Fuzzy Gain Research Articles

A New Random Forest Ensemble of Intuitionistic Fuzzy Decision Trees

Classification is essential to the applications in the field of data mining, artificial intelligence, and fault detection. There exists a strong need in developing accurate, suitable, and efficient classification methods and algorithms with broad applicability. Random forest is a general algorithm that is often used for classification under complex conditions. Although it has been widely adopted, its combination with diverse fuzzy theory is still worth exploring. In this paper, we propose the intuitionistic fuzzy random forest (IFRF), a new random forest ensemble of intuitionistic fuzzy decision trees (IFDT). Such trees in forest use intuitionistic fuzzy information gain to select features and consider hesitation in information transmission. The proposed method enjoys the power of the randomness from bootstrapped sampling and feature selection, the flexibility of fuzzy logic and fuzzy sets, and the robustness of multiple classifier systems. Extensive experiments demonstrate that the IFRF has competitive and superior performance compared to other state-of-the-art fuzzy and ensemble algorithms. IFDT is more suitable for ensemble learning with outstanding classification accuracy. This study is the first to propose a random forest ensemble based on the intuitionistic fuzzy theory.

Adaptive second order sliding mode guidance law for missile-target interception with fuzzy logic system

This paper presents the design of a 3D missile guidance law based on a second order sliding mode control technique employing an adaptive tuning law and a fuzzy gain scheduling. At the outset, the super-twisting sliding mode guidance law is obtained to overcome the chattering phenomenon. Then, without the knowledge about the bounds of disturbances, an adaptive law is used to determine the control gains. The results are enhanced using a fuzzy module that provides the controller parameters according to a set of linguistic rules. Finally, a comparative set of simulation results are given. To verify the performance and effectiveness of the proposed guidance law, we compare the performances of the traditional sliding mode (SM) guidance law, the traditional super-twisting sliding mode (STWSM) guidance law, the adaptive super-twisting sliding mode (ASTWSM) guidance law and the adaptive fuzzy super-twisting, sliding mode (AFSTWSM) guidance law. The simulation scenarios consider fundamental target movements. The results demonstrate that the proposed adaptive guidance laws display better performance in terms of miss distance, intercept time, and final closing velocity compared the alternatives considered in this study.

Design of Vector Control Strategies Based on Fuzzy Gain Scheduling PID Controllers for a Grid-Connected Wind Energy Conversion System: Hardware FPGA-in-the-Loop Verification

This paper presents a hardware implementation upon a Field Programmable Gate Array (FPGA) of improved Vector Control Strategies (VCSs) based on a Fuzzy Logic System (FLS) of a grid-connected wind energy conversion system. Usually, the classical VCS is based on fixed-gain Proportional Integral Derivative (PID) controllers, which are known to exhibit limited performance against the nonlinear behavior of wind systems, such as rapid fluctuations of the wind speed and uncertainties of the system parameters. In order to overcome this limitation, an improved VCS based on Fuzzy Gain Scheduling PID controllers (VCS-FGS-PID) is suggested in this work to guarantee good tracking, high accuracy and good robustness under system parameter variations. Indeed, the PID controller gains are tuned, in real-time, by the FLS. In addition, the proposed VCS-FGS-PID methods are implemented on the FPGA in order to reduce the delays and the period of the system of the control loop, thanks to its parallel processing. In fact, the performance of the suggested VCS-FGS-PID approaches are proved by digital simulation with the Xilinx system generator tool under Matlab/Simulink, in addition to an experimental hardware-in-the-loop implementation using the FPGA. The obtained results demonstrate that the proposed VCS-FGS-PID techniques offer better performance as regards good tracking and high accuracy and good robustness against stator resistance variability compared to VCS-PI.

Fuzzy gain scheduling PID control of a hybrid robot based on dynamic characteristics

In this paper, a fuzzy gain scheduling proportional-integral-differential (PID) controller based on dynamic characteristics of a hybrid robot named TriMule is presented. Since the equivalent inertia and gravity imposed on motor shafts vary with the robot configuration, it is difficult to obtain satisfactory control performance with a linear, fixed gain PID controller. After analyzing the effect mechanism of dynamic characteristics on control performance, the clustering analysis algorithm and fuzzy logic are combined to deduce the control parameters online according to the command acceleration, equivalent inertia and gravity. The experimental results on a prototype machine show that, compared with the conventional PID and fuzzy gain scheduling PID controller, the proposed method can significantly reduce the effect of dynamic characteristics on control system, thus ensuring satisfactory control performance under any configuration in the task workspace.

Intelligent PIV Fuzzy Navigation and Attitude Controller for an Octorotor Mini-UAV

In this research, a proportional plus integral plus velocity (PIV) fuzzy gain scheduling flight controller for an octorotor mini-unmanned aerial vehicle is developed. The designed flight controller scheme, with a PIV term, is combined with a fuzzy gain scheduling approach. The tracking controller PIV fuzzy gain scheduling is based on two controllers connected in cascade with a saturation approach. The Newton–Euler equations of motion are applied to obtain a mathematical model for the octorotor mini-unmanned aerial vehicle (mini-UAV). The flight controller approach is applied to obtain coupling moments and forces with interconnected attitude and navigation tracking trajectory. In the design of a flight navigation controller with two layers, the inner layer consists of a PIV fuzzy gain scheduling controller that is applied to the attitude dynamics, obtaining the references for the coupling outer layer PIV fuzzy gain scheduling controller, which manipulates the translational dynamics. The navigation PIV fuzzy gain scheduling controller is saturated for bounding in translational forces to avoid large deviations of commands to Euler angles pitch and roll, and another saturated controller is implemented for the bounded thrust rotor to avoid the excessive angular speed of these rotors. The octorotor mini-UAV flight navigation simulation is performed to validate the tracking control of a sequence of motions in each axis, which is presented as a validation for the proposed control scheme.

Enhanced Fuzzy Random Forest by Using Doubly Randomness and Copying From Dynamic Dictionary Attributes

While fuzzy random forest (FRF) as a fuzzy implementation of random forest has earned its strong ambiguity/uncertainty handling capability on a rich variety of considerably low dimensional datasets, this article revisits FRF and attempts to enhance its generalization capability and computational speed on high dimensional datasets. For the first issue, in addition to the original use of randomness in FRF, a doubly randomness is newly introduced into the generation of both the candidate attributes and the best splitting attributes in FRF. For the computational speed issue, while the proposed new fuzzy information gain (NFG) measure does not apply to all candidate attributes, the remaining NFG values can be quickly retrieved by copying from the dynamically generated dictionary. As a result, a new method called enhanced fuzzy random forest (E-FRF) is proposed and justified theoretically from the consistency perspective. Our extensive experimental results indicate that the proposed method E-FRF has at least comparable performance to the comparative methods, and is an advantageous alternative to FRF in terms of both testing accuracy and running speed in most of the adopted high dimensional datasets.

Research on model identification and compound control of vibration mirror swing scan system

According to the principle of system identification, the least squares algorithm and MATLAB system identification toolbox is used to identify the model of the test bench of the vibration mirror swing scan system with the step signal and the vibration mirror swing-sweep angular displacement as the input and output quantities. Taking the identification model as the object, a sliding mode variable structures composite controller of fuzzy switching gain adjustment is designed. In the controller, according to the actual conditions achieved by the sliding mode, the switching gain is effectively estimated, so as to eliminate the interference of uncertain factors and high-frequency vibration. At the same time, the sliding mode function and sliding mode controller of the system needs to be designed. The switching hyperplane of the system is designed using the fuzzy switching method and the desired dynamic characteristics of the system, to ensure the smooth transition of the system state from outside the hyperplane to the convergence of the switching hyperplane. The simulation results show that: the new composite control method eliminates the shortcomings of PD series control, improves the response speed and tracking accuracy of the vibration mirror swing scan system, and improves the overall performance of the system. And it has a strong ability to inhibit the adverse effects caused by changes in the parameters of the controlled object.

Adaptive fuzzy gain scheduling PID controller for frequency regulation in modern power system

AbstractHere an adaptive fuzzy gain scheduling proportional, integral and derivative (FGPID) controller has been proposed for the load frequency control of a three‐area inter‐connected modern power system with the large‐scale wind‐power penetration in the presence of the vehicle‐to‐grid. The proposed FGPID controller benefits from two main advantages that it has an adaptive structure to deal with changes in the parameters of the power system and also its stability has been shown using Lyapunov theory. The adaptive terms for calculations the parameters of this controller are given and discussed, which are derived from an appropriate Lyapunov’s function, are not depend on the controlled system parameters which makes it possible to be employed in many applications even with considering uncertainty. This method is investigated in the case study considering plug in electric vehicles and it was observed that the tuning parameters are realized successfully and frequency control can be improved. A comparison among a fixed structure controller and the proposed FGPID controller is presented. The comparison study indicates that the proposed FGPID controller has better performance than the other fixed structure controllers and can generate the best dynamic response following load and wind uncertanities which makes it to implement practically.

Adjustable Event-Triggered Load Frequency Control of Power Systems Using Control-Performance-Standard-Based Fuzzy Logic

This article proposesa control performance standard (CPS)-based fuzzy event-triggered scheme for load frequency control (LFC) of power systems with a limited communication bandwidth. First, a CPS-based fuzzy LFC system is established to reduce the wear and tear of the generating unit equipment. Then, based on the Lyapunov stability theory, a stability criterion of the LFC system is proposed to ensure the stable operation of the LFC system, which considers the threshold parameter of the event-triggered condition and the fuzzy gain in the fuzzy LFC system. Next, based on the stability criterion and the Gaussian-type curve-fitting method, a functional expression between the fuzzy gain and the threshold parameter is obtained. According to the expression, the threshold parameter is updated in real time with the change of fuzzy gain, so as to further save usage of the communication network bandwidth. Case studies based on a one-area power system and an IEEE 39-bus benchmark test system are undertaken. Simulation results show that the proposed scheme achieves three objectives: 1) to comply with CPS1 and CPS2 in the North American Electric Reliability Council; 2) to reduce wear and tear of the generating unit equipment; and 3) tosave more communication network resources.

An Algebraic Fuzzy Pole Placement Approach to Stabilize Nonlinear Mechanical Systems

Based on the general structure of mechanical systems described by their state-space representation, the Takagi–Sugeno fuzzy modeling, and the controllability property of fuzzy systems, an algebraic and practical approach to computing the fuzzy gain capable of ensuring the stability property of the Takagi–Sugeno fuzzy model is proposed in this article. The main idea consists of finding a continuous fuzzy gain such that any linear behavior, defined by the adequate selection of eigenvalues, is induced in the closed-loop fuzzy system. Therefore, by continuity, if the fuzzy model is an approximation sufficiently close to the mechanical system, then such a nonlinear system is also stabilized by the fuzzy controller. A notable advantage of the proposed method, when compared with similar approaches, is the simplicity of the resulting gain. The validity of the approach is illustrated through the numerical simulation of a sufficiently complex nonlinear system.

Fuzzy membership function-dependent switched control for nonlinear systems with memory sampled-data information

In this paper, a fuzzy memory-based coupling sampled-data control (SDC) is designed for nonlinear systems through the switched approach. Compared with the usual SDC scheme, by employing the Bernoulli sequence, a more general coupling switched SDC that involving the signal transmission delay is designed. The Lyapunov–Krasovskii functional (LKF) is presented with the available characteristics of the membership function, and a coupling sampling pattern, for the T-S fuzzy systems. Based on LKF, together with time derivative information of membership function, and the generalized N-order free-matrix-based inequality, the suitable conditions are obtained in terms of linear matrix inequalities (LMIs) for guaranteeing the asymptotic stability and stabilization of the concerned system. Then, the desired fuzzy coupling SDC gain is attained from the solvable LMIs. In the end, two examples are given to validate the derived theoretical results.

Intelligent Fuzzy Controller Design: Disturbance Rejection Cases

In this paper, a modified fuzzy gain scheduling (GS) Proportional–Integral–Derivative (PID) controller where the output of the integral term of PID, ui, heuristically used as the scheduling variable is discussed. Then, it is demonstrated that ui can effectively represent the inaccessible low-frequency disturbances that alter the system operating point. In addition, since GS design involves time-consuming multiple runs of nonlinear optimization tasks, a fast hybrid method using PSO-GA and classical fminsearch is worked out. Interpolation of the designs, for GS build up, is carried out using an adaptive neuro-fuzzy inference system (ANFIS) to provide PID continuous gain surfaces. The algorithm is applied to the speed control of a combined cycle power plant under unmeasured bounded load disturbances. The simulation outputs indicate that the design quality is effectively preserved in case of small perturbations where the achievements are also superior to the responses of some published methods. In the case of large perturbations, the fuzzy scheduling PID (FSPID) supersedes successfully two other optimally designed regulators. Therefore, it is concluded that the proposed approach renders a better transient response in facing both small and large stepwise load disturbances. The findings have been verified under various working loads through extensive simulations.

Automatic Artificial Pancreas Systems Using an Intelligent Multiple-Model PID Strategy.

In this paper, an individualized intelligent multiple-model technique is proposed to design automatic artificial pancreas (AP) systems for the glycemic regulation of type 1 diabetic patients. At first, using the multiple-model concept, the insulin-glucose regulatory system is mathematically identified by constructing some local models. In this step, trade-offs between the number of local models and the complexity of the overall closed-loop system are made by defining and solving a bi-objective optimization problem. Then, optimal AP systems are designed by tuning a bank of proportional-integral-derivative (PID) controllers via the genetic algorithm (GA). A fuzzy gain scheduling strategy is employed to determine the participation percentages of the PID controllers in the control action. Finally, two safety mechanisms, called insulin on board (IOB) constraint and pump shut-off, are installed in the AP systems to enhance their performance. To assess the proposed AP systems, in silico experiments are performed on virtual patients of the UVA/Padova metabolic simulator. The obtained results reveal that the proposed intelligent multiple-model methodology leads to AP systems with limited hyperglycemia and no severe hypoglycemia.

Fuzzy gain Scheduled PID Controller for Power Quality Enhancement in Grid Connected Hybrid Solar PV-PEMFC Energy System

Power quality improvement is inevitable due to the presence of power converters and non-linear loads in grid connected renewable based hybrid energy system (HES). The harmonics introduced by the distributed energy systems may lead to output voltage distortion andequipment failure which may lead to system losses. It may also cause for direct and indirecteconomic impacts. Development of active filter, passive filter and hybrid filter for harmonics mitigation would be an expensive solutions. Therefore, various DC/AC converter switchingschemes are proposed for power quality improvements in grid connected Solar Photovoltaic(PV) and fuel cell based HES. Initially, Solar PV and Proton Exchange Membrane Fuel cell (PEMFC) are modeled using MATLAB/Simulink. Further the harmonic behavior withProportional plus Integral (PI), Proportional plus Integral plus Derivative (PID) and Fuzzygain scheduled PID controllers are demonstrated using Pulse Width Modulator (PWM).Converter output voltage waveform, frequency spectrum and total harmonics distortion (THD) values at the Point of Common Coupling (PCC) have witnessed that the fuzzy gain scheduled PID controller suppresses the voltage distortions completely. It also yields 4 percentage THD value which satisfies the harmonic level recommended by IEEE 519 standard. Comparative analysis shows that the fuzzy gain scheduled PID control scheme exhibits superior performance than the traditional PI and PID controllers. Therefore, it is identified as an effective control scheme for power quality improvement in hybrid renewable energy system.

Robust fuzzy gain scheduling power control of a generation IV nuclear reactor

Gen-IV nuclear reactors are deemed to actively participate in the load following operations in order to meet the ever increasing energy demands. Robust fuzzy gain scheduling is proposed in this work as an advanced and efficient control strategy for the power cycling of the BREST design of lead cooled fast reactors (LFR). To this end, a reasonably accurate coupled thermo-neutronic simulator is developed at first. System identification techniques are employed thereafter to extract a multi-model set at various operating levels relying on the data obtained from this simulator. Uncertainties incurred as a result of the statistically based identification schemes are moreover accounted for using a narrow band (99%) confidence level for the determined model parameters. A set of robust controllers are contrived through a mixed sensitivity analysis framework and are blended afterwards by way of a fuzzy inference mechanism to appropriately schedule this regulation practice with the reactor power level. Certain load following scenarios are finally examined wherein the closed loop system exhibits an efficient and quite feasible control attempt.

M-generalised q-neutrosophic extension of CoCoSo method

Nowadays fuzzy approaches gain popularity to model multi-criteria decision making (MCDM) problems emerging in real-life applications. Modern modelling trends in this field include evaluation of the criteria information uncertainty and vagueness. Traditional neutrosophic sets are considered as the effective tool to express uncertainty of the information. However, in some cases, it cannot cover all recently proposed cases of the fuzzy sets. The m-generalized q-neutrosophic sets (mGqNNs) can effectively deal with this situation. The novel MCDM methodology CoCoSomGqNN is presented in this paper. An illustrative example presents the analysis of the effectiveness of different retrofit strategy selection decisions for the application in the civil engineering industry.

Fuzzy-PID Strategy Based on PSO Optimization for pH Control in Water and Fertilizer Integration

In the process of crop growth, irrational fertilizer application methods have caused waste of fertilizer resources and water, as well as damaged the soil’s structure. To puzzle the above problems out, the research constructs the model of water-fertilizer machine by gathering relevant parameters in the field. Considering the system’s plenty of defects and combining it with the MATLAB/simulink system, such as non-linearity, time-varying, large inertia, uncertain mathematical model and severe lag, a fuzzy proportional integral differential control based on particle swarm optimization is proposed in this paper for water and fertilizer integration system. Primarily, the research is done for precision fertilization control of fertilizer integration system and water, and the parameters of fuzzy proportional integral differential gain that schedules controllers are optimized through a particle swarm algorithm. The effectiveness of the suggested controller has been validated by comparing with the control algorithms (proportional integral differential control, fuzzy proportional integral differential control) commonly applied in current fertilizer application systems. Simulation experiments for this research are devised through MATLAB/Simulink simulation platform. Significant improvement in the system’s tuning capabilities by incorporating particle swarm algorithm in the hysteretic non-linear system. Eventually, four control algorithms are experimentally validated in this research at different pH values through Experiments čáre designed in the experimental field. The outcomes demonstrate that the control algorithm in this paper possesses better regulation effect, smaller overshoot, excellent stability and more inadequate rising steady state time compared with the previous controls, which can enables precise control of the fertiliser system.

PMSG Based Wind Energy Conversion System Using Intelligent MPPT with HGRSC Converter

Wind power conversion systems play a significant position in grid-coupled renewable source networks. In this paper, a permanent magnet based synchronous alternator type wind energy scheme is considered for analysis. The enhanced performance of wind power conversion could be reached by improving maximum power point tracking (MPPT) and by modernising the control circuit of the power electronic circuit. The main task is to enrich its performance level by proposing fuzzy gain scheduling (FGS) based optimal torque management for maximum power point tracking. In addition to the improved MPPT, this article analyses different topologies of direct current–direct current (DC–DC) converters such as boost converter and high-gain resonant switched capacitor converter. Comparative simulation analysis of converters with conventional optimal torque control and proposed FGS-based optimal torque control using MATLAB/simulink are presented with various wind speeds.

Development of Fuzzy Observer Gain Design Algorithm for Ship Path Estimation Based on AIS Data

This paper develops a Takagi-Sugeno fuzzy observer gain design algorithm to estimate ship motion based on Automatic Identification System (AIS) data. Nowadays, AIS data is widely applied in the maritime field. To solve the problem of safety, it is necessary to accurately estimate the trajectory of ships. Firstly, a nonlinear ship dynamic system is considered to represent the dynamic behaviors of ships. In the literature, nonlinear observer design methods have been studied to estimate the ship path based on AIS data. However, the nonlinear observer design method is challenging to create directly since some dynamic ship systems are more complex. This paper represents nonlinear ship dynamic systems by the Takagi-Sugeno fuzzy model. Based on the Takagi-Sugeno fuzzy model, a fuzzy observer design method is developed to solve the problem of estimating using AIS data. Moreover, the observer gains of the fuzzy observer can be adjusted systemically by a novel algorithm. Via the proposed algorithm, a more suitable or better observer can be obtained to achieve the objectives of estimation. Corresponding to different AIS data, the better results can also be obtained individually. Finally, the simulation results are presented to show the effectiveness and applicability of the proposed fuzzy observer design method. Some comparisons with the previous nonlinear observer design method are also given in the simulations.

Takagi–Sugeno State Delayed Feedback and Integral Control for PV Systems: Modeling, Simulation, and Control

The integration of the large-scale photovoltaic systems has experienced significant growth, which is similarly expected to occur with small-scale photovoltaic systems. Since small-scale systems must be simple in cost-effective components, control strategies must be implemented in low complexity circuits. However, current maximum power point tracking (MPPT) algorithms are generally complex and require electronic components to support variable control gains for different irradiance conditions, preventing simple MPPT implementations suitable for small-scale photovoltaic systems. This paper proposes a new control strategy to tackle the power tracking problem of the power systems. First, a dynamic model of the photovoltaic system is described and converted into a Takagi–Sugeno (T-S) model. Then, an MPPT scheme is proposed in series with a fixed integral and a fuzzy gain state delay feedback controller, which avoids the need for a variable control gain, simplifying the electronic implementation of the control strategy. New delay-dependent stabilization conditions based on the Lyapunov-Krasovskii functional are proposed in terms of a convex optimization problem, where the delayed feedback and integral gains are designed simultaneously. Simulation results using Matlab and Simulink are used to validate the proposed method.