Search

Recent advancements in patient rehabilitation integrate both traditional and modern techniques to enhance treatment efficacy and accessibility. Hydrotherapy, leveraging water's physical properties, is crucial for reducing joint stress, alleviating pain, and improving circulation. The rehabilitation of upper limbs benefits from technologies like virtual reality and robotics which, when combined with hydrotherapy, can accelerate recovery. Exoskeletons, which support and enhance movement, have shown promise for patients with neurological conditions or injuries. This study focused on implementing and comparing proportional–integral–derivative (PID) and fuzzy logic controllers (FLCs) in a lower limb exoskeleton. Initial PID control tests revealed instability, leading to a switch to a PI controller for better stability and the development of a fuzzy control system. A hybrid strategy was then applied, using FLC for smooth initial movements and PID for precise tracking, with optimized weighting to improve performance. The combination of PID and fuzzy controllers, with tailored weighting (70% for moderate angles and 100% for extensive movements), enhanced the exoskeleton's stability and precision. This study also explored quantum computing techniques, such as the quantum approximate optimization algorithm (QAOA) and the quantum Fourier transform (QFT), to optimize controller tuning and improve real-time control, highlighting the potential of these advanced tools in refining rehabilitation devices. [ABSTRACT FROM AUTHOR]

This study introduces the improved fractional ordered Darwinian particle swarm optimization (IFODPSO) method combined with fuzzy logic controllers to optimize motor performance. By utilizing direct torque control (DTC) techniques controlled by IFODPSO, the system achieves quick response times and instantaneous torque generation. The research addresses the optimization of motor performance using the IFODPSO approach for combination with fuzzy logic controllers. The specific problem being addressed is the control of torque in motor applications. The scope of the research includes comparing the performance of the IFODPSO-FLC approach with common field-oriented control (FOC) as well as DTC approaches. The significance of the results attained is noteworthy as the IFODPSO-FLC approach demonstrates promising outcomes in terms of torque control compared to traditional FOC and DTC techniques. Additionally, the proposed PI-fuzzy opposition estimation helps improve system efficiency at low speeds by compensating for variations in stator resistance. The combination of fuzzy logic controllers and Darwinian particle swarm optimization of fractional order approaches presents a novel and efficient solution. The results are evaluated using MATLAB-Simulink, and the obtained performance demonstrates the potential for successful motor control applications. [ABSTRACT FROM AUTHOR]

In smart cities, bicycle-sharing systems have become an essential component of the transportation services available in major urban centers around the globe. Due to environmental sustainability, research on the power-assisted control of electric bikes has attracted much attention. Recently, fuzzy logic controllers (FLCs) have been successfully applied to such systems. However, most existing FLC approaches have a fixed fuzzy rule base and cannot adapt to environmental changes, such as different riders and roads. In this paper, a modified FLC, named self-tuning FLC (STFLC), is proposed for power-assisted bicycles. In addition to a typical FLC, the presented scheme adds a rule-tuning module to dynamically adjust the rule base during fuzzy inference processes. Simulation and experimental results indicate that the presented self-tuning module leads to comfortable and safe riding as compared with other approaches. The technique established in this paper is thought to have the potential for broader application in public bicycle-sharing systems utilized by a diverse range of riders. [ABSTRACT FROM AUTHOR]

Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed. [ABSTRACT FROM AUTHOR]

The double-inverted pendulum (DIP) constitutes a classical problem in mechanics, whereas the control methods for stabilizing around the equilibrium positions represent the classic standards of control system theory and various control methods in robotics. For instance, it functions as a typical model for the calculation and stability of walking robots. The present study depicts the controlling of a double-inverted pendulum (DIP) on a cart using a fuzzy logic controller (FLC). A linear-quadratic controller (LQR) was used as a benchmark to assess the effectiveness of our method, and the results showed that the proposed FLC can perform significantly better than the LQR under a variety of initial system conditions. This performance is considered very important when the reduction of the peak system output is concerned. The proposed controller equilibration and velocity tracking performance were explored through simulation, and the results obtained point to the validity of the control method. [ABSTRACT FROM AUTHOR]

This article explains the design of fuzzy logic controllers (FLCs) for level processes which is generally used in numerous control operations. The main purpose of the proposed design is to maintain the liquid level in the tank at the desired level. In this paper, the fuzzy logic controller is chosen as the controller for the level process because of its fault tolerance, knowledge representation, expertise, nonlinearity, uncertainty, and real-time manipulation. Fuzzy logic controllers have been developed and compared in the Mamdani version. Performance on proportional derivatives (PD) and proportional-integral-derivatives (PID) controllers. Whereas traditional PD and PID controllers are simple, dependable and eliminate steady-state errors, fuzzy logic controllers are rule-based systems that are a logical model of human behavior in processes of the proposed design. The response is provided as follows: The LabVIEW software has been validated. It is used to simulate the proposed system. Comparing error indicators such as PD controller, PID controller, fuzzy logic controller integral absolute error, integral quadratic error, time and absolute error integral, time and quadratic error integral, fuzzy logic controller is observed from the simulation results. increase. It offers better performance than other controllers. [ABSTRACT FROM AUTHOR]

• Introduction of the notion of semantic similarity controller. • Design of semantic similarity controllers being both accurate and inter- pretable. • Evaluation using a pool of methods and datasets for semantic similarity measurement. Recent advances in machine learning have been able to make improvements over the state-of-the-art regarding semantic similarity measurement techniques. In fact, we have all seen how classical techniques have given way to promising neural techniques. Nonetheless, these new techniques have a weak point: they are hardly interpretable. For this reason, we have oriented our research towards the design of strategies being able to be accurate enough but without sacrificing their interpretability. As a result, we have obtained a strategy for the automatic design of semantic similarity controllers based on fuzzy logics, which are automatically identified using genetic algorithms (GAs). After an exhaustive evaluation using a number of well-known benchmark datasets, we can conclude that our strategy fulfills both expectations: it is able of achieving reasonably good results, and at the same time, it can offer high degrees of interpretability. [ABSTRACT FROM AUTHOR]

Utilisation of renewable energy sources (RES) is increasing day by day to reduce greenhouse emissions. The toxic emission from ship is the main concern in marine sector. Here, utilisation of renewable energy for propulsion and electrification of accessories in a ship are proposed. Microgrid with AC and DC bus is developed using solar panels, wind mills, fuel cell, diesel generator, and energy storage devices. Energy management system with two fuzzy logic controllers (FLCs) is used to select and manage energy in the microgrid. Selection of source is decided by FLC1 based on the availability of RES. Generation of control pulses for inter‐linking converters is decided by FLC2 based on variation in solar irradiance and wind velocity. The microgrid with RES is simulated using MATLAB/SIMULINK. The results show that uncertainty in RES can be handled by FLCs to provide a continuous power supply for transportation of ship and its accessories. [ABSTRACT FROM AUTHOR]

In this paper, an integration of fuzzy logic controllers (FLCs) with hybrid genetic algorithms (HGAs) is developed with a view to make the design process fully automatic, without requiring any human expert and numerical data. Our approach consists of two phases: first phase involves selection and definition of fuzzy control rules as well as adjustment of membership functions parameters, while the second phase performs an optimal selection of membership function types corresponding to fuzzy control rules. Learning both parts concurrently represents a way to improve the accuracy of the FLCs to minimize the errors. It has been argued that the performance of FLCs greatly depends on the parameters as well as types of membership functions. Thus, the aforementioned HGAs are a viable solution for designing an efficient adaptive FLCs system. To demonstrate the effectiveness of the proposed method for optimal design of the FLCs, the proposed approach is applied to a well-known benchmark controller design tasks, car and truck-and-trailer like robot system. Simulation results illustrates that proposed optimization approach can find optimal fuzzy rules and their corresponding membership functions types with a high rate of accuracy. The new HGAs optimized adaptive FLCs outperforms not only a passive control strategy but also human-designed FLCs, a neural coded controller with clustering and a neural-fuzzy control algorithm. [ABSTRACT FROM AUTHOR]

The integration of variable renewable energy sources such as wind and solar power has brought a significant challenges and uncertainty in power system operation, making it essential to take measures for advanced coordinated control. Moreover, by using the batteries of plug-in electrical vehicles (PEVs), the active and reactive power support functions can be provided at certain buses in a power system. Hence, parking lots with a large number of PEVs, referred to as SmartParks, can provide these functions at bulk level. Thus, by appropriate control of SmartParks, the active and reactive power flows in a power system can be regulated to achieve the functionalities of oscillation damping control, transmission line power flow regulation and bus voltage control. In this paper, in order to realize these three functionalities concurrently by using SmartParks, fuzzy-logic based controllers are proposed. Wide area measurements from a power system can be used to generate the active and reactive power output dispatch command signals for SmartParks. The parameters of the fuzzy logic controllers are heuristically determined to provide optimal system-wide performances for oscillation damping, transmission line power flow regulation and bus voltage control. Typical results are provided on a 12-bus power system with 400 MW wind farm implemented on a real-time simulation platform. [ABSTRACT FROM AUTHOR]

This paper presents a study on a sensorless decoupled P-Q control for a doubly fed induction generator (DFIG)-based wind energy conversion system (WECS) with intelligent controllers via Matlab/Simulink. In the absence of mechanical sensors, an algorithm based on modified phase locked loop (MPLL) can estimate the rotor speed and position. An experimental validation of the proposed speed algorithm in steady state was conducted and is presented. In addition, this study focuses on the implementation of fuzzy logic controllers combined with the conventional PI (Proportional-Integral) for the adjustment of active power, reactive power, rotor speed and voltage in dc-link. The combination of fuzzy logic and PI concerns the use of the first as gain tuner of the second one, bearing in mind the significance and simultaneously the difficulty of gain finding. The simulation results of this study are presented to clarify the enhanced effectiveness of the combination of fuzzy logic with PI controllers. Finally, a comparative study between three control strategies, namely, case 1 – ‘with mechanical sensors and PI controllers’, case 2 – ‘sensorless with PI controllers’ and case 3 – ‘sensorless with fuzzy logic controllers’ was conducted and is also presented. [ABSTRACT FROM AUTHOR]

This paper presents a numerical strategy to reanalyze the modified frequency stability analysis of friction induced vibration problem. The stability analysis of a mechanical system relies on several coupling steps, namely a non-linear static analysis followed by linear and complex eigenvalue problems. We thus propose a numerical strategy to perform more rapidly multiple complex eigenvalue analyses. This strategy couples three methods namely, Fuzzy Logic Controllers to manage frictional contact problem, homotopy developments and projection techniques to reanalyze the projection matrices and component mode synthesis to calculate the modified eigensolutions. A numerical application is performed to highlight the efficiency of the strategy and a discussion is proposed in terms of precision and computational time. [ABSTRACT FROM AUTHOR]

In this study, the general structure of interval type-2 fuzzy PID (IT2-FPID) controllers and a self-tuning mechanism for IT2-FPID controller is presented. In this context, we will present and examine the controller structures of the type-1 fuzzy PID (T1-FPID) and IT2-FPID controllers on a generic a symmetrical 3x3 rule base. Then, an online self-tuning mechanism for IT2-FPID controllers is presented. The presented selftuning mechanism, which was firstly presented for T1-FPID, controllers, tunes the scaling factors of IT2-FPID with respect to the current error value of the control system. A systematic design approach has been also presented for the self-tuning IT2-FPID structure. The performance of the T1-FPID, IT2-FPID and Self-Tuning IT2-FPID structures has been investigated on a simulation study conducted on a nonlinear tank system. The results have shown that, since the proposed approach has more extra degree of freedom provided by its interval type-2 fuzzy sets and self-tuning mechanism, the self-tuning IT2FPID resulted with a superior control performance in comparison with its type-1 and interval type-2 counterparts. [ABSTRACT FROM AUTHOR]

In recent years, Multi-Objective Evolutionary Algorithms ( moeas) that consider diversity as an objective have been used to tackle single-objective optimisation problems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand-tuning of algorithms, the use of automated parameter control approaches that are able to adapt parameter values during the course of an evolutionary run are becoming more common in the field of Evolutionary Computation ( ec). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parameter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self-adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark problems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a significant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the benefits of solving single-objective problems through diversity-based multi-objective schemes. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach to dynamically manage the traffic lights cycles and phases in an isolated intersection. The target of the work is a system that, comparing with previous solutions, offers improved performance, is flexible and can be implemented on off-the-shelf components. The challenge here is to find an effective design that achieves the target while avoiding complex and computationally expensive solutions, which would not be appropriate for the problem at hand and would impair the practical applicability of the approach in real scenarios. The proposed solution is a traffic lights dynamic control system that combines an IEEE 802.15.4 Wireless Sensor Network (WSN) for real-time traffic monitoring with multiple fuzzy logic controllers, one for each phase, that work in parallel. Each fuzzy controller addresses vehicles turning movements and dynamically manages both the phase and the green time of traffic lights. The proposed system combines the advantages of the WSN, such as easy deployment and maintenance, flexibility, low cost, noninvasiveness, and scalability, with the benefits of using four parallel fuzzy controllers, i.e., better performance, fault-tolerance, and support for phase-specific management. Simulation results show that the proposed system outperforms other solutions in the literature, significantly reducing the vehicles waiting times. A proof-of-concept implementation on an off-the-shelf device proves that the proposed controller does not require powerful hardware and can be easily implemented on a low-cost device, thus paving the way for extensive usage in practice. [ABSTRACT FROM AUTHOR]

This paper describes the design of a high speed min/max architecture based on a new current comparator. The main advantage of the proposed circuit which employs a novel preamplifier-latch comparator is the higher operating frequency feature in comparison with previous works. Because the comparator can work in voltage mode, the min/max structure can be redesigned either in voltage or current mode. The designed comparator is refreshed without any external clock. Therefore, it does not degrade the speed performance of proposed min/max structure. These features along with low power consumption qualify the proposed architecture to be widely used in high speed fuzzy logic controllers (FLCs). Post-layout simulation results confirm 3.4 GS/s comparison rate with 9-bit resolution for a 0.9 V peak-to-peak input signal range for the comparator and 800 MHz operating frequency for min/max circuit. The power consumption of whole structure is 912 μW from a 1.8 V power supply using TSMC 0.18-μm CMOS technology. [ABSTRACT FROM AUTHOR]

This paper proposes a new approach to the stable design of fuzzy logic control systems that deal with a general class of chaotic processes. The stable design is carried out on the basis of a stability analysis theorem, which employs Lyapunov's direct method and the separate stability analysis of each rule in the fuzzy logic controller (FLC). The stability analysis theorem offers sufficient conditions for the stability of a general class of chaotic processes controlled by Takagi-Sugeno-Kang FLCs. The approach suggested in this paper is advantageous because inserting a new rule requires the fulfillment of only one of the conditions of the stability analysis theorem. Two case studies concerning the fuzzy logic control of representative chaotic systems that belong to the general class of chaotic systems are included in order to illustrate our stable design approach. A set of simulation results is given to validate the theoretical results. [ABSTRACT FROM AUTHOR]

The work presented herein illustrates the use of computational intelligence and optimization approaches for improving the fuzzy controller׳s performance in architectural heating, ventilation, and air conditioning system (HVAC). The primary purpose of the performed research is to find a method to moderate the energy use without compromising the comforts of the inhabitants. The control design used to meet this purpose includes the predicted mean vote (PMV) and predicted percentage dissatisfied (PPD) indices. The software of choice for evaluating PMV and PPD is EnergyPlus. Whereas, for the fuzzy controller and the evolutionary optimization framework, the co-simulation tool with building controls virtual test bed (BCVTB) is used in conjunction with Simulink. The ensuing comparison between EnergyPlus׳s thermal control of HVAC and our fuzzy approach is the outcome of the present research. [ABSTRACT FROM AUTHOR]

SUMMARY This paper presents the use of evolutionary optimization approach to design and tune smart fuzzy controllers for heating, ventilation, and air conditioning systems or HVAC. The objective is to optimize energy consumption while accounting for user comfort requirements. The problem of energy conservation in air conditioning systems becomes a multi-objective optimization constrained problem, which enlarges the solution search space. To solve this problem, a multi-objective evolutionary optimization technique based on genetic algorithm (GA) is proposed. A physical experimental setup is constructed for data collection and formulation of mathematical model. A fuzzy controller is initially designed through expert knowledge, and GA is then used to tune the rules and membership functions of the fuzzy controller in order to optimize multiple objectives. Simulations and real experiments are compared to determine the effectiveness of the proposed strategy. As compared to the controller present in the real experimental air conditioner, approximately 15% energy is successfully saved with no increase in average individual dissatisfaction or discomfort index. Also, a decrease in peak individual dissatisfaction or discomfort index from 91% to 62% is observed. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Abstract: One of the most commonly known weaknesses of Evolutionary Algorithms (eas) is the large dependency between the values selected for their parameters and the results. Parameter control approaches that adapt the parameter values during the course of an evolutionary run are becoming more common in recent years. The aim of these schemes is not only to improve the robustness of the controlled approaches, but also to boost their efficiency. In this paper we investigate the application of parameter control schemes to address a well-known variant of the Frequency Assignment Problem (fap). The controlled ea is a highly efficient diversity-based multi-objective memetic scheme. In this work, a novel general parameter control method based on Fuzzy Logic is devised. In addition, a hyper-heuristic is also considered as an established parameter control scheme. An extensive experimental evaluation of both methods is carried out that includes a comparison to a wide-range of fixed-parameter schemes. The results show that the fuzzy logic method is able to find similar or even better solutions than the hyper-heuristic and the fixed-parameter methods for several instances of the fap. In fact, this method yielded frequency plans that outperform the best previously published solutions. Finally, the generality of the fuzzy logic-based scheme is demonstrated by controlling different kinds of parameters. [Copyright &y& Elsevier]

This paper presents a new stability analysis method dedicated to a class of fuzzy control systems (FCSs) controlling multi input-multi output (MIMO) nonlinear processes by means of Takagi-Sugeno-Kang fuzzy logic controllers (FLCs). The stability analysis of the FCSs is carried out using LaSalle's global invariant set theorem by the separate stability analysis of each fuzzy rule in MIMO fuzzy control systems. Therefore the complexity of the stability analysis is reduced and the adding of new fuzzy rules can be conducted easily; this modification of FLC structure requires the fulfillment of only one of the conditions of the stability analysis theorem suggested in this paper. Another advantage of the stability analysis approach proposed in this paper is that the derivative of the Lyapunov function candidate must be only negative semi-definite in comparison with Lyapunov's stability theorem where it must be negative definite. The conservativeness of stability conditions is thus reduced, and this enables the convenient design of FLCs. The applicability and efficiency of the theoretical results are illustrated by numerical simulations for a representative MIMO process which deals with the level control in a three spherical tank system. [ABSTRACT FROM AUTHOR]

Abstract: The paper describes detailed structure of the integral automated regulation system for the control of indoor environment (ICsIE) which harmonises occupant comfort requirements and principles of bioclimatic approach. The ICsIE regulates indoor illumination, thermal conditions and air quality. The control unit of the system is designed as a hybrid cascade system incorporating fuzzy logic and conventional PI controllers. Regulation is executed with the use of sensors, which monitor the environmental conditions and adherent actuators. The ICsIE is currently installed in an occupied office in the building of the Faculty of Civil and Geodetic Engineering, University of Ljubljana, Slovenia. During the operation the ICsIE was able to synchronise the aspects of thermal, illuminance and ventilation control and maintain indoor environmental conditions in the desired range of the set-point values. From the occupant point of view the ICsIE performed adequately, as manual interventions into the functioning of the system were rare. [Copyright &y& Elsevier]

The design analysis of a multi‐input converter using an intelligent controller based on fuzzy logic control algorithm is encompassed. The use of dedicated dc–dc converters is going through a transitory phase in smart‐grid applications. Different characteristics of inputs can be combined to give desired output operation by using a multi‐input converter which is more or less the combination of individual converters sharing a common load thus simultaneously transferring power to the load. The converter with the help of an intelligent algorithm will ensure the buffering time for each input thus multiplexing between various inputs according to the set demand and fluctuating conditions on the input side. Such a converter will behave as a power electronic interface between the utility, user and the renewable energy sources. A three‐input cuk converter has been designed for interfacing wind energy sources with the dc load bus and the main grid. Sending power to the main grid incorporates the power quality enhancement features which will be accommodated using the bi‐directional inverter connected with the grid to perform the active filtration. Moreover, three fuzzy logic controllers will set the output currents corresponding to each input current by changing their reference voltages according to the power demand. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, an equalizer chip for digital communication channel is presented. The proposed system can equalize the CAT5e UTP cable from 20 to 400m with a 30MHz bandwidth. A novel structure has been used to realize GM-C filter. It uses only one GM-cell stage in contrast of conventional structures. This technique leads to improvement excess-phase, area and power consumption. An intelligent fuzzy controller tunes this filter. It changes the filter parameters based on reference eye pattern to achieve the best tuning for the filter. Layout of all circuits has been done in standard CMOS process. Total power consumption, area and power supply are 29.3mW, and 3.3V. [Copyright &y& Elsevier]

Abstract: In this paper, we propose the use of weighted linguistic fuzzy rules in combination with a rule selection process to develop accurate fuzzy logic controllers dedicated to the intelligent control of heating, ventilating and air conditioning systems concerning energy performance and indoor comfort requirements. To do so, a genetic optimization process considering an efficient approach to perform rule weight derivation and rule selection is developed. This allows the tuning of the system to be developed at the rule level. The proposed technique was tested considering a physical modelization of a real test site. [Copyright &y& Elsevier]

Two robust non-linear controllers have been developed in this study to control the rigid and flexible motions of a single-link robotic manipulator. The controllers consist of a conventional sliding mode controller (CSMC) and a fuzzy sliding mode controller (FSMC). The effects of fuzzy-tuning some of the CSMC control parameters on the overall performance of the arm have been investigated in this study. Furthermore, the proposed FSMC, whose parameters are determined by fuzzy inference systems, has been designed herein based on two Lyapunov functions. The rationale is to considerably reduce the momentum of the system before entering the boundary layer neighboring the sliding surface. This will significantly attenuate the structural deformations of the arm. The digital simulations have demonstrated that the structural deformations, incurred by the beam at the onset of its movement, can be significantly reduced by fuzzy-tuning some of the control parameters. Furthermore, the results have illustrated the superiority of the FSMC over the CSMC in producing a less oscillatory and more accurate response of the angular displacement at the base joint, in damping out the unwanted vibrations of the beam, and in requiting significantly smaller control torques. [ABSTRACT FROM AUTHOR]

A data-driven fuzzy approach is developed for solving the motion planning problem of a mobile robot in the presence of moving obstacles. The approach consists of devising a general method for the derivation of input–output data to construct a fuzzy logic controller (FLC) off-line. The FLC is constructed based on the use of a recently developed data-driven and efficient fuzzy controller modeling algorithm, and it can then be used on-line by the robot to navigate among moving obstacles. The novelty in the presented approach, as compared to the most recent fuzzy ones, stems from its generality. That is, the devised data-derivation method enables the construction of a single FLC to accommodate a wide range of scenarios. Also, care has been taken to find optimal or near optimal FLC solution in the sense of leading to a sufficiently small robot travel time and collision-free path between the start and target points. Furthermore, since the algorithm has been shown efficient in the representation of non-linear control functions, in terms of combating noise and possessing a good generalization capability, these aspects are also tested in this practical control problem. Comparison of the results with those obtained by fuzzy-genetic and another hybrid and data-driven design approach is also done. [Copyright &y& Elsevier]

In recent times, we have seen an explosion in the number of new solutions to address the problem of semantic similarity. In this context, solutions of a neuronal nature seem to obtain the best results. However, there are some problems related to their low interpretability as well as the large number of resources needed for their training. In this work, we focus on the data-driven approach for the design of semantic similarity controllers. The goal is to offer the human operator a set of solutions in the form of a Pareto front that allows choosing the configuration that best suits a specific use case. To do that, we have explored the use of multi-objective evolutionary algorithms that can help find break-even points for the problem of accuracy versus interpretability. [ABSTRACT FROM AUTHOR]

Since the seminal fuzzy sets paper was published, many graduate and undergraduate academic programs have included Fuzzy logic courses, as one of the most important topics regarding Artificial Intelligence (AI). Yet, it is worth highlighting the fact that AI as a tool for education has its main roots in the way AI is taught to students, leading to the development of many educational models, seeking to accelerate the learning curve of fuzzy logic concepts and their suitability for control systems. Nevertheless, most of those methodologies are mainly focused on theoretical concepts, without improving disciplinary and transversal competences that are developed through experimental challenges; consequently, knowledge acquisition is incomplete, and the industrial requirements are not fulfilled. Henceforth, this work proposes a model inspired by the Systems Development V-Model, which allows students to understand the entire fuzzy logic controllers design process through an experimental product development process that improves lifelong learning features in students. Besides, this model supports the Challenge-Based Learning (CBL) in students, encouraging new skills development and experience gain for fuzzy logic controllers designs. Moreover, the proposed methodology in this paper, covers the adapted V-model for education to understand the designing process for fuzzy logic controllers, through a Quadcopter case study, selected due to the complexity of the system, which (as supported from the results) also leads to improve the background skills in engineering students. Therefore, results show that this model enhances student's capacity to develop functional prototypes and promotes technological products development. Then, as validated through an expert review committee within the engineering technical field, this proposal could be easily suited into several engineering topics; such as, Electronic circuits design, Power electronics and so on. [Display omitted] • The proposal supports CBL, which encourages learning AI controller's implementation. • The V-model through the Quadcopter testbed, improved student's background skills. • The V-model enhances student's skills to develop functional technological prototypes. • The V-Model for education fills the gap between theory and experimental validations. • The experimentation relevance to consolidate AI controller's knowledge was validated. [ABSTRACT FROM AUTHOR]