shaft and has no sliding phenomenon)

      Here: N_m[pulse]- encoder pulses on a rotation (encoder resolution); T_p[s]- pulse counting cycle; D[m]- conveyor roller diameter mounted coaxially with EN; p[pulse]- counted pulses.

      Material density is proportional to the weight of material on the weighing table measured by loadcell [11]:

      Here: q_w[kg]- material weight on weighing table measured by loadcell; L_w[m]- converted weighing table length, L_B[m]- conveyor length between two rollers on weighing table, L_A[m]- conveyor length between two rollers on feeding side, L_B[m]- conveyer length between two rollers on exiting side.

      The following analysis makes the simplifying assumption that any section of conveyor and the bulk material it carries is fully supported by the two closest rollers. This being the case, the full weight of product distributed on the conveyor and indeed the weight of the conveyor in the region L_B of Figure 3.

Figure 3. Schematic representation of the belt weigher

      Momentary material mass flowrate on the conveyor equals to multiplication of material density and conveyor velocity, according to the following formula as:

      The mass flowrate of material (mass per time) is determined by multiplying the linear density (mass per length) by the velocity of the material (length per time). The velocity of the material is obtained by measuring the velocity of the conveyor and making the assumption that the material moves at the same velocity. However, care has to be taken to ensure that this is the case. The most common way of measuring conveyor velocity is through determination of the revolutions of a wheel in contact with the clean side of the conveyor.

      The actual average mass flowrate on conveyor is determined by taking the instantaneous mass flowrate integration with the integral time constant T_q[s], according to the following formula:

3. Design of controllers for material weighing conveyor

3.1.  Structure block diagram of material flowrate control system

      Structure block diagram of the material mass flowrate control system on the conveyor consists of two control loops: the inner conveyor velocity control loop and the outer mass flowrate control loop, as shown in Figure 4.

Figure 4. Structure block diagram of the weighing conveyor control system

      Here: R_q– mass flowrate controller, R_v– conveyor velocity controller, BT- conveyor, DL- delay block when considering the time delay of the material transport on the conveyor, AQ- average mass flowrate calculation block, q_sp– flow set point, U_v– conveyor velocity control signal, G_ov– conveyor velocity control object, including BD converter based on MOSFET, motor and gearbox DC+HS, conveyor BT, encoder measuring and calculating conveyor velocity EN+KV.

      Mass flowrate controller Rq is designed based on the principle of PID control law with the parameters k_PF, k_IF, k_DF that are adjusted online by fuzzy calculating block FCQ based on initial parameters k_P0, k_I0, k_D0 synthesized according to traditional methods.

3.2.   Identify the object of the conveyor velocity control loop

      Experimental diagram to identify the control object of the loop to control the conveyor velocity as shown Figure 5.

      The laptop/PC (with monitoring interface based on Wincc) communicates with PLC controller. Pulse width modulation mode setting time: T_PWM=1ms. To achieve the conveyor velocity characteristic, we do the following: on the computer, setting the velocity value v_sp=0.2m/s; at this time, the PLC outputs feed the control pulse with time T_ON=T_PWM., corresponding to the control signal U_v=100%. This pulse is sent to the BD power converter to fully open the MOSFET supply U_dm for the DC motor and therefore the BT conveyor operates. Conveyor velocity is determined by counting number of pulses at PLC input (connected to the encoder). The characteristic of conveyor velocity is collected on the WinCC interface on the computer, as shown in Figure 6.

Figure 5. Block diagram for identifying object of velocity control loop

Figure 6. Response of the conveyor velocity control loop

Based on the characteristic type of the velocity loop control object, allow to approximate this control object as the first order inertia object, with the transfer function as follow:

Here: k_v là gain coefficient; T_v inertial time.

From graph on Figure 6, to determine T_v, drawing the tangent to the characteristic, the inertial time T_v can specify: T_v=0.5[s].

Gain coefficient k is determine as follow:

Thus the transfer function of the velocity loop control object, in approximate form, as follow:

3.3.  Design of the conveyor velocity controller

      The block diagram of the conveyor velocity control loop is shown in Figure 7.

Figure 7. Block diagram of velocity control loop

      Applying the module optimal method for the conveyor velocity control object, the velocity controller is determining as the integral control law (I) with the transfer function:

3.4.  Design of the material mass flowrate controller

      The approximate transfer function of the conveyor velocity control loop takes the form as:

      The block diagram of the material mass flowrate control loop on the conveyor, with the assumption of constant material density, taking into account the delay time of material transport on the conveyor, as shown in Figure 8.

Figure 8. Block diagram of mass-flow control loop

      In this diagram the parameters are as follows: k_g =0.5[kg/m] is the coefficient of calculating the material mass flowrate when the velocity is in a steady state;t=1[s] is the time of material transport delay on the conveyor; G_aq(s)=1/(T_qs) is the transfer function of the material mass flowrate average calculation block, T_q=1[s].

      The control object of the material mass flowrate control loop has the equivalent transfer function as follows:

      Applying symmetrical optimum (SO) method to the mass flowrate control object, the mass flowrate controller is determined as proportional – integral – differential control law (PID-SO) with the transfer function as follows:

3.5.  Design of the fuzzy calculation block for the seft-tunning parameters of the mass flowrate PID controller

      The FCQ fuzzy calculation block has two inputs: the error of the material mass flowrate EQ and the deviation differential of the material mass flowrate DEQ, corresponding to the signal e=q_sp-q and the deviation differential signal de/dt. The FCQ block has three outputs, proportional coefficient-PQ, integral coefficient-IQ, the differential coefficient – DQ, corresponding to the three output values are k_PF, k_IF, k_DF. With the above structure, the coefficients of the PID controller in combination with the fuzzy calculation block (PID-F) are determined as follows:

Using the membership functions of triangles, implementing the input variables by 5 fuzzy sets {NB (Negative Big), NS (Negative Small), ZE (ZEro), PS (Positive Small), PB (Positive Big)} and output variables are equal to 5 fuzzy sets {SM (SMall), ME (MEdium), BI (BIg), QB (Quite Big), VB (Very Big)}. The physical value range of the input variables and the output variables are as follows: EQÎ[-1.0,1.0], DEQÎ[-10.0,10.0], PQÎ[0,1.0], IQÎ[0,0.01], DQÎ[0,0.5]. Based on the weight conveyor operation experience, the characteristic of the material mass flowrate control process and the PID control principle to improve the control quality for the system, the fuzzy rule table for FCQ is built as Table 1. Using the Max-Min inferential law and defuzzification according to the centroid point method, the output clear values of FCQ block k_PF, k_IF, k_DF are defined.

Table 1. Fuzzy rules of the FCQ block

3.6.  Simulation of the mass flowrate control system on MATLAB

      Building the material mass flowrate control system on Matlab with the R_v controller according to (8), the R_q controller has been re-calibrated with the toolbox PID Tuner so that the control quality meets the requirements, Figure 9 shows the step response of this system. The R_q controller parameters, after adjusting by toolbox PID Tuner as follows:

Figure 9. Step response of the weight control system: 1- reference input, 2- controller PID-F, 3- controller PID

      Simulation results show that the PID controller combined with self-tuning parameters (PID-F) gives better control quality than the traditional PID controller: quick response, reduced overshoot, small steady state error, reduced the impact of noise (the level of material in the hopper).

Table 2. Quality Improvement of PID-F controller and others

4. Experimental setup of the real-time weight system

4.1.  Hardware wiring diagram

      In this experimental system, PLC programmable logic controller Siemens S7-200 is used to install PID algorithm for velocity control loop and PID-F algorithm for the mass flowrate control loop. The equipment of the system is shown in Figure 1. The complete system is shown in Figure 10.

Figure 10. Experimental setup of the weighing conveyor

4.2.  Algorithm for controlling material mass flowrate

The general algorithm to control the material mass flowrate for continuous weight conveyor system is shown in Figure 11.

After initializing the initial values for the system, the main program in turn calls subroutines: calculating conveyor velocity, calculating the mass of the material, calculating the material mass flowrate, calculating the error of the material mass flowrate and its deviation differential. Then the main program calls the subroutine to execute the R_q, R_v controller algorithms. Finally, the main program calls the PLC’s pulse calculation function to send to the BD converter, thereby controlling the motor velocity and thus meeting the required material mass flowrate.

Figure 11. General algorithm for controlling mass flowrate

The algorithm for programming material mass flowrate controller R_q on PLC is shown in Figure 12.

The algorithm to program the conveyor velocity controller R_v on PLC is shown in Figure 13.

5. Experimental results

      The experimental setup is shown in Figure 1, 10. In this study, the PLC S7-200 and expansion module analog EM235 are used. The laptop is communicated with PLC S7-200 through the PC Adapter USB cable to download the user program (control algorithm) into PLC S7-200 station. The output channel (PWM) from the PLC is supplied to the driver (BD) of the conveyor’s motor. The conveyor’s velocity measured by the encoder LPD3806 is fed back to the PLC via the high velocity counter input channel. The mass of materials moving on the conveyor measured by the loadcell OBU is fed back to the analog input channel of the EM235 module, after having amplified by the KD amplifier device.

Figure 12. Algorithm of the controller R_q

The PID algorithms for the velocity control loop and the mass flowrate control loop are organized in PLC S7-200, as shown in Figure 14. The fuzzy logic calculation algorithm for adjusting PID parameters of the mass flowrate control loop is also programmed with the separate subroutine on this PLC.

Figure 13. Algorithm of the controller R_v

Figure 14. PID algorithms organization in PLC

 The screen for monitoring the weighing conveyor was built on WinCC, as described in Figure 15.

Figure 15. Screen for monitoring the weighing conveyor

This experiment was carried out with the materials – rice. The tests were done at the material’s mass flowrate q_sp=80[kg/h], and q_sp=100[kg/h].The experimental results with algorithm PID combined fuzzy calculating block (PID-F) to adjust the PID’s parameters are shown as Figure 16-17.

Figure 16. Real-time response of the material mass flowrate as using PLC with PID-F algorithm at q_sp=80[kg/h]: 1- reference, 2- response

Figure 17. Real-time response of the material’s mass flowrate as using PLC with PID-F algorithm at q_sp=100[kg/h]: 1- reference, 2- response

      From the real-time response of the material’s mass flowrate, shows that the control object of the mass flowrate loop exits delay time with the constant delay time approximate 1 second; rising time about 5 second; after about 6.5 second, the steady state is reached; then, mass flowrate is small oscillated around the input reference. This oscillation is caused by noise measurement of the material’s mass on the conveyor by loadcell combined with the no-filter amplifier circuit.

6. Conclusion

In this article, a nonlinear model of the weighing conveyor system is developed as considering the delay time of material transport on conveyor. This is more suitable for the actual weighing process in industries. And then, a novel two-control-loop strategy is applied for controlling the material mass flowrate on conveyor. The integral control algorithm-based inner control loop is designed for the conveyor velocity object which is identified from the experimental device system at UTC’s laboratory. The outer control loop is developed for the material mass-flowrate object based on PID controller combined self-tuning parameters by the fuzzy logic calculation block. Thereafter, the proposed control strategy is verified and evaluated by simulation on MATLAB and performed experimentally on the physical equipment system at UTC’s laboratory. The experimental results show that PLC controller is capable of performing the proposed control strategy with reasonable cost, but bring better control quality, thanks to the application of advanced control techniques.

Future research focuses on improving quality of controller or/and combined with IMC, GA, GPC algorithms, as considering disturbance and applying for other materials with different density and changing parameters of the object in real time.

Conflict of interest

The authors declare no conflict of interest.

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39. Slim Dhahri, Essia Ben Alaia, "Robust Static Output-Feedback Fault Tolerant Control for a Class of T-S Fuzzy Systems using Adaptive Sliding Mode Observer Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 4, pp. 70–80, 2020. doi: 10.25046/aj050410
40. Trinh Luong Mien, Vo Van An, Bui Thanh Tam, "A Fuzzy-PID Controller Combined with PSO Algorithm for the Resistance Furnace", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 568–575, 2020. doi: 10.25046/aj050371
41. Pham Van Bach Ngoc, Bui Trung Thanh, "Dynamics Model and Design of SMC-type-PID Control for 4DOF Car Motion Simulator", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 557–562, 2020. doi: 10.25046/aj050369
42. Fadoua Tamtam, Amina Tourabi, "A Framework for Measuring Workforce Agility: Fuzzy Logic Approach Applied in a Moroccan Manufacturing Company", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 411–418, 2020. doi: 10.25046/aj050352
43. Efrain Mendez, German Baltazar-Reyes, Israel Macias, Adriana Vargas-Martinez, Jorge de Jesus Lozoya-Santos, Ricardo Ramirez-Mendoza, Ruben Morales-Menendez and Arturo Molina, "ANN Based MRAC-PID Controller Implementation for a Furuta Pendulum System Stabilization", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 324–333, 2020. doi: 10.25046/aj050342
44. Kapil Kumar Gupta, Namrata Dhanda, Upendra Kumar, "A Novel Hybrid Method for Segmentation and Analysis of Brain MRI for Tumor Diagnosis", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 3, pp. 16–27, 2020. doi: 10.25046/aj050303
45. Aditi Haresh Vyas, Mayuri A. Mehta, "A Comprehensive Survey on Image Modality Based Computerized Dry Eye Disease Detection Techniques", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 2, pp. 748–756, 2020. doi: 10.25046/aj050293
46. Ilyas Lahlouh, Ahmed Elakkary, Nacer Sefiani, "Design and Implementation of State-PID Feedback Controller for Poultry House System: Application for Winter Climate", Advances in Science, Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 135–141, 2020. doi: 10.25046/aj050118
47. Daniel Szabo, Emese Gincsaine Szadeczky-Kardoss, "Novel Cost Function based Motion-planning Method for Robotic Manipulators", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 386–396, 2019. doi: 10.25046/aj040649
48. Noor Syahirah Nordin, Mohd Arfian Ismail, Vitaliy Mezhuyev, Shahreen Kasim, Mohd Saberi Mohamad, Ashraf Osman Ibrahim, "Fuzzy Modelling using Firefly Algorithm for Phishing Detection", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 6, pp. 291–296, 2019. doi: 10.25046/aj040637
49. Olena Basalkevych, Olexandr Basalkevych, "Fuzzy Simulation of Historical Associative Thesaurus", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 224–233, 2019. doi: 10.25046/aj040528
50. Samruan Wiangsamut, Phatthanaphong Chomphuwiset, Suchart Khummanee, "Chatting with Plants (Orchids) in Automated Smart Farming using IoT, Fuzzy Logic and Chatbot", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 5, pp. 163–173, 2019. doi: 10.25046/aj040522
51. Stephen Craig Stubberud, Kathleen Ann Kramer, Allen Roger Stubberud, "Estimation of Target Maneuvers from Tracked Behavior Using Fuzzy Evidence Accrual", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 468–477, 2019. doi: 10.25046/aj040457
52. Mohamed Hamada, Abdulsalam Latifat Ometere, Odu Nkiruka Bridget, Mohammed Hassan, Saratu Yusuf Ilu, "A Fuzzy-Based Approach and Adaptive Genetic Algorithm in Multi-Criteria Recommender Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 449–457, 2019. doi: 10.25046/aj040454
53. Hun Choi, Gyeongyong Heo, "An Enhanced Fuzzy Clustering with Cluster Density Immunity", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 239–243, 2019. doi: 10.25046/aj040429
54. Wanwanut Boongsood, Chiranuwat Jadram, "Effects of Using Fuzzy Material Handling Inputs in the Genetic Algorithm for Machine Layout", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 4, pp. 133–140, 2019. doi: 10.25046/aj040416
55. Trinh Luong Mien, "An Adaptive Fuzzy-Sliding Mode Controller for The Bridge Crane", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 164–170, 2019. doi: 10.25046/aj040322
56. Ilya Vasilyevich Germashev, Tatyana Petrovna Mashihina, Evgeniya Vyacheslavovna Derbisher, Vyacheslav Evgenyevich Derbisher, Mikhail Alexeevich Kharitonov, "Analysis of Fuzzy Data for the Selection of Ingredients of the Polymer Composition by Technical Functions in Quality Management", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 3, pp. 88–99, 2019. doi: 10.25046/aj040313
57. Husni Teja Sukmana, Luh Kesuma Wardhani, Sarah Khairunnisa, Kyung Oh Lee, Risa Wati, "ITSM Software Ranking for Small Medium Enterprises Based on ITIL V3 Quick Win Criteria using Fuzzy SIR Method", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 288–298, 2019. doi: 10.25046/aj040237
58. Andro Majid, Djoko Budiyanto Setyohadi, Suyoto, "Estimation of Software Development Project Success using Fuzzy Logics", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 2, pp. 280–287, 2019. doi: 10.25046/aj040236
59. Nosiri Onyebuchi Chikezie, Onyenwe Ezinne Maureen, Ekwueme Emmanuel Uchenna, "Fuzzy Logic Implementation for Enhanced WCDMA Network Using Selected KPIs", Advances in Science, Technology and Engineering Systems Journal, vol. 4, no. 1, pp. 114–124, 2019. doi: 10.25046/aj040112
60. Abdullah Y. Al-Maliki, Kamran Iqbal, "PID-Type FLC Controller Design and Tuning for Sensorless Speed Control of DC Motor", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 515–522, 2018. doi: 10.25046/aj030660
61. Paolo Mercorelli, "Using Fuzzy PD Controllers for Soft Motions in a Car-like Robot", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 380–390, 2018. doi: 10.25046/aj030646
62. Veena Deshmukh, Srinivas Mangalwede, Dandina Hulikunta Rao, "Student Performance Evaluation Using Data Mining Techniques for Engineering Education", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 259–264, 2018. doi: 10.25046/aj030634
63. Francesco Nucci, "Fuzzy Uncertainty Management in Multi-Shift Single-Vehicle Routing Problem", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 6, pp. 33–45, 2018. doi: 10.25046/aj030603
64. Samuel Carvalho de Aragão, Agnaldo Reis Pontes, Luis Manuel Borges Gouveia, Samuel Franco Lopes, Pier Kenji Rauschkolb Katsuda, Anirene Galvão Tavares Pereira, Márcio Teixeira Oliveira, Jefferson Pinto de Oliveira, Rita do Socorro Brito Coroa, Gilson Ferreira Araújo, Marcio Merêncio Panza de Siqueira, "The Visualization of Cattle Movement Data in The State of Pará in 2016 Through Networks of Animal Transit Graphs and Guides", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 92–96, 2018. doi: 10.25046/aj030512
65. Emil Brajković, Daniel Vasić, Tomislav Volarić, "Evaluation of Methods for Sentence Similarity for Use in Intelligent Tutoring System", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 5, pp. 1–5, 2018. doi: 10.25046/aj030501
66. Abyad Mohamed, Karama Asma, Khallouq Abdelmounaim, "A Comparative Analysis of two Controllers for Trajectory Tracking Control: Application to a Biological Process", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 4, pp. 318–326, 2018. doi: 10.25046/aj030432
67. Zeynep Bala Duranay, Hanifi Guldemir, "Fuzzy Logic Based Selective Harmonic Elimination for Single Phase Inverters", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 161–167, 2018. doi: 10.25046/aj030322
68. Kanasottu Anil Naik, Chandra Prakash Gupta, Eugene Fernandez, "Performance improvement of a wind energy system using fuzzy logic based pitch angle control", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 3, pp. 30–37, 2018. doi: 10.25046/aj030304
69. Salma Charmi, Bassem El Badsi, Abderrazak Yangui, "Direct Torque Control Strategy Based on the Emulation of Six-Switch Inverter Operation by a Four-Switch Inverter Using an Adaptive Fuzzy Controller", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 346–356, 2018. doi: 10.25046/aj030237
70. Rasel Ahmmed, Md. Asadur Rahman, Md. Foisal Hossain, "An Advanced Algorithm Combining SVM and ANN Classifiers to Categorize Tumor with Position from Brain MRI Images", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 2, pp. 40–48, 2018. doi: 10.25046/aj030205
71. Gints Poiss, Sandra Vitolina, Janis Marks, "Development of Indicators for Technical Condition Indexing of Power Transformers", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 148–154, 2018. doi: 10.25046/aj030118
72. Sina Razvarz, Raheleh Jafari, Wen Yu, "Numerical Solution of Fuzzy Differential Equations with Z-numbers using Fuzzy Sumudu Transforms", Advances in Science, Technology and Engineering Systems Journal, vol. 3, no. 1, pp. 66–75, 2018. doi: 10.25046/aj030108
73. Wafa Gritli, Hajer Gharsallaoui, Mohamed Benrejeb, "Fault Tolerant Control Based on PID-type Fuzzy Logic Controller for Switched Discrete-time Systems: An Electronic Throttle Valve Application", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 6, pp. 186–193, 2017. doi: 10.25046/aj020623
74. Deyan Chen, Dingwei Wang, "Multi-attribute Reverse Auction Design Based on Fuzzy Data Envelopment Analysis Approach", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1323–1329, 2017. doi: 10.25046/aj0203167
75. Ammar Al-Gizi, Sarab Al-Chlaihawi, Aurelian Craciunescu, "Efficiency of Photovoltaic Maximum Power Point Tracking Controller Based on a Fuzzy Logic", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 1245–1251, 2017. doi: 10.25046/aj0203157
76. Imad Elzein, Yury Petrenko, "Adapting Model Predictive Control for a PV Station and Evaluating two different MPPT Algorithms P&O and FLC", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 741–748, 2017. doi: 10.25046/aj020394
77. Maulana Erwin Saputra, Safrizal, "Analysis of Learning Development With Sugeno Fuzzy Logic And Clustering", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 5, pp. 26–30, 2017. doi: 10.25046/aj020505
78. Ouafae Kasmi, Amine Baina, Mostafa Bellafkih, "Multi Level Integrity Management in LTE/LTE-A Networks", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 658–668, 2017. doi: 10.25046/aj020384
79. Alvaro Anzueto-Rios, Jose Antonio Moreno-Cadenas, Felipe Gómez-Castañeda, Sergio Garduza-Gonzalez, "Image Segmentation Using Fuzzy Inference System on YCbCr Color Model", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 460–468, 2017. doi: 10.25046/aj020359
80. Hanene Medhaffar, Moez Feki, Nabil Derbel, "Adaptive Discrete-time Fuzzy Sliding Mode Control For a Class of Chaotic Systems", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 395–400, 2017. doi: 10.25046/aj020351
81. Khalid Al-Mutib, Foudil Abdessemed, "Indoor Mobile Robot Navigation in Unknown Environment Using Fuzzy Logic Based Behaviors", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 327–337, 2017. doi: 10.25046/aj020342
82. Fabian Bustamante, Walter Fuertes, Paul Diaz, Theofilos Toulqueridis, "Methodology for Management of Information Security in Industrial Control Systems: A Proof of Concept aligned with Enterprise Objectives.", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 3, pp. 88–99, 2017. doi: 10.25046/aj020313
83. Sorin Mihai Radu, Elena-Roxana Tudoroiu, Wilhelm Kecs, Nicolae Ilias, Nicolae Tudoroiu, "Real Time Implementation of an Improved Hybrid Fuzzy Sliding Mode Observer Estimator", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 214–226, 2017. doi: 10.25046/aj020126
84. Sender Rocha dos Santos, Jorge L. M. Amaral, José Franco M. Amaral, "Adaptive Intelligent Systems applied to two-wheeled robot and the effect of different terrains on performance", Advances in Science, Technology and Engineering Systems Journal, vol. 2, no. 1, pp. 1–5, 2017. doi: 10.25046/aj020101
85. Niaz Ahmed, Kashif Ahmed, "Chemical and Different Nutritional Characteristics of Brown Seaweed Lipids", Advances in Science, Technology and Engineering Systems Journal, vol. 1, no. 1, pp. 23–25, 2016. doi: 10.25046/aj010104