Type-2 Fuzzy Logic Controller Research Articles

Retracted: Type-2 Fuzzy Logic Controller of a Doubly Fed Induction Machine

Retracted: Type-2 Fuzzy Logic Controller of a Doubly Fed Induction Machine

The Type-2 Fuzzy Logic Controller-Based Maximum Power Point Tracking Algorithm and the Quadratic Boost Converter for Pv System

An interval type-2 fuzzy logic controller-based maximum power point tracking algorithm and direct current–direct current (DC–DC) converter topology are proposed for photovoltaic (PV) systems. The proposed maximum power point tracking algorithm is designed based on an interval type-2 fuzzy logic controller that has an ability to handle uncertainties. The change in PV power and the change in PV voltage are determined as inputs of the proposed controller, while the change in duty cycle is determined as the output of the controller. Seven interval type-2 fuzzy sets are determined and used as membership functions for input and output variables. The quadratic boost converter provides high voltage step-up ability without any reduction in performance and stability of the system. The performance of the proposed system is validated through MATLAB/Simulink simulations. It is seen that the proposed system provides high maximum power point tracking speed and accuracy even for fast changing atmospheric conditions and high voltage step-up requirements.

Robust resource allocation scheme under channel uncertainties for LTE-A systems

Nowadays, resource allocation is one of the major problems in the cellular networks. Due to the increasing number of autonomous heterogeneous devices in future mobile networks, a proper scheduling scheme is required to provide the adequate resources for the service flow. However, provisioning quality-of-service (QoS) for real-time applications with the constraint on transporting delay is hard to achieve without compromising other QoS parameters. In this paper, an intelligent QoS-aware bandwidth allocation solution is proposed for the uplink traffic when the channel condition is uncertain. The system is designed based on a specific maximum latency assurance for real-time applications as well as considering fairness to the throughput of non-real-time services. The scheduling system employs a channel-aware Kalman filter based interval type-2 fuzzy logic controller to estimate channel uncertainty as well as satisfying the QoS requirements for user equipment. Through simulations, the performance of the proposed system in terms of optimal bandwidth allocation, bandwidth wastage, fairness, jitters, various delays and throughputs for delay sensitive and delay tolerant services is analyzed. The numerical results show that the proposed scheme provides reliable scheduling for real-time services without harming the performance of non-real-time QoS parameters.

Chaos driven instability control using interval type-2 fuzzy logic controller for better performance

Chaos driven instability control using interval type-2 fuzzy logic controller for better performance

Comparative analysis of noise robustness of type 2 fuzzy logic controllers

Nowadays Fuzzy logic in control applications is a well-recognized alternative, and this is thanks to its inherent advantages as its robustness. However, the Type-2 Fuzzy Logic approach, allows managing uncertainty in the model. Type-2 Fuzzy Logic has recently shown to provide significant improvement in image processing applications, however it is also important to analyze its impact in controller performance. This paper is presenting a comparison in the robustness of Interval Type-2 and Generalized Type-2 Fuzzy Logic Controllers, in order to generate criteria to decide which type of controller is better in specific applications. The plants considered in the experimentation are two benchmark control plants and we report the Integral Squared Error (ISE), Integral Absolute Error (IAE) and Integral Time-weighted Absolute Error (ITAE) performance metrics, and also another important metric reported is the execution time. Based on the experimental results, Fuzzy Logic Controller selection criteria are proposed according to the performance and execution time requirements.

INTELLIGENT TRACKING CONTROL USING PSO-BASED INTERVAL TYPE-2 FUZZY LOGIC FOR A MIMO MANEUVERING SYSTEM

Air vehicle modeling like the helicopter is very challenging assignment because of the highly nonlinear effects, effective cross-coupling between its axes, and the uncertainties and complexity in its aerodynamics. The Twin Rotor Mutli-Input Multi-Output System (TRMS) represents in its behavior a helicopter. TRMS has been widely used as an apparatus in Laboratories for experiments of control applications. The system consists of two degrees of freedom (DOF) model; that is yawing and pitching. This paper discusses the design of Four Interval Type-2 fuzzy logic controllers (IT2FLC) for yaw and pitch axes and their cross-couplings of a twin rotor MIMO system. The objectives of the designed controllers are to maintain the TRMS position within the pre-defined desired trajectories when exposed to changes during its maneuver. This must be achieved under uncertain or unknown dynamics of the system and due to external disturbances applied on the yaw and pitch angles. The coupling effects are determined as the uncertainties in the nonlinear TRMS. A PSO algorithm is used to tune the Inputs and output gains of the four Proportional-Derivative (PD) Like IT2FLCs to enhance the tracking characteristics of the TRMS model. Simulation results show the substantial enhancement in the performance using PSO-Based Interval Type-2 fuzzy logic controllers compared with that of using IT2FLCs only. The maximum percentage of enhancements reaches about 33% and the average percentage of enhancements is about 17.1%. They also show the proposed controller effectiveness improving time domain characteristics and the simplicity of the controllers.

Ant Colony Optimization Based Type-2 Fuzzy Force-Position Control for Backhoe Excavator Robot

This paper proposes the design and simulation of Interval Type-2 Fuzzy Logic Control using MATLAB/Simulink to control the position of the bucket of the backhoe excavator robot during digging operations. In order to reach accurate position responses with minimum overshoot and minimum steady state error, Ant Colony Optimization (ACO) algorithm is used to tune the gains of the position and force parts for the force-position controllers to obtain the best position responses. The joints are actuated by the electro-hydraulic actuators. The force-position control incorporating two-Mamdani type-Proportional-Derivative-Interval Type-2 Fuzzy Logic Controllers for position control and 3-Proportional-Derivative Controllers for force control. The nonlinearity and uncertainty in the model that inherit in the electro hydraulic actuator system are also studied. The nonlinearity includes oil leakage and frictions in the joints. The friction model is represented as a Modified LuGre friction model in actuators. The excavator robot joints are subjected to Coulomb, viscous and stribeck friction. The uncertainty is represented by the variation of bulk modulus. It can be shown from the results that the ACO obtain the best gains of the controllers which enhances the position responses within the range of (19, 23 %) compared with the controllers tuned manually.

Seismic control of buildings with active tuned mass damper through interval type-2 fuzzy logic controller including soil–structure interaction

This paper presents the application of interval type-2 fuzzy logic controller (IT2FLC) in ATMD for the response control of a building considering soil–structure interaction (SSI). One of the main constraints of the current fuzzy systems is their inability to consider uncertainty in fuzzy rules. Interval type-2 fuzzy systems have the ability to handle this deficiency. It also takes into account uncertainty in loading and structural behavior. To evaluate the influence of soil types on the behavior of structure, an 11-story shear building is used. This structure has been analyzed under the earthquake excitations recommended by the International Association of structural control (IASC) committee. The uncontrolled and controlled responses of structure with ATMD through IT2FLC are calculated with and without SSI effects. Numerical results indicate that SSI would significantly affect the structural response of the building. It is also found that when a structure is built on soft soil sites, the effectiveness of ATMD with IT2FLC in the case of suppressing the structural response is increased.

On the Comparison of Type 1 and Interval Type 2 Fuzzy Logic Controllers Used in a Laser Tracking System

This study is an extension of our previous researches for a type-1 fuzzy logic controller used in a Laser Tracking System (LTS). In a LTS the control objective is for a laser beam to track a retro-reflecting moving target through adjustment of joint angles and velocities of a two Degrees-Of-Freedom (DOF) tracking gimbals on which tracking mirrors are mounted. This is done based on two types of feedback signals: Distance reading, obtained by a laser interferometer and off-center distance error of the returning laser beam, as measured by a four-quadrant photo-detector. The LTS control system should effectively provide a high-speed and smooth tracking on the target with lowest steady state errors. A complete LTS consists of laser interferometer transducer subsystem, opto-electronic beam steering subsystem, gimbals driving subsystem and the digital control subsystem. As high tracking accuracy and high tracking speed are both vital for LTS operation, a design of a high performance controller is critical. This paper shows that an interval type 2 fuzzy logic controller (IT2FLC) can outperform a type 1 fuzzy logic controller (T1FLC) in such applications. Complete and extensive simulation studies are discussed and analyzed with a performance comparison between a type 1 and an interval type 2 FLC. The advantages of utilizing an IT2FLC over T1FLC to improve the control and tracking performance for a LTS with random noises are confirmed by simulation results.

Interval type-2 fuzzy logic based MPPT algorithm for direct drive PMSG wind turbine system

This paper proposes a maximum power point tracking (MPPT) scheme for direct drive permanent magnet synchronous generator (PMSG) wind turbine system. In this scheme, the machine side converter (MSC) is controlled using the interval type-2 fuzzy logic control (IT2-FLC). The IT2-FLC is proposed owing to its ability to handle the uncertainties of the system parameters. The additional degree of freedom of type-2 fuzzy sets is due to the third dimension in the membership function which handles power oscillations damping and voltage recovery following parameter uncertainties in the network. The field oriented control is used to control the machine side and grid side converter. The design of the proposed control scheme of the MSC is presented in this paper. Simulation results, based on MATLAB-Simulink, for a 1.5 MW PMSG wind turbine are carried out to validate the proposed control scheme considering the various uncertainties. Also, experimental results with hardware in loop (HIL) configuration are obtained for reduced scale PMSG wind turbine to demonstrate the feasibility of the controller for real time applications. The results show a significant enhancement in speed regulation, mechanical stress reduction and power harvest with the proposed control scheme.

Hardware Implementation of IT2FLC using FPGA for Control Applications

Interval Type2 Fuzzy Logic Control (IT2FLC) has been applied to a number of industrial, medical, home and military applications. Hardware implementation of IT2FLC can be achieved in a number of ways. One of these ways is the use of a Field Programmable Gate Array (FPGA).         In this paper, the design and implementation of an IT2FLC using FPGA has been presented. The proposed controller is of Mamdani type. It works in different structures (P/PI/PD/PID like IT2FLC) depending on two control lines, different number of triangular shape memberships (2-7) depending on three control lines, six tunable gains and within a range of sampling time of (0.01-1024) seconds. Three type reduction algorithms are used and it is found that the Enhanced Iterative Algorithm with Stop Condition (EIASC) produced the minimum reduction in FPGA size. Thus less execution time. The reduction size is about 75% than Karnick Mendel (KM) and is about 3% than Enhanced KM (EKM). Linear and nonlinear models are used to test the designed Controller. Gains are tuned manually to reach minimum overshoot, settling time and steady state error.                                                Simulation and Implementation results showed that the proposed controller works in an efficient way under no-load, load and uncertainty in the nonlinear model parameters.

FLC control for tuning exploration phase in bio-inspired metaheuristic

<p>Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey.</p>

Trajectory tracking control for rotary steerable systems using interval type-2 fuzzy logic and reinforcement learning

Rotary steerable system (RSS) is a directional drilling technique which has been applied in oil and gas exploration under complex environment for the requirements of fossil energy and geological prospecting. The nonlinearities and uncertainties which are caused by dynamical device, mechanical structure, extreme downhole environment and requirements of complex trajectory design in the actual drilling work increase the difficulties of accurate trajectory tracking. This paper proposes a model-based dual-loop feedback cooperative control method based on interval type-2 fuzzy logic control (IT2FLC) and actor-critic reinforcement learning (RL) algorithms with one-order digital low-pass filters (LPF) for three-dimensional trajectory tracking of RSS. In the proposed RSS trajectory tracking control architecture, an IT2FLC is utilized to deal with system nonlinearities and uncertainties, and an online iterative actor-critic RL controller structured by radial basis function neural networks (RBFNN) and adaptive dynamic programming (ADP) is exploited to eliminate the stick–slip oscillations relying on its approximate properties both in action function (actor) and value function (critic). The two control effects are fused to constitute cooperative controller to realize accurate trajectory tracking of RSS. The effectiveness of our controller is validated by simulations on designed function tests for angle building hole rate and complete downhole trajectory tracking, and by comparisons with other control methods.

A high-performance control scheme for photovoltaic pumping system under sudden irradiance and load changes

A low-cost photovoltaic (PV) pumping system based on three phase induction motor (IM) without the use of chemical energy storage elements is presented in this paper. The PV generator-side boost converter performs the maximum power point tracking (MPPT), while the IM−side two-level inverter regulates the net DC-link voltage and the developed electromagnetic torque by IM, which is coupled with a centrifugal pump. An improved variable step size perturb and observe (P&O) algorithm is proposed to reduce the steady-state PV power fluctuation, to accelerate the tracking operation under sudden irradiance changes, and to protect IM under load drops. The proposed algorithm is based on a current control approach of the boost converter with a model predictive current controller to select the optimal control action. Moreover, predictive torque and flux control (PTC) is used to control IM drive, due to its advantages such as faster torque response, lower torque ripple, and simplicity of implementation. Furthermore, a Takagi-Sugeno (T-S) type fuzzy logic controller (FLC) is developed in order to regulate the DC-link voltage, by producing the torque reference for PTC algorithm. In order to examine and assess the performance of the proposed control scheme for PV pumping system, a complete simulation model is developed using MATLAB/SimulinkTM environment and confirmed through real-time hardware in the loop (HIL) system. The obtained results indicate the excellent performance of the proposed control scheme, which is much better than the conventional scheme based on conventional techniques (P&O algorithm and direct torque control (DTC)).

A generalized type-2 fuzzy logic approach for dynamic parameter adaptation in bee colony optimization applied to fuzzy controller design

In this work a Generalized type-2 Fuzzy Logic System (GT2FLS) approach for dynamic parameter adaptation in metaheuristics and for optimal fuzzy controller design is presented. In these two cases, the efficiency of the GT2FLS approach is verified with simulation results. In the first case, the GT2FLS provides an approach to dynamically find the optimal values of the heuristic parameters that are a critical part of the Bee Colony Optimization (BCO) algorithm performance. In the second case, the GT2FLS approach provides the basis for building a Generalized type-2 Fuzzy Logic Controller (GT2FLC), which can be optimized with the traditional BCO, specifically to find the optimal design of the Membership Functions (MFs) in the Fuzzy Controller. In both cases, the GT2FLS approach shows advantages in the optimization of the solutions to the problems. For both cases, we can considered them as hybrid systems combining GT2FLS and BCO although the combination is made in a different way, and it can be noted that a GT2FLS presents better stability in the minimization of the errors when applied to benchmark control problems. Simulation results illustrate that the implementation of the Generalized type-2 Fuzzy Logic Controller (GT2FLC) approach improves its performance when using the BCO algorithm and the stability of the fuzzy controller is better when compared with respect to a type-1 Fuzzy Logic Controller (T1FLC) and an Interval type-2 Fuzzy Logic Controller (IT2FLC).

Performance analysis of optimal hybrid novel interval type-2 fractional order fuzzy logic controllers for fractional order systems

The main objective of this paper is to propose the family of hybrid novel interval type-2 fractional order fuzzy PID (IT2FO-FPID) controllers, which incorporates fractional order integro-differentiator, taking into its consideration the advantages of the interval type-2 fuzzy controllers (IT2FLCs) and fractional order PID (FOPID) controllers. The incorporation of IT2FLCs with FOPID controllers has been investigated in terms of time response measure for unit set-point response and unit load disturbance. The resulting different hybrid IT2FO-FPID controller structures are examined on different fractional order processes followed by robustness analysis. It is observed that incorporation of fractional order integro-differential operators made the proposed control structures less sensitive to the parameters change. For this reason, fractional order integro-differential operators are adopted as design variables along with input/output scaling factors for designing of effective novel controllers. In order to do reasonable comparison, the tuning of the family of hybrid IT2FO-FPID controllers' parameters is done with newly Artificial Bee Colony-Genetic Algorithm (ABC-GA) optimization technique to develop optimal closed loop performance by considering the objective function as weighted sum of control loop error index and control output. The simulation analyses are executed for two different non-integer order plus time delay fractional order process plants (NIOPTD-I and NIOPTD-II). These processes represent a sensible estimation of most process plants and are employed to examine the comparative qualities of the presented hybrid IT2FO-FPID controllers. Further, the performance comparison and the significance of the different proposed hybrid controllers are investigated under unit set-point response, load disturbance rejection, and small control signal or actuators requirement. In order to test the efficacy of proposed controllers, the robustness analysis such as parameter variations is also investigated. Finally, the simulation investigations explicitly suggest that proposed different hybrid IT2FO-FPID control structures can be recommended in terms of better step response, better load disturbance rejection, small control signal response, and parameters variation.

Non-fragile control for interval type-2 TSK fuzzy logic control systems with time-delay

The study is concerned with non-fragile controller design for nonlinear systems with time-delay which are described by the interval type-2 Takagi–Sugeno–Kang fuzzy logic control system. Interval type-2 Takagi–Sugeno–Kang fuzzy logic control systems have been used in many applications and shown better outperforms in some cases than their type-1 counterparts. In this paper, a Lyapunov–Krasovskii functional is constructed through the Wirtinger-based integral inequality which is less conservative than the Jensen inequality, then sufficient conditions are obtained to stabilize the interval type-2 Takagi–Sugeno–Kang fuzzy logic control system with time-delay. Two types of controllers are designed, one is normal and the other is non-fragile. Non-fragile controllers make their implementation easier allowing to tune controller parameters automatically. The results are established in the form of linear matrix inequalities, which can be easily solved by MATLAB toolbox, then the controller gain matrices can be developed. Simulation examples in different cases are provided to demonstrate the effectiveness of the proposed method.

Interval type-2 fuzzy logic and its application to occupational safety risk performance in industries

In this paper, we have developed an interval type-2 fuzzy logic controller (T2FLC) approach for assessment of the risks that workers expose to at construction sites. Using this novel approach, past accident data, subjective judgments of experts, and the current safety level of a construction site are to be combined. The method is then implemented on a tunneling construction site and risk level for all type of accidents is formulated. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of risk assessment can be predicted in industry. Finally, a comparative study has been successfully performed with type-1 and type-2 fuzzy dataset for improving risk assessment that can be easily determined in the type-2 fuzzy prediction model for improving accuracy. Validity of the proposed model is done with the help of statistical analysis and multiple linear regressions.

Model predictive fuzzy control for enhancing FRT capability of DFIG-based WT in real-time simulation environment

Fault ride through (FRT) capability creates a challenging condition for wind turbines (WTs) with a doubly fed induction generator (DFIG). In order to fulfill the FRT requirement for WTs with DFIG, the over-current in rotor circuit and theDC-link over-voltage during fault conditions must be addressed. This paper proposes an application of the model predictive control (MPC) system to power converters and uses Takagi–Sugeno–Kang type fuzzy logic control for improving the FRT capability of WTs with DFIG. The effectiveness of the proposed MPC method is compared with conventional proportional-plus-integral controllers with pulse-width modulation. The real-time simulation results illustrate the improved performance offered by the proposed control scheme for maintaining the rotor current and the DC-link voltage within permissible ranges when power grid faults occur.

Type-2 fuzzy logic control for underactuated truss-like robotic finger with comparison of a type-1 case1

Type-2 fuzzy logic control for underactuated truss-like robotic finger with comparison of a type-1 case1