Type-2 Fuzzy Logic Controller Research Articles

A New Intelligent Hybrid Control Approach for DC–DC Converters in Zero-Emission Ferry Ships

Nowadays, sea traveling is increasing due to its practicality and low-cost. Ferry boats play a significant role in the marine tourism industry to transfer passengers and tourists. Nevertheless, traditional ferry ships consume massive amounts of fossil fuels to generate the required energy for their motors and demanded loads. Also, by consuming fossil fuels, ferries spatter the atmosphere with CO2 emissions and detrimental particles. In order to address these issues, ferry-building industries try to utilize renewable energy sources (RESs) and energy storage systems (ESSs), instead of fossil fuels, to provide the required power in the ferry boats. In general, full-electric ferry (FEF) boats are a new concept to reduce the cost of fossil fuels and air emissions. Hence, FEF can be regarded as a kind of dc stand-alone microgrid with constant power loads (CPLs). This article proposes a new structure of a FEF ship based on RESs and ESSs. In order to solve the negative impedance induced instabilities in dc power electronic based RESs, a new intelligent single input interval type-2 fuzzy logic controller based on sliding mode control is proposed for the dc-dc converters feeding CLPs. The main feature of the suggested technique is that it is mode-free and regulates the plant without requiring the knowledge of converter dynamics. Finally, we conduct a dSPACE-based real-time experiment to examine the effectiveness of the proposed energy management system for FEF vessels.

An interval type-2 fuzzy logic controller design method for hydraulic actuators of a human-like robot by using improved drone squadron optimization

Hydraulic actuator becomes an increasingly concerned driver for human-like robots. However, its dynamic performance under the control should be still further improved because hydraulic system is a typical nonlinearity system. Interval type-2 fuzzy logic controller is an advanced control method featured with high performance to deal with uncertain and nonlinear dynamics, so designing an interval type-2 fuzzy logic controller for the control of hydraulic is a feasible method. In this article, an improved drone squadron optimization-based approach is proposed to optimize interval type-2 fuzzy logic controller parameters. To verify the feasibility and priority of improved drone squadron optimization, a comparison on three different typical plants including proportional-derivative (PD) system, proportional-integral (PI) system, and PI nonlinear system between improved drone squadron optimization and other meta-heuristic algorithms is carried out. Simulation results demonstrate that improved drone squadron optimization not only gets an appropriate interval type-2 fuzzy logic controller for system control but also outperforms other popular algorithms in accuracy of performance.

Speed control of brushless direct current motor using a genetic algorithm–optimized fuzzy proportional integral differential controller

In this article, a genetic algorithm–based proportional integral differential–type fuzzy logic controller for speed control of brushless direct current motors is presented to improve the performance of a conventional proportional integral differential controller and a fuzzy proportional integral differential controller, which consists of a genetic algorithm–based fuzzy gain tuner and a conventional proportional integral differential controller. The tuner is used to adjust the gain parameters of the conventional proportional integral differential controller by a new fuzzy logic controller. Different from the conventional fuzzy logic controller based on expert experience, the proposed fuzzy logic controller adaptively tunes the membership functions and control rules by using an improved genetic algorithm. Moreover, the genetic algorithm utilizes a novel reproduction operator combined with the fitness value and the Euclidean distance of individuals to optimize the shape of the membership functions and the contents of the rule base. The performance of the genetic algorithm–based proportional integral differential–type fuzzy logic controller is evaluated through extensive simulations under different operating conditions such as varying set speed, constant load, and varying load conditions in terms of overshoot, undershoot, settling time, recovery time, and steady-state error. The results show that the genetic algorithm–based proportional integral differential–type fuzzy logic controller has superior performance than the conventional proportional integral differential controller, gain tuned proportional integral differential controller, conventional fuzzy proportional integral differential controller, and scaling factor tuned fuzzy proportional integral differential controller.

An optimal interval type-2 fuzzy logic control based closed-loop drug administration to regulate the mean arterial blood pressure

Background and ObjectiveThe main aim of this work is to present an optimal and robust controller design in order to improve the drug infusion to the automatic control of mean arterial blood pressure in conditions like critically-ill or post-operative or anaesthesia administration. The physiological systems also have uncertainty issues such as parameter variations with time or external disturbances and noise. Therefore, a controlled drug administration is necessary to regulate the mean arterial blood pressure of a person during surgery/observation. Over the years, the proportional-integral-derivative (PID) controller is the most commonly used controller in industries due to its easy structure and simplicity. However, this controller does not meet the desired performance with the complex and uncertain plants. Therefore, a robust controller is required to regulate the physiological variables that are uncertain in nature and can affect the human life. MethodsIn this work, a hybrid control scheme consisting of an interval type-2-fuzzy logic controller which acts as pre-compensator to the traditional PID controller is presented, to regulate the mean arterial blood pressure of a patient by administering the drug sodium nitroprusside in a controlled manner. An effective and well-established nature-inspired optimization technique namely cuckoo search algorithm is employed for obtaining the optimal parameters for the presented scheme. ResultsSimulation results are presented to show the effectiveness and robustness of proposed interval type-2-fuzzy logic controller based PID controller scheme, for maintaining the mean arterial pressure to 100 mmHg within considerable limit through SNP infusion. The results are further compared with other two controllers namely type-1 fuzzy logic based PID and traditional PID controllers for the parameter variations and external noise. ConclusionIn this study, the proposed interval type-2-fuzzy logic controller pre-compensator based PID controller provides an effective control than traditional type-1 fuzzy logic based control scheme and PID controller in terms of overshoot, settling-time and error which are the prime performance objectives of the closed-loop controlled drug delivery of human blood pressure. The presented study provides a firm base for initial design considerations for development of a low-cost closed-loop drug delivery system for blood pressure regulation.

A new adjusting technique for PID type fuzzy logic controller using PSOSCALF optimization algorithm

The main aim of this work consists of proposing a new three-step adjusting approach for an improved version of PID-type fuzzy structure in order to determine its design parameters based on a novel hybrid PSO search technique called PSOSCALF, combining Sine Cosine Algorithm (SCA) and Levy Flight (LF) distribution. In addition, conventional and self-tuning controllers are designed to get a better understanding of the performance and robustness of the proposed PID-type FLC approach. At first, the proposed PID-type FLC structure is defined as an optimization problem and then the PSOSCALF algorithm is applied to resolve it systematically. Evaluation of the performance quality of the proposed fuzzy structure is accomplished based on the stabilization and tracking control of a nonlinear Inverted Pendulum (IP) system. To make a complete comparison, the performance of three other optimization techniques namely simple PSO, Differential Evolution (DE) and Cuckoo Search (CS) are examined against the hybrid PSOSCALF algorithm. The simulation results demonstrate that the proposed PSOSCALF-tuned PID-type FLC structure is able to decrease the overshoot and integral square error amounts by about 25% and 10%, respectively compared to the self-tuning controllers. Finally, for more validation, all the controllers are tested under four different disturbance scenarios. Obtained results show that the proposed PID-type FLC can better stabilize the pendulum angle under all the scenarios compared to the PID and self-tuning controllers.

Online learning of an interval type-2 TSK fuzzy logic controller for nonlinear systems

In this study, an adaptive interval type-2 Takagi-Sugeno-Kang fuzzy logic controller based on reinforcement learning (AIT2-TSK-FLC-RL) is proposed. The proposed controller consists of an actor, a critic and a reward signal. The actor is represented by the IT2-TSK-FLC in which the antecedents and the consequents are interval type-2 fuzzy sets (IT2FSs) and type-1 fuzzy sets (T1FSs), respectively, which are named A2-C1. The critic is represented by a neural network, which approximates the optimal guaranteed cost in the control design to ensure the system stability for all admissible uncertainties and noise. The use of a reward signal to formalize the idea of a goal is one of the most distinctive features of RL. Thus, the proposed controller evolves in time as a result of the online learning algorithm. The parameters of the proposed controller are learned online based on the Lyapunov theorem to guarantee the stability, overcome the shortcomings of the gradient descent, such as the local minima and instability, and determine the learning rate of the IT2-TSK-FLC controller. Furthermore, the critic stability is discussed for determining the optimal learning rate. The proposed controller is applied to uncertain nonlinear systems to show its robustness in reducing the effect of system uncertainties and external disturbances and is compared to other controllers.

Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system: A comparative assessment

This paper presents investigations into the development of an interval type-2 fuzzy logic control (IT2FLC) mechanism integrated with particle swarm optimization and spiral dynamic algorithm. The particle swarm optimization and spiral dynamic algorithm are used for enhanced performance of the IT2FLC by finding optimised values for input and output controller gains and parameter values of IT2FLC membership function as comparison purpose in order to identify better solution for the system. A new model of triple-link inverted pendulum on two-wheels system, developed within SimWise 4D software environment and integrated with Matlab/Simulink for control purpose. Several tests comprising system stabilization, disturbance rejection and convergence accuracy of the algorithms are carried out to demonstrate the robustness of the control approach. It is shown that the particle swarm optimization-based control mechanism performs better than the spiral dynamic algorithm-based control in terms of system stability, disturbance rejection and reduce noise. Moreover, the particle swarm optimization-based IT2FLC shows better performance in comparison to previous research. It is envisaged that this system and control algorithm can be very useful for the development of a mobile robot with extended functionality.

Hybrid fuzzy control for the goethite process in zinc production plant combining type-1 and type-2 fuzzy logics

The goethite process is a complicated reaction system which holds a significant position in zinc hydrometallurgy process. It exhibits nonlinear behavior and time-delay nature because of the chemical reactions. To achieve the stable and real-time control performance, a hybrid fuzzy control strategy integrating a type-1 and a type-2 fuzzy logic controllers is proposed in this paper. According to the on-line measured pH, the type-1 fuzzy controller (T1 FLC) via the Takagi–Sugeno fuzzy control is employed to control the zinc oxide additive rate. A parameter tuning method that does not require the system model is designed for the T1 FLC. Because the ferrous ion concentration cannot be measured on-line, the interval type-2 fuzzy logic controller (IT2 FLC) is utilized to control the oxygen flow rate. According to the feedback information, gradient descent algorithm is used to update the parameters in the IT2 FLC. Because of the unknown system structure, gradient descent information is estimated by the simultaneous perturbation stochastic approximation. Finally, simulations are conducted using the practical production data. The simulation results show the effectiveness of the proposed strategy. The hybrid fuzzy control strategy improves the control performance of the goethite process, compared with PID controller.

Online adaptive type-2 fuzzy logic control for load frequency of multi-area power system

Online adaptive type-2 fuzzy logic control for load frequency of multi-area power system

DTC-SVM based on Interval Type-2 Fuzzy Logic Controller of Double Stator Induction Machine fed by Six-Phase Inverter

DTC-SVM based on Interval Type-2 Fuzzy Logic Controller of Double Stator Induction Machine fed by Six-Phase Inverter

Double Closed-Loop General Type-2 Fuzzy Sliding Model Control for Trajectory Tracking of Wheeled Mobile Robots

In this paper, a double-loop control scheme, combining general type-2 fuzzy logic controller (GT2FLC) and non-singular terminal sliding mode control (NTSMC), is proposed to stabilize the non-holonomic wheeled mobile robot. In the double-loop tracking controller, the outer ring adopts the exponential approach law used to track the position state quickly and the inner ring adopts the double-power approaching law, which makes the attitude convergence faster than the position to ensure the stability of the closed-loop system and deal with the random disturbances. NTSMC is designed for both loops to achieve fast convergence of the system in a finite time. The GT2FLC is used to adjust the gain of the approaching law, so as to enhance the adaptability to random disturbances and weaken the chattering of sliding mode input. The simulation results show that the proposed method can achieve small error and fast tracking of position and attitude under random disturbance, and its performance is better than other controllers.

Analysis and Control of Fault Ride-Through Capability Improvement for Wind Turbine Based on a Permanent Magnet Synchronous Generator Using an Interval Type-2 Fuzzy Logic System

Recently, wind energy conversion systems in renewable energy sources have attracted attention due to their effective application. Wind turbine systems have a complex structure; however, traditional control systems are inadequate in answering the demands of complex systems. Therefore, expert control systems are applied to wind turbines, such as type-1 and interval type-2 fuzzy logic control (IT-2 FLC) systems. An IT-2 FLC system is used to solve the complexity of the wind turbine system and increases the efficiency of the wind turbine. This paper proposes a new control approach using the IT-2 FLC method applied to a wind turbine based on a permanent magnet synchronous generator (PMSG) to improve the transient stability during grid faults. An IT-2 FLC was designed to enhance the fault ride-through performance of a wind turbine and was implemented to control the machine side converter and grid side converter of a wind turbine. The proposed algorithm performance of a wind turbine based on a PMSG was investigated for different types of grid fault. The analysis results verify that the interval type-2 fuzzy logic control system is robustly utilized under different operational conditions.

Dynamic parameter adaptation in the harmony search algorithm for the optimization of interval type-2 fuzzy logic controllers

At the present time there are several types of metaheuristics which have been used to solve various types of problems in the real world. These metaheuristics contain parameters that are usually fixed throughout the iterations. However, various techniques exist to adjust the parameters of an algorithm such as probabilistic, fuzzy logic, among others. This work describes the methodology and equations for building Triangular and Gaussian interval type-2 membership functions, and this methodology was applied to the optimization of a benchmark control problem with an interval type-2 fuzzy logic controller. To validate in the best way the effect of uncertainty we perform experiments using noise (Pulse generator) and without noise. Also, a statistical z-test is presented to verify the effectiveness of the proposed method. The main contribution of this article is the proposed use of the theory of interval type-2 fuzzy logic to the dynamic adjustment of parameters for the harmony search algorithm and then its application to the optimal design of interval type-2 fuzzy logic controller.

Non-singleton General Type-2 Fuzzy Control for a Two-Wheeled Self-Balancing Robot

This paper presents several non-singleton general type-2 fuzzy logic controllers (NGT2FLCs) for an under-actuated mobile two-wheeled self-balancing robot to improve the anti-interference capability of the system. Four kinds of fuzzifiers, including singleton fuzzifier, type-1 non-singleton fuzzifier, interval type-2 non-singleton fuzzifier and general type-2 non-singleton fuzzifier, are considered to construct different general type-2 fuzzy logic controllers (GT2FLCs). In order to show the superiority of the GT2FLCs, three kinds of interval type-2 fuzzy logic controllers (IT2FLCs), including singleton IT2FLCs, type-1 non-singleton IT2FLCs (N1IT2FLCs) and interval type-2 non-singleton IT2FLCs (N2IT2FLCs), are also presented. A comparative study between singleton fuzzy controllers and non-singleton fuzzy controllers, and IT2FLCs and GT2FLCs is also shown. All simulation results show that the performance of non-singleton fuzzy logic controllers is better than that of singleton fuzzy logic controllers. The NGT2FLCs get the best performance in the presence of uncertainties and external disturbances.

Advanced Type-2 fuzzy logic–based pitch-angle control strategy for wind energy system

In general, pitch-angle controller regulates the generator output power when the wind speed exceeds the rated wind turbine speed. Besides this, it can also be employed to stabilize the wind energy system rotor speed during the transient disturbances. In this article, therefore, a logical pitch-angle controller strategy (in power and speed control modes) has been developed and an interval Type-2 fuzzy logic technique is proposed to design the controller. To evaluate the effectiveness of the Type-2 fuzzy logic–based pitch-angle controller, the simulations have been carried out for severe network faults and fluctuating wind conditions, and the results are compared with conventional proportional–integral and fuzzy logic controller (called as Type-1 fuzzy logic controller). Moreover, some key factors that affect the transient stability of wind generator have also been investigated. The electrical torque and mechanical torque versus rotor speed results are obtained under different pitch-angle conditions, and the concept of stable and unstable electrical–mechanical equilibrium points is established.

Intuit before tuning: Type-1 and type-2 fuzzy logic controllers

Although a considerable amount of effort has been put in to show that fuzzy logic controllers have exceptional capabilities of dealing with uncertainty, there are still noteworthy concerns, e.g., the design of fuzzy logic controllers is an arduous task due to the lack of closed-form input–output relationships which is a limitation to interpretability of these controllers. The role of design parameters in fuzzy logic controllers, such as position, shape, and height of membership functions, is not straightforward. Motivated by the fact that the availability of an interpretable relationship from input to output will simplify the design procedure of fuzzy logic controllers, the main aims in this work are derive fuzzy mappings for both type-1 and interval type-2 fuzzy logic controllers, analyse them, and eventually benefit from such a nonlinear mapping to design fuzzy logic controllers. Thereafter, simulation and real-time experimental results support the presented theoretical findings.

Trajectory and vibration control of a flexible joint manipulator using interval type-2 fuzzy logic

This study presents the development of a novel interval type-2 fuzzy logic controller for real time trajectory and vibration control of a flexible joint manipulator. The controller is designed on the Mamdani based interval type-2 fuzzy logic toolbox, which is developed by the authors, using interval triangular membership functions and Karnik–Mendel type reduction algorithm. The closed-loop stability of the system is proved based on Lyapunov stability theorem. In order to observe the effectiveness and robustness of the proposed controller to variations of system parameters (change in link length and payload), the experimental results of interval type-2 and conventional type-1 fuzzy logic controllers are compared. The results show that proposed controller clearly improves the link vibration and trajectory tracking behavior of the system.

Prediction of maximum oil-yield from almond seed in a chemical industry: A novel type-2 fuzzy logic approach

Almond (Termanilia catappa) is one of the most popular tree nuts on a world-wide basis due to its huge benefits in human health. Since it is not readily available in every country, research and investigations are still going on this particular. Therefore, here we have developed a unique model using type-2 fuzzy logic controller (T2FLC) approach to predict the optimum process conditions for the extraction of oil at maximum from almond seeds. Here, we have taken pressure, temperature, heating time and moisture content as input parameters and oil yield is taken as an output parameter to optimize the process. Though hundred percent recovery of oil by extraction of almond seeds is never possible, but controlling the process parameters at suitable condition, oil yield may vary within the range of 40%–45%. Using these four input and one output parameters, four Mamdani fuzzy inference systems are formed depending on the different membership functions of the variables. In T2FLC assists to trace inputs and outputs in a well-organized manner for building the inferences train so that various types of oil yield and its assessment can be predicted during extraction. Finally, a statistical analysis has been done using type-2 fuzzy data set to improve the control of process parameters that can be easily determined in the type-2 fuzzy prediction model to get high yield.

Intelligent System Stability using Type-2 Fuzzy Controller

To avoid mathematical complexity, Interval T2FSs (IT2FSs) have been pertained in majority of the fields. Type-2 Fuzzy sets (T2FSs) handle a greater modelling and uncertainties which exist in the real world applications especially in control systems. One of the important components which influence the fuzzy controller is the triangular norms which are the aggregation operators. For getting the stability of a control system T Norm operator can be preferred. Gaussian Interval Type-2 Membership Function (GIT2MF) has been used in this paper. Also mathematical properties of aggregation operator are proved using Gaussian Interval Type-2 Weighted Arithmetic (GIT2WA) Operator. The aim of this work is analyzing the stability of an inverted pendulum using Interval Type-2 Fuzzy Logic controller (IT2FLC) and the results are compared with traditional Proportional Integrated Derivative (PID) Controller. It is observed that IT2FL controller gives better stability under imprecise condition.

Fire monitoring in coal mines using wireless underground sensor network and interval type-2 fuzzy logic controller

From the view of underground coal mining safety system, it is extremely important to continuous monitoring of coal mines for the prompt detection of fires or related problems inspite of its uncertainty and imprecise characteristics. Therefore, evaluation and inferring the data perfectly to prevent fire related accidental risk in underground coal mining (UMC) system are very necessary. In the present article, we have proposed a novel type-2 fuzzy logic system (T2FLS) for the prediction of fire intensity and its risk assessment for risk reduction in an underground coal mine. Recently, for the observation of underground coal mines, wireless underground sensor network (WUSN) are being concerned frequently. To implement this technique IT2FLS, main functional components are sensor nodes which are installed in coal mines to accumulate different imprecise environmental data like, temperature, relative humidity, different gas concentrations etc. and these are sent to a base station which is connected to the ground observation system through network. In the present context, a WUSN based fire monitoring system is developed using fuzzy logic approach to enhance the consistency in decision making system to improve the risk chances of fire during coal mining. We have taken Mamdani IT2FLS as fuzzy model on coal mine monitoring data to consider real-time decision making (DM). It is predicted from the simulated results that the recommended system is highly acceptable and amenable in the case of fire hazard safety with compared to the wired and off-line monitoring system for UMC. Legitimacy of the suggested model is prepared using statistical analysis and multiple linear regression analysis.