Type-2 Fuzzy Logic Controller Research Articles

Hybrid active control of adjacent buildings interconnected by viscous dampers utilizing type-2 fuzzy controller considering soil-structure interaction

Viscous dampers (VDs) connecting adjacent multi-story buildings are significantly useful not only for preventing severe earthquake-induced pounding, but also for mitigating the response of buildings. In the current study, in addition to utilizing viscous dampers, enhanced hybrid active tuned mass dampers (EHATMDs) are used at the top of asymmetric buildings and active tendons (ATs) are used at the sixth floor of symmetric buildings, while all these buildings are interconnected by VDs. The interval type-2 fuzzy logic controller (IT2FLC) is employed to produce real-time active forces applied to the actuators of EHATMDs and ATs. Moreover, soil-structure interaction (SSI) effects are considered. The aim is to assess the performance of all the mentioned controllers in mitigating both the translational and torsional responses of structures rested on soft soil. To reach these goals, three different layouts of adjacent buildings, including asymmetric and symmetric buildings, are taken into account. The results showed that the layout where asymmetric buildings were controlled by VDs in both main directions had the highest performance.

Fixed-time adaptive general type-2 fuzzy logic control for air-breathing hypersonic vehicle

This paper proposes a novel fixed-time adaptive general type-2 fuzzy logical control (FAGT2FLC) scheme for an air-breathing hypersonic vehicle (AHV) with uncertainties. Firstly, the AHV dynamic model is transformed into a strict feedback form. Then, the FAGT2FLC is designed based on the transformed model to improve robustness and guarantee fixed-time convergence of the closed-loop system. The general type-2 fuzzy logic system (GT2FLS) is utilized to approximate the model uncertainties; for the purpose of designing adaptive laws, the [Formula: see text]-plane method is employed to represent the GT2FLS. A parameter projection operator is used to solve the possible singularity problem of parameter adaption. Besides, a fixed-time differentiator is used to deal with the “explosion of terms” inherent in backstepping method. Theoretical analysis based on relevant lemmas shows that the closed-loop system will converge into a small error band in fixed time. Lastly, detailed simulations are carried out to demonstrate the effectiveness and superiority of the proposed control scheme.

Comparative Study between Fuzzy Logic and Interval Type-2 Fuzzy Logic Controllers for the Trajectory Planning of a Mobile Robot

In this study, Fuzzy Logic (FL) and Interval Type-2 FL (IT-2FL) controllers were applied to a mobile robot in order to determine which method facilitates navigation and enables the robot to overcome real-world uncertainties and track an optimal trajectory in a very short time. The robot under consideration is a non-holonomic unicycle mobile robot, represented by a kinematic model, evolving in two different environments. The first environment is barrier-free, and moving the robot from an initial to a target position requires the introduction of a single action module. Subsequently, the same problem was approached in an environment closer to reality, with objects hindering the robot's movement. This case requires another controller, called obstacle avoidance. This system allows the robot to reach autonomously a well-defined target by avoiding collision with obstacles. The robustness of the structures of the defined controllers is tested in Matlab simulations of the studied controllers. The results show that the IT-2FL controller performs better than the FL controller.

A Robust Interval Type-2 Fuzzy Logic Controller for Variable Speed Wind Turbines Based on a Doubly Fed Induction Generator

This paper presents an implementation of a new robust control strategy based on an interval type-2 fuzzy logic controller (IT2-FLC) applied to the wind energy conversion system (WECS). The wind generator used was a variable speed wind turbine based on a doubly fed induction generator (DFIG). Fuzzy logic concepts have been applied with great success in many applications worldwide. So far, the vast majority of systems have used type-1 fuzzy logic controllers. However, T1-FLC cannot handle the high level of uncertainty in systems (complex and non-linear systems). The amount of uncertainty in a system could be reduced by using type-2 fuzzy logic since it offers better capabilities to handle linguistic uncertainties by modeling vagueness and unreliability of information. A new concept based on an interval type-2 fuzzy logic controller (IT-2 FLC) was developed because of its uncertainty management capabilities. Both these control strategies were designed and their performances compared for the purpose of showing the control most efficient in terms of reference tracking and robustness. We made a comparison between the performance of the type-1 fuzzy logic controller (T1-FLC) and interval type-2 fuzzy logic controller (IT2-FLC). The simulation results clearly manifest the height robustness of the interval type-2 fuzzy logic controller in comparison to the T1-FLC in terms of rise time, settling time, and overshoot value. The simulations were realized by MATLAB/Simulink software.

Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

Nonsingular Terminal Sliding Mode Control With Ultra-Local Model and Single Input Interval Type-2 Fuzzy Logic Control for Pitch Control of Wind Turbines

As wind energy is becoming one of the fastest-growing renewable energy resources, controlling large-scale wind turbines remains a challenging task due to its system model nonlinearities and high external uncertainties. The main goal of the current work is to propose an intelligent control of the wind turbine system without the need for model identification. For this purpose, a novel model-independent nonsingular terminal sliding-mode control (MINTSMC) using the basic principles of the ultra-local model (ULM) and combined with the single input interval type-2 fuzzy logic control (SIT2-FLC) is developed for non-linear wind turbine pitch angle control. In the suggested control framework, the MINTSMC scheme is designed to regulate the wind turbine speed rotor, and a sliding-mode (SM) observer is adopted to estimate the unknown phenomena of the ULM. The auxiliary SIT2-FLC is added in the model-independent control structure to improve the rotor speed regulation and compensate for the SM observation estimation error. Extensive examinations and comparative analyses were made using a real-time software-in-the-loop (RT-SiL) based on the dSPACE 1202 board to appraise the efficiency and applicability of the suggested model-independent scheme in a real-time testbed.

Improvement of the induction motor sensorless control based on the type-2 fuzzy logic

This paper presents MRAS speed sensorless control of induction motor using type-2 fuzzy logic controller (T2FLC). These controllers replace the PI ones, in the new MRAS strategy proposed in Benlaloui et al. (IEEE Trans Energy Convers 30(2):588–595, 2015), in order to improve the induction motor performances and robustness at low speed region. Indeed, the choice of these controllers is made because of their adaptation-based schemes which permit to handle nonlinear uncertain systems without the need of precise mathematical model required when using PI controllers. Comparative study had shown a better rejection of disturbance and high insensitivity to stator resistance compared to the PI and the T1FLC controllers. The effectiveness of the proposed speed-based T2FLC estimation method and its good robustness are validated by simulation and by experimental results.

GWO-Based Optimal Tuning of Type-1 and Type-2 Fuzzy Controllers for Electromagnetic Actuated Clutch Systems

Abstract The goal of this paper is to develop the optimal fuzzy controllers for nonlinear processed represented by electromagnetic actuated clutch systems in order to improve the control system performance. A Takagi-Sugeno type-1 fuzzy logic controller and a Takagi-Sugeno type-2 fuzzy logic controller are designed here to enhance position tracking performance of electromagnetic actuated clutch systems, which are subjected to parametric uncertainties. The parameters of membership functions and the rules of the proposed fuzzy controllers are optimally tuned using a recent metaheuristic optimization algorithm, namely the Grey Wolf Optimizer. A comparison of Takagi-Sugeno type-1 and Takagi-Sugeno type-2 fuzzy control systems is carried out in various simulation scenarios, which are discussed and analyzed. The results highlight that the type-2 fuzzy controller performs efficiently for these complex processes.

Design of type-2 fuzzy logic controller for fluid catalytic cracking unit

Design of type-2 fuzzy logic controller for fluid catalytic cracking unit

Adaptive Interval Type-2 Fuzzy Controller Based Direct Torque Control of Permanent Magnet Synchronous Motor

This paper develops an adaptive type-2 fuzzy logic controller for direct torque control of a permanent magnet synchronous motor. The type-2 fuzzy logic systems are used for modeling th ...

Power Quality Enhancement in Sensitive Local Distribution Grid Using Interval Type-II Fuzzy Logic Controlled DSTATCOM

In the current scenario, integration of renewables, growth of non-linear industrial and commercial loads results in various power quality issues. Among commercial utilities connected to the grid, hospital-operated loads include sensitive, linear, non-linear, and unbalanced loads. These loads are diverse as well as prioritized, which also causes major power quality issues in the local distribution system. Due to its widespread divergence, it leads to harmonic injection and reactive power imbalance. Distribution Static Compensator (DSTATCOM) is proposed as a solution for harmonic mitigation, load balancing, reactive power imbalances, and neutral current compensation. The present work utilizes Interval Type-2 Fuzzy Logic Controller (IT2FLC) with Recursive Least Square (RLS) filter for generating switching pulses for IGBT switches in the DSTATCOM to improve power quality in the Local Distribution Grid. The proposed approach also shows superior performance over Type 1 fuzzy logic controller and Conventional PI controller in mitigating harmonics. For effective realization, the proposed system is simulated using MATLAB software.

Sensorless Brushless DC Motor Control Using Type-2 Fuzzy Logic

BLDC motors have many advantages such as a better speed versus torque characteristics, high dynamic response, high efficiency and reliability, low cost drives, long operating life (no brush erosion), noiseless operation, higher speed ranges, and reduction of electromagnetic interference. For this reason, it is used in many different fields today. A BLDC motor requires an inverter and a sensor to achieve commutation process. However, the hall sensor presents quite a few disadvantages from the standpoint of drive’s cost, machine size, temperature sensitivity requiring special arrangements and noise protection. As a result, with the increasing power of embedded computing in recent years sensorless control techniques have been developed and widely used. Additionly, conventional controllers do not give the desired results as these motors are non-linear in nature. Many techniques for BLDC motor speed control have been developed such as PID, Fuzzy logic controller, adaptive neuro fuzzy inference system. But the responses obtained were oscillatory and when a load was applied to these systems, the system's responses were much lower than the reference value. To remove oscillations and achieve a better performance, some new techniques were required. Due to their ability to handle uncertainty with robust and adaptive structure against complex systems, type-2 fuzzy logic systems, which is one of the artificial intelligence techniques started to use in recently years. In this study, the brushless motor was used as without sensor with back emf technique and zero crossing detection. Type-2 fuzzy logic controller was used to better resolve the uncertainties in the system. Simulation performed in Matlab – Simulink and PID, type-1, type-2 fuzzy logic controller results were compared and as a result, it was observed that type-2 fuzzy logic controller gave the most suitable system response to reference value.

Stabilizing control of two-wheeled wheelchair with movable payload using optimized interval type-2 fuzzy logic

The control schemes of a wheelchair having two wheels with movable payload utilizing the concept of a double-link inverted pendulum have been investigated in this article. The proposed wheelchair has been simulated using SimWise 4D software considering the most efficient parameters. These parameters are extracted using the spiral dynamic algorithm while being controlled with interval type-2 fuzzy logic controller (IT2FLC). The robustness and stability of the implemented controller are assessed under different situations including standing upright, forward motion and application of varying directions and magnitudes of outer disturbances to movable (up and down) system payload. It is shown that the two-wheeled wheelchair adopted by the newly introduced controller has achieved a 94% drop in torque for both Link1 and Link2 and more than 98% fall in distance travelled in comparison with fuzzy logic control type-1 (FLCT1) controller employed in an earlier design. The present study has further considered the increased nonlinearity and complexity of the additional moving payload. From the outcome of this study, it is obvious that the proposed IT2FLC-spiral dynamic algorithm demonstrates better performance than FLCT1 to manage the uncertainties and nonlinearities in case of a movable payload two-wheel wheelchair system.

Artificial potential field neuro-fuzzy controller for autonomous navigation of mobile robots

Some autonomous navigation methods, when implemented alone, can lead to poor performance, whereas their combinations, when well thought out, can yield exceptional performances. We have demonstrated this by combining the artificial potential field and fuzzy logic methods in the framework of mobile robots’ autonomous navigation. In this article, we investigate a possible combination of three methods widely used in the autonomous navigation of mobile robots, and whose individual implementation still does not yield the expected performances. These are as follows: the artificial potential field, which is quick and easy to implement but faces local minima and robustness problems. Fuzzy logic is robust but computationally intensive. Finally, neural networks have an exceptional generalization capacity, but face data collection problems for the learning base and robustness. This article aims to exploit the advantages offered by each of these approaches to design a robust, intelligent, and computationally efficient controller. The combination of the artificial potential field and interval type-2 fuzzy logic resulted in an interval type-2 fuzzy logic controller whose advantage over the classical interval type-2 fuzzy logic controller was the small size of the rule base. However, it kept all the classical interval type-2 fuzzy logic controller characteristics, with the major disadvantage that type-reduction remains the main cause of high computation time. In this article, the type-reduction process is replaced with two layers of neural networks. The resulting controller is an interval type-2 fuzzy neural network controller with the artificial potential field controller’s outputs as auxiliary inputs. The results obtained by performing a series of experiments on a mobile platform demonstrate the proposed navigation system’s efficiency.

Interval Type-2 Fuzzy Hybrid Control of a High-Rise Building Including Soil–Structure Interaction Under Near-Field and Far-Field Ground Motions

The interval type-2 fuzzy logic controller (IT2FLC) has emerged as one of the most effective high-tech control strategies to improve the performance of structures under near-field and far-field ground motions. The present study discusses the application of this strategy in the hybrid control of the behaviour of a real high-rise shear building by considering soil–structure interaction. In this regard, a hybrid control technique, consisting of a tuned mass damper (TMD) and a magneto-rheological (MR) damper is proposed. Using an MR damper on a TMD (MR + TMD) helps to reduce the seismic responses of a high-rise building in soil–structure interaction. The utilization of this hybrid device is very useful because it does not require huge power sources. By applying the observer–teacher–learner–based optimization (OTLBO) method, the fuzzy rules of IT2FLC, the TMD parameters, and the voltage of the MR damper have been optimized for a fifteen-storey shear building under four seismic near-field and far-field excitations. Minimization of the peak displacement of the top floor by using the square root of sum of squares (SRSS) of each response to each earthquake was utilized as the optimization criterion. Then, the optimized type-2 fuzzy rules and optimized TMD parameters were examined under seven new earthquake records in the fifteen-storey building. The results obtained by comparing the proposed control and uncontrolled systems in terms of the structure interaction with soft and dense soils show that the IT2FLC- using OTLBO is an efficient control method. It is also shown that the responses of this building to the hybrid controller have been effectively improved.

Ride Comfort-Road Holding Trade-off Improvement of Full Vehicle Active Suspension System by Interval Type-2 Fuzzy Control

This paper presents a hybrid fuzzy controller structure to improve ride comfort-road holding trade-off characteristics of a full vehicle active suspension system. The controller includes two interval type-2 fuzzy logic controllers, optimized for ride comfort and road holding, for each tire. Type C random road profiles according to ISO 8608 are applied to each tire in the model and ride index, road holding, crest factor, and RMS acceleration values are evaluated. Stability of the overall closed loop system is presented by phase plane analysis. The simulation results reveal that simultaneous improvement of ride index and road holding can be made possible with the proposed controller.

ANFIS Based DTC SVPWM Controller for Induction Motor

This paper presents three PWM techniques, space vector PWM (SVPWM). SVPWM gives the more rms stator flux ripples in induction motor at 0.76 modulation indices, which gives more ripples in current and torque distortion. The flux performance of the induction motor is improved using three PWM (TPWM) due to twin switching transition with less area of rms stator flux ripple. The PI-control (PIC) doesn’t give the good dynamic performance of the induction motor drive using two level Voltage Source Inverter due to sudden changes in the load or speed. Further to improve dynamic performance in the torque and Flux of induction motor drive is simulated using type-2 fuzzy logic control (TP2FLC) and ANN in place of PIC because; TP2FLC and ANN is effectively dealing with uncertainties, and simulation results of speed, torque, flux and stator current of induction motor is compare with PIC of SVPWM and TPWM.

Optimization of Type-2 Fuzzy Logic Controller Design Using the GSO and FA Algorithms

This paper presents a comparative study between the firefly algorithm (FA) and the galactic swarm optimization (GSO) method, where the performance of both methods is observed and tested in the optimization of a fuzzy controller for path tracking of an autonomous mobile robot. The main contribution of this work is finding the best method that generates an optimal vector of values for the membership function optimization of the fuzzy controller. This with the goal of improving the performance of the controller and thus the trajectory generated by the autonomous robot is closer to the desired trajectory. It should be noted that the fuzzy controller that is optimized is an interval type-2 fuzzy controller, which has a greater capability for managing uncertainty than a type-1 fuzzy controller. In this case, the limiting membership functions in the interval type-2 fuzzy sets are themselves type-1 fuzzy sets that define the footprint of uncertainty. Type-2 fuzzy controllers have been shown in previous works to handle better the control of robotic systems under noisy and dynamic conditions and this is why their optimal design is very important. Simulation results show that GSO outperforms FA in the optimal design of interval type-2 fuzzy controllers.

Antiretroviral therapy of HIV infection using a novel optimal type-2 fuzzy control strategy

The human immunodeficiency virus (HIV), as one of the most hazardous viruses, causes destructive effects on the human bodies’ immune system. Hence, an immense body of research has focused on developing antiretroviral therapies for HIV infection. In the current study, we propose a new control technique for a fractional-order HIV infection model. Firstly, a fractional model of the HIV model is investigated, and the importance of the fractional-order derivative in the modeling of the system is shown. Afterward, a type-2 fuzzy logic controller is proposed for antiretroviral therapy of HIV infection. The developed control scheme consists of two individual controllers and an aggregator. The optimal aggregator modifies the output of each individual controller. Simulations for two different strategies are conducted. In the first strategy, only reverse transcriptase inhibitor (RTI) is used, and the superiority of the proposed controller over a conventional fuzzy controller is demonstrated. Lastly, in the second strategy, both RTI and protease inhibitors (PI) are used simultaneously. In this case, an optimal type-2 fuzzy aggregator is also proposed to modify the output of the individual controllers based on optimal rules. Simulations results demonstrate the appropriate performance of the designed control scheme for the uncertain system.

Evaluation of the High Performance Indirect Field Oriented Controlled Dual Induction Motor Drive Fed by a Single Inverter using Type-2 Fuzzy Logic Control

In this paper, a high-performance indirect field-oriented controlled dual Induction Motor (IM) drive fed by a single inverter using type-2 fuzzy logic control will be presented. At first, the mathematical model of the IM is implemented in the d-q reference frame. Then, the speed control of the Dual Induction Motor (DIM) operating in parallel configuration with Indirect Field Oriented Control (IFOC) using PI and type-2 Fuzzy Logic Controller (T2-FLC) will be presented. For the control of this system, a DC supply and a Space Vector Pulse Width Modulation (SVPWM) voltage source inverter are introduced with constant switching frequency. Also, the performance of T2-FLC, which is based on the IFOC, is tested and compared to those achieved using the PI controller. The simulation results demonstrate that the T2-FLC is more robust, efficient, and has superior dynamic performance for traction system applications.