Hybrid Fuzzy Controller Research Articles

A novel advanced hybrid fuzzy MPPT controllers for renewable energy systems

At present, the availability of nonrenewable sources and their usage for electric vehicle technology is reducing gradually because of their disadvantages are more environmental pollution, direct effect on human health, less reliability, and taking more time to start functioning. So, in this article, the proton exchange membrane fuel cell (PEMFC) is considered for the automotive application because of its advantages quick startup, more power density, more safety to handle, high efficiency, and capability of operating at very low operational temperature conditions. However, the drawback of PEMFC is very difficult to identify the accurate MPP position of the fuel system. Here, the improved variable step genetic algorithm is added with the adaptive neuro-fuzzy inference system for tracking the operational point of the proposed system with high efficiency. These hybrid MPPT controller features are easy to understand, more accurate, have a better dynamic response, and have low design complexity. The evaluated proposed MPPT controller operational efficiency, and settling time of the converter voltage at different fuel stack temperature conditions are 98.7402%, and 0.01607 s respectively. Finally, the boost converter is used in this work to enhance the voltage supply capability of the entire system. The proposed system is investigated by applying the MATLAB tool.

Intelligent controller design of an autonomous system using a social spider optimizer for path navigation and obstacle avoidance

This research paper proposes a hybrid fuzzy logic controller for achieving autonomous path navigation and obstacle avoidance through the use of the Social Spider Optimizer algorithm. The proposed controller employs kinematic modelling to determine the mobile robot’s path navigation and utilizes a fuzzy logic system for effective control. The Social Spider Optimizer algorithm optimizes the parameters of the fuzzy controller, while the FLC is responsible for obstacle avoidance. The effectiveness of the proposed controller has been analyzed, and a comparative study has been carried out with optimization techniques like particle swarm optimization (PSO) and cuckoo search optimization (CSO) controllers. The study aims to propose a hybrid fuzzy logic controller, that provides efficient navigation and obstacle avoidance for mobile robots. In a simulation, the starting point is considered as (0,0) and the destination point is set as Xk = 1.1 and Yk = 1.2. The performance of the proposed method is compared with FLC and methods like PSO and CSO. With the SSO-based FLC, the proposed mobile robot identifies the obstacle distance and travels towards the destination with smooth navigation. The results show the efficacy of the proposed controller in comparison to other controllers for mobile robots in terms of path navigation and obstacle avoidance.

Robust Fuzzy-PID Technique for the Automatic Generation Control of Interconnected Power System with Integrated Renewable Energy Sources

This paper proposes a robust hybrid fuzzy PID controller, with the PID gain values tuned using the particle swarm optimization (PSO) technique for the automatic generation control (AGC) of the two-area hybrid power system. PSO Algorithm is applied to reduce the integral of time-weighted absolute error (ITAE) of the frequency and tie-line power variations of the two-area interconnected power system. The suggested method’s efficacy is examined on an interconnected two-area power system with Area 1 containing thermal reheat plants, hydropower plants, and diesel generating units, and Area 2 comprising thermal reheat plants and renewable energy sources (RES): wind energy conversion system, solar photovoltaic system, and electric vehicles. The thermal reheat power plants are modelled taking into consideration the governor dead band (GDB) and the generation rate constraints (GRC). The performance of the presented controller is compared with the genetic algorithm-optimized PID AGC and the PSO-optimised PID AGC. The simulation outcomes show superior results of the proposed controller against other controllers. Further, sensitivity analysis reflects that the proposed technique provides better dynamic response and robustness than the other techniques.

Improved Drycooler control by custom hybrid controller

Refrigeration devices designed for use in industrial environments are typically equipped with universal control algorithms, which require a minimal number of signals and parameters to ensure satisfactory device operation. These algorithms are typically of the PID type. This study elaborates upon the impact of using a dedicated hybrid controller, which was designed with specific consideration of the operating conditions of a given refrigeration device. The identified nonlinear Drycooler characteristics were used to support the controller at steady-state operating conditions. The system dynamics were supervised by a dedicated, experience-based designed hybrid fuzzy logic Mamdani type controller for both freecooling and compressor modes. The switchable configuration of the MISO control architecture results in a reduction in overshoots and oscillations in the system, a decrease in the time necessary for stabilization, a reduction in CO2, and an increase in control quality and energy efficiency.

Position Control for Automatic Assembly Equipment Using a New Hybrid Fuzzy Controller

Position Control for Automatic Assembly Equipment Using a New Hybrid Fuzzy Controller

A novel design and analysis of hybrid fuzzy logic MPPT controller for solar PV system under partial shading conditions

Renewable energy resources are more useful when associated with the thermal power generation network because of their high accessibility in the environment, good system response, easy manufacturing, plus high scalable. So, the present research is going on solar power to reduce consumer grid dependency. The running of the PV network is quite easier, plus less human sources are involved. However, the solar modules’ power generation is nonlinear fashion. So, the collection of peak power from the sunlight-dependent systems is a highly challenging task. In this article, a Modified Differential Step Grey Wolf Optimization with Adaptive Fuzzy Logic Controller (MDSGWO with FLC) is developed for collecting the maximum power from renewable energy resources under diverse Partial Shading Conditions (PSCs). The introduced method comprehensive analysis has been done along with the other recently existing MPPT methods in terms of convergence speed, MPP tracking accuracy, operating efficiency of the introduced method, functioning duty value of the DC–DC boost power converter, dependence of MPPT on sunlight system, total number of sensing devices are needed, plus peak power extraction from the proposed system. Here, the sunlight power generation cost is more to limit this issue, a power converter is selected in the second objective to develop the voltage source capability of the PV network. The overall PV-interfaced power converter network is examined by utilizing the MATLAB environment.

Automatic Generation Control of a Multi-Area Hybrid Renewable Energy System Using a Proposed Novel GA-Fuzzy Logic Self-Tuning PID Controller

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources.

Review on PID, fuzzy and hybrid fuzzy PID controllers for controlling non-linear dynamic behaviour of chemical plants

The chemical production process is tedious due to the integration of different types of equipment and variables. Designing the controller is crucial in the chemical industry due to the interactive and non-linear system behaviour. An intelligent autonomous controller can improve the operating efficiency of the industry. Although several controllers have been developed, different system failures are frequently reported. Hence, controllers such as proportional integral derivative (PID), fuzzy logic controller (FLC), and hybrid fuzzy PID (F-PID) applied in the chemical industries are critically reviewed in the paper. Initially, the PID controller-based approaches are reviewed for different purposes in the chemical industry. After that, the FLC-based controllers-based papers are reviewed. In order to satisfy the issues in both controllers, the H-PID controllers have been reviewed. This review paper will provide an effective solution for operation control in the chemical industry under different operating conditions.

Identification of the Yield Rate by a Hybrid Fuzzy Control PID-Based Four-Stage Model: A Case Study of Optical Filter Industry

With the vigorous development of emerging technology and the advent of the Internet generation, high-speed Internet and fast transmission 5G wireless networks contribute to interpersonal communication. Now, the Internet has become popular and widely available, and human life is inseparable from various experiences on the Internet. Many base stations and data centers have been established to convert and switch from electrical transmission to optical transmission; thus, it is entering the new era of optical fiber networks and optical communication technologies. For optical communication, the manufacturing of components for the purpose of high-speed networks is a key process, and the requirement for the stability of its production conditions is very strict. In particular, product yields are always low due to the restriction of high-precision specifications associated with the limitations of too many factors. Given these reasons, this study proposes a hybrid fuzzy control-based model for industry data applications to organize advanced techniques of box-and-whisker plot method, association rule, and decision trees to find out the determinants that affect the yield rate of products and then use the fuzzy control Proportional-Integral-Derivative (PID) method to manage the determinants. Since it is unrealistic to test the real machine online operation at the manufacturing stage, the simulation software supersedes this for improved results, and a mathematical neural network is used to verify the given data to confirm whether its result is similar to that of the simulation. The study suggests that excessive temperature differentials between substrate and cavity can lead to low yields. It suggests using fuzzy control technology for temperature management, which could increase yield, reduce labor costs, and accelerate the transition to high-speed networks by mass-producing high-precision optical filters.

Frequency and voltage stability of multi microgrid system using 2-DOF TIDF FUZZY controller

ABSTRACT Two parameters, voltage and frequency, are required for smooth delivery of quality power to consumer. Therefore, in this paper enhancement of frequency and voltage stability of multi microgrid system with a proposed hybrid fuzzy logic controller with two-degree of freedom tilt-integral derivative with filter (2DOFTIDFFUZZY) are analyzed. A novel algorithm known as artificial electric field algorithm (AEFA) technique is employed to obtain optimal controller parameters. Firstly, a two-area system widely used in literature is considered for proper comparison. Efficacy of 2DOFTIDFFUZZY controller is confirmed by comparing it with different controllers for the same system. Further, a multi microgrid system is considered for unified voltage-frequency control. Automatic Voltage Regulator (AVR) effect states a substantial improvement in system dynamics with the proposed controller. Under different unfavorable conditions like source and load variation, dynamic system performances are studied to enlighten robustness of proposed controller. Simulation results elucidate that proposed 2DOFTIDFFUZZY controller optimized with AEFA technique can achieve better system stability in case of any vulnerability.

Stability analysis of nonlinear fuzzy hybrid control systems subject to saturation and delays via step‐function method

AbstractWithin the framework of the step‐function method, the stability of a nonlinear fuzzy hybrid control system that incorporates both an impulsive controller and a continuous state feedback controller is investigated. Both the two controllers are assumed to be subject to both actuator saturation and time‐varying delays, which has been given scant attention, if any, in the existing studies. A new assumption is introduced in this article, thereby enabling the use of generalized sector conditions to tackle the double saturation, and the conservatism inherent in stability results is remarkably reduced owing to the improved step‐function approach employed. The stability theorem proposed in this article removes constraints imposed on the time delays of both controllers, which can be also applied to wider scopes of systems, encompassing hybrid control systems with both stabilizing and instabilizing impulses, systems with varying impulsive gain, and systems with Zeno behavior. Numerical simulations of stabilization for different systems by delayed saturated hybrid control have been conducted, which demonstrate the validity of the proposed theorems.

Damping Magnetorheological Systems Based on Optimal Neural Networks Preview Control Integrated with New Hybrid Fuzzy Controller to Improve Ride Comfort

<div>Adaptive neural networks (ANNs) have become famous for modeling and controlling dynamic systems. However, because of their failure to precisely reflect the intricate dynamics of the system, these have limited use in practical applications and perform poorly during training and testing. This research explores novel approaches to this issue, including modifying the simple neuron unit and developing a generalized neuron (GN). The revised version of the neuron unit helps to develop the system controller, which is responsible for providing the desired control signal based on the inputs received from the dynamic responses of the vehicle suspension system. The controller is then tested and evaluated based on the performance of the magnetorheological (MR) damper for the main suspension system. These results of the tests show that the optimal preview controller designed using the GN both ∑-Π-ANN and Π-∑-ANN can accurately capture the complex dynamics of the MR damper and improve their damping characteristics compared with other methods. The seat and main suspension systems work together to provide more support and comfort for the driver and passengers. The short stroke of the MR damper is used in seat suspension as it allows for more precise control over the suspension and can provide a smoother ride. The new hybrid fuzzy type-2 (T-2) control is designed to accurately estimate the desired damping force for the seat MR damper. This system also allows for the damping force to be adjusted to meet the desired requirements of the seat MR damper. This integration of damping systems allows better control and stability of the vehicle and provides a smoother ride for drivers and passengers. Furthermore, integrating the damping systems increases the overall performance of the vehicle, making it better able to handle various road conditions.</div>

Hybrid fuzzy and fire fly algorithm-based MPPT controller for PV system using super lift boost converter

This study suggests a new simplified firefly algorithm (SFA) for maximum power point tracking (MPPT) of the solar system under conditions of partial shadowing. The disregarded and coefficients are present in the simplified firefly method, which is different from the regular firefly algorithm. The updated β coefficient for each iteration step is the second new feature, which helps to accelerate convergence. This approach is suggested to find the best PV system MPPT solution for three different shaded circumstances. The proposed method produced results with the highest possible power and efficiency. The ripple performed better than the conventional FA under steady-state conditions. The suggested algorithm’s key advantages over the conventional firefly algorithm are its simplicity, quicker convergence, and accuracy.

Synthesis of Hybrid Fuzzy Logic Law for Stable Control of Magnetic Levitation System

In this paper, we present a method to design a hybrid fuzzy logic controller (FLC) for a magnetic levitation system (MLS) based on the linear feedforward control method combined with FLC. MLS has many applications in industry, transportation, but the system is strongly nonlinear and unstable at equilibrium. The fast response linear control law ensures that the ball is kept at the desired point, but does not remain stable at that point in the presence of noise or deviation from the desired position. The controller that combines linear feedforward control and FLC is designed to ensure ball stability and increase the system's fast-response when deviating from equilibrium and improve control quality. Simulation results in the presence of noise show that the proposed control law has a fast and stable effect on external noise. The advantages of the proposed controller are shown through the comparison results with conventional PID and FLC control laws.

Stick-slip stabilization in oil well drill-strings via optimal hybrid fractional order fuzzy logic control scheme

Stick-slip vibration is regarded as one of the major factors that negatively influence the efficiency of the drilling processes. Therefore, it would be highly preferable to overcome the stick-slip vibration challenge through an effective control scheme. This study aims to address the stick-slip vibration problem in oil well drill-strings through a novel concept of an optimal hybrid fractional order fuzzy logic control (OH-FOFLC) scheme. By combining the robustness of fuzzy logic with the convergence speed of the fractional order method, the proposed scheme may enhance tracking performance in drill-strings under various operating conditions. In addition, the fuzzy logic controller for the OH-FOFLC scheme was designed by considering the minimum rules to decrease the computational burden as well as to reduce the controller’s cost. Furthermore, a particle swarm optimization algorithm was implemented for optimizing the coefficients of the OH-FOFLC scheme. To illustrate the overall performance and effectiveness of the OH-FOFLC, four different controllers, including proportional–integral–derivative (PID), fractional order PID, sliding mode control, and optimal hybrid fuzzy logic control approaches, are considered. The validity of the suggested OH-FOFLC was demonstrated through numerical simulations and comprehensive comparative studies which demonstrated that the OH-FOFLC scheme performs better than the four other controllers under various operating conditions.

Performance enhancement of UPQC based on optimized GBSSA hybrid fuzzy controller with EPLL

In this research, UPQC (Unified Power Quality Conditioner) with optimized hybrid fuzzy controller based GBSSA (Gaussian Barebone Salp Swarm Algorithm) with EPLL (Enhanced Phase Locked Loop) have been proposed for power quality enhancement in power distribution networks. Using the proposed method, the difficulties in major of the power distribution system networks can be solved, related to power quality issues. GBSSA has been employed in this research, to improve solution accuracy and optimization efficiency. Given that, it is permissible to add some extra time cost to acquire a better solution, based on the Non-Free Lunch (NFL) theory, and that the time consumption of function evaluation is rather large, when addressing actual optimization problems, the extra time consumption can be overlooked to some extent. The EPLL control method improves the standard PLL, by reducing its fundamental flaw, which is the occurrence of main frequency errors, as well as double frequency errors. It controls the DC-bus voltage of unified power quality conditioners, during supply voltage and load voltage turbulences. The proposed UPQC control technique has been found to be resilient, to a variety of source and load perturbations, including unbalanced, transient distorted supply, voltage sag, unbalanced load and voltage swell. The proposed optimized GBSSA hybrid fuzzy controller with EPLL has been proven to be more effective in reducing the THD (Total Harmonic Distortion) to 3.22%. Moreover, comparative analysis with a conventional TSF-PLL has been performed with that of Takagi-Sugeno fuzzy controller and implemented using MATLAB (MATrix Laboratory).

Hybrid Half-Gaussian Selectively Adaptive Fuzzy Control of an Actuated Ankle–Foot Orthosis

To control an actuated ankle–foot orthosis (AAFO) during walking, a selectively adaptive hybrid fuzzy control employing particle-swarm optimization was used in conjunction with a Lyapunov-theory-based adaptive fuzzy-logic control. Adaptation (a computationally expensive process) was performed only when the tracking error exceeded a certain half-Gaussian function. The stability of the overall closed-loop system was proved using Lyapunov theory. The proposed control strategy was verified both by simulations and by experiments with five healthy subjects. The proposed control strategy significantly reduced both tracking error and required control torque when compared to other competing control schemes.

Temperature control algorithm for polymerase chain reaction (PCR) instrumentation based upon improved hybrid fuzzy proportional integral derivative (PID) control

Here is reported an adaptive polymerase chain reaction (PCR) temperature control algorithm based on improved hybrid fuzzy proportional integral derivative (PID) control. The algorithm adopts fuzzy control in the rapid temperature changing stage for monitoring and reduces the overshoot. In the constant temperature stage, the PID controller's initial parameters are automatically calculated online through the relay self-tuning algorithm. The output of the system is pre-compensated by feedforward compensation algorithm, and adjusted by the variable universe fuzzy PID algorithm, which avoids the explosion of fuzzy rules to a certain extent. The experimental results show that the average heating rate of the improved hybrid fuzzy PID control algorithm is 4.2 °C/s, with an average cooling rate is 3.2 °C/s. The system stabilizes within 5 s with a maximum overshoot of less than 1.2 °C and a static error of ± 0.1 °C at various ambient temperatures.

Actuator fault tolerance based on continuous‐time fuzzy singularly perturbed model

AbstractActuator fault tolerance based on fuzzy singularly perturbed model (FSPM) for nonlinear singularly perturbed systems (NSPSs) is investigated. A continuous‐time FSPM with subtractive faults on control input is presented to describe NSPSs with actuator faults, which is less conservative than slow–fast submodel methods. A hybrid fuzzy controller (HFC) consisting of a fuzzy detector and a fault‐tolerant controller can realize real‐time fault tolerance, such that the target system with actuator faults is stabilized. The theorem to solve controller gains is derived by using Lyapunov, singular perturbation, , and Schur complement theories, which can avoid the problem that the feasible solutions cannot be obtained by solving linear matrix inequalities (LMIs). A nonlinear item was replaced by a linear item, which provides a good idea for solving nonlinear matrix inequalities. The sudden and remittent actuator faults of a motor system are suppressed by using the above approaches.

Design of a robust hybrid fuzzy super-twisting speed controller for induction motor vector control systems

Design of a robust hybrid fuzzy super-twisting speed controller for induction motor vector control systems