Type-2 Fuzzy Logic Controller Research Articles

Interval Type-2 Fuzzy Logic PID Controller Based on Differential Evolution with Better and Nearest Option for Hydraulic Serial Elastic Actuator

Interval type-2 fuzzy logic controller (IT2FLC) owns good performance under uncertainty and nonlinearity environments while its optimization is hard and complicated. In this work, we propose an optimization method based on differential evolution with better and nearest option (NbDE) for interval type-2 fuzzy logic PID controller (IT2FL-PID-C) in order to control the position of hydraulic serial elastic actuator (SEA). Firstly, a simplified IT2FL-PID-C structure with fewer parameters is proposed to reduce the difficulty of the optimization of IT2FL-PID-C. To balance its frequency and step performance, an objective function with weighted integral time absolute error and integral square error is given. Secondly, to investigate the performance of NbDE based IT2FL-PID-C, three experiments are conducted. A set of experiments is taken to determine the weight for fitness function. Then we compare NbDE with other algorithms. In addition, NbDE-IT2FL-PID-C is also compared with other optimization methods. At last, NbDE-IT2FL-PID-C is applied to hydraulic SEA and compared with PID. And a range for the weight of fitness function is given. The results have shown the superiority of NbDE with proposed fitness function to optimize IT2FL-PID-C and the superiority of NbDE-IT2FL-PID-C to control the position of hydraulic SEA.

Multi‐objective emperor penguin handover optimisation for IEEE 802.21 in heterogeneous networks

IEEE concentrates on the development of effective media independent handover (IEEE 802.21 MIH) services. The major aim of IEEE 802.21 MIH is to optimise the handover process to make an uninterrupted handover service with less delay. The handover process is categorised into two types a horizontal and vertical process. Among them, vertical handover (VH) needs parameter optimisation for better performance. The optimised result depends on the parameter selection to avoid the rate of handover failure. Although the availability of various optimisation procedures for VH management, many existing works consider one/two parameters for VH optimisation. So that resultant optimal handover solution will not be better in terms of failure rate, latency, and accuracy. Thus, a method of multi-objective emperor penguin handover optimisation (MOEPHO) is developed in the proposed work, which includes almost overall network parameters for VH optimisation. So that, accurate handover with less delay and the minimum energy consumption is achieved in this work. Network simulator 2 working platform is used for the research evaluation. The resultant performances are compared with whale optimisation algorithm-based neural network, adaptive cross-layer design, fuzzy intelligent decision making and novel type 2-fuzzy logic controller to show the effectiveness of MOEPHO.

A PID-Type Fuzzy Logic Controller-Based Approach for Motion Control Applications

Motion control is widely used in industrial applications since machinery, robots, conveyor bands use smooth movements in order to reach a desired position decreasing the steady error and energy consumption. In this paper, a new Proportional-Integral-Derivative (PID) -type fuzzy logic controller (FLC) tuning strategy that is based on direct fuzzy relations is proposed in order to compute the PID constants. The motion control algorithm is composed by PID-type FLC and S-curve velocity profile, which is developed in C/C++ programming language; therefore, a license is not required to reproduce the code among embedded systems. The self-tuning controller is carried out online, it depends on error and change in error to adapt according to the system variations. The experimental results were obtained in a linear platform integrated by a direct current (DC) motor connected to an encoder to measure the position. The shaft of the motor is connected to an endless screw; a cart is placed on the screw to control its position. The rise time, overshoot, and settling time values measured in the experimentation are 0.124 s, 8.985% and 0.248 s, respectively. These results presented in part 6 demonstrate the performance of the controller, since the rise time and settling time are improved according to the state of the art. Besides, these parameters are compared with different control architectures reported in the literature. This comparison is made after applying a step input signal to the DC motor.

Torsional Oscillations Mitigation via Interval Type-2 Fuzzy Logic Brake Control

Turbine-generator shaft torsional oscillations is an interdisciplinary power system dynamic problem because it encompasses mechanical and electrical sectors of power grids. They give rise to a premature expenditure of fatigue life of the turbine-generator shaft metal which could lead to shaft cracks. This paper introduces an interval type-2 fuzzy logic controller to regularize the dynamic braking interventions of a novel braking resistor model for mitigation of torsional oscillations resulting from unsuccessful autoreclosure procedures near generation stations. The effectiveness of proposed scheme is elucidated by considering the unsuccessful autoreclosure of three-phase-to-ground fault in a single machine infinite bus power system via MATLAB/Simulink-based modeling and simulation environment with the help of interval type-2 fuzzy logic controller toolbox. The comparative simulation results with and without the suggested mitigation regime show that the proposed scheme is effective in the mitigation of torsional torque oscillations

A Secure Clustering Protocol With Fuzzy Trust Evaluation and Outlier Detection for Industrial Wireless Sensor Networks

Security is one of the major concerns in Industrial Wireless Sensor Networks (IWSNs). To assure the security in clustered IWSNs, this paper presents a secure clustering protocol with fuzzy trust evaluation and outlier detection (SCFTO). Firstly, to deal with the transmission uncertainty in an open wireless medium, an interval type-2 fuzzy logic controller is adopted to estimate the trusts. And then a density based outlier detection mechanism is introduced to acquire an adaptive trust threshold used to isolate the malicious nodes from being cluster heads. Finally, a fuzzy based cluster heads election method is proposed to achieve a balance between energy saving and security assurance, so that a normal sensor node with more residual energy or less confidence on other nodes has higher probability to be the cluster head. Extensive experiments verify that our secure clustering protocol can effectively defend the network against attacks from internal malicious or compromised nodes.

Towards Systematic Design of General Type-2 Fuzzy Logic Controllers: Analysis, Interpretation, and Tuning

This article aims to provide a new perspective on how the deployment of general type-2 (GT2) fuzzy sets affects the mapping of a class of fuzzy logic controllers (FLCs). It is shown that an α-plane represented a GT2-FLC is easily designed via baseline type-1 and interval type-2 FLCs and two design parameters (DPs). The DPs are the total number of α planes and the tuning parameter of the secondary membership function that are interpreted as sensitivity and shape DPs, respectively. We provide a clear understanding and interpretation of the sensitivity and shape DPs on controller performance through various comparative analyses. We present design approaches on how to tune the shape DP by providing a tradeoff between robustness and performance. We also propose two online scheduling mechanisms to tune the shape DP. We explore the effect of the sensitivity DP on the GT2-FLC and provide practical insights on how to tune the sensitivity DP. We present an algorithm for tuning the sensitivity DP that provides a compromise between computational time and sensitivity. We validate our analyses, interpretations, and design methods with experimental results conducted on a drone. We believe that this article provides clear explanations on the role of DPs on the performance, robustness, sensitivity, and computational time of GT2-FLCs.

Modified Synchronous Reference Frame Control of Solar Photovoltaic-Based Microgrid for Power Quality Improvement

In this paper, solar photovoltaic (SPV)-based microgrid has been connected to grid. Modified synchronous reference frame control of PV inverter has been proposed to estimate reference current for power quality improvement. Conventionally, DC link voltage of the PV inverter is regulated using proportional integral (PI) controller, which suffers from undershoot/overshoot and long settling time during load variation. Intelligent control technique can be the better solution with more accuracy and fast dynamic response. In the present work, interval type-2 (IT-2)-based fuzzy logic controller (FLC) has been proposed to control inverter’s DC link voltage. Performance of the grid-connected SPV system is analyzed using proposed IT-2 FLC and compared with conventional PI as well as type-1 FLC. It compensates the harmonics of the nonlinear load and maintains the grid at unity power factor by supplying the reactive demand of the load. Total harmonic distortion at grid side of the system is within the limits as per IEEE-519 standard. To test the efficacy of the proposed IT-2 FLC for grid-connected SPV system MATLAB software2016(a) has been used.

Interval Type 2 Fuzzy Logic Control for Energy Management of Hybrid Electric Autonomous Vehicles

Autonomous vehicles are aimed to reduce accidents and traffic congestion. Since hybrid electric vehicles offer feasible solutions to reduce energy consumption and emission to the environment, it is expected that autonomous vehicles will be powered through a hybrid electric system compared to other alternatives. In this paper, a hybrid electric autonomous vehicle is studied under significant amount of uncertainty and ambiguity in the road environment and driver behavior. A Type 1 fuzzy logic controller is constructed here to address the uncertainties of driving conditions. The design involves building an intelligent energy management system for the hybrid electric autonomous vehicle. We have also examined the potentials of the Interval Type 2 fuzzy logic control, especially for energy consumption management. Two simulations are implemented, to demonstrate that the intelligent system, proposed trough Type 1 and Interval Type 2 fuzzy logic control, decreases the fuel usage of the vehicle from 6.74 to 6.58 L/100km, respectively. It is also demonstrated that the Interval Type 2 fuzzy logic controller saves more battery life compared to the Type-1 when the vehicle works under uncertain and ambiguous road conditions. Finally, Interval Type-2 fuzzy logic controller facilitates a reduction of carbon footprint in the autonomous vehicle as desired by the automotive industry stakeholders.

An Optimal Interval Type-2 Fuzzy Logic Controller for Maximum Power Capture of Point Absorbers

An optimal interval Type-2 Fuzzy logic controller is proposed for maximum power capture from point absorbers. First, based on an introduced point absorber, a model-based feedback-simplifier is proposed to reduce the complexity of the nonlinear part of the model. Then, two interval Type-2 Fuzzy systems are designed for finding the optimal values of damping and stiffness coefficients so that the maximum power absorption is realised. A black hole optimisation algorithm is then introduced to find the optimal values of the parameters of the Fuzzy system. Finally, the high performance of the proposed scheme is confirmed using simulations. The contributions of this paper are: (1) designing a model-based feedback simplifier for point absorbers, (2) introducing a state space of the final closed-loop system, (3) designing an interval Type-2 Fuzzy controller for the final system, (4) training the Fuzzy controller parameters using a black hole optimisation algorithm.

Robust State Estimation Method for Adaptive Load Frequency Control of Interconnected Power System in a Restructured Environment

Handling varying uncertainties in renewable energy generation and load demands are major challenges to the restructured power system environment. This article proposes the design of a novel control methodology using a robust UIO for dynamic state estimation and an interval type-2 fuzzy logic controller for adaptive LFC of interconnected power systems with high penetration of renewable energy in a restructured power system environment. The capability of the proposed robust UIO to independently estimate the true state of the power system regardless of unknown inputs or disturbances that impact the frequency stability in real time is demonstrated by the asymptotic zero property of the state estimation error. This is supported by using an interval type-2 fuzzy logic controller in the LFC loop of each area of the interconnected system to efficiently damp frequency oscillations, thereby improving power system stability and resilience. The performance of the proposed methodology is evaluated using a modified two-area power system in the restructured environment. The results obtained from the stability and LFC analyses during severe operating conditions demonstrated the robustness of the proposed control framework to unknown inputs and unprecedented variations that can affect the frequency stability of the system.

Interval type-2 and type-1 Fuzzy Logic Controllers for congestion avoidance in internet routers

Active queue management (AQM) scheme is an important research area in congestion control. The AQM mechanism attains the congestion controller by regularize the length of queue from evaluation or dropping the packets at the moderate routers. In this paper the interval type-2 and type-1 fuzzy logic controller are designed to desist the congestion in the networks. Several evolutionary optimization algorithms are used to select the parameters of two controllers as particle swarm optimization (PSO), social spider optimization (SSO) and ant colony Optimization (ACO). The simulation of linearized model of TCP/AQM are introduced in MATLAB (R2018a). Results of the PID like fuzzy type-1controller is compared with PID like interval type-2 fuzzy controller (IT2-FLC) based on optimization algorithms in each controller and shows that IT2-FLC with SSO has been given the best result.

Inter-Area Oscillations Stabilization by Type-2 Fuzzy Based Novel Resistor Brake Model

Inter-area power oscillation is an inherent problem of weakly interconnected power systems. This paper introduces fuzzy logic controller of Type-2 to organize the interventions of novel resistor bake model for inter-area oscillation stabilization in Kundur's two-area benchmark test power system via MATLAB/Simulink-based modelling and simulation environment with the help of Interval Type-2 fuzzy logic controller toolbox. The propositioned controller uses the relative kinetic energy deviation between the two areas as an input control signal. Three case studies, including small, mild, and severe disturbances should elucidate the effectuality of the propositioned scheme. The obtained comparative time-domain simulation results should prove that the suggested stabilization regime is a reliable mean for treating the issue of inter-area power oscillations.

Invasive Weed Optimization Algorithm for Tuning Transitioning from Type-1 to Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters

Invasive Weed Optimization Algorithm for Tuning Transitioning from Type-1 to Interval Type-2 Fuzzy Logic Controller for Boost DC-DC Converters

Optimization of Threshing Quality Control Strategy Based on Type-2 Fuzzy Logic Controller

At present, loss rate and grain damage are the most complicated problems for combine harvester. Besides, existing controllers that control threshing quality do not take into account environmental noise, data fluctuations, and other detection factors. In this paper, considering the interference of uncertain factors, a control strategy with low crashed rate and reduced loss rate using type-2 Fuzzy Logic Controller (FLC) is proposed. This control strategy takes threshing separation component as a research object and adopts combination of theory with experimental analysis. Firstly, basic data acquisition experiment of threshing system is designed. Secondly, a control strategy is established by adequately previous filled test data. In the process of control strategy design, Fuzzy Logic Toolbox evaluates the performance of type-2 FLC. Integral of Square Error (ISE) and Integral of the Absolute value of the Error (IAE) show a better performance of type-2 FLC than of type-1 FLC. At last, field experiments are designed to verify the effectiveness of type-2 control strategy. The field test shows that the maximum reduction of grain damage rate and loss rate can reach to 44.08 % and 29.6 %. Experiment results show that the type-2 FLC can significantly reduce loss rate, crashed rate, and improve threshing quality.

An Optimized Type-2 Self-Organizing Fuzzy Logic Controller Applied in Anesthesia for Propofol Dosing to Regulate BIS

During general anesthesia, anesthesiologists who provide anesthetic dosage traditionally play a fundamental role to regulate bispectral index (BIS). However, in this article, an optimized type-2 self-organizing fuzzy logic controller (SOFLC) is designed for a target controlled infusion pump related to propofol dosing guided by BIS, to realize automatic control of general anesthesia. The type-2 SOFLC combines a type-2 fuzzy logic controller with a self-organizing mechanism to facilitate online training while able to contend with operational uncertainties. A novel data-driven surrogate model and genetic programming-based strategy is introduced for optimizing the type-2 SOFLC parameters offline to handle interpatient variability. A pharmacological model is built for simulation in which different optimization strategies are tested and compared. Simulation results are presented to demonstrate the applicability of our approach and show that the proposed optimization strategy can achieve better control performance in terms of steady-state error and robustness.

Type-2 fuzzy logic controller optimized by wavelet networks for mobile robot navigation

<p>In this work, we will use a new control strategy based on the integration of a type-2 fuzzy reasoning optimized by wavelet networks as part of a navigation system of a mobile robot. The proposed approach is able to facilitate the navigation task in an autonomous manner, in order to determine which commands must be sent at each moment to the mobile robot. This operation must take into account convergence towards a goal with the shortest possible path in the minimum delay between the starting position and the target position. Once the goal is reached, the robot stops. </p><p> </p>

Design of Type 2 Fuzzy Logic Controller Based Inverter for Isolated PV System

In this article, a Type 2 fuzzy logic controller (T2FLC) is implemented in the PV system to improve the magnitude and frequency as well as the transient time response of the photovoltaic (PV) system. T2FLC is capable to handle more uncertainty because of the PV characteristic variable with atmospheric temperature and irradiation of the sun. Simple and most widely used Perturb & Observation (P& O) based MPPT technique has been implemented to extract maximum power to generate the desired switching pulse for DC-DC converter in the proposed standalone PV system. Deployed T2FLC based inverter in the proposed standalone PV system with different loads can improve the response of the system and reduce the oscillations as well as noise. The performance of the system validates over with proportional-integral (PI) and T1FLC controller. The proposed T2FLC algorithm deployed in a standalone three-phase PV system shows performance better than the other two controllers and simulate in MATLAB/SIMULINK environment.

An Intelligent Type-2 Fuzzy Stabilization of Multi-DC Nano Power Grids

This paper proposes a robust and intelligent droop control method to stabilize a cluster of DC nanogrids (DCNGs) with constant power loads (CPLs). Conventional droop methods suffer from voltage regulation in parallel converters due to the CPLs destructive effects, which cause instability in the DCNGs. To address the shortcomings of the conventional methodologies, this paper proposes a new droop-based single input interval type-2 fuzzy logic controller (SI-IT2-FLC), which enhances the transient response and improve the steady-state performance. The suggested method aims to boost the stability of the paralleled DCNGs in a cluster under the effect of the CPLs, which is inevitable in the practical applications. The efficacy of the suggested scheme is validated in different operating conditions. Moreover, real-time model-in-the-loop (MiL) results show the performance improvements in the transient and steady-state obtained by the novel suggested control approach.

A new fractional-order general type-2 fuzzy predictive control system and its application for glucose level regulation

In this paper a new robust fractional-order predictive controller is presented and employed to regulate the glucose level in type-1 diabetes. The dynamics of the system is fully unknown an it is online estimated by a fractional-order model using interval Type-2 (T2) fuzzy logic system. The proposed control system is composed of two main controllers which are the predictive General T2 Fuzzy Logic Controller (GT2-FLC) and compensator controller. In this structure, the main controller is the GT2-FLC which is optimized via the Biogeography-based Optimization (BBO) algorithm such that to minimize a cost function in a fixed prediction horizon. The compensator controller is designed to guarantee the closed-loop asymptotic stability. The performance of proposed control strategy is examined on the modified Bergman’s model of some patients with time-varying parameters, external noise perturbation and meal disturbances. The effectiveness of the proposed control scheme is verified and is compared with the other T2 fuzzy and well-known model predictive controllers. The results of the paper clearly show the superiority of the proposed T2 fuzzy logic control system.

Balance Adjustment of Power-Line Inspection Robot Using General Type-2 Fractional Order Fuzzy PID Controller

In this study, a general type-2 fractional order fuzzy PID (GT2FO-FPID) controller is proposed to fulfil the balance adjustment of the Power-line Inspection (PLI) robot system. It is a combination of Mamdani general type-2 fuzzy logic controller (GT2-FLC) and fractional PID controller. Since the PLI robot system is an under-actuated system, it’s necessary to get complete information of the system. However, when all state variables are treated as input to the controller, there is a problem with the rule explosion. Because of this, the information fusion method is adopt to solve the problem and simplify the controller design. At the same time, fractional-order integral-differential operators and input-output scaling factors, which are taken as design variables and optimized by genetic algorithm (GA). To assess the performance of proposed controller based on symmetry criterion, we compared it against existing controllers, i.e., interval type-2 fractional order fuzzy PID (IT2FO-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), and conventional fractional order (FOPID) controllers. Furthermore, to show the anti-inference ability of the proposed controller, three common perturbed process are tested. Finally, simulation results show that the GT2FO-FPID controller outperforms other controllers in the presence of external perturbations on the PLI robot system.