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Electromagnetic tomography (EMT) uses the mutual inductance of the coil to visualise the conductivity distribution of interesting regions. Since the conductivity of defects and metal samples are different, the metal samples with defects can be treated as binary-valued material distributions. This paper investigates the closed-loop fuzzy proportional, integral and derivative (PID)-controlled iterative Landweber method. The whole method includes fuzzy PID controller, the Landweber reconstruction method, and the Dirichlet-to-Neumann map. Specifically, the differential signal between the mutual inductance of the coil and the feedback signal is used as the input of the fuzzy PID controller. The fuzzy controller can automatically adjust three parameters (${K_p}$ K p , ${K_i}$ K i and ${K_d}$ K d ) of PID controller. Subsequently, the output of the PID controller can serve as the input of the Landweber algorithm to reconstruct the distribution of conductivity. Furthermore, the Dirichlet-to-Neumann map is used to calculate the mutual inductance, acting as the feedback signal based on the reconstruction conductivity distribution. Finally, both the numerical simulation and experiments are applied to verify the proposed method. The results indicate that the proposed method can reconstruct the image with a clear edge, and the average correlation coefficient can reach 0.792. [ABSTRACT FROM AUTHOR]

Fuzzy logic control is the most common method utilized to tune proportional integral derivative (PID) controller parameters online. However, proportional integral derivative controllers often perform poorly in the control of nonlinear and/or complicated systems, such as direct current motors, where the model parameters are not exactly known if the scaling factors are not properly selected besides the membership function and rule sets in a fuzzy logic controller design. Finding the most suitable scaling factors for complex systems where the model parameters are not exactly known or nonlinear systems is a challenging task. Furthermore, traditional trial and error techniques of determining appropriate scaling factors are experience based, time consuming, and may not always provide optimal response. In this paper, a particle swarm optimization algorithm is suggested for optimizing the input and output gains of the fuzzy PID controller. The robustness and effectiveness of the suggested controller was validated using MATLAB/Simulink. The performance of the suggested controller is compared with the Ziegler Nichols and Particle Swarm Optimization Algorithm tuned PIDs, and fuzzy PID controllers. The simulation result show that the fuzzy PID controller whose scaling factor was tuned using particle swarm optimization outperforms the other controllers in avoiding disturbance and has a better trajectory tracking capability. [ABSTRACT FROM AUTHOR]

This paper addresses the stabilization of voltage and frequency in multi-area diverse source power systems (MADSPS). Consequently, to attain this, an investigation was carried out on the load frequency control and automatic voltage regulation in combined (CLFC-AVR) analysis. The MADSPS comprises multiple generation units, such as Thermal-Hydro-Wind in each area, and performs the analysis by laying area-1 with a 10% step load disturbance (10%SLD) under the supervision of a fuzzy-aided proportional-integral-derivative (PID) optimally rendered with a novel searching strategy of hybrid (H) artificial electric field (HAEFA) algorithm. However, the tuning of the fuzzy PID controller using the HAEFA technique is performed under different time-domain objective functions and their combinations to reveal the most suitable index for optimization of the considered model. Moreover, the efficacy of the HAEFA-tuned fuzzy PID was compared with classical PI/PID. Further, the CLFC-AVR investigation on MADSPS is stretched with the integration of superconducting magnetic energy storage (SMES) in each area and the flexible AC transmission device of the thyristor-controlled series compensator (TCSC) with a tie line for performance boost.

A suspension system plays a crucial role in ensuring the smoothness of a car when traveling on roads. This paper proposes using the active suspension system equipped with a hydraulic actuator to replace a conventional passive suspension. Unlike previous publications, this study uses a Hybrid proportional-integral-derivative (PID) algorithm, combining a conventional PID controller and a fuzzy solution with two distinct inputs. The coefficients of the PID controller are flexibly adjusted by the fuzzy algorithm. Therefore, it can more respond to complex motion conditions. Calculations are performed with the MATLAB-Simulink software for four specific cases. According to the research findings, the maximum and root mean square (RMS) values of vehicle body acceleration and displacement significantly decrease when applying the hybrid algorithm to the active suspension system. In the last case, these values are only 16.69% and 36.90% when comparing Fuzzy PID and Passive situations. In general, the smoothness of the sprung mass can be guaranteed in all survey cases. [ABSTRACT FROM AUTHOR]

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup's recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. [ABSTRACT FROM AUTHOR]

Temperature control is an important factor which influences the accuracy of refrigerant heat transfer experimental results. In this paper, the three temperature control methods for the electric heating water tank (EHWT) in the single tube heat transfer experimental rig are investigated. The error of proportional-integral-derivative (PID) controller is ±1 °C and the stability time is 390 seconds. The control performance is not satisfactory. A fuzzy controller and a fuzzy PID controller are designed to improve temperature control performance. The designed controllers are simulated by MATLAB/SIMULINK and the results prove that the designed controllers is suitable for EHWT. The experimental results show that the performance of the designed controllers are improved concerning. The error of two controllers is ±0.1 ℃. Compared to the PID controller, the stability time of the fuzzy controller and the fuzzy PID controller are decreased by 14.9% and 43.1% and the overshoot of the two controllers are reduced by 100% and 62.5%, respectively. The results and the control method have great significance for the refrigerant heat transfer experiment. [ABSTRACT FROM AUTHOR]

This paper addresses the stabilization of voltage and frequency in multi-area diverse source power systems (MADSPS). Consequently, to attain this, an investigation was carried out on the load frequency control and automatic voltage regulation in combined (CLFC-AVR) analysis. The MADSPS comprises multiple generation units, such as Thermal-Hydro-Wind in each area, and performs the analysis by laying area-1 with a 10% step load disturbance (10%SLD) under the supervision of a fuzzy-aided proportional-integral-derivative (PID) optimally rendered with a novel searching strategy of hybrid (H) artificial electric field (HAEFA) algorithm. However, the tuning of the fuzzy PID controller using the HAEFA technique is performed under different time-domain objective functions and their combinations to reveal the most suitable index for optimization of the considered model. Moreover, the efficacy of the HAEFA-tuned fuzzy PID was compared with classical PI/PID. Further, the CLFC-AVR investigation on MADSPS is stretched with the integration of superconducting magnetic energy storage (SMES) in each area and the flexible AC transmission device of the thyristor-controlled series compensator (TCSC) with a tie line for performance boost. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to design an adaptive neuro-fuzzy controller to control the speed of the BLDC motor. This paper throws overall view of the performance of fuzzy PID controller and compares with fuzzy - adaptive neural controller. Take characteristics of suitable in PID controller is difficult. But fuzzy is the ability to take appropriate control parameters and calculations easier. An adaptive neuro fuzzy control system has the advantages of both types of fuzzy control system and neuro system. Paper examines the BLDC motor speed control based on adaptive neuro - fuzzy First tried to have a simulated fuzzy PID controller And then designing controller adaptive neuro - fuzzy using ANFIS toolbox for the motor. And we compare the characteristics of speed, torque, current and voltage in the three controllers finally, we conclude that the characteristic of the controller adaptive neuro - fuzzy (ANFIS) is better than the other two controllers.

To improve the applicability of automatic emergency braking system (AEBS) in urban traffic conditions, a control strategy based on driving behavior characteristics was proposed. Firstly, a driver-in-the-loop test platform was constructed to collect driving data from different drivers during braking processes, and analyze the relationship between driving behavior characteristics and the relative motion state between vehicles. Then, a two-level warning and braking control strategy was designed based on the idea of hierarchical control. The upper-level controller determined the braking deceleration, warning, and braking trigger time thresholds, while the lower-level controller designed a fuzzy PID controller to control the brake master cylinder pressure. Finally, CarSim and Matlab/Simulink were used to simulate a rear-end collision scenario with a stationary vehicle, and an unmanned driving wire-controlled chassis test platform was constructed for semi-physical experiment verification. The results showed that both the two-level warning and braking functions can be effectively triggered, the actual braking deceleration can closely follow the desired braking deceleration during braking, and the inter-vehicle distance can be controlled at around 2.15 m after braking, indicating the excellent warning and emergency avoidance performance of the designed AEBS. [ABSTRACT FROM AUTHOR]

The transmission system efficiency and control accuracy of traditional large hydraulic press are low. Under the concept of energy saving, low carbon and environmental protection, the integration of high-precision pump control technology and high-performance hydraulic press control system has been widely concerned by the industry. In order to solve the problem of insufficient position control accuracy of open radial variable piston pump-controlled hydraulic press system, an adaptive fuzzy PID control system was designed, and the characteristics of fast forging system under different working conditions were studied. Firstly, the mathematical models of the digital control enhanced radial piston pump and the open pump controlled asymmetric cylinder were established, the mathematical model of the open radial variable piston pump controlled hydraulic press system was derived, and the adaptive fuzzy PID controller was designed. Then, the simulation study of the fast-forging control characteristics under different working conditions was carried out by AMESim-Simulink simulation. Finally, based on 0.6MN pump-controlled hydraulic press platform, the effectiveness of adaptive fuzzy PID and the control characteristics of the fast-forging system were verified. The results show that under the two working conditions of fast forging frequency of 1Hz, stroke of 20mm and load of 1.2×105N, and fast forging frequency of 0.5Hz, stroke of 40mm and load of 2.0×105N, the open radial variable piston pump-controlled hydraulic press system with adaptive fuzzy PID control operates stably, responds quickly, and the position accuracy error is less than 0.5mm. The validity of the simulation model and the effectiveness of the controller of open radial variable piston pump-controlled hydraulic press are verified. [ABSTRACT FROM AUTHOR]

This work suggests a hybrid African Vultures Optimization Algorithm (AVOA) and Pattern search (hAVOA-PS) based Fuzzy PID (FPID) structure for frequency control of a nonlinear power system with Electric Vehicles (EVs). To illustrate the dominance of the projected hAVOA-PS algorithm, initially PI controllers are considered and results are compared with AVOA, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. To further enhance the dynamic performance, PID and FPID controllers are considered. The dominance of FPID over PID and PI controllers is shown. In the next step, EVs are incorporated in the test system and a comparative analysis of hAVOA-PS based PI/PID/FPID and FPID+EV is presented. To exhibit the superiority of projected frequency control scheme in maintaining the stability of system under different disturbance conditions like load increase in area-1 only, load decrease/increase in all areas, and large load increase in all areas are considered. It is noticed that the suggested hAVOA-PS based FPID controller n presence of EV is able to maintain system stability for all the considered cases where as other compared approaches fail to maintain stability in some cases.

In this paper, a novel peak observer based adaptive multi-input multi-output (MIMO) fuzzy proportional-integral-derivative (PID) controller has been introduced for MIMO time delay systems. The adaptation mechanism proposed by Qiao and Mizumoto [1] for single-input single-output (SISO) systems has been enhanced for MIMO system adaptive control. The tracking, stabilization and disturbance rejection performances of the proposed adaptation mechanism have been evaluated for MIMO systems by comparing with non-adaptive fuzzy PID and classical PID controllers. The obtained results indicate that the introduced adjustment mechanism for MIMO fuzzy PID controller can be successfully deployed for MIMO time delay systems.

Aiming at the problem that the constant speed control strategy of the aquaculture water pump equipped by a large aquaculture worker ship will cause a lot of power waste and increase the aquaculture cost, a Whale Optimization Algorithm(WOA)Fuzzy PID controller for the aquaculture water pump is designed. The whale algorithm is used to optimize the quantitative factor and scaling factor in the Fuzzy PID controller,which makes the controller can dynamically adjust the speed of the aquaculture pump for the complex and changeable conditions of water changing,feeding,cleaning,and surface foam discharge,so as to realize the regulation of seawater flow. Simulation results show that compared with the PID controller and Fuzzy PID controller,the overshoot and steady-state error are smaller. The adjustment time is shortened by 73. 14% compared with the PID controller and 84. 49% compared with the Fuzzy PID controller,which can better meet the control requirements of the aquaculture water pump and achieve the goal of energy saving. [ABSTRACT FROM AUTHOR]

This study presents the design and real-time applications of an Interval Type-2 Fuzzy PID (IT2-FPID) control system on an unmanned aerial vehicle (UAV) with a flexible cable-connected payload in comparison to the PID and Type-1 Fuzzy PID (T1-FPID) counterparts. The IT2-FPID control has significant stability, disturbance rejection, and response time advantages. To prove and show these advantages, the DJI Tello, a commercial UAV, is used with a flexible cable-connected payload to test the robustness of PID, T1-FPID, and IT2-FPID controllers. First, the optimal coefficients of the compared controllers are found using the Big Bang–Big Crunch algorithm via the nonlinear UAV model without the payload. Second, once optimised, the controllers are tested using several scenarios, including disturbing the payload and the coverage path planning area to examine their robustness. Third, the controller performance results are evaluated according to reference achievement and point-based tracking under disturbances. Finally, the superiority of the IT2-FPID controller is shown via simulations and real-time experiments with a better overshoot, a faster settling time, and good properties of disturbance rejection compared with the PID and the T1-FPID controllers. [ABSTRACT FROM AUTHOR]

In this paper, a novel peak observer based adaptive multi-input multi-output (MIMO) fuzzy proportional-integral-derivative (PID) controller has been introduced for MIMO time delay systems. The adaptation mechanism proposed by Qiao and Mizumoto [1] for single-input single-output (SISO) systems has been enhanced for MIMO system adaptive control. The tracking, stabilization and disturbance rejection performances of the proposed adaptation mechanism have been evaluated for MIMO systems by comparing with non-adaptive fuzzy PID and classical PID controllers. The obtained results indicate that the introduced adjustment mechanism for MIMO fuzzy PID controller can be successfully deployed for MIMO time delay systems. [ABSTRACT FROM AUTHOR]

When the Parallel Hybrid Vehicle (PHV) wheel's torque is coordinated and controlled, the issue of coordination control of wheel torque under mode switching has some limitations. Therefore, the experiment proposed a novel wheel torque coordination control technique for parallel hybrid vehicles. The feedforward and feedback joint control technique of the Wheel Drive Torque (WDT) control algorithm may be used to implement the wheel torque drive control. The technique for the wheel braking Torque Control (TC) is applied to obtain the wheel braking torque through the Fuzzy PID controller (F-PID). A Dynamic Coordination Controller (DCC) is created and the engine output torque is adjusted using the PID control technique of wheel angular velocity difference. The motor provides the compensation torque. The deviation of the intended angular velocity from the actual angular velocity is adjusted intime to realize the wheel's dynamic coordination while being controlled by a mode switch. The results demonstrates that the reaction time of the proposed algorithm can be reduced to 0.08s; The overall output TC precision is improved by 11.1%. The precision of the predicted velocity of vehicle speed tracking has risen by 8.0%. The vehicle dynamics during the transition procedure is improved by 4.4%, which enhances the ride comfort and the PHV's power. [ABSTRACT FROM AUTHOR]

An innovative Semi-Active Pendulum Tuned Mass Damper (SAPTMD) with variable frequency is introduced in this paper. The SAPTMD consists of two identical pendulum systems, each consisting of a pendulum and two springs attached to the pendulum mass. The pendulums are mounted on two boxes that can rotate symmetrically around the horizontal shafts that pass through them. Due to the change in inclination of the pendulum systems and, consequently, the orientation of the oscillating pendulums, the frequency of the proposed system can change within a predefined range. Besides, the need for less height for installation is another advantage of the SAPTMD over the conventional pendulum tuned mass dampers. In order to show the efficiency of the SAPTMD, it is attached to a single degree of freedom primary system excited similar to excitation due to an unbalanced rotating machinery during its start-up period. The SAPTMD's performance is compared with a classical tuned mass damper. As it is shown, the response of the primary system is reduced noticeably. Furthermore, the performance of the system is improved by utilizing a fuzzy Proportional-Integral-Derivative (PID) controller. [ABSTRACT FROM AUTHOR]

The regulation process of gas distribution systems for atmosphere packaging has the characteristics of being nonlinear time varying and having hysteric delay. When the conventional proportional-integral-derivative (PID) control algorithm is applied to this kind of system, it is difficult to set the parameters as the process is time consuming and has poor reliability. For these reasons, this paper designs a gas distribution system for fresh food atmosphere packaging based on a fuzzy PID controller. The step response method is used to construct the system's mathematical model under the given conditions and to optimize the gas distribution control flow. A simulation experimental platform to compare between the fuzzy PID controller and a conventional PID controller is designed, and the effectiveness of the fuzzy PID control strategy is verified, which proves that it can improve the performance of the monitoring system. The system can realize the remote monitoring of the gas distribution processes through the use of a mobile phone communication network. The data transmission is reliable, the operation is convenient, and, at the same time, the overall efficiency is improved. The results of the system simulation and the gas distribution for atmosphere packaging show that the fuzzy PID algorithm has a faster gas distribution speed and good environmental adaptability as the controller of the gas distribution system. The results show that the stability time of the fuzzy PID controller is about 38 s, while the stability time of the conventional PID controller is about 85 s. The concentration error of fresh gases is ±0.25% floating, the accuracy is increased by 12 times, and the gas distribution speed is increased by about 50% when the system is stable. [ABSTRACT FROM AUTHOR]

This study developed a predictive direct torque control (PDTC) application-specific integrated circuit (ASIC) with a fuzzy proportional–integral–derivative (PID) controller and a new round-off calculation circuit for improving the ripple response of a hysteresis controller when sampling and calculating delay times in an induction motor drive. The proposed PDTC ASIC not only calculates the stator’s magnetic flux and torque by detecting three-phase currents, three-phase voltages, and rotor speed but also eliminates large ripples in the torque and flux by using the fuzzy PID controller. Furthermore, the proposed round-off algorithm reduces the calculation error of the composite flux. A fuzzy voltage vector switching table is proposed not only to speed up the calculating speed but also to resolve the instability generated by its large torque and flux ripples. The Verilog hardware description language was used to implement the hardware architecture, and the aforementioned ASIC was fabricated using the 0.18- $\mu \text{m}$ 1P6M CMOS process of the TSMC by employing the cell-based design method. The predictive calculations, fuzzy PID controller, fuzzy voltage vector switching table, and round-off calculation algorithm improved not only the ripple issue faced in traditional direct torque control but also the control stability and robustness. The measurement results indicate that the proposed PDTC ASIC has an operating frequency, a sampling rate, and a dead time of 50 MHz, 100 kS/s, and 100 ns, respectively, at a supply voltage of 1.8 V. The power consumption and chip area of this ASIC are 1.0027 mW and 1.169 $\times $ 1.168 mm2, respectively. The main advantages of the proposed PDTC ASIC are its low power consumption, small chip area, robustness, and convenience.

This paper develops a high-precision time synchronization control method based on IEEE 1588 protocol. A novel control technique based on the hybridization of the improved wolf colony algorithm and cuckoo search algorithm (hybrid IWCA-CS) is used to dynamically adjust the fuzzy PID controller parameters to reduce the error of the master-slave clock. To make better performance of the conventional wolf colony algorithm (WCA), it is improved by using the Bloch coordinate encoding to determine the initial population and adding inertial weights to balance the search ability of the algorithm. The proposed hybrid IWCA-CS combines the best attributes of WCA’s global search and local search of cuckoo search (CS). In optimizing benchmark function experiments, the proposed hybrid IWCA-CS presents a superior performance compared to other soft computing methods. Furthermore, the hybrid IWCA-CS optimized controllers with different structures are presented to demonstrate its dynamic response characteristics. The results show that IWCA-CS tuned fuzzy PID controller shows the best dynamic performance as compared to PID/PI controllers. And the IWCA-CS based fuzzy PID controller is less sensitive to step load disturbance and parameter changes. Finally, the effectiveness of the proposed time synchronization control method in this paper over published methods is further confirmed by employing in power information collection system (PICS).

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability. [ABSTRACT FROM AUTHOR]

It is necessary to upgrade and transform the sorting equipment in the industrial production line. In order to improve production efficiency and reduce labor intensity, a high-speed lightweight parallel mechanism control system for the high-speed sorting and packaging of light items was studied. A fuzzy PID controller based on genetic algorithm (GA) optimization is proposed according to the nonlinear and strong coupling characteristics of the parallel mechanism (PM) control system. The inverse kinematic analysis was conducted to map the workspace trajectory tracking problem to the joint space. It was transformed into the trajectory planning and solving problems in the joint space. The motion trajectory was obtained utilizing quintic polynomial interpolation. Finally, the servo control system model was established, and the PID control parameters were optimized and self-tuned by the GA. They were applied to the fuzzy PID controller for simulation experiments. The simulation results showed that the GA-optimized fuzzy PID control system compared with the fuzzy PID control system had a 23.39% shorter rise time, 22.32% less regulation time, and 7.18% less steady-state error. The control system had a good dynamic and steady-state performance. [ABSTRACT FROM AUTHOR]

In order to improve the control performance of the micro gas turbine in the full range of operating conditions, this paper proposes a new control technique based on the hybridization of improved particle swarm optimization algorithm and cuckoo search algorithm (HIPSO_CS) to tune the fuzzy PID controller parameters. Firstly, the traditional particle swarm optimization algorithm is improved by linearly decreasing the number of particles and the value of the inertia weight. Secondly, the cuckoo algorithm’s local random walk strategy is introduced into the particle swarm optimization algorithm to enhance particles’ diversity. Through comparing with traditional optimization algorithms, the proposed HIPSO_CS algorithm is verified to have high convergence accuracy and fast iteration speed. To improve the dynamic response performance of the micro gas turbine, controllers with different structures are designed, and a comparative study of HIPSO_CS optimized Fuzzy PID/PID/PI is presented. The simulation results show that the micro gas turbine controlled by the fuzzy PID controller has a rapid response to fuel flow, minor speed overshoot, and shorter stabilization time during load increase or decrease. In addition, the designed control method can also achieve significant control effects under load disturbance, model parameter changes, and extreme operating condition.

At present, the local ventilator control mainly uses traditional PID controllers. The parameters of the controller are fixed and selected based on experience. It is difficult to achieve the optimal combination of membership functions and fuzzy rules, which makes it difficult for the controller to meet the adaptive control requirements of local ventilator. In order to solve this problem, a fuzzy PID controller for mine local ventilator optimized by improved genetic algorithm is designed. In the improved genetic algorithm, the Euclidean distance is introduced to increase the diversity of the population, and the adaptive crossover and mutation probability are introduced to improve the convergence of the algorithm. In the encoding process, the proportional fraction is used to indirectly optimize the membership functions, which enables the improved genetic algorithm to optimize both the membership functions and fuzzy rules. The optimized membership functions and fuzzy rules are imported into the fuzzy PID controller. The controller can adaptively adjust the air volume of the local ventilator through the inverter according to the different working status of the local ventilator so as to realize the dynamic adjustment of the local ventilator. The simulation results show that compared with the traditional fuzzy PID controller, the improved fuzzy PID controller can basically achieve no overshoot, the rise time is shortened by 56.25%, and the stabilization time is shortened by 47.06%, which can better meet the control requirements of mine local ventilator.

Abstract This study presents a conventional Ziegler-Nichols (ZN) Proportional Integral Derivative (PID) controller, having reviewed the mathematical modeling of the Micro Electro Mechanical Systems (MEMS) Tunable Capacitors (TCs), and also proposes a fuzzy PID controller which demonstrates a better tracking performance in the presence of measurement noise, in comparison with conventional ZN-based PID controllers. Referring to importance and impact of this research, the proposed controller takes advantage of fuzzy control properties such as robustness against noise. TCs are responsible for regulating the reference voltage when integrated into Alternating Current (AC) Voltage Reference Sources (VRS). Capacitance regulation for tunable capacitors in VRS is carried out by modulating the distance of a movable plate. A successful modulation depends on maintaining the stability around the pull-in point. This distance regulation can be achieved by the proposed controller which guarantees the tracking performance of the movable plate in moving towards the pull-in point, and remaining in this critical position. The simulation results of the tracking performance and capacitance tuning are very promising, subjected to measurement noise. Article Highlights This article deals with MEMS tunable capacitor dynamics and modeling, considering measurement noise. It designs and applies fuzzy PID control system for regulating MEMS voltage reference output. This paper contributes to robustness increase in pull-in performance of the tunable capacitor.

Abstract The objective of this work is to design a fuzzy rule-based set point weighting mechanism for fuzzy PID (FPID) controller so that an overall improved closed-loop performance may be achieved for linear as well as nonlinear process models. Till date, tuning criteria for FPID controllers are not well defined. Trial-and-error approach is primarily adopted and it is quite time-consuming and does not always ensure improved overall closed-loop behaviour. Hence, to ascertain satisfactory closed-loop performance with an initially tuned fuzzy controller, a fuzzy rule-based set point weighting mechanism is reported here. The proposed scheme is capable of providing performance enhancement with instantaneous weighting factor calculated online for each instant based on the latest process operating conditions. The proposed methodology is capable of ascertaining acceptable performances during set point tracking as well as load recovery phases. Efficacy of the proposed scheme is verified for linear as well as nonlinear process models through simulation study along with real-time verification on servo position control in comparison with the others’ reported performance augmentation schemes as well as fuzzy sliding mode control.

Abstract In this paper, we present a mathematical model and control strategy to regulate the yaw of a horizontal axis wind turbine. In order to obtain the dynamic equations of the yaw motion, the mathematical model is developed using the Euler–Lagrange formulation and considers the wind turbine as a manipulator robot with three degrees of freedom (DOF). A fuzzy proportional‐integral‐derivative (PID) controller is proposed to manipulate the yaw angle for power regulation. In addition, we have developed a low cost experimental platform to implement the designed controller to obtain experimental results for the dynamic behavior of the closed‐loop system.

In this paper, a Fuzzy proportional–integral–derivative (Fuzzy PID) controller design is presented to improve the automatic voltage regulator (AVR) transient characteristics and increase the robustness of the AVR. Fuzzy PID controller parameters are determined by a genetic algorithm (GA)-based optimization method using a novel multi-objective function. The multi-objective function, which is important for tuning the controller parameters, obtains the optimal solution using the Integrated Time multiplied Absolute Error (ITAE) criterion and the peak value of the output response. The proposed method is tested on two AVR models with different parameters and compared with studies in the literature. It is observed that the proposed method improves the AVR transient response properties and is also robust to parameter changes. [ABSTRACT FROM AUTHOR]

This paper proposes a modified predictive direct torque control (MPDTC) application-specific integrated circuit (ASIC) with multistage hysteresis and fuzzy controller to address the ripple problem of hysteresis controllers and to have a low power consumption chip. The proposed MPDTC ASIC calculates the stator's magnetic flux and torque by detecting three-phase currents, three-phase voltages, and the rotor speed. Moreover, it eliminates large ripples in the torque and flux by passing through the modified discrete multiple-voltage vector (MDMVV), and four voltage vectors were obtained on the basis of the calculated flux and torque in a cycle. In addition, the speed error was converted into a torque command by using the fuzzy PID controller, and rounding-off calculation was employed to decrease the calculation error of the composite flux. The proposed MDMVV switching table provides 294 combined voltage vectors to the following inverter. The proposed MPDTC scheme generates four voltage vectors in a cycle that can quickly achieve DTC function. The Verilog hardware description language (HDL) was used to implement the hardware architecture, and an ASIC was fabricated with a TSMC 0.18 μm 1P6M CMOS process by using a cell-based design method. Measurement results revealed that the proposed MPDTC ASIC performed with operating frequency, sampling rate, and dead time of 10 MHz, 100 kS/s, and 100 ns, respectively, at a supply voltage of 1.8 V. The power consumption and chip area of the circuit were 2.457 mW and 1.193 mm × 1.190 mm, respectively. The proposed MPDTC ASIC occupied a smaller chip area and exhibited a lower power consumption than the conventional DTC system did in the adopted FPGA development board. The robustness and convenience of the proposed MPDTC ASIC are especially advantageous. [ABSTRACT FROM AUTHOR]

In recent years, the heuristic algorithms used to improve the PID controller have attracted increasing attention. Additionally, there have been many significative explorations into the field of the fuzzy PID controller. Therefore, this article presents a modified biogeography‐based optimization (BBO) algorithm to optimize the fuzzy PID controller. We utilize cloud theory to adapt migration operator and mutation operator of the BBO algorithm to enhance its exploration ability and exploitation ability for better convergence speed and accuracy. The final algorithm is called cloud theory‐biogeography‐based optimization algorithm (CTBBO). The great performance of the CTBBO algorithm is illustrated in the benchmark functions testing compared with other heuristic algorithms. Lastly, the CTBBO algorithm was used in an engineering case—optimizing the fuzzy PID controller in a 180°C‐die heater to demonstrate its practical application value. [ABSTRACT FROM AUTHOR]

To further improve the control system of the manipulator, the manipulator of the jumbo is taken as the research object. According to the working process of the jumbo and the structure of the manipulator, the electronic drive control system of the hydraulic joints of the jumbo manipulator is designed. Firstly, the overall scheme of the electronic drive control system of the hydraulic joints of the jumbo manipulator is designed, which includes three parts: information collection, information processing, and actuators. Secondly, through the selection of fuzzy subsets and membership functions, the determination of variable domains, quantization factors and proportional factors, and the establishment of fuzzy control rules, fuzzy Proportion Integration Differentiation (PID) controller is designed, and the overall design and software and hardware system of the electronic drive control system of the hydraulic joints of the jumbo manipulator are studied. Finally, the fuzzy controller is designed and simulated through Matrix Laboratory (MATLAB) software. The results show that the rising time of the fuzzy PID controller is about 0.3 seconds, while the rising time of the conventional PID control is about 1 second. Compared with the conventional PID control, the overshoot of the fuzzy PID controller is smaller and can be fast and stable. When disturbed by the 10,000N external load, the fuzzy PID controller responds faster. The maximum peak range is about 1.13, the adjustment time is about 3 seconds. However, the maximum peak range of the conventional PID controller is about 1. 15. The adjustment time is about 3.8 seconds, indicating that compared with the conventional PID controller, the fuzzy PID controller has good dynamic performance, stability, fast response speed, strong tamper-resistance ability and better control effect. The results are conducive to improving the working efficiency of the jumbo manipulator, and providing certain research experience for the research of automatic jumbo. [ABSTRACT FROM AUTHOR]

The regulation process of gas distribution systems for atmosphere packaging has the characteristics of being nonlinear time varying and having hysteric delay. When the conventional proportional-integral-derivative (PID) control algorithm is applied to this kind of system, it is difficult to set the parameters as the process is time consuming and has poor reliability. For these reasons, this paper designs a gas distribution system for fresh food atmosphere packaging based on a fuzzy PID controller. The step response method is used to construct the system’s mathematical model under the given conditions and to optimize the gas distribution control flow. A simulation experimental platform to compare between the fuzzy PID controller and a conventional PID controller is designed, and the effectiveness of the fuzzy PID control strategy is verified, which proves that it can improve the performance of the monitoring system. The system can realize the remote monitoring of the gas distribution processes through the use of a mobile phone communication network. The data transmission is reliable, the operation is convenient, and, at the same time, the overall efficiency is improved. The results of the system simulation and the gas distribution for atmosphere packaging show that the fuzzy PID algorithm has a faster gas distribution speed and good environmental adaptability as the controller of the gas distribution system. The results show that the stability time of the fuzzy PID controller is about 38 s, while the stability time of the conventional PID controller is about 85 s. The concentration error of fresh gases is ±0.25% floating, the accuracy is increased by 12 times, and the gas distribution speed is increased by about 50% when the system is stable.

Speed profile design and tracking control are two important issues to ensure the performance of the automatic train operation system. Aiming to realize the maximum optimization probability of train running, this paper constructs an integrated optimal method through speed profile design and fuzzy PID controller design. In the recommended speed profile design, three typical running strategies are included in the proposed comprehensive scheme to extend solution space, and the constructed train performance simulation is combined with NSGA-II algorithm to solve such multiobjective problem. As for recommended speed profile tracking, in order to improve the tracking performance of the train, which is a nonlinear system, a fuzzy PID controller is designed to adaptively adjust PID gains. Note that multiple indicators of punctually, energy consumption, stopping accuracy and comfort are all considered in the integrated design. Taking Beijing Subway Line 8 as the numerical example, the results show that the design of speed profile and fuzzy PID controller are both effective, and energy consumption saves about 10.40% with other better indicators compared with the original data.

This paper develops an adaptive control method for controlling frequency and voltage of an islanded mini/micro grid (M/µG) using reinforcement learning method. Reinforcement learning (RL) is one of the branches of the machine learning, which is the main solution method of Markov decision process (MDPs). Among the several solution methods of RL, the Q-learning method is used for solving RL in this paper because it is a model-free strategy and has a simple structure. The proposed control mechanism is consisting of two main parts. The first part is a classical PID controller which is fixed tuned using Salp swarm algorithm (SSA). The second part is a Q-learning based control strategy which is consistent and updates it's characteristics according to the changes in the system continuously. Eventually, the dynamic performance of the proposed control method is evaluated in a real M/µG compared to fuzzy PID and classical PID controllers. The considered M/µG is a part of Denmark distribution system which is consist of three combined heat and power (CHP) and three WTGs. Simulation results indicate that the proposed control strategy has an excellent dynamic response compared to both intelligent and traditional controllers for damping the voltage and frequency oscillations.

A new method for optimizing the fuzzy PID controller, based on an artificial electric field algorithm (AEFA), is proposed in this paper, aiming at improving the stability indicator of the Brushless DC (BLDC) motor for the small satellite attitude control flywheel. The BLDC motor is the basic part of the small satellite attitude control flywheel. In order to accurately control the attitude of the small satellite, a good motor control system is very important. Firstly, the mathematical model of the BLDC motor is established and the BLDC motor speed control system using traditional PID control is designed. Secondly, considering that the small satellite speed control system is a nonlinear system, a fuzzy PID control is designed to solve the shortcomings of the fixed parameters of the traditional PID control. Finally, we find that the control accuracy of the fuzzy PID control will change with the range of the input. Therefore, we introduce the AEFA to optimize fuzzy PID to achieve high-precision attitude control of small satellites. By simulating the BLDC motor system, the proposed fuzzy PID controller based on AEFA is compared with the traditional PID controller and the fuzzy PID controller. Results from different controllers show that the proposed control method could effectively reduce steady state error. In addition, the proposed fuzzy PID–AEFA controller has the better anti-jamming capability.

It is necessary to upgrade and transform the sorting equipment in the industrial production line. In order to improve production efficiency and reduce labor intensity, a high-speed lightweight parallel mechanism control system for the high-speed sorting and packaging of light items was studied. A fuzzy PID controller based on genetic algorithm (GA) optimization is proposed according to the nonlinear and strong coupling characteristics of the parallel mechanism (PM) control system. The inverse kinematic analysis was conducted to map the workspace trajectory tracking problem to the joint space. It was transformed into the trajectory planning and solving problems in the joint space. The motion trajectory was obtained utilizing quintic polynomial interpolation. Finally, the servo control system model was established, and the PID control parameters were optimized and self-tuned by the GA. They were applied to the fuzzy PID controller for simulation experiments. The simulation results showed that the GA-optimized fuzzy PID control system compared with the fuzzy PID control system had a 23.39% shorter rise time, 22.32% less regulation time, and 7.18% less steady-state error. The control system had a good dynamic and steady-state performance.

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Electrical capacitance tomography (ECT) utilizes measured mutual capacitances across a region of interest to visualize distributions inside. As typical two-phase flows can be roughly treated as binary-valued material distributions, in this article, a fuzzy PID-controlled iterative algorithm is proposed for image reconstruction in cases of binary distributions. A closed-loop control system includes a fuzzy PID controller, Calderon’s method, and fast calculation of the Dirichlet-to-Neumann map. Capacitances measured in an electrode array of the ECT sensor are compared with the feedback, and the difference is input to the controller. Fuzzy rules are used to automatically adjust the three parameters of the controller, i.e., $K_{P}$ , $K_{I}$ , and $K_{D}$. The controller passes the difference to Calderon’s method for reconstructing permittivity distribution. Reconstructed distribution is used to calculate a boundary map for feedback, by fast calculation of the Dirichlet-to-Neumann map, and serves as an updated reference for measured capacitances. A smooth segmentation method is also introduced to deal with the binary distribution and release the fluctuation in the tuning of the PID controller. Numerical simulations were done to verify the performance of the proposed iterative Calderon’s method for binary distributions. Experiments on real phantoms were also carried out using an ECT system to evaluate the proposed method. Several distributions were set up with solid particles and air. The results show that the proposed method can produce images with clear edges and shapes of binary distributions. [ABSTRACT FROM AUTHOR]