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Traffic on highways has increased significantly in the past few years. Consequently, this has caused delays for the drivers in reaching their final destination and increased the highway's congestion level. Many options have been proposed to ease these issues. In this paper, a model of the highway drivers' population was built based on several factors, including the behavioral patterns of the drivers, like drivers' time flexibility to reach the destination, their carpool eligibility, and their tolerance to pay the toll price, in addition to the traffic information from the system. A fuzzy logic decision-making model is presented to emulate how drivers would choose the lane to use based on the aforementioned factors and the current congestion levels of all the lanes on the highway. The presented model, along with the simulation results from applying the model to different simulation scenarios, show the usefulness of such a model in predicting an optimal toll value. Such optimal value would reduce congestion on the highway at one end while maximizing the revenue for the toll company. [ABSTRACT FROM AUTHOR]

As significant carriers of the application of fuzzy set theories, fuzzy systems have been widely used in many fields. However, selecting fuzzifications, fuzzy reasoning engines, and defuzzifications is subjective for Mamdani fuzzy systems, and the fuzzy rule of Takagi-Sugeno-Kang fuzzy systems is less of a linguistic interpretation. Regarding these shortcomings, this paper proposes a fuzzy system based on vague partitions processing information directly from the fuzzy rule base, in which fuzzy rules have explicit semantics. Firstly, the n-dimensional vague partition of the n-dimensional universe is defined based on 1-dimensional vague partitions and the aggregation function, and its properties are discussed. Based on these, we design the new fuzzy system, and investigate its approximation properties which is the theoretical guarantee for applying the fuzzy system. As an application, we combine the fuzzy system with PID control system to deal with autonomous vehicle path tracking control problems. A series of experiments are constructed, and experimental results indicate that the fuzzy system based on vague partitions makes the fuzzy PID control system strong robustness, and has obvious advantages compared with other traditional fuzzy systems for path tracking control problems. [ABSTRACT FROM AUTHOR]

Weather Forecasting is very essential as it is helpful in saving lives and materials by predicting disasters such as cyclonic storms, tsunamis, extreme rainfall, etc. Within the defined range of rainfall rate approximation, this study investigates the application of Fuzzy Logic (FL) to forecast rainfall using the Interval Type –2 Fuzzy Inference System (IT2FIS). Environmental parameters which influence rainfall have been applied in this analysis and every implementation is carried out using MATLAB 9.13. The performance of IT2FIS model is compared with the actual data. Correlation coefficient (R2) and Root Mean-Squared Error (RMSE) have been used to evaluate the performance metrics of the proposed model. The results suggest that the IT2FIS model can capture the dynamic behavior of rainfall data and generate reasonable results, implying that it might be beneficial in long-term rainfall prediction. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

In this paper, the robust stability of nonlinear system with unknown perturbation is considered combining operator-based right coprime factorization and fuzzy control method from the input-output view of point. In detail, fuzzy logic system is firstly combined with operator-based right coprime factorization method to study the uncertain nonlinear system. By using the operator-based fuzzy controller, the unknown perturbation is formulated, and a sufficient condition of guaranteeing robust stability is given by systematic calculation, which reduces difficulties in designing controller and calculating inverse of Bezout identity. Implications of the results related to former results are briefly compared and discussed. Finally, a simulation example is shown to confirm effectiveness of the proposed design scheme of this paper. [ABSTRACT FROM AUTHOR]

In this paper, a variable impedance control method is proposed for uncertain robotic systems based on a nonlinear force contact-based flexible environmental model. First, a nonlinear force contact model between the rigid manipulator and flexible environment is applied to the compliant control of the manipulator, which can avoid excessive force overshoot that usually exists in the traditional spring-damping environmental model. Then, to achieve better force/position tracking performances, a fuzzy-based adaptive variable impedance controller is designed based on the force contact-based flexible environmental model, where the impedance parameters are adjusted online through the force and position feedback of the robotic system, and the fuzzy logic system is used to compensate the uncertainties. Moreover, the stability of the adaptive variable impedance control scheme is proved by the Routh stability criterion, and the boundness of all the signals in the closed-loop control system is guaranteed by the Lyapunov stability theorem. Finally, the effectiveness of the proposed method is verified by the simulation of a two-link manipulator, and the results demonstrate that the performances of position tracking are improved, while the force overshoot and oscillation time are reduced. [ABSTRACT FROM AUTHOR]

In this paper, the high accuracy motion output feedback control of a kind of launching platforms driven by motors is focused. The launching platform is used to launch kinetic load to hit the target so it is susceptible to external disturbance. In addition, significant issues arise due to limitations on the plant inputs, such as actuator energy limits and velocity state is usually unavailable due to the limitation of system cost and volume. A new adaptive fuzzy output feedback controller based on dual observers is proposed for solving these problems. A smooth and continuous model is established for input saturation to compensate it. A sliding mode observer and a fuzzy observer with proper membership function are combined to estimate the unmeasured system states more accurately. An adaptive robust controller and the fuzzy observer are combined to realize a motion control with disturbance rejection, which allows correct adaptation while the plant input is saturated. Lyapunov theorem proves the bounded stability of the proposed controller when there exists observation error. Extensive comparative simulation and experiment results verify the effectiveness and practicability of the proposed controller and show that the control accuracy can be improved by an order of magnitude compared with the traditional PID controller and better than some other nonlinear controllers. [ABSTRACT FROM AUTHOR]

In this paper, the containment control problem of second-order nonlinear heterogeneous multi-agent system is studied. In order to deal with complex uncertainties such as unknown parts, uncertainties, and input constraints in the system, we designed a distributed fuzzy adaptive controller. The interval type-II (IT2) fuzzy set is adopted to deal with the uncertainty of membership functions. We construct a matrix equality and a matrix inequality to deal with the asymmetric Laplace matrix. The controller designed is simple and the designed controller only uses the information of itself and its neighbors. Therefore, it is very easy to be compensated in practice. Finally, a simulation example is introduced to verify the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Multi-objective optimization can be used to address possible conflicting relationships between multiple objectives. However, some objectives have a fuzzy temporal relationship between them, making it difficult to give a common method to portray the fuzzy temporal relationship. To fill this gap, we propose the concept of complex objectives, which can be described by fuzzy temporal logic that includes both temporal and logical operators. Furthermore, we investigated the optimal control problems of complex objectives and developed a fuzzy system called possibilistic decision systems (PDSs) to establish a framework for optimal control. In PDSs, states of fuzzy systems are determined by a family of variables, and transitions induced by actions between fuzzy states of systems are also fuzzy uncertain and determined by a possibility degree. Importantly, we proved that memoryless strategies are sufficient for optimal control of complex objectives. Finally, the theory presented in this paper is illustrated by a mobile robot simulation. [ABSTRACT FROM AUTHOR]

Here, we develop a fuzzy controller using a new online self-adapting design. The objective of this work is to control a nonlinear process by using a one-dimensional input rule variable, instead of error and error variation. The initial limits of the fuzzy logic membership functions are mostly depend on experiments and previous knowledge of the dynamic process behaviors. Generally, the membership function parameters have a significant impact on control signal amplitude and, consequently on the convergence and stability of the controller-plant system. The proposed technique determines the limits of the antecedent membership functions online using the kth and k - 1th outputs of the controlled plant and reference model, respectively. Meanwhile, the limits of the consequent membership functions are calculated using error and error variation. This approach ensures: (i) that the input/output variables have the required fuzzy space, (ii) the controlled plant follows the desired reference model, and (iii) the control signal amplitude is within acceptable limits. Additionally, (iiii) it takes into account the dynamic variability of the process and the existence of an overshoot. The membership function parameters are updated continuously through a self-adapting procedure, ensuring improved control performance. Ultimately, the proposed approach is improved using two nonlinear systems. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a future-generation networking environment in which distributed smart objects can communicate directly and create a connection between different types of heterogeneous networks. Knowing the accurate localization of IoT-based devices is one of the most challenging issues in expanding the IoT network performance. This paper was done to propose a new fuzzy type2-based scheme to enhance the position accurateness of sensors deployed in the Internet of Things environments. Our proposed scheme is based on the weighted centralized localization strategy, in which the location of unknown nodes calculates using the fuzzy type-2 system. The flow measurement via the wireless channel to calculate the separation distance between the sensor/anchor nodes is employed as the fuzzy system input. Also, the fuzzy membership functions to better adaptivity of our scheme with lossy IoT environments via learning automata algorithm are tuned. Then, in the proposed method, the fuzzy type-2 calculations are restricted by comparing the received signal strength with a predefined threshold value to extend the network lifetime. The effectiveness of the proposed scheme has been proven through extensive simulation. Based on the simulation results, our scheme, on average, reduced the localization error by 35.9% and 9.5% decreased the energy consumption by 13% and 7.2%, and reduced the convergence rate by 33.1% and 12.37 % compared to the HSPPSO and IMRL methods, respectively. [ABSTRACT FROM AUTHOR]

In recent years, the use of Gas Turbines (GTs) to generate electricity has grown exponentially. Therefore, for the optimal performance of gas power plants, a lot of research has been done on modeling different parts of GTs, estimating model parameters, and controlling them. But most of the available methods are not accurate enough, like most linear methods, or are model-based, which require an accurate model of the system (like most nonlinear methods), or there is a constant need to adjust the controller parameters. To address these shortcomings, this study uses a new hybrid method including the brain emotional learning-based intelligent controller, the nonlinear multivariate method in the form of feedback linearization, and an adaptive control method of mode predictive reference model used to quickly control the GT. The Rowen model is used to simulate the nonlinear model of the GT. Owing to the influence of exhaust temperature on the speed of GT and the multivariate system model, nonlinear multivariate controller design is considered. First, the adaptive control method of the state-predictive reference model for a multi-output multi-input system, in general, is presented, and then, the proposed method for a GT with real dynamic values is implemented. The simulation results show the ability of the proposed controller to control the GT. In order to prove the efficiency of the proposed method, the obtained results are compared with the PID industrial controller method and the classical reference model method. [ABSTRACT FROM AUTHOR]

This paper focuses on simplifying the structure of fuzzy systems and improving the precision. By regarding the fuzzy rule base as a mapping from the vague partition on the input universe to the vague partition on the output universe, we first design a new type of fuzzy system using the complete and continuous fuzzy rule base in terms of vague partitions. We then exploit Weierstrass's approximation theorem to show that this new type of fuzzy system can approximate any real continuous function on a closed interval to arbitrary accuracy and provide the corresponding approximation accuracy with respect to infinite norms. We also provide two numerical examples to illustrate the effectiveness of this new type of fuzzy system. Both theoretical and numerical results show that this new type of fuzzy system achieves the quite approximation effect with a few fuzzy rules. [ABSTRACT FROM AUTHOR]

The aim of this study is to improve randomized methods for designing a Takagi-Sugeno-Kang (TSK) fuzzy system. A novel adaptive incremental TSK fuzzy system based on stochastic configuration, named stochastic configuration fuzzy system (SCFS), is proposed in this paper. The proposed SCFS determines the appropriate number of fuzzy rules in TSK fuzzy system by incremental learning approach. From the initial system, new fuzzy rules are added incrementally to improve the system performance until the specified performance is achieved. In the process of generation of fuzzy rules, the stochastic configuration supervision mechanism is applied to ensure that the addition of fuzzy rules can continuously improve the performance. The premise parameters of new adding fuzzy rules are randomly assigned adaptively under the supervisory mechanism, and the consequent parameters are evaluated by Moore-Penrose generalized inverse. It has been proved theoretically that the supervisory mechanism can help to ensure the universal approximation of SCFS. The proposed SCFS can reach any predetermined tolerance level when there are enough fuzzy rules, and the training process is finite. A series of synthetic data and benchmark datasets are used to verify SCFS's performance. According to the experimental results, SCFS achieves satisfactory prediction accuracy compared to other models. [ABSTRACT FROM AUTHOR]

Flight performance of unmanned aerial vehicles (UAVs) strongly depends on implemented attitude tracking control. For designing better controllers, nonlinear control design techniques are often opted instead of control design based on linearized models. Uncertainty in nonlinear dynamics estimation may arise due to inaccuracies in aerodynamic derivatives and simplifications/assumptions made during the derivation of nonlinear models. This paper considers attitude tracking control of fixed-wing UAVs having uncertain dynamics and corrupted gyro sensor outputs. An integral chain differentiator (ICD) is used to provide the analytical redundancy to the gyros used to measure the angular rates. Two control design schemes are proposed, a neuro-fuzzy adaptive sliding mode control (NFASMC) and an ICD approximation-based fuzzy adaptive sliding mode control (ICD-FASMC). In NFASMC, the uncertain part of the dynamics is estimated using an adaptive radial basis function neural network. Gyro sensor output errors are estimated in real-time, using ICD based error estimation scheme and used in the control law along with the sensor's corrupted outputs. In ICD-FASMC, the uncertain dynamics and angular rates of UAV are estimated using the ICD such that the requirement of the gyro sensor outputs for control design is bypassed. The switching gain of the designed controllers is made adaptive using fuzzy logic to mitigate the chattering effect. The stability of the proposed controllers is proved using the Lyapunov approach. The proposed schemes are implemented using a nonlinear simulation of a fixed-wing UAV. Simulation results are presented to show the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

One of the most important issues for FLC systems is the problem of finding the right balance between interpretability and accuracy. For this delicate balance, several methods which can be integrated into fuzzy logic, and tune the fuzzy logic parameters adaptively, have been proposed. One of these popular approaches is the heuristic optimization method. However, in terms of optimization, designing fuzzy logic control is a complex optimization problem that is discrete in terms of rule optimization and numerical in terms of optimization of membership degrees parameters. In this context, heuristic-based adaptive fuzzy control systems focus on either fuzzy rule optimization, weighting fuzzy rules, or parameter optimization. In this paper, unlike the others, an adaptive weighted fuzzy logic control (awFLC) method, which weights the inputs instead of the rules, is proposed. First, the membership degree of each input is calculated. Then, the resultant weight is determined by combining the weighted input membership degrees. For a crisp result, the average of the membership degrees of the resultant weight to the output membership functions is calculated. In awFLC, the interaction between membership functions is achieved by average membership degree, communication between inputs is achieved by the weighting of inputs, and mapping between inputs-outputs is achieved by the resultant weight value. Thus, the approach, which turns into a purely numerical optimization problem, provides convenience for heuristic search. In awFLC, optimal values for input weights and variable parameters are searched by the genetic algorithm. The performance of the method was tested on traction power control, and the results were compared with the ANFIS results. With awFLC, an 8.13% average error was obtained, while ANFIS produced solutions with an average error rate of 8.97%. [ABSTRACT FROM AUTHOR]

In order to solve the problem of multi-attribute decision-making with unknown weights under probabilistic hesitant fuzzy information, considering the shortcomings of the existing probabilistic hesitant fuzzy distance measure, such as weak distinguishing ability, a probabilistic hesitant fuzzy multi-attribute decision-making method based on improved distance measures is proposed. Firstly, the hesitancy degree of probabilistic hesitant fuzzy element and the improved difference measure of probabilistic hesitant fuzzy element are defined, and an improved probabilistic hesitant fuzzy distance measure based on hesitancy degree, incompleteness degree and improved difference measure is proposed. Secondly, based on the improved distance measure, a mathematical programming model with the goal of minimizing the relative approach degree is con-structed to determine the attribute weights of evaluation indexes in multi-attribute decision making problems. Using it as a base, a new probabilistic hesitant fuzzy multi-attribute decision-making method is proposed by combining the improved probabilistic hesitant fuzzy distance measure with the compromise ratio method. Finally, the proposed method is applied to the problem of green supplier selection, and the feasibility and effectiveness of the proposed method are verified by case analysis and comparison with other methods. [ABSTRACT FROM AUTHOR]

Recently, Heterogeneous Networks (HetNet) are concentrated more in order to improve transmission coverage and spectrum efficiency. When compared to HetNet, Non-Orthogonal Multiple Access (NOMA) is used to allow multiple users to share the same frequency band resource. Moreover, cross-tier links are improved in realistic HetNets due to channel delay and random perturbation. In this research, a fuzzy logic-based user association and pairing was proposed to improve the system robustness and energy utilization. Here, fuzzy logic overcomes the limitations of traditional channel-based and gain-based pairing schemes. The proposed fuzzy logic-based system evaluates distance, channel gain, and reference signal power for mode selection and enhances pairing features. Nature-inspired cuckoo search optimization is also used to improve coverage probability, feasible rate, and energy efficiency by optimizing the path loss, transmit power, and distance between user and base station. The proposed model analysis is carried out through intense simulation to demonstrate improved performance when compared to conventional state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This paper proposes a disturbance observer supported Takagi-Sugeno (TS) fuzzy model-based control scheme for uncertain systems. The baseline controller is a guaranteed performance fuzzy model based parallel distributed controller (PDC) which is constructed using the nominal system's parameters. The model approximation error and parametric uncertainties are treated as a lumped disturbance and a nonlinear disturbance observer (NDOB) is introduced to counter the lumped disturbance. The applicability of the proposed scheme is demonstrated on the bilateral control of nonlinear teleoperation system in MATLAB/Simulink/QUARC environment through simulations as well as semi-real time experiments. [ABSTRACT FROM AUTHOR]

The complexity of the coalface environment determines the non-linear and fuzzy characteristics of the drum adjustment height. To overcome this challenge, this study proposes an adaptive fuzzy reasoning Petri net (AFRPN) model based on fuzzy reasoning and fuzzy Petri net (FPN) and then applies it to the intelligent adjustment height of the shearer drum. This study constructs adaptive and reasoning algorithms. The former was used to optimize the AFRPN parameters, and the latter made the AFRPN model run. AFRPN could represent rules that had non-linear and attribute mapping relationships and could adjust the parameters adaptively to improve the accuracy of the output. Subsequently, the drum adjustment height model was established and compared to three models neural network (NN), classification and regression tree(CART) and gradient boosting decision tree (GBDT). The experimental results showed that this method is superior to other drum adjustment height methods and that AFRPN can achieve intelligent adjustment of the shearer drum height by constructing fuzzy inference rules. [ABSTRACT FROM AUTHOR]

Pipelines are a nonlinear and complex component to transfer fluid or gas from one place to another. From economic and environmental points of view, the safety of transmission lines is incredibly important. Furthermore, condition monitoring and effective data analysis are important to leak detection and localization in pipelines. Thus, an effective technique for leak detection and localization is presented in this study. The proposed scheme has four main steps. First, the learning autoregressive technique is selected to approximate the flow signal under normal conditions and extract the mathematical state-space formulation with uncertainty estimations using a combination of robust autoregressive and support vector regression techniques. In the next step, the intelligence-based learning observer is designed using a combination of the robust learning backstepping method and a fuzzy-based technique. The learning backstepping algorithm is the main part of the algorithm that determines the leak estimation. After estimating the signals, in the third step, their classification is performed by the support vector machine algorithm. Finally, to find the size and position of the leak, the multivariable backstepping algorithm is recommended. The effectiveness of the proposed learning control algorithm is analyzed using both experimental and simulation setups. [ABSTRACT FROM AUTHOR]

A special ceramic roller bearing press (SCRBP) is developed to press two bearings efficiently and precisely at the same time. A speed control mathematical model of the bearing press is built to obtain stability bearing pressing speed. The fuzzy adaptive PID controller of the bearing pressing speed of SCRBP is designed. The simulation model is also built. Fuzzy adaptive PID control is compared with conventional PID control. By simulation analysis, the simulation results show that adjusting time of fuzzy adaptive PID control is short, and its overshoot is very small, almost coincides with the set pressing speed. Moreover, fuzzy adaptive PID is suitable for the pressing speed control of the bearing pressing speed system with step interference signal. The pressing stability speed is obtained by fuzzy adaptive PID control. [ABSTRACT FROM AUTHOR]

This work presents a comparative analysis of Type-1 and Type-2 fuzzy controllers to drive an omnidirectional mobile robot in line-following tasks using line detection images. Image processing uses a Prewitt filter for edge detection and determines the error from the line location. The control systems are tested using four different paths from the Robotino® SIM simulator. Also, two different strategies in the design and implementation of the controllers are presented. In the first one, a PD controller scheme is extended by using a fuzzy system to have adaptive parameters P and D, additionally, Type-2 Fuzzy sets are used to give robustness to the controller. In the second case, a Fuzzy controller is designed to compute in a direct way the control variables and it is extended to Type-2 Fuzzy controller. Finally, experimental results and comparative analysis are presented for the five control schemes by comparing the running time and the standard deviation to measure the robustness of the control systems. [ABSTRACT FROM AUTHOR]

With running speed increases, the dynamics characteristic of railway vehicle system behaves intensively, such as, snaking motion, bifurcation problem, even digression accident. These questions effect ride comfort and run stationary. The magneto-rheological (MR) damper can continually change its state in a few milliseconds and has low energy requirement and insensitivity to the temperature and circumstance. MR dampers have turned out to be a promising device in vibration control. According to the nonlinear of MR damper and the vibration characteristic of semi-active suspension of high-speed vehicle, a seventeen-degree-of-freedom lateral semi-control model of high-speed whole vehicle with MR dampers is established. Taking into account of the vibrations of vehicle and electromagnetic interference, a novel adaptive fuzzy control strategy is put forward. The simulation results show that adaptive fuzzy control method can improve the ride comfort and restrain electromagnetic interference. The electromagnetic interference noise problems in high-speed railway vehicle system with MRD are analyzed and discussed according to EN 55022 for the first time. [ABSTRACT FROM AUTHOR]

An extended state observer (ESO) based fuzzy adaptive sliding mode control (FASMC) for permanent magnet synchronous motor (PMSM) drive system is proposed in this paper. In order to improve the disturbance rejection property, a dynamic disturbance compensation method is developed based on extended state observer. A fuzzy adaptive sliding mode control method with a novel sliding mode reaching law is presented to enhance the tracking performance. Then, the proposed method called FASMC-ESO, is applied to regulate the speed of PMSM. The global asymptotic stability analysis of the system is proved by Lyapunov functions. Comparative simulation and experiment results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents an identifier based intelligent adaptive fuzzy control scheme with regulating blood glucose concentration in normoglycemic level of 70 mg/dl for type 1 diabetic patients. The identifier is built with fuzzy neural network (FNN) to predict the blood glucose concentration of the diabetic patient. The fuzzy based controller with generic operating regimes which cluster all the adaptive control rules is designed to robustly reject the multiple meal disturbances resulting from food intake and deal with the parametric uncertainties in model and measurement noise. All the parameters of the FNN and of the fuzzy logic system are tuned by backpropagation (BP), to achieve the control objectives. The numerical simulations are performed to show that the set point tracking, meal disturbances and measurement noise rejection can be realized within this method. [ABSTRACT FROM AUTHOR]

As a key theoretical basis of fuzzy analysis, a new representation of the two-dimension fuzzy numbers is proposed in this paper. We also present a new method for solving the fuzzy system of linear equations with the two-dimension fuzzy data based on the new representation of the two-dimension fuzzy numbers, which may translate a fuzzy system of linear equations into two real systems of linear equations, one is an n × n real system of linear equations and the other is a (2n) × (2n) real system of linear equations, where the coefficients, the right hand term and the unknown term of the (2n) × (2n) real systems are all positive real numbers. Accordingly, we compare the classical methods with the technique on the basis of the study of the two-dimensional fuzzy linear system proposed in this paper for an one-dimensional fuzzy linear system, and the results shows that they have the same solutions. Finally, the conditions of the existence of the solutions for a fuzzy system of linear equations are discussed, and the examples are given to show the efficiency and effectiveness of the method investigated in this article. [ABSTRACT FROM AUTHOR]

Human-beings in general and pregnant subjects (characterized by a sophisticated dynamic system) in particular are sensitive to whole-body vibrations (WBV) in sitting position while driving. The published literature is thickly populated with the investigation of WBV effects on non-pregnant subjects but sparsely populated with the biodynamic response enhancement of the pregnant subjects in driving conditions. In this paper, the biodynamic response analysis of eleven degrees of freedom (11-DoF) seated pregnant subject at the driver position is carried out while being exposed to vibrations induced by inherent road irregularities. An advanced adaptive NeuroFuzzy (AdaptNeuroFuzzy) control strategy for active suspensions of nineteen degrees of freedom (19-DoF) integrated vehicle-pregnant subject model is designed to attenuate harmful vibrations and protect the seated pregnant subject and fetus against the risk of damages. Matlab/simulink is employed to carry out simulations. Performance validation of the proposed advanced intelligent control strategy based suspension system is accomplished through comparison with the passive, PID, adaptive PID (AdapPID) and adaptive fuzzy logic control (AdapFLC) via standard performance indices, using an ISO-classified standard random road profile. [ABSTRACT FROM AUTHOR]

Network virtualization technology releases human resources to some extent through network and cloud computing technology, reducing the workload of staff. The application of network virtualization technology in cloud computing data centers is based on this condition to improve work quality and efficiency. The purpose of this paper is to use the fuzzy algorithm to realize network virtualization of cloud computing data center. In this paper, we study the adaptive fuzzy control in depth, and conduct the practical application based on the basic knowledge of adaptive fuzzy control we learned, achieved "learn to use". Apply the design of adaptive fuzzy control to the load balancing algorithm of the network virtual cloud computing data center, realized the load balancing algorithm of the network virtual cloud computing data center based on adaptive fuzzy control. According to the load balancing algorithm based on adaptive fuzzy control to achieve this algorithm by using Internet knowledge, and designed the load balancing system of the whole network virtual cloud computing data center. Test the whole load balancing system which has been achieved, and obtained the performance variance curve of the system under different algorithms. Then obtained advantages and disadvantages of the algorithm by analyzing the experimental data. The experimental results show that the proposed method can effectively improve the execution performance of communication-intensive applications and ensure the stable execution of the application. At the same time, the algorithm inherits the advantages of the general fuzzy control load balancing algorithm. The stability is strong and the variance curve does not appear pulsed fluctuation. There is also no divergence phenomenon with time increased. [ABSTRACT FROM AUTHOR]

In this paper, a type-2 fuzzy neural network is used to adaptive inverse control of a class of nonlinear systems. The proposed method has proper performance for real-time control of nonlinear and time-invariant systems and has a quick response to following changes. In this method, the high approximation ability of type 2 fuzzy systems is used to reverse system modeling and this inverse model is used immediately to control the system. In order to measure the efficiency of the proposed method, the control of dynamic, highly nonlinear and time varying water both temperature system with certain and uncertain water pressure is experimentally considered. The results show the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

As a key theoretical basis of fuzzy analysis, a new representation of the two-dimension fuzzy numbers is proposed in this paper. We also present a new method for solving the fuzzy system of linear equations with the two-dimension fuzzy data based on the new representation of the two-dimension fuzzy numbers, which may translate a fuzzy system of linear equations into two real systems of linear equations, one is an n × n real system of linear equations and the other is a (2n) × (2n) real system of linear equations, where the coefficients, the right hand term and the unknown term of the (2n) × (2n) real systems are all positive real numbers. Accordingly, we compare the classical methods with the technique on the basis of the study of the two-dimensional fuzzy linear system proposed in this paper for an one-dimensional fuzzy linear system, and the results shows that they have the same solutions. Finally, the conditions of the existence of the solutions for a fuzzy system of linear equations are discussed, and the examples are given to show the efficiency and effectiveness of the method investigated in this article. [ABSTRACT FROM AUTHOR]

In this paper, a novel observer based direct adaptive interval type-2 (IT2) fuzzy sliding mode control (SMC) method is proposed for a certain class of high order unknown nonlinear dynamical systems with unmeasured states. Firstly, a high-gain observer is designed to estimate the tracking errors of the system states. Then on the basis of the observer, an IT2 fuzzy logic system (FLS) is established to approximate the equivalent control law, in which the estimate of the sliding surface function is applied as the input. The sliding mode control law is developed to guarantee the robustness of the system, in which an adaptive switching control gain is designed to handle the chattering problem. The free parameters of the controller are adjusted online by the adaptive laws. Finally, the stability of the overall closed-loop system is proved in the Lyapunov sense based on Meyer-Kalman-Yakubovich (MKY) lemma. In the proposed control system, the constraints on the knowledge of mathematical model, disturbances, and state vector can be avoid completely, and the trajectory tracking control can be achieved only using the information of system output. Simulation results demonstrate the effectiveness and the high control performance of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper proposes an intuitionistic fuzzy control method for twin rotor multi-input and multi-output (twin rotor MIMO) systems. Twin rotor MIMO systems are often used to measure the performance of control systems as they are extremely sensitive to environmental factors. The use of the intuitionistic fuzzy control method for modeling these uncertainties offers an effective way to increase the robustness of the control system to uncertainties in the structure of twin rotor MIMO systems. In this study, two intuitionistic fuzzy controllers are designed, namely for the main and tail rotors separately and then combine the outputs of these rotors. Also, this method is compared with the classical optimal PID method in terms of stability and performance by various simulations and experiments. [ABSTRACT FROM AUTHOR]

An adaptive fuzzy backstepping control method is proposed for uncertain fractional-order chaotic systems subject to system constraints, including unknown external disturbance and input saturation in this article. The system constraints are resolved by modeling the saturation limiter as portions of unknown functions and a backstepping recursive algorithm is proposed. In each step, the system unknown nonlinearity is approximated by a fuzzy logic system, and fuzzy parameters are updated by the corresponding fractional-order adaptation laws online. In the end, an adaptive fuzzy controller that assures the convergence of tracking error is designed. Formal proof and two simulations are applied for confirming the effectiveness of the designed controller. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) is one of the important control problems in design and operation of power systems as permanent deviation of frequency from nominal value affects power system operation and reliability. This paper presents a control method based on neural network for LFC of a two-area power system containing re-heat thermal plants. System parameters are assumed to be unknown and the proposed type-2 fuzzy controller is designed online, is adaptive and does not require initial adjustment by the operator. The training method of the type-2 fuzzy controller includes error back-propagation and gradient descent. In this paper, since the dynamics of the system is unknown, it is modelled using multilayer perceptron (MLP) structure, and Jacobian of the system is extracted to determine system model. In order to evaluate the robustness of proposed online adaptive fuzzy type-2 controller (OADF) against parameter changes, a time-variant parameter is added to the system. The performance of the controller is compared with the PI, PID, N-PID, fuzzy-PI and neural network controllers. Simulation results illustrate the improved performance of LFC and its capability to overcome uncertain and time-variant parameters. [ABSTRACT FROM AUTHOR]

In the conventional fuzzy k-NN classification rule, the vote cast by each nearest neighboring known (labelled) sample on the class membership grades of the unknown (unlabelled) sample is formed by weighting the nearest neighbor's class membership grades by the inverse of the nearest neighbor's distance to the unknown sample. This paper proposes a modification of the weight (distance) used for each nearest neighbor by employing the geometrical relation among the nearest neighbor, its most informative known neighbor of the same class and the unknown sample. It is also proposed that this modification be only (conditionally) applied when the feature vector of the unknown sample lies outside the convex hull of the feature vectors of the known samples of each class. Results on a large number of datasets from the UCI and KEEL repositories and synthetically generated datasets show that, in return for a modest increase in classification complexity over the original fuzzy k-NN rule, the proposed fuzzy k-NN rule offers a better classification accuracy than the accuracies of the original fuzzy kNN rule and most other nearest neighbor type algorithms. [ABSTRACT FROM AUTHOR]

Two-stage model free fuzzy adaptive control (MFFAC) has been proposed which rejects the multiplicative input disturbance while also reducing the unnecessary control activity arising due to the sensor noise. Noisy sensor measurements of the input disturbance severely degrades the control performance and might destabilize the control loop. In this work, a novel approach has been proposed which initially develops a model of the multiplicative input disturbance whereas in the second stage the measurement uncertainty is modeled. The uncertainty is ultimately represented as the fuzziness of the control signal. Adaptive conditional defuzzification is employed which utilizes the fuzziness resulting in reduced control activity. The proposed control scheme has shown good control performance in cases where the plant dynamics varies and the sensor noise in the control loop cannot be simply rejected by low pass filtering. Simulation and experimental results adequately validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The current study presents a new method for allocation of the contribution of voltage harmonic distortion for consumers and utilities. This method utilizes three-phase voltage and current waveforms at the point of common coupling (PCC) by applying an adaptive Kalman filter to estimate the amplitude and phase angle of the waveform. The fuzzy adaptation part of the Kalman filter allows resetting of the Kalman gain for fast tracking of system variations under transient conditions. Singular value decomposition based on recessive least square is used to estimate the Norton equivalent circuit for the entire system. In the proposed method an Indicator of the voltage value is used to determine the contribution of harmonic distortion for both sides. The characteristics of the proposed method were investigated through simulation of a nonlinear load which is connected to the distribution system and practical data from 130 kV distribution system in north-west of Iran. [ABSTRACT FROM AUTHOR]

In this paper, a multiple-input single-output (MISO) fuzzy rules system is decomposed into its equivalent collection of single-input single-output (SISO) fuzzy rules systems. First, the constructive and destructive linguistic models are reviewed. Under certain conditions, the final consequence of the constructive linguistic model working on a MISO fuzzy rules system is equivalent to the final consequence of the constructive linguistic model working on a collection of SISO fuzzy rules systems. Meanwhile, under another conditions, the final consequence of the destructive linguistic model working on a MISO fuzzy rules system is equivalent to the final consequence of the destructive linguistic model working on a collection of the SISO fuzzy rules systems. The decomposition of the MISO fuzzy rules system with fuzzy singleton consequent parts is also investigated. This paper shows the MISO fuzzy rules system requires many more rules than the SISO fuzzy rules system. Through the decomposing process, we can realize the inference result with fewer rules. [ABSTRACT FROM AUTHOR]

The release planning process concerns with assigning requirements to the different future releases of the software. This paper considers three factors that govern the release planning process: stakeholders' satisfaction, risk, and availability of resources. All of these factors depend on human knowledge, which is always incomplete, imprecise, and approximated. This classifies release planning as an under-uncertainty decision-making problem. This paper proposes a prioritization approach for generating a release plan for the next release of the software. The proposed approach employs a fuzzy inference system engine in order to tackle the uncertainty in the release planning process. The artifacts of the fuzzy inference (FIS) process (the membership functions and the IF-rules) are constructed using adaptive network-based fuzzy inference system (ANFIS). ANFIS helps to reinforce the human knowledge with the knowledge obtained from the historical data. Experiments show that the outputs of the proposed framework are affected by the reliability, accuracy, and the orientation of the historical data used to train the ANFIS module. For example, when training the ANFIS module using data that concentrates on the factor of stakeholders' satisfaction, the proposed framework has shown very good results from the perspective of this factor. [ABSTRACT FROM AUTHOR]

In this paper, the adaptive sliding mode control problem of fractional-order nonlinear systems in the presence of model uncertainties and external disturbances is considered. More particularly, an adaptive interval type-2 fuzzy sliding mode controller is designed for achieving finite-time stability of unknown fractional-order nonlinear systems. First, a nonsingular terminal sliding mode surface is designed to reduce chattering and improve the robustness and effectiveness of the controller. Then, the interval type-2 fuzzy logic is used to approximate the unknown parts appearing in the fractional-order nonlinear system model. Following these are augmented by a novel adaptive interval type-2 fuzzy sliding mode controller which can guarantee the stability of the target system in a finite time. In terms of Lyapunov approach, the adaptive laws are designed, meanwhile the proof of the corresponding finite-time property is presented. Finally, three examples are given to illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy controller design methodology via MultiObjective Particle Swarm Optimization (MOPSO) based on robust stability criterion, is proposed. The plant to be controlled is modeled from its input-output experimental data considering a Takagi-Sugeno (TS) fuzzy NARX model, by using the fuzzy C-Means clustering algorithm (antecedent parameters estimation) and Weighted Recursive Least Squares (WRLS) algorithm (consequent parameters estimation). An adaptation mechanism as MOPSO problem for online tuning of a fuzzy model based digital PID controller parameters, based on the gain and phase margins specifications, is formulated. Experimental results for adaptive fuzzy digital PID control of a thermal plant with time varying delay is presented to illustrate the efficiency and applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Engine idle speed control problem has significant effects on fuel consumption, combustion stability and pollutions produced in urban traffics. In this paper, a Multi-Agent adaptive Critic based NeuroFuzzy Controller (MACNFC) for engine idle speed is presented to improve fuel efficiency, reduce emissions, and increase disturbance rejection capability while preventing the engine from stalling. The proposed MACNFC consists of a neurofuzzy controller, a learning mechanism and a set of fuzzy critic agents. It is assumed that each critic has been well designed to serve a particular objective. The role of the critics is to evaluate the controller performance in terms of satisfactory achievements of the control objectives through evaluation of plant output and provide appropriate continues reinforcement signals. If these signals become zero, it means that the critics are satisfied by the performance of the controller from their own point of view. If deviation of signals from zero become larger, it shows the more stress and more dissatisfaction. The reinforcement signals provided by critics contribute collaboratively to update neurofuzzy controller parameters via the learning mechanism for achieving predefined criteria and goals. The controller should modify its characteristics so that all reinforcement signals are decreased and consequently all critics are satisfied. To illustrate the effectiveness of the proposed method some simulations are given. Obtained results show that proposed method not only maintains the engine speed as close as possible to a desired value in the presence of various disturbances, but also improves fuel consumption for large production volumes of engine operating in different idle speed regimes. [ABSTRACT FROM AUTHOR]

As a typical, nonlinear system with instability, high order, strong coupling and multiple variations, the inverted pendulum model is the focus and research object of many experts and scholars in the control field. We propose the PD control method of the system based on the adaptive fuzzy compensation in order to weaken the impact of uncertainty in regards to external disturbances, as well as improve the precise control of the inverted pendulum system when the parameters are unknown. It uses the fuzzy method to conduct fuzzy approximation on the nonlinear system of the inverted pendulum by modeling the nonlinear inverted pendulum system to weaken the impact of uncertainty and achieve complete compensation for the nonlinear system. Then, it establishes the adaptive PD fuzzy controller, forms the adaptive control law, applies the Lyapunov function to verify the stability and robustness of the system and finally achieves the intelligent, optimal control of the system. Simulation results show that the control method can achieve the optimal control of the tracking error and parameter error, has a better anti-interference ability and assures system stability. [ABSTRACT FROM AUTHOR]

Micro grids (MG) are seen as the future power system providing clear economic and environmental benefits. Most MG energy management solutions rely on centralized controller which is not suitable to guarantee the flexibility and the adaptability that modern electricity market needs. In other hand, multi objective optimization for fully decentralized system in MG environment is not realizable without certain level of coordination between agents. In this paper, we present an adaptive multi-agents system (AMAS) for MG power management based on enhanced fuzzy decision using multi swarm optimization (MS-PSO) algorithm. In the proposed architecture each agent presents a different MG unit. Fuzzy logic is used by each agent to estimate the amount of energy to be generated in order to cover the uncertainty and imprecision related to renewable energy sources and the MG constraints. For the MAS coordination, a MS-PSO algorithm is used by a coordinator agent to find the best compromised solution to satisfy economical/environmental objective based on agent proposals in order to improve them. Simulation results show the importance of the chosen optimization algorithm for the AMAS with MS-PSO algorithm which is compared to the basic particle swarm optimization for the same encapsulated knowledge. [ABSTRACT FROM AUTHOR]

The paper is concerned with the design of H∞ state estimator of a dynamical system based on fuzzy modeling. At first, a universal linear model is considered with some unknown variables. Since the variables are not time invariant in modeling of a real nonlinear system so they are modeled by fuzzy approximates with some free parameters. The system states constitute input variables of the fuzzy system. Then, a stable state and parameter estimator based on H∞ criterion and some circumstances is proposed. By assuming some conditions, it will be proved that the parameter estimator is just modified normalized least mean squares (NLMS) algorithm which follows H∞ optimality. It is used for estimating the fuzzy model parameters. In this way, it reduces required tuning parameters of the filter. The proposed model can be used in signal processing such as time series and speech processing or in nonlinear circuit modeling. In this paper, suggested algorithm is implemented on a practical data gathered from a real nonlinear system. The results show stability, good performance, fast convergence time, and robustness to far initial conditions of the suggested estimator. For comparison, the proposed method is compared with the one in which the identified free parameters are not adjusting during estimation process. [ABSTRACT FROM AUTHOR]

This paper proposes a new method to estimate power loss characteristics of metal oxide surge arresters. The method was used to compute surge arrester power loss curve based on adaptive network based fuzzy inference system (ANFIS) and artificial neural network (ANN). Surge arrester operating history is an important factor that bears influence on its power loss. Degradation was, in this paper, introduced as a new index to represent operating history of metal oxide surge arrester. Therefore, applied voltage, temperature and degradation factor were considered as inputs in ANFIS system and ANN models to obtain accurate power loss which is a very important factor in surge arrester thermal stability. Degrading effect was undertaken by measurement voltage and current in varistors degraded by utilization in network. The results of the two artificial models were compared. Results show that ANFIS was more accurate than ANN. This study shows that the power loss characteristics of surge arrester are to a great extent accurately predictable using proposed artificial model. [ABSTRACT FROM AUTHOR]

Product collaborative design scheduling problem (PCDSP) has received increasing attention in the engineering field for its popularity in manufacturing and supply chain systems. Although many scholars have achieved many useful results in Job-shop scheduling problem (JSP), the research work of PCDSP is still lacking. Moreover, the fuzziness, dynamics and multi-objective of PCD pose a great challenge for enterprises making quick response to unplanned perturbations. Thus, in this paper, multi-objective dynamic fuzzy scheduling and its solution algorithm are presented. Emergencies cases are analyzed and dynamic scheduling procedure based on event-driven and lifecycle-driven is developed firstly. The fuzziness of PCD process is studied meanwhile fuzzy triangular number and fuzzy trapezoid number are used to express the fuzziness. Based on this, a multi-objective dynamic fuzzy scheduling model aiming to minimum the makespan and tardiness penalty is constructed. As a solution, multi-objective dynamic adaptive scheduling algorithm, based on bi-layer coding strategy and self-adaptive double point crossover and selfadaptive mutation, is proposed following closely. Finally, analytic results from a case of a wind turbine are used to illustrate the model and method proposed in this paper. With the analysis, it can provide insight into ways of improving the strategic and operational decision making for enterprises. [ABSTRACT FROM AUTHOR]

System modeling is one of the most important tasks of dynamic analysis and prediction systems, and imprecise model may lead to high bias. The presence of noise in sample data can make it more difficult to obtain precise system models. A new modeling algorithm called ANFIS-GMDH is presented in this paper, which builds upon the traditional ANFIS structure and utilizes the self-organizing mechanisms of GMDH. The aim of ANFIS-GMDH is to improve upon the traditional ANFIS method and prevent overfitting of noisy data. The well-studied Box-Jenkins gas furnace data is utilized to validate the algorithm, with results showing that the proposed algorithm performs better than traditional ANFIS, GMDH and subtractive clustering for both noisy and noiseless data, without any significant increase in execution time. [ABSTRACT FROM AUTHOR]

The primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. In soil mechanics relationship between the causes and effects can be observed with laboratory tests but is difficult to develop analytical functions or numerical relations between input and output. In geotechnical optimization the most usual constraints represent state variables of structural response for each loading case. The aim of this paper is to define the soft constrain with adaptive network-based fuzzy inference system (ANFIS) in the soil mechanics. The developed soft constrain is than applied in non-linear programming (NLP) to obtain optimal solution. In the case of soil compaction the performance of the proposed optimization algorithm is evaluated. The main aim of soil compaction is to define optimal water content at which soil can be compacted to a densest state that improve their mechanical and physical properties. [ABSTRACT FROM AUTHOR]