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Recently, synchronization of complex networks has been studied deeply; however, most existing works take the standard proportional control strategy approach to pinning controllers, which produces a steady-state error and has a significant time of fluctuation. In order to solve this problem, we propose a new method using the Proportional-Integral-Differential (PID) control to design a pinning control method for synchronization of complex networks. Based on Lyapunov theory, the stability of the network is proved, and sufficient synchronization conditions are obtained. Finally, examples are given to evaluate the effectiveness of the proposed PID pinning method and compare it to the traditional proportional pinning controller and adaptive pinning controller.

In the present paper, the problem of digital implementation of retuning fractional-order (FO) controllers for an unstable plant — a real-life laboratory model of a Magnetic Levitation System (MLS) — is investigated. Retuning control is applied to an existing closed-loop PID control system through the use of a hardware FO controller prototype. An implementation method for the retuning controller is proposed. Three types of hardware-in-the-loop (HIL) experiments are considered. First, the plant is simulated using a suitable dynamic model. Second, the real-life laboratory model of the MLS is controlled. In the third type of experiment, a MATLAB implementation of the retuning controller is evaluated as a benchmark. Three controllers obtained in an earlier work are tested with this setup. Experimental results are compared and discussed.

This paper presents the design and modeling of power electronic converters such as buck–boost and Ćuk operated under continuous conduction mode (CCM). The open-loop behavior of buck–boost and Ćuk converters needs modeling and simulation using modeled equations. The closed-loop control of these converters has a propositional–integral–derivative (PID) controller. PID controller parameters are obtained from Ziegler–Nichols step response method. These converters can be analyzed using the state equation. The MATLAB/SIMULINK tool is used for simulation of those state equations. Ćuk and buck–boost converters are designed and analyzed. The mathematical model of power Converter for simulation has been carried out using SIMULINK with/without any Sim Power System Elements. The open- and closed-loop results are compared.

Power quality (PQ) issue is referred to as any problem that exposes in the voltage and current or in frequency value that causes a malfunction of protection devices or maloperation of the system. The improvement of the PQ is important at the load side when the production processes get more complicated and require a bigger liability level. An Elephant Herding Optimization (EHO) algorithm is presented for improving the PQ and reducing the harmonic distortion using the Static Switched Filter Compensation (SSFC) in Photovoltaic (PV) interconnected wind energy conversion system (WECS). The novelty of the proposed system is enhancing the performance of the grid-connected hybrid energy system such as stabilizing the voltage, reducing the power loss and mitigating the harmonic distortion using the SSFC. Here, the proposed controller is used to optimize the control pulses for SSFC. The SSFC and voltage-source converters with smart dynamic controllers are emerging as stabilization and power filtering equipment to improve the PQ. The proposed method has implemented in MATLAB/Simulink platform and their performances are evaluated and contrasted with the existing technologies such as Bat algorithm (BA) and Firefly algorithm (FA) techniques.

A new voltage mode Proportional-Integral-Derivative (PID) controller employing Current Differencing Buffered Amplifier (CDBA) is presented. The proposed PID controller employs a canonical number of capacitors without requiring any passive components matching conditions. The element values are expressed in terms of PID parameters. Workability of the proposed controller is demonstrated through SPICE simulations for which CDBA is realized using Current Feedback Amplifier (CFA). The simulation results are found in close agreement with the theoretical results.

Eight new immittance function simulators (IFSs) with only grounded passive elements are proposed in this paper. All of the IFSs consist of only two DVCC+s and a minimum number of passive components without needing any passive element matching constraints. Each of the proposed IFSs can provide one of $\pm$L with series $\pm$R and $\pm$L with parallel $\pm$R. As an application example, a second-order mixed-mode (MM) multifunction filter is developed from the proposed +L with series +R and +L with parallel +R. Furthermore, a proportional integral derivative (PID) controller is derived from the proposed +L with series +R. Many simulation results through the SPICE program and several experimental ones are included to verify the theory.

In this paper, a current-mode (CM) proportional integral derivative (PID) controller based on second-generation voltage conveyor (VCII) is presented. The proposed circuit consists of two-plus type VCIIs, two resistors, and two capacitors. There is no need of critical matching condition. Considering the parasitic impedance, the operating frequency ranges of the proposed PID network are examined. The magnitude and phase responses, Monte Carlo, temperature and input-output noise analyses have been simulated. The simulation results are obtained with the LTspice program using AD844 SPICE macro-model under $\pm$9V DC supply voltages. The total power consumption of the proposed CM PID controller is 235mW.

This paper aims to bring a voltage differencing inverting buffered amplifier (VDIBA)-based current-mode (CM) proportional integral derivative (PID) controller circuit. This CM PID controller is designed with a single VDIBA, three resistors, and two grounded capacitors. The proposed circuit is easy to design, and the control parameters can be tuned without changing the design configuration. A sensitivity analysis of the control parameters to electronic components has been conducted. The Simulation Program with Integrated Circuit Emphasis (SPICE) simulation has been performed using Taiwan Semiconductor Manufacturing Company (TSMC) $0.18\mu$m complementary metal-oxide semiconductor (CMOS) technology parameters. An application circuit example is given to demonstrate the reliability of the proposed PID design. A comparison table of the PID controllers previously reported in the literature is also presented.

Z-source inverters are used in controller applications, which offer higher performance than the buck-boost converter. This work presents the Improved Embedded Z (IEZ)-source inverter used for the impedance network. The Z-source inverter includes the switching topologies, so the double inductor is added to the EZ source inverter circuit. IEZ source inverter manages the Total Harmonic Distortion (THD), and the PID controller controls the inverter voltage. The Social Ski Driver (SSD) Optimization algorithm is used to tune the parameters of the PID controller. The harmonic content of the Z-source inverter is reduced by the carrier based sinusoidal Pulse Width Modulation (SPWM) technique. The Improved Embedded Z-source Inverter (IE-ZSI) is utilized to apply a grid-connected system. The inverters receive the power from the DC voltage and supply the grid-connected load with controllable voltage. The proposed work is implemented in the platform MATLAB/SIMULINK to evaluate the performance of the voltage across the capacitor, the voltage across the inductor, grid current and grid voltage. Inverter DC link voltage performance was compared to conventional Z-source inverters and embedded Z-source inverters. The proposed work achieved 1.52% THD compared with existing techniques.

In this paper, the output voltage of a buck power converter is controlled by means of a quasi-sliding scheme. The Fixed Point Inducting Control (FPIC) technique is used for the control design, based on the Zero Average Dynamics (ZAD) strategy, including load estimation by means of the Least Mean Squares (LMS) method. The control scheme is tested in a Rapid Control Prototyping (RCP) system based on Digital Signal Processing (DSP) for dSPACE platform. The closed loop system shows adequate performance. The experimental and simulation results match. The main contribution of this paper is to introduce the load estimator by means of LMS, to make ZAD and FPIC control feasible in load variation conditions. In addition, comparison results for controlled buck converter with SMC, PID and ZAD–FPIC control techniques are shown.

This paper presents a new Synchronization Adaptive Fuzzy Gain Scheduling PID controller (SAFGS-PIDc) for a class of Multiple-input multiple-output (MIMO) nonlinear systems with uncertainties. To achieve better adaptation properties, weighting factor is adapted to sum together the adaptive fuzzy control scheme and Fuzzy Gain Scheduler PID control (FGS-PIDc) method, such that both controllers can be incorporated at the same time. The FGS adjusts online the parameters of the conventional PID controller, and an Indirect Adaptive Fuzzy control (IAFc) scheme that uses feedback error function as inputs is constructed. In addition, each subsystem of MIMO system is able to adaptively compensate for uncertainties and external disturbance. Also, a robust control term is designed that aims to provide added robustness in the presence of uncertainties. The proposed scheme can overcome the controller singularity problem. While the proposed controller scheme requires the uncertainties to be bounded, it does not require this bound to be known. Thus, this control law can be used for the systems that the system’s models are quite unknown. The proposed method guarantees the stability of the closed-loop system based on Lyapunov theory. Finally, simulation studies demonstrate the usefulness and effectiveness of the proposed technique for controlling nonlinear.

This paper presents a sensitivity analysis on the performance of optimal proportional–integral–derivative (PID) controller for use in nonlinear smart base-isolated structures with uncertainties. A set of nine performance indices is defined to evaluate the performance of the controller in the presence of uncertainties in the superstructure, lead rubber bearing (LRB) seismic isolation system, and applied loads. The time delay effect on the stability and performance of the PID controller is also examined. The results show that the PID controller is robust against the uncertainties up to $\pm 15$% in the damping and stiffness coefficients of the superstructure, the yield force of the LRB and the artificial earthquake. In the case with $\pm 15$% of uncertainty, the input energy entering into the structure is increased with respect to the nominal model. However, the changes of the performance indices related to the damage energies are negligible. An uncertainty of $-$20% in the stiffness coefficient and stiffness ratio of the LRB gives an increase of 15% in the maximum and root mean square (RMS) of the structural responses. In the case with $-$20% of uncertainty, the damage and damping energies do not change in comparison with the case of the nominal model, but a significant decay in the performance index related to the input energy response is obtained in this case. Not all performance indices are sensitive to time delay. For large time delays, the performance index for the seismic input energy increases significantly, while the maximum damage and damping energies increase up to 5% and 10%, respectively.

Due to the expanded industrialization, the necessity of variable speed machines/drives keeps on expanding. The vast majority of computerized Brushless Direct Current (BLDC) motor frame-works are utilized because of their speedier reaction and high stablity. In this paper, an innovative technique, i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS) with Fractional-Order PID (FOPID) controllers for controlling a portion of the parameters, for example, speed, and torque of the BLDC motor are exhibited. With a specific end goal being the performance of the proposed controller under outrageous working conditions, for example, varying load and set speed conditions, simulation results are taken for deliberation. An Opposition-based Elephant Herding Optimization (OEHO) optimization algorithm is utilized to improve the tuning parameters of FOPID controller. At that point, the ANFIS is gladly proposed to adequately control the speed and torque of the motor. The simulation result exhibited that the composed FOPID controller understands a decent dynamic behavior of the BLDC, an immaculate speed tracking with less ascent and gives better execution. The performance investigation of the proposed strategy lessened the error signal contrasted with the existing strategies, for example, FOPID-based Elephant Herding Optimization (EHO), Proportional–Integral–Derivative BAT (PID-BAT), and PID-ANFIS.

The main objective of this research is to propose a multidisciplinary approach for the development and design of Computer Numerical Control (CNC) machine tools using numerical optimization methods combined Multi-Body Dynamic (MBD) analysis and to control design co-simulation. Metamodels based Sequential Approximate Optimization (SAO) for the co-simulation optimization problems are developed. The metamodels are constructed as approximate models for exact dynamic analysis responses by using simultaneous Kriging metamodeling method. SAO problems for single objective and multi-objective optimization designs are carried out based on the augmented Lagrange multiplier (ALM) method. An application of the proposed method on optimizing Proportional-Integral-Derivative (P-I-D) coefficients of PID controllers of a CNC machine tool model is performed to demonstrate the usefulness of integrating different research methods in numerical simulation. Therefore, this work overcomes a difficult task in tuning the PID controller which requires extensive experience and understandings of research and development (R&D) engineers. Moreover, the optimal PID controllers obtained by the multidisciplinary approach can help to increase the contouring accuracy of the CNC machine tools.

The accurate analysis and controller design for high-order systems are important issues. In this paper, a design of controller is suggested to Single Machine Infinite Bus (SMIB) System in which its order is reduced by the proposed self-adaptive Firefly Algorithm (FA). First, the dynamic adaption to the dominant parameters of the standard FA is proposed for overcoming its disadvantages and improving its ability in searching the global optimum solution with application to reduce the model order of SMIB. The parameters which are proposed to self-adaption are both of the light absorption based on the mean distance of fireflies’ positions and the step setting based on the fitness information to the status of preceding firefly and the current fireflies. Second, proportional–integral–derivative (PID) controller is suggested to be designed by pole zero cancellation method via the step response characteristics of the reduced-order SMIB to be applied to its high order. The efficiency of the proposed methods is tested on high-order SMIB to get its corresponding low order and to control it. The experimental results prove the efficacy of self-adaptive FA compared to the standard FA and the methods in the literature in terms of step response characteristics using error indices. The proposed methods assure the stability of the reduced order and the ability of the designed controller to handle both high and low-order model.

Cardiovascular diseases have been recently shown to have a pivotal role in human death and endangers lives of many people around the world. One of the most common cardiovascular diseases is poor performance of left ventricle. In this case, the ventricle cannot pump the blood into the aorta and circulatory system with a suitable power which is required for normal circulatory system. AVICENA is a new cardiac assist device which is implanted into the aorta to help the ventricle to pump the blood into circulatory system with more power and to make a better perfusion of the coronary arteries as well.

To reach a desire value of rotational speed of the pump, a control circuit is designed for counterpulsation of AVICENA based on the outcomes from previous studies. This control circuit uses a PID controller.

The present study aims to simulate the blood flow through the balloon part of AVICENA in a heart cycle with focusing on the calculation of its pump rotational speed by controlling the electrical current of the pump. Results revealed that the desired rotational speed of the pump can be achieved according to the previous aorta pressure cycle by electrical current control which is higher during balloon inflation in comparison with balloon deflation.

These findings may have implications not only for understanding the performance of AVICENA but also to help cardiac mechanics experts to improve the shortcoming of this newborn device.

This paper concerns with the adaptive based autolanding controller design by the means of MRAS (Model Reference Adaptive Systems) method. In that, Adaptive controller is used to design a controller which can land the aircraft smoothly on runway in severe conditions like gusts. A PID controller has also been designed to compare its performance with that of Adaptive controller. Two kinds of wind patterns, Strong and Very Strong in comparison to JFK Airport Downburst, have been induced to investigate the performance of the proposed controllers. Simulation results show that both controllers satisfy necessary conditions in presence of the strong wind but only Adaptive controller meets the necessary performance conditions, in presence of Very Strong wind. Therefore, this controller can expand the safety flight envelope of the aircraft.

This chapter presents a new practical control system that can apply conventional PID controllers to nonlinear field by using fuzzy reasoning. The proposed system is a hierarchical one consisting of two components: (a) a Fuzzy-PID controller, and (b) a supervisor for setting the control target of this controller. The fuzzy controller in the Fuzzy-PID controller compensates the output error of the conventional PID controller. The supervisor calculates the control target by fuzzy reasoning. This hierarchical control system is applied to the temperature control in a petroleum plant. The parameters in fuzzy controller are tuned on-line in the actual plant and the system can control the temperature effectively in the transient state, such as feed property changing or operation mode changing, as well as in the steady state

This paper proposes an integral predictive control strategy, a hierarchical scheme consisting of the Runge-kutta controller and PID controller to stabilize the rotational movements of the AR Drone. The Runge-kutta algorithm can predict states in advance and hence we utilize this robust algorithm to build the Runge-kutta controller. The control strategy combining both the Runge-kutta controller and PID controller can be considered as a Robust PID controller, which improves the drone’s robust performance especially when flying at high speed. The controller reduces the positioning errorin conditions of continuous disturbances in the operation environment. The experimental results demonstrate that the Robust PID controller possess good accuracy and robustness in drone control for both indoor and outdoor environments.

In the temperature control, the object has a non-linear, strongly coupled system with large time delay and other characteristics, traditional PID can't achieve good results because the adjustment of the PID controller's parameter cannot be achieved. This paper presents an algorithm for optimization of PID controller's parameters based on fruit flies optimization algorithm, which can achieve optimal control by tuning PID controller's parameters. On this basis, we design a different regional temperature optimal control system, which makes pulse combustion furnace in different areas to achieve uniform temperature. The simulation results show that the organic combination of fruit flies optimization algorithm and the different regional temperature optimal control system improves the accuracy of temperature in furnace.