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Background and Objective: The 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., Methods: In 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., Results: Simulation 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., Conclusion: In 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., Competing Interests: Declaration of Competing Interest The authors declared that they do not have any associated financial or commercial interests that present any conflict of interest with the reported work in this paper., (Copyright © 2019. Published by Elsevier B.V.)

This work presents an unprecedented resolution for the lack of harmony between wired network TCP, which transacts with stationary routers, and the Ad-hoc, which appoints mobile hosts as routers, causing failure. This is done by modifying the TCP's queue via fuzzy logic as a controller (FLC). The novelty is that when the mobile router changes location, leading to failure, the controller keeps tracking error signals, which become a behavior prediction base for the next error before occurring. The transfer function is designed with respect to the I/O parameters to optimize the system, leading to the steady with free of fluctuation and overshoot. Other than previous techniques, which depend on fixed criteria or proactive strategies such as AQM, the proposed system is distinguished by dynamic adaptation with continuous changes like mobility and signal power. Using FLC, the system response is improved by reducing time delay, packet loss, and enhancing stability and spectral efficiency. A simulation is designed to continuously monitor these parameters' results, represented by the metrics rise time, undershoot, overshoot, and steady time. For comparison purposes, the PID controller is applied, and the results show surpassing this system where the metrics rise time, undershoot, overshoot, and steady time values are for FLC 0.55, 0.017, 0, and 2.5, respectively, with fluctuation-free, while for PID 0.6, 0.526, 9.341, and 3.6533, respectively, with fluctuation. Furthermore, by dealing the FLC with the challenges of high mobility and the signal power changes, it exceeds the traditional PID controller, as the simulation results show that in terms of reducing the delay time by 28.6%, packet loss by 50%. Besides, there is a remarkable increase in the throughput by 20% and spectral efficiency by 16.7%. [ABSTRACT FROM AUTHOR]

Buck-boost converters are one of the important devices in power conversion applications that are capable of increasing or decreasing the voltage according to the load requirements. However, the main challenge in operating a buck-boost converter is maintaining the stability of the output voltage. The selection of PID control is one of the ways that can be used to keep the output voltage stable, in this study, buck boost with 12 volts input is used and PID control is set with several parameters that can reduce errors and stabilize the output voltage, the PID constant values are Kp = 2 and Ki = 0.2 with these parameters obtained less stable results with time rise = 72.45 ms and overshoot = 0.995 % for boost mode with voltage 24 volt output and time rise = 123.2 ms and overshoot = 0.995 % for buck mode with an output voltage of 5 volts. Thus, to maximize the system with a stable output voltage, a fuzzy logic control is used which has an adaptive time response and is able to adapt to load variations, the simulation results show that the output voltage of the fuzzy logic control has a time rise = 114 ms and overshoot = 0.761 % in boost mode and time rise = 104 ms with overshoot = 0.971 % in buck mode. The advantage of fuzzy logic control is that it is more optimal and able to improve system performance responsively compared to other conventional controls. [ABSTRACT FROM AUTHOR]

This study presents a comparative analysis of control strategies designed to enhance the performance of Anti-Lock Braking Systems (ABS) and improve vehicle safety. The research explores three key approaches: First, it evaluates Fuzzy Logic-Controlled ABS, comparing five defuzzification algorithms using MATLAB's Fuzzy Logic Toolbox. Second, it investigates a Neural Network-Based Fault-Tolerant Control strategy, emphasizing improved fault tolerance during braking. Third, it assesses the performance of three ABS controllers--fuzzy logic, bangbang, and PID controllers. The findings reveal that Fuzzy Logic-Controlled ABS significantly enhances braking performance and directional stability, while Neural Networks demonstrate rapid response and accuracy in generating real-time substitute signals, thereby boosting system reliability. Among the controllers, the PID controller excels in reducing stopping distance and time, though the Fuzzy Logic Controller shows superior control over relative slip, enhancing steerability despite longer stopping distances and times. This comparative analysis provides valuable insights into ABS control strategies and their implications for vehicle safety. Future research should focus on refining ABS algorithms, developing robust fault detection mechanisms, and optimizing controller designs to further advance automotive safety and ABS efficiency. [ABSTRACT FROM AUTHOR]

Automotive air conditioning (ACC) system designers faced unique challenges in satisfying customer demands for efficient and comfortable operation across a broad temperature range. The AAC system's effective or fixed temperature setting makes this possible. Many climate control models, like the state flow switching controller in MATLAB control, must handle heating and cooling due to a single Simulink/environment software and frequently contain steady-state error (SSE). The present research proposes to design an automatic temperature control scheme and fuzzy logic control (FLC) for an automotive air-conditioned (AAC) system. In addition, it employs MATLAB Simulink/environment software to model the fuzzy control technique to analyze the AAC system's response. A simulation has been set up to evaluate the suggested system performance to match a selection of user-specified reference temperatures and compressor speeds. Compared with a PID-controlled AAC system, the proposed FLC-based system reduced the undershoot to only 2.30% from 33.30% respectively, and was robust, quicker, and better at controlling temperature. • Challenges in automotive air conditioning systems for designers were addressed. • An automatic temperature control scheme was proposed via fuzzy logic control. • Improved efficiency and comfort in the AAC system was achieved. • The proposed FLC-based system reduced the undershoot to 2.30% from 33.30%. • The FLC-based was more robust, quicker, and better at controlling temperature. [ABSTRACT FROM AUTHOR]

This study investigates advanced control techniques to evaluate the trajectory tracking control of a two-degrees-of-freedom (2-DOF) helicopter system based on simulation and hardware implementation experiments. For this, a Quanser Aero 2 platform and its QUARC software, integrated within MATLAB/Simulink, are used to design and implement multiple controllers, including Proportional Integral Derivative (PID), Fuzzy PID, and Adaptive Neuro Fuzzy Inference System (ANFIS) controllers. A two-phase approach was followed to assess and compare these controllers' ability to handle parametric uncertainties, unmodeled dynamics, and matched disturbances. Firstly, simulation experiments were conducted using an uncertain system model, considering the controller's responses in scenarios with and without cross-coupling and matched disturbances. Subsequently, hardware experiments were conducted under the same conditions to validate the simulation results, providing real-time performance comparisons. Finally, a rigorous quantitative assessment based on multiple performance metrics including Root Mean Square Error (RMSE), peak value, Integral Square Error (ISE), Integral of Absolute Error (IAE), and Integral of Time-multiplied Absolute Error (ITAE) demonstrated overperformance achieved using ANFIS for pitch control and Fuzzy PID for yaw control. [ABSTRACT FROM AUTHOR]

This paper presents simplest fuzzy logic controller (SFLC) PLC's based implementation applied to the cascade control strategy. Level and flow control loops are important and widely used in the oil and manufacturing industries to ensure a quality product. The control is used to maintain the level of the liquid in the tank at the desired value by manipulating the liquid in the reservoir. Two fuzzy sets on each input variable, five fuzzy sets on the output variable, five linear control rules, algebraic product bounded AND/OR operator, Larsen product inference and Centre of Sums (CoS) defuzzification are the components of this simplest nonlinear fuzzy controller to be implemented. The proposed work deals with the real implementation of a simplest fuzzy logic cascade control strategy designed on SIMATIC S7-300 Plc based on ladder diagram (LD) programming. In this paper, we have presented the results of the experimental tests of the conventional PI control strategy as well as the simplest fuzzy PI implementation applied to a PUL-2/EV cascade control device. Experimental results using this simplest fuzzy PI controller with the setpoint error shown that the setpoint tracking rise time can be reduced by 30% and the disturbance rejection time is decreased at 7sec compared to the conventional PID-PLC based controller, and concluded by stressing the importance of this new controller implementation. [ABSTRACT FROM AUTHOR]

This research focuses on developing an adaptive fuzzy-neural control system to manage and maintain temperature in brick tunnel kilns. The problem is how to optimize performance and energy consumption in the brick production process. A control algorithm using a combination of a fuzzy logic system and neural network is proposed to automatically adjust temperature parameters, optimize production efficiency, and reduce energy consumption. Simulation and experimental results demonstrate the outstanding performance of the presented system, with significant improvements in energy efficiency and product quality compared to traditional control methods. Moreover, the obtained results exhibit the potential for wide application of the adaptive neuro-fuzzy control method in academic study or industrial production processes. [ABSTRACT FROM AUTHOR]

Hardware implementations of networked control systems (NCS) are still rarely found in various research publications so that technical issues, such as how to measure random delay and substitute it into the control equation, still need to be studied further. This study is therefore aimed to compare the performance of NCS based on the used protocol in the ethernet network, i.e. user datagram protocol (UDP) or transmission control protocol (TCP), by applying it in room temperature plant. In this study, the controlled plant is influenced by the fan speed, heating, and window position plant. The heating plant is employed as the main control, while the others are set as sub-plants. The three plants use proportional–integral–derivative (PID) controller where they are regulated by fuzzy logic as the master control unit (MCU). The MCU and three PID controllers are located in the master terminal unit (MTU) while the actuators and sensors are located in the three different remote terminal units (RTU). The verification experiment shows that there is no overshoot on TCP-based NCS, while UDP has 0.725%. For the risetime, the response on UDP is faster than TCP (110.22 as compared to 138.18 s). The same thing also happened to the settling time, where the time with UDP was 101.86 s and 128.44 s for TCP.

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute error (8.246), integral square error (3.3); integral time absolute error(1762);integral time square error (43.77) while for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature. More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations.

Tuning a nonlinear inverted pendulum is a complex and uncertain optimization problem. In this paper, we develop two new GWO variants by introducing a DLH (Dimension Learning-based Hunting) module and new formulas to enhance the exploitation/exploration ratio aiming to avoid local minima. A statistical analysis is carried out to compare the two proposed approaches with five GWO variants. After that, they are used to tune a PID and FSMC controller. The obtained results are promising even when compared to other approaches. [ABSTRACT FROM AUTHOR]

The heat exchanger system (HES) of an air conditioner (AC) has difficulty in maintaining the proper output temperature during heat exchange in the existing system. Therefore, a controller has been created to control this outlet procedure in HES. However, it has major vibrations, heat and exchanger leakages, energy consumption, fouling, and other difficulties that cause it to overheat. To achieve optimal tuning, a mixture of gases, pressure, and humidity must be optimized. Thus, to achieve the exact solution, the paper proposes a novel pine sleek PID (PSP) controller that uses fuzzy logic in the adaption of Gaussian member functions and a multivariable exponential method to eliminate vibration and thermal exchanger leakages. Also, the integral square error (ISE) was optimized using the global optimization principle of the Luus–Jaakola (LJ) algorithm, which reduces energy usage and minimizes the error. Meanwhile, to optimize the maximum peak value with the shorter feedback process, it controls the various factors to get the desired temperature. In comparison to the previous technique, the proposed technique achieves a peak time of 1.0 s, which is 0.8 s faster than the auto-tuned PID. As a result, the proposed method effectively provides a PID control system in an AC heat exchanger with the largest peak value in the smallest time, proving efficiency. [ABSTRACT FROM AUTHOR]

The Type 2 Fuzzy Logic System (T2FLS) is an enhanced form of the classical Fuzzy Logic System (FLS). The T2FLS based control technics demonstrated a lot of improvements for the past few decades. This is based on the advantage of its membership function (MF). Many experimental studies indicated the superiority of Type 2 Fuzzy Logic Controller (T2FLC) over the ordinary Type 1 Fuzzy Logic Controller (T1FLC), particularly in the event of non-linearities and complex uncertainties. However, the organized design method of T2FLCs is still an interesting problem in the control engineering community. This is due to the difficulties in computing the parameters associated it. A novel application of the Modified Flower Pollination (MFP) optimization algorithm in the design of T2FL is presented. The optimized Cascade Interval Type 2 Fuzzy PID Controller (IT2FPIDC) structure is proposed in this study. The best values of the parameters of the antecedent MFs and the PID gains of IT2FPIDC are found using the MFP algorithm. The MFP optimization technique was used because of its lower computational effort and high convergence speed, in view of the higher number of variables to be optimized in cascaded IT2FPIDC. The MFP-based Type-1 Fuzzy Proportional Integral Derivative Controller (T1FPIDC) is compared with the proposed MFP-based cascade-optimized IT2FPIDC. The rotary inverted pendulum (RIP) which is a non-minimum phase, non-linear, and unstable system is employed as a benchmark for testing the proposed controller. Balance and trajectory-tracking controls of the RIP are considered. Furthermore, the disturbance rejection ability of the proposed controller is analysed. The presented control methos is evaluated on the RIP manufactured by Quanser over many simulations and real-world experiments. The performance characteristics considered are steady state error (E s s) , settling time (t s) , maximum overshoot (M p) and rise time (t r) . The improvement of the effectiveness and robustness proposed controller in the presence of load disturbance, noise effects and parameter variation is shown. [ABSTRACT FROM AUTHOR]

Electro-hydraulic actuators (EHA) have recently played a significant role in modern industrial applications, especially in systems requiring extremely high precision. This can be explained by EHA's ability to precisely control the position and force through advanced sensors and innovative control algorithms. One of the promising approaches to improve control accuracy for EHA systems is applying classical to modern control algorithms, in which the proportional-integral-derivative (PID) algorithm, fuzzy logic controller, and a hybrid of these methods are popular options. In this paper, we developed a novel version of the fuzzy control algorithm and linear feedback control method, namely fuzzy linear feedback control, to improve the control performance. To achieve the highest performance, we first designed a mathematical EHA model based on the Matlab/Simulink software packages thanks to the selected parameters, which are similar to a real EHA system. Then, we respectively applied PID, fuzzy PID (FPID), and fuzzy linear feedback control (FLFC) before comparing them to have a full view of the outstanding advantages of the proposed algorithm. The simulation results showed that the proposed FLFC algorithm is approximately 99% and 77% superior in performance to the PID and feedback control algorithms, respectively. [ABSTRACT FROM AUTHOR]

Robotic manipulators are extremely nonlinear complex and, uncertain systems. They have multi-input multi-output (MIMO) dynamics, which makes controlling manipulators difficult. Robotic manipulators have wide applications in many industries like processes, medicine, and space. Effective control of these manipulators is extremely important to perform these industrial tasks. Researchers are working on the control of robotic manipulators using conventional and intelligent control methods. Conventional control methods are proportional integral and derivative (PID), Fractional order proportional integral and derivative (FOPID), sliding mode control (SMC), and optimal & robust control while intelligent control method includes Artificial Neural network (ANN), Fuzzy logic control (FLC) and metaheuristic optimization algorithms based control schemes. This paper presents the trajectory control of a robotic manipulator using a PID controller. Four different meta-heuristic algorithms namely Sooty tern optimization (STO), Spotted Hyena optimizer (SHO), Atom Search optimization (ASO), and Arithmetic Optimization algorithm (AOA) are used to optimize the gains of PID controller for trajectory control of a two-link robotic manipulator and a novel hybrid sooty tern and particle swarm optimization (STOPSO) has been designed. These optimization techniques are nature-inspired algorithms that give the optimal gain values while minimizing the performance indices. A performance index comprising Integral time absolute error (ITAE) having weights for both links has been considered to achieve the desired trajectory. These optimization techniques are stochastic in nature so statistical analysis and Freidman's ranking test has been performed to evaluate the effectiveness of these algorithms. The proposed hybrid STOPSO provided a fitness value of 0.04541 and showed a standard deviation of 0.0002. A comparative study of these optimization techniques is presented and as a result, hybrid STOPSO provides the best results with minimum fitness value followed by STO, AOA, ASO, and SHO algorithms. [ABSTRACT FROM AUTHOR]

A challenge for inspecting transmission power lines with Unmanned Aerial Vehicles (UAVs) is to precisely determine their position and orientation, considering that the geo-location of these elements via GPS often needs to be more consistent. Therefore, a viable alternative is to use visual information from cameras attached to the central part of the UAV, enabling a control technique that allows the lines to be positioned at the center of the image. Therefore, this work proposes a PID (proportional–integral–derivative) controller tuned through interval type-2 fuzzy logic (IT2_PID) for the transmission line follower problem. The PID gains are selected online as the position and orientation errors and their respective derivatives change. The methodology was built in Python with the Robot Operating System (ROS) interface. The key point of the proposed methodology is its easy reproducibility, since the designed control loop does not require the mathematical model of the UAV. The tests were performed using the Gazebo simulator. The outcomes demonstrated that the proposed type-2 fuzzy variant displayed lower error values for both stabilization tests (keeping the UAV centered and oriented with the lines) and the following step in which the trajectory is time-variant, compared to the analogous T1_PID control and a classical PID controller tuned by the Zigler–Nichols method. [ABSTRACT FROM AUTHOR]

The output voltage of microbial fuel cells (MFCs) needs to be controlled effectively to improve the operating efficiency of MFC system against the load variations, disturbances, and uncertainties. The output voltage control is purposed to provide the MFC with an appropriate amount of fuel required to maintain the stability of output voltage. In the applications, the MFC output voltage can be requested to track the variable reference voltage or to track the constant output voltage under changing conditions such as load change. For this purpose, two adaptive fuzzy PID (FPID) controllers powered by optimization algorithms were designed in this study to keep the output voltage value of the MFC at the desired values. This study extends the previous works by using optimization algorithms, such as particle swarm optimization (PSO) and gray wolf optimization (GWO) algorithms, to adjust fuzzy logic parameters, which are used for tuning Proportional‐Integral‐Derivative (PID) controllers. The optimization‐based adaptive FPID controllers play an active role in efficient, fast, and robust tracking of the reference voltage value with its adaptive structure. The designed adaptive control algorithms were tested under different cases, which involve the variation of the reference voltage and load conditions. The results show that both of these controllers can effectively control the output voltage of a MFC by regulating the flow rate of the fuel. In addition, the performances of the designed controller strategies according to the results obtained vary according to the reference signal. While PSO‐based FPID gives more successful results for positive step changes, GWO‐based FPID gives more successful results for negative step changes. These two control strategies show more efficient, robust, successful, and faster performances compared to the traditional PID controller. [ABSTRACT FROM AUTHOR]

The Hybrid Electrical Energy Storage System (HESS) with supercapacitors in "GESITS" electric motorcycle offers greater power density and cycle life as well as a wider operating temperature range compared to batteries so as to maximize the existing regenerative braking features. In this study, the Four Switch Buck Boost Converter (FSBB) with Power Transfer is used as a current regulator to connect HESS. Fuzzy PID control is used to regulate the current that passes through the power transfer so that the braking force can be controlled based on the brake pull variable, vehicle speed and SOC supercapacitor. The results of the fuzzy control test with the MATLAB Fuzzy Designer obtained an average error of 1.44 %. While the results of the ITAE PID tuning method obtained a rise time of 3.33 seconds, a settling time of 3.11 seconds, an overshoot of 9.20 % and a steady state error of 2.20 %. From the results of the regenerative mode test, when given an input of 28-4 VDC, the average output voltage is 16.34 VDC with an average current of 8 A. While in the drive mode test with 28 VDC input, the average output voltage is 23.98 VDC and average current is 3.99 A with FSBB efficiency of 90.54 %. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for PID (proportional–integral–derivative) controller auto-tuning based on expert knowledge incorporated into a fuzzy logic inference system. The proposed scheme iteratively tries to improve the performance of the closed-loop system. As performance measures, the proposed scheme uses the characteristics of the step response (rise time, overshoot, and settling time). PID parameters in the first iteration can be calculated based on the basic open-loop step response experiment or it is possible to use current parameters. In each successive iteration, step response characteristics are measured and the relative changes expressed in the percentage of value in the first iteration are calculated and converted into linguistic values. The fuzzy expert system computes fuzzy values that are used after defuzzification as multiplying factors for current PID parameters. To achieve a balance between the aggressive and robust closed-loop response, as well as between the slower and the faster one, the fuzzy expert system works in three operating modes: the one for speeding up the system, the one for reducing the overshoot, and the one for a balanced reduction of rise time and overshoot. The performance and robustness are verified by computer simulation using an extensive range of different processes.

This paper presents a novel method for PID (proportional–integral–derivative) controller auto-tuning based on expert knowledge incorporated into a fuzzy logic inference system. The proposed scheme iteratively tries to improve the performance of the closed-loop system. As performance measures, the proposed scheme uses the characteristics of the step response (rise time, overshoot, and settling time). PID parameters in the first iteration can be calculated based on the basic open-loop step response experiment or it is possible to use current parameters. In each successive iteration, step response characteristics are measured and the relative changes expressed in the percentage of value in the first iteration are calculated and converted into linguistic values. The fuzzy expert system computes fuzzy values that are used after defuzzification as multiplying factors for current PID parameters. To achieve a balance between the aggressive and robust closed-loop response, as well as between the slower and the faster one, the fuzzy expert system works in three operating modes: the one for speeding up the system, the one for reducing the overshoot, and the one for a balanced reduction of rise time and overshoot. The performance and robustness are verified by computer simulation using an extensive range of different processes. [ABSTRACT FROM AUTHOR]

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This paper aims to control of satellite position using the two-phase Hybrid Stepping Motor. it's used to receive satellite signal and must accurately track the satellite as it moves across the sky. This system applies the traditional Proportional Integral Derivative (PID) control, the fuzzy PID control methods Fuzzy Logic Control (FLC) and Fractional Order PID (FOPID)control. Firstly, the PID controller and FLC was designed by Ziegler-Nichols tuning method to obtain control parameters. secondly, the PID controller and FOPID controller was designed by Particle Swarm Optimization (PSO) tuning method to obtain control parameters. finally, the PID controller and FOPID controller was designed by Crazy PSO tuning method to obtain control parameters. The system was simulated by MATLAB, SIMULINK and compared be control methods.

In this work a comparison among three control strategies is presented, with application to a multi-articulated system. The proposed control scheme is based on the dynamic model derived using LagrangeEuler formulation. Our robot manipulator ANFIS system control’s simulated in Matlab Simulink environment; the results obtained present the efficiency and the robustness of the proposed control with good performances compared with PID and the FIS method.

In this study, active suspension control of the interaction between the bridge can be modeled according to the Euler-Bernoulli beam theory, and the quarter car model with three degrees of freedom is studied. The active suspension system consists of a spring, damper, and linear actuator. The active suspension control is designed using classical PID and self-tuning fuzzy PID (STFPID) to reduce the vehicle body's disruptive effects. To determine the performance of the designed controllers, two different road profiles with the bridge oscillations caused by the bridge flexibility were considered as the disruptive effect of the vehicle. When the simulation results were examined in terms of passenger seat displacement and acceleration, the proposed STFPID method significantly increased road holding and ride comfort. [ABSTRACT FROM AUTHOR]

In this paper, modeling of doubly fed induction generator (DFIG) based wind energy conversion system (WECS) and generator speed controller are presented. The System Identification Toolbox of MatLab is used to develop the linear model of the WECS by considering the wind speed as input and speed of the generator as output. Two models, namely Auto Regressive with eXogenous Input (ARX) and Auto-Regressive Moving Average with eXogenous Input (ARMAX), are estimated. We used the ARX221 model structure with the best fit of 84.31%, Final Prediction Error (FPE) of 0.0433 and Mean Square Error (MSE) of 0.0432. The Ziegler-Nichols (Z-N) method and the fuzzy logic technique are employed in the proportional integral derivative (PID) controller design to control the speed of the generator. The classical Z-N PID used for the responses of the system is observed to be insufficient for both uniform and variable inputs, hence, a better response has been obtained by applying the fuzzy logic-based PID controller. The present study proves that the fuzzy logic based control enhances the speed regulation of generator in the WECS by overcoming the effect of varying wind speed. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been widely used in various applications such as health and safety, smart homes, greenhouses, and industrial application. It has been increasingly utilized in the industry owing to its benefits in terms of enhancing the overall performance of a given system. This study appeared from a real need in many local industries. In this paper, a prototype system has been implemented for artificial control on the temperature of the industrial panel. The paper includes two control systems executed; classical PID (Proportional Integral Derivative) and fuzzy logic with a comparison between them. Fuzzy control algorithm is developing based on Sugeno method inside PLC (Programmable Logic Controller). The connection of PLC with sensors is used by the Modbus protocol. Arduino UNO and Ethernet shield are used to connect the sensor to the router and then to PLC by Modbus. [ABSTRACT FROM AUTHOR]

Microgrids, comprising distributed generation, energy storage systems, and loads, have recently piqued users’ interest as a potentially viable renewable energy solution for combating climate change. According to the upstream electricity grid conditions, microgrid can operate in grid-connected and islanded modes. Energy storage systems play a critical role in maintaining the frequency and voltage stability of an islanded microgrid. As a result, several energy management systems techniques have been proposed. This paper introduces a microgrid system, an overview of local control in a microgrid, and an efficient EMS for effective microgrid operations using three smart controllers for optimal microgrid stability. We designed a microgrid consisting of renewable sources, Li-ion batteries, the main grid as a backup system, and AC/DC loads. The proposed system control was based on supplying loads as efficiently as possible using renewable energy sources and monitoring the battery’s state of charge. The simulation results using MATLAB Simulink demonstrate the performance of the three proposed microgrid stability strategies (PID, artificial neural network, and fuzzy logic). The comparison results confirmed the viability and effectiveness of the proposed technique for energy management in a microgrid which is based on fuzzy logic controllers.

The main aim of this study consists of proposing a simple but effective and robust approach for PID type fuzzy controller (Fuzzy-PID) in order to improve the dynamics and stability of a magnetic ball levitation system. The design parameters of the proposed controller are optimally determined based on Cuckoo Search (CS) algorithm. During the optimization, a time domain objective function is used for minimizing the values of common step response characteristics for the optimal selection of the controller parameters. Robustness tests are performed to evaluate the performance of the proposed controller through extensive simulations under load disturbance, parametric variation and changes in references. Moreover, to show the advantage and compare the performance of the proposed controller, the PID and Fractional Order PID (FOPID) controllers tuned with CS are designed. The simulation results and comparisons with the CS based PID and FOPID controllers demonstrate that the CS based Fuzzy-PID controller has superior performance depending on small overshoot, short settling time, fast rise time and minimum steady state error. Compared with the PID and FOPID controller tuned with CS, the simulation results show that the proposed Fuzzy-PID controller tuned with CS outperforms in terms of the accuracy, robustness and the least control effort. [ABSTRACT FROM AUTHOR]

When the standard operating temperatures are exceeded in hydroxy generators, production performance decreases and power consumption increases. Self-adaptive fuzzy proportional integral derivative systems have been used to prevent this situation and to maintain optimum production conditions. The Fuzzy Logic is provided to tune the optimum PID parameters by using a gain scheduling method. The tuning scheme is demonstrated by a fuzzy decision process, which consists of fuzzification, knowledge and rule base, inference and defuzzification. Results of developed fuzzy proportional integral derivative (FPID), on-off, conventional PID and fuzzy control approach are discussed and compared. The presented result shows that self-adaptive fuzzy PID controllers achieve better control performance than conventional control methods mentioned. The last but not the least, developed approach minimizes the expertise needed to apply pulse width modulation technique. • Fuzzy proportional integral derivative controller is presented for hydroxy generators. • The controller protected the hydroxy reactor from extreme temperature effects. • Hydrogen production is optimized with artificial intelligence control technique. • Fuzzy rules determined from practical experience. • Performance evaluation criteria are determined for different control methods. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of power electronics and micro-electronics technology, the use of brushless DC motors has expanded considerably. Brushless DC motor has many advantages such as small volume, light weight, high efficiency, energy saving, easy speed adjustment, simple structure, reliable operation and easy maintenance. Today, DC motor control has become an important problem since brushless DC motor is widely used in industrial control processes. For this aim, PID and Fuzzy Logic controllers are used for position control of DC motor in this study. DC Motor's dynamic equations and transfer function are obtained by taking necessary parameters from the document of DC Motor. PID and Fuzzy Logic coefficients were calculated by making simulation studies on transfer function. Calculated coefficients and required block diagrams were created with Waijung blocks in Matlab Simulink environment and embedded in STM32F4 microcontroller kit with Code-Genaration tool. PID and Fuzzy Logic controller structures are embedded in microcontroller and successful experiments on DC motor position control have been performed. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters' uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, we introduce three different nacelle yaw controllers that use distinct techniques and study their performances in improving the captured energy by the turbine. The first one is a carefully tuned Proportional-Integral- Differential (PID) controller with its simple design; the second one is a linguistic fuzzy logic controller with its intuitive flexible design; and the third one is a Model-Predictive-Controller (MPC) with its adaptive functionality. The control objective of the developed controllers is to effectively track the wind direction by the yaw motion of the turbine nacelle, and consequently to improve the energy capture. A comparative study and a thorough analysis of the performances of three controllers are carried out using extensive MATLAB/SIMULINK simulations. [ABSTRACT FROM AUTHOR]

This work aims at developing a new dynamic model for the vibration control using the finite element analysis using comparing the effects of different controllers on system performance. The finite element method (FEM) is used to derive the flexible beam model. Frequency analysis (modal and harmonic) of the model is performed using ANSYS software in 4 different cases. In each of the 4 cases, the state space model of the beam was extracted in workspace of MATLAB based on result of its frequency analysis done in ANSYS. A PID controller was designed based on the model obtained from the first case in a MATLAB based environment and validated on the other 3 cases. To achieve better system performance, three more intelligent controller systems were considered: PD like Fuzzy, PID-AT (PID auto-tuning) and STFC (self-tuning fuzzy controller). In comparison with PID, PD like Fuzzy and PID-AT controllers, the system under effect of STFC shows the least overshoot, least rise time and least settling time. The three studied intelligent controller types based on fuzzy logic (PD like Fuzzy, PID-AT and STFC) achieved much improved system performance with the STFC showing the best system performance. [ABSTRACT FROM AUTHOR]