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The time delay (TD) in the levitation control system significantly affects the dynamic performance of the closed-loop system in electromagnetic suspension (EMS) maglev vehicles. Excessive TD can cause levitation instability, making it essential to explore effective mitigation methods. To address this issue, a Smith Predictor (SP) is integrated into the traditional PID levitation control system. The combination of theoretical analysis and numerical simulation is employed to assess the stability of the time-delay levitation control system after the integration of the Smith Predictor. Theoretical analysis reveals that when TD exceeds a critical threshold, the levitation system becomes unstable. The addition of SP alters the root trajectory of the system characteristic equation from positive to negative, and recovers the levitation system to stable status. Assuming complete knowledge of the dynamic system, the TD compensation value in the SP becomes a key parameter that determines its performance. A minimum effective value (MEV) for TD compensation is identified, correlating with the system's stability region. Under the influence of TD, more complex systems and higher running speeds of the maglev vehicle lead to a narrower stable region and a larger MEV for TD compensation. Given the simulation parameters in this paper, with a system TD of 15 ms and a maximum vehicle speed of 160 km/h, the MEV for TD compensation in the SP should be set at 12 ms. • Smith Predictor is introduced into maglev control system to mitigate the adverse impact from TD. • Analyzed application value of Smith Predictor in levitation control system with time delay. • Provided suggestions for the broader implementation of Smith Predictor in practical engineering. [ABSTRACT FROM AUTHOR]

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller's parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively. • An EXP-PID controller is introduced for LFR of power systems. • The new controller enjoys advantages of nonlinear feature and adaptive gains. • Corrected variant of snake optimizer is presented for controller optimization. • Several power systems are studied for performance validation. • The dominance of our proposal is vividly affirmed over the state-of-the-art. [ABSTRACT FROM AUTHOR]

This study proposes a direct synthesis-based two-degree-of-freedom (2-DOF) controller for various types of integrating processes with time delays. This 2-DOF controller includes a proportional-integral-derivative (PID) controller to enhance load disturbance rejection performance and a set-point filter to improve servo response performance. The main PID controller parameters are expressed as process model parameters and a single adjustment variable, while the set-point filter is composed of PID controller parameters with weighted factors. The adjustment variable is tuned to achieve an optimal balance between response performance and robustness, based on the maximum magnitude of the sensitivity function (Ms). Controller parameters for various Ms values and guidelines for setting these parameters are provided in a consistent formulaic form using a curve-fitting method. These parameter-setting formulas facilitate the accurate implementation of PID controllers with specified Ms values and allow the controller design to be extended to processes with larger dimensionless time delays for a given Ms value. Although a 2-DOF controller was proposed, the adjustment variable for setting the parameters of the main PID controller and the set-point filter was solely the desired time constant. The proposed method was applied to various integrating processes with time delays, and its performance was compared with existing methods reported in the literature, based on performance indices such as settling time, overshoot, integral of absolute error, total variation in input usage, and global performance index. Simulations were conducted using six examples of various integrating processes with time delays to verify the effectiveness and applicability of the proposed controller. • All parameters of a PID controller are given the same form of formulas. • Formulas for PID controller parameter setting are derived through a curve fitting method • Controller parameters that almost match the specified level of robustness are obtained. • In 2-DOF PID controller including a set-point filter, there is only one adjustment variable. [ABSTRACT FROM AUTHOR]

Robotic arms have become increasingly popular and widely used in various industrial applications. However, conventional control methods are not capable of adequately controlling a robotic arm in tasks that require contact with a surface. To address this issue, this study proposes a Cartesian impedance control method to control a 3-DOF robotic arm in real-time during contact with a surface. The proposed controller consists of two control loops: an inner loop and an outer loop. The inner loop utilizes a motion control method in the joint space, with the parameters of the controller being calculated through system identification. The outer loop implements Cartesian impedance control in the Cartesian space using a mass-spring-damper model. The coefficients of the Cartesian impedance control were determined based on the over-damped response with real-time applications. By selecting the inner loop in the joint space and the outer loop in the Cartesian space, the control of the robotic arm is guaranteed. The proposed method was tested in real-time, and its performance was compared with the PID with gravity compensation control in the Cartesian space. The results indicated that the proposed method was able to successfully follow reference trajectories and reduce the contact force. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive probabilistic recurrent Takagi-Sugeno-Kang fuzzy neural PID controller for handling the problems of uncertainties in nonlinear systems. The proposed controller combines probabilistic processing with a Takagi-Sugeno-Kang fuzzy neural system to proficiently address stochastic uncertainties in controlled systems. The stability of the controlled system is ensured through the utilization of Lyapunov function to adjust the controller parameters. By tuning the probability parameters of the controller design, an additional level of control is achieved, leading to enhance the controller performance. Furthermore, it can operate without relying on the system's mathematical model. The proposed control approach is employed in nonlinear dynamical plants and compared to other existing controllers to validate its applicability in engineering domains. Simulation and experimental investigations demonstrate that the proposed controller surpasses alternative controllers in effectively managing external disturbances, random noise, and a broad spectrum of system uncertainties. • We propose an adaptive probabilistic recurrent TSK fuzzy neural PID controller. • The Lyapunov stability criterion is used to update the proposed controller parameters. • The proposed controller is applied practically for controlling a servo motor. [ABSTRACT FROM AUTHOR]

This study proposes a one-shot data-driven tuning method for a fractional-order proportional-integral-derivative (FOPID) controller. The proposed method tunes the FOPID controller in the model-reference control formulation. A loss function is defined to evaluate the match between a given reference model and the closed-loop response while explicitly considering the closed-loop stability. A loss function value is based on the fictitious reference signal computed using the input/output data. Model matching is achieved via loss function minimization. The proposed method is simple and practical: it needs only one-shot input/output data of a plant (no plant model required), considers the bounded-input bounded-output stability of the closed-loop system from a bounded reference input to a bounded output, and automatically determines the appropriate parameter value via optimization. Numerical simulations show that the proposed approach facilitates good control performance, and destabilization can be avoided even if perfect model matching is unachievable. • Practical data-driven design method for FOPID controller is developed. • The proposed method requires only one-shot input/output experimental data. • Closed-loop stability is explicitly considered in the proposed scheme. • Model matching is achieved via numerical optimization. • Numerical simulations show the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents an altitude and attitude control system for a newly designed rocket-type unmanned aerial vehicle (UAV) propelled by a gimbal-based coaxial rotor system (GCRS) enabling thrust vector control (TVC). The GCRS is the only means of actuation available to control the UAV's orientation, and the flight dynamics identify the primary control difficulty as the highly nonlinear and tightly coupled control distribution problem. To address this, the study presents detailed derivations of attitude flight dynamics and a control strategy to track the desired attitude trajectory. First, a Proportional-Integral-Derivative (PID) control algorithm is developed based on the formulation of linear matrix inequality (LMI) to ensure robust stability and performance. Second, an optimization algorithm using the Levenberg–Marquardt (LM) method is introduced to solve the nonlinear inverse mapping problem between the control law and the actual actuator outputs, addressing the nonlinear coupled control input distribution problem of the GCRS. In summary, the main contribution is the proposal of a new TVC UAV system based on GCRS. The PID control algorithm and LM algorithm were designed to solve the distribution problem of the actuation model and confirm altitude and attitude tracking missions. Finally, to validate the flight properties of the rocket-type UAV and the performance of the proposed control algorithm, several numerical simulations were conducted. The results indicate that the tightly coupled control input nonlinear inverse problem was successfully solved, and the proposed control algorithm achieved effective attitude stabilization even in the presence of disturbances. • A newly designed thrust vector control based rocket-type UAV prototype is presented. • The governing equations of the under-actuated and unstable dynamics system are derived. • A robust PID controller based on the formulation of LMI guaranteeing robust performance in the sense of the Lyapunov is designed. • A configuration of the GCRS is designed to realize the thrust vector control. • A LM-based optimal algorithm is presented to solve the nonlinear force inverse mapping problem induced by the GCRS. [ABSTRACT FROM AUTHOR]

A hybrid compensation scheme for piezoelectric ceramic actuators (PEAs) is proposed. In the hybrid compensation scheme, the input rate-dependent hysteresis characteristics of the PEAs are compensated. The feedforward controller is a novel input rate-dependent neural network hysteresis inverse model, while the feedback controller is a proportion integration differentiation (PID) controller. In the proposed inverse model, an input rate-dependent auxiliary inverse operator (RAIO) and output of the hysteresis construct the expanded input space (EIS) of the inverse model which transforms the hysteresis inverse with multi-valued mapping into single-valued mapping, and the wiping-out, rate-dependent and continuous properties of the RAIO are analyzed in theories. Based on the EIS method, a hysteresis neural network inverse model, namely the dynamic back propagation neural network (DBPNN) model, is established. Moreover, a hybrid compensation scheme for the PEAs is designed to compensate for the hysteresis. Finally, the proposed method, the conventional PID controller and the hybrid controller with the modified input rate-dependent Prandtl-Ishlinskii (MRPI) model are all applied in the experimental platform. Experimental results show that the proposed method has obvious superiorities in the performance of the system. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) technology is developing and requires effective and adaptive controllers to enhance performance and be the best sustainable solution to the population concerns caused by oil from other vehicles. This paper proposes a nonlinear Proportional Derivative (PD) controller to control motor speed in EV. The controller's optimal gains are tuned using Gorilla Troops Optimization (GTO). Two classical controllers (PID, PD) are compared with nonlinear PD controller, all tuned controller variables are determined using the GTO algorithm, and the Integral Time Absolute Error (ITAE) fitness function is adopted to maintain the system performance. To simulate the system response, MATLAB (Platforms for m-file and Simulink) is used. The achieved results are compared with the classical controllers (PD &PID) to demonstrate the efficacy of the suggested nonlinear PD controller. The benefits of using the nonlinear hyperbolic function effect on system behavior can be seen in its quick settling time, which is 9.6% faster than the traditional PID controller and 47% faster than the traditional PD controller. It also became apparent in its stable response, which was free of noise and overshoot while the PID controller overshoots with a value of (8.8). While in rise time, the proposed controller with 0.0083s outperforms the PID controller 0.4905s, and the PD controller 0.1315s. According to the simulation results, the nonlinear PD controller settling time is faster and more smoothly (0.0187s) than the PID (0.18s) and PD (0.88s) controllers, which are also optimized with the same GTO tuning algorithm. The suggested controller and the intelligent tuning algorithm guided the system's response to produce the best outputs. The simulation results demonstrated a steady behavior and an effective response in tracking the intended speed value; transient analysis is utilized to explain the performance of the suggested controller. [ABSTRACT FROM AUTHOR]

In the realm of autonomous robotics, navigating differential drive mobile robots through unknown environments poses significant challenges due to their complex nonholonomic constraints. This issue is particularly acute in applications requiring precise trajectory tracking and effective obstacle avoidance without prior knowledge of the surroundings. Traditional navigation systems often struggle with these demands, leading to inefficiencies and potential safety risks. To address this problem, our studies propose an algorithm that integrates machine learning and control concepts, especially through the synergistic software of a Q-learning set of rules and a (PID) controller. This technique leverages the adaptability of Q-learning pathfinding and the precision of PID control for actual-time trajectory adjustment, aiming to beautify the robotics' navigation skills. Our comprehensive technique includes growing a country-area version that integrates Q-values with the dynamics of differential power robots, employing Bellman's equation for iterative coverage refinement. This version enables the robotics' capacity to dynamically adapt its navigation techniques in reaction to instant environmental feedback, thereby optimizing efficiency and protection in actual time. The effects of our full-size simulations exhibit a marked improvement in trajectory-tracking accuracy and impediment-avoidance competencies. These findings underscore the capability of combining machine learning algorithms with traditional methods to increase autonomous navigation technology in robotic systems. Our effects, derived from full-size simulations, suggest that the integration of Q-learning with PID controller markedly improves trajectory tracking accuracy, reduces tour times to targets, and complements the robotics' ability to navigate round barriers. This incorporated method demonstrates a tremendous advantage over conventional navigation systems, providing a sturdy way to the challenges of autonomous robot navigation in unpredictable environments. [ABSTRACT FROM AUTHOR]

For improving the energy efficacy and control performance, integration of swarm optimization with controller design could successfully reach this objective. In this study, a comparative analysis has been conducted between two decentralized control structures based on optimized Proportional-Integral-Derivative (PID) and PID-Proportional (PID-P) controllers for optimal controlling of heating system in multi-zone building. Based on the energy balance equation, the mathematical dynamics model of the heating system is established in the building. In order to enhance and optimize the performances of both controllers, their design parameters are tuned based on Whale Optimization Algorithm (WOA). Two objectives have been considered in the optimization process of heating system. The first objective is to minimize the error in temperature, between the desired and real temperatures, based on IAE (Integral of Absolute Error) index, while the second objective is the minimization of the heat energy consumption. The normalization method has been used to adjust between the two differently-scaled objectives. Simulation results based on MATLAB reveal that the PID-P controller achieved better performance in terms of providing comfort indoor temperature with energy savings as compared to the PID controller. [ABSTRACT FROM AUTHOR]

At present, the controllable parameters of micro turbojet engines in engineering applications are mainly speed-fuel flow (hereinafter referred to as flow) control, in which closed-loop proportional–integral–derivative (PID) control is mostly used to achieve a stable control of engine speed under slow engine conditions. For the optimal adjustment of PID parameters, this paper designs an improved evolutionary strategy for the self-tuning of control parameters in the engine speed and flow control system and formulates an improved PI controller based on a neural network. The simulation experimental results show that the method can realistically achieve stable and fast control of the engine under above slow conditions. [ABSTRACT FROM AUTHOR]

The relay feedback auto-tuning method, which was an early commercialized approach, has maintained its popularity due to its simplicity and robustness. However, the classical proportional integral derivative (PID) controller auto-tuning method often results in unacceptable overshoot, especially for integrating and higher-order processes. By integrating the TID controller with the relay feedback technique, we significantly enhance dynamic control performance without relying on prior knowledge of the system. However, the direct application of the classical auto-tuning method to the TID controller encounters challenges due to the additional fractional-order transfer function s − 1 n . Therefore, we have developed a fractional-order Ziegler–Nichols (FOZ-N) approach, specifically designed to adjust the parameters of the TID controller. The proposed FOZ-N method is fast, simple, and capable of achieving the desired performance. In contrast to the previous Ziegler–Nichols tuning method, the TID controller parameters K t and K i are determined to shift the critical point (− 1 / K u , j 0) to the FOZ-N point (− 0. 5 , − j 0. 7) on the Nyquist curve, ensuring system robustness and dynamic performance. The impact of the fractional order parameter s − 1 n and ratio r is explored through time-domain analysis, where these parameters are determined to ensure optimal dynamic performance. Additionally, we provide a detailed tuning procedure along with a helpful example. To demonstrate the advantages of the proposed auto-tuning TID controller over the Ziegler–Nichols PID controller, Optimal PID controller, simple internal model control (SIMC) PID controller, and Ziegler–Nichols FOPID controller, we present a simulation illustration involving multiple different systems. To validate the practical outcomes of this paper, we present experimental results on the temperature control of a Peltier cell. • A fast auto-tuning TID controller is introduced without needing accurate plant model. • A fractional-order Ziegler-Nichols approach is presented for TID controller. • Experiments show the proposed TID controller surpasses traditional controllers. [ABSTRACT FROM AUTHOR]

This paper explores the advantages of the Two Degree of Freedom Proportional-Integral-Derivative (2DOF_PID) controller in tracking the reference signal and rejecting the disturbance signal at the same time. Three types of metaheuristic optimization algorithms are employed for tuning the controller's parameters which are Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Dragonfly Algorithm (DA). These three algorithms have in common that they combine the exploratory concept (global search) and the exploitative concept (local search) in order to reach the optimal global solution. The effectiveness of these algorithms was taken advantage of to improve the performance of the control system that contains the controller. Second and third order plants were adopted for the purpose of testing, evaluating, and comparing the performance of the control system. This aim was accomplished by using each of the optimization algorithms for each plant. The simulation results showed the superiority of the DA in terms of obtaining the lowest value of the Integral Absolute Error (IAE) as the cost function. [ABSTRACT FROM AUTHOR]

The mathematical model for a full actuated T-shaped Tri-copter is derived in details and a suitable controller is proposed in this work. The system is governed by three tiltable motor– propeller sets to change the thrust direction to a desired angle. This will lead to the achievement of maneuverability and reduce power consumption. To control the model, a feedback linearization method is used to decouple and get a direct control on the copter degree of freedoms. Attitude and Altitude PID controller is designed to control the system and fulfil the desired trajectory. The PID parameters are tuned using Genetic algorithm (GA) optimization method. The system and the controller performance are proved using MATLAB Simulink platform through several flight scenarios. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms have significantly advanced robotics by enabling the creation of efficient and intelligent robotic systems. This study aims to evaluate the effectiveness of two optimization algorithms, artificial bee colony (ABC) and spiral dynamic algorithm (SDA), in controlling the position of a flexible-link manipulator. By integrating the ABC algorithm into the manipulator's control system, the goal is to enhance its ability to plan paths and optimize trajectories. Additionally, the spiral dynamics algorithm, which draws on principles from complex adaptive systems and human values, provides a framework for modelling system evolution. The study hypothesizes that combining these two algorithms will improve the flexible link manipulator's adaptability and flexibility in dynamic environments and varied task conditions. The results support this hypothesis, demonstrate that the combined ABC and spiral dynamics approach outperforms conventional methods in several key performance metrics, including PID parameter tuning, overshoot, rise time, settling time, and steady-state error. In industry application such a motoring machinery, it is crucial to achieve these metrics at best, which kept the overshoot below 10%, settling time and rise time within a second. Interestingly, the manipulator's behaviours using the spiral dynamics algorithm for PID controller tuning were superior to those using alternative methods. Specifically, the spiral dynamics approach yielded the lowest overshoot at 5.83%, compared to 16.64% with the heuristic method and 8.82% with the ABC method. The SDA has the fastest rise time and settling time which is 0.03267 s and 0.18445 s respectively. Overall, the simulation results indicate that employing these algorithms for PID parameter optimization effectively enhances the manipulator's transient response and minimizes steady-state error, offering promising implications for real-world robotic applications. [ABSTRACT FROM AUTHOR]

Drilling operation has a direct impact on the quality of the production. Insufficiently controlling the cutting force during the drilling process leads to the risk of early drill failure. Typically, the selection of the drilling parameters is determined based on machining-data handbook where the experience and skill of the operator are required. This paper presents an optimal framework to control the cutting force of the drilling process. A mathematical model that captures complex drilling dynamics between cutting force and feed rate based on system identification is used. Then, a Proportional-Integral-Derivative (PID) controller is proposed to control the cutting force. Taking advantage of up-to-date swam-based optimization technique, an Enhance Bat Algorithm (EBA) approach is used to tune the design variables of the PID controller based on the Integral Absolute Error (IAE) criterion. The results are compared with another two swam optimization, the Particle Swarm Optimization (PSO) and the Whale Optimization Algorithm (WOA). The comparison reveals that EBA can give better results in terms of improving time domain specifications and reducing error performance indices. [ABSTRACT FROM AUTHOR]

Electric vehicles (EVs) have assumed prominence due to their enhanced performance, efficiency, and zero carbon emission. This paper proposes an efficient adaptive neuro-fuzzy inference system (ANFIS) based fractional order PID (FOPID) controller for an EV speed tracking control driven by a DC motor. The optimal controller parameters of the FOPID controller are found via an Ant Colony Optimization (ACO) method. The ANFIS controllers are well trained, tested, and validated using the data set sextracted from the fuzzy-based controllers. The performance and accuracy of the ANFIS model are evaluated using statistical parameters such as mean square error (MSE), coefficient of correlation (R), and root mean square error (RMSE). The controller performance, energy consumption, and robustness are tested using the new European drive cycle (NEDC) test. The efficacy of the ANFIS-based controller is demonstrated by comparing its performance with properly tuned fuzzy-based controllers. The proposed controller shows robustness towards external disturbances and offers promising EV speed regulation control. The comparative results illustrate the superior performance of ANFIS-based FOPID controller with high prediction and low error rates. MATLAB- Simulink platform is used for system modeling, controller design, and numerical simulation. [ABSTRACT FROM AUTHOR]

An overview of intelligent control techniques for the speed control of a direct current (DC) motor has been described in this study. Using the MATLAB SIMULINK platform, the individually excited DC motor speed control system implemented as a physical model. A mathematical model for both the sliding mode control (SMC) method and PID control, a traditional control methodology that ensures the speed controller has been developed. For comparison's sake, fuzzy logic is constructed using the Mamdani Technique with two inputs to obtain the necessary speed control. In addition, to select the optimal gain, the output signal of The PID controller and SMC were contrasted with fuzzy logic in terms of overshoot peak and stability time period. The outcomes prove the SMC's superiority over the PI controller and fuzzy logic approach. Based on time domain characteristics, this article presents a comparative study between Proportional-Integral-Derivative (PID), a sliding mode control (SMC), and fuzzy logic controller (FLC) controllers. The study concludes the less overshoot peak and fast response through fixed Properties for the DC motor and its mechanical variations due to operating conditions. Based on transient response study, the results show that SMC is superior to fuzzy logic and classical controllers PID. [ABSTRACT FROM AUTHOR]

Space-based gravitational wave detection missions use multiple satellites to form a very large scale Michelson laser interferometer in space. This requires extremely high precision displacement measurements at the picometer level between test masses even millions of kilometers apart. Drag-free control is a key technology to ensure the ultra-static and ultra-stable space experiment platform for space-based gravitational wave detection. This paper proposes an innovative ground simulation scheme for drag-free control principle based on the Stewart platform. The kinematics and dynamics modeling of the Stewart platform used in the experiment is presented. A drag-free ground simulation experimental equipment is designed and built. A two-degree-of-freedom (2-DOF) drag-free controller is designed based on the H ∞ loop shaping algorithm which outperforms a PID controller in Simulink simulation. A semi-physical simulation experiment is conducted to verify the controller designed using rapid control prototyping technology. The experimental results show that the control performance reaches the limit accuracy of the hardware device, thus verifying the effectiveness of the drag-free control algorithm. • Introduce a novel ground simulation scheme for drag-free control using the Stewart platform. • Design a drag-free controller based on H ∞ loop shaping. • Validate our approach through a two-degree-of-freedom semi-physical simulation experiment. [ABSTRACT FROM AUTHOR]

The Proportional-Integral-Derivative (PID) controller is a widely used industrial feedback control algorithm that consists of proportional, integral, and derivative components. This PID control can measure process variables and calculate errors to generate accurate control signals. PID controllers are often used for DC motor speed control applications based on desired parameters. By applying artificial intelligence, PID controller performance can be improved by determining optimal values, automatically adjusting parameters, and responding quickly and accurately to changes in process variables. This article discusses the design of a DC motor PID controller for CNC machines using the MATLAB 2017a application. In this research, a numerical approach is employed using the Action Research (AR) method to enhance practitioners' methods/ways of understanding material learned in higher education. Implementing a DC motor controller with a closed loop PID controller block can reduce the rise time value in the design transfer function. The lowest settling time and the overshoot in the PID control can result in the lowest rise time value of 136,893 ms. These findings can serve as a reference for determining the appropriate tuning method to adjust the values of proportional gain, integral gain, and derivative gain in the PID control system, thereby enhancing the production effectiveness of CNC machines. [ABSTRACT FROM AUTHOR]

Considering the superior control characteristics and increased tuning flexibility of the Fractional-Order Proportional Integral Derivative (FOPID) controller than the conventional PID regulator, this article attempts to explore its application in the optimal design of the Automatic Voltage Regulator (AVR). Since FOPID has two additional tuning parameters (µ and ʎ) than its mentioned conventional counterpart, its tuning process is comparatively difficult. To overcome the stated issue, a self-regulated off-line optimal tuning method based on the Gradient-Based Optimization (GBO) algorithm is adopted in the current study. The optimal FOPID gains are obtained by minimizing the selected Fitness Function (FF) that is chosen as Integral Time Absolute Error (ITAE) in the current study. The simulations are performed using MATLAB/SIMULINK 2018a to test and compare the performance of the proposed GBO-based optimal AVR design based on the dynamic response, stability, and robustness evaluation metrics with some of the recently published metaheuristic optimization algorithm based optimal AVR designs in the literature. The results show that the proposed AVR design provides the most optimal dynamic response and enhanced stability among the considered AVR designs, thus proves its efficacy and essence. [ABSTRACT FROM AUTHOR]

This paper presents the implementation for the first time of a Multi-Particle Swarm Optimization (MPSO) algorithm in the tuning of a PID controller for Power Factor Correction (PFC), applied to a 100W AC-DC boost converter. MPSO algorithm navigates in a search space where each dimension of the space corresponds to the controller constants (Proportional, Integral, Derivative and the Derivative Filter), prioritizing communication over exploration in the algorithm. The controller parameters are randomly initialized in a reduced sector of the space [ K p , K i , K d , K n ], to optimize the search for a PID solution. In the first step, the algorithm is validated using a simulation model in Simulink and Matlab. Subsequently, a final implementation using a real converter is implemented with the PID tuned by MPSO, improving the PFC obtained in previous work. Although previous works have used evolutionary algorithms applied to heuristic optimization to tunning PID controllers, the MPSO algorithm is not usually used for this purpose, particularly to tunning a PID controller in a power electronics system. One advantage of MPSO over the PSO classical algorithm is the search at different points if the vectorial field looks for an optimal solution. PSO presents problems such as getting stuck in a locally optimal solution. The PID controller is trained offline, with the advantage of allowing the risk of damage in the Boost converter for transitory response, increasing the performance of the Power Factor Correction in the converter. This research opens the possibility to use the extended version of the PSO bioinspired algorithm to tune offline controllers to improve the power converter's performance, minimizing the risk presented in the real-time tuning process. [ABSTRACT FROM AUTHOR]

This paper introduces a novel neural network model predictive controller based on Archimedes optimization algorithm to control constrained nonlinear systems, namely robot manipulators. Neural network models of a simple structure are used to accurately predict the system's future outputs. The new Archimedes optimization algorithm is utilized to solve the optimization problem of model predictive control and compute the optimal control action; this algorithm was widely used in different engineering fields due to its good accuracy and convergence speed. The performances of the suggested control algorithm are investigated by simulating a two-degrees-of-freedom robot manipulator. The obtained results are compared with those of various techniques, namely the PID controller, the computed torque controller, and the neural network-based model predictive control using the teaching–learning-based optimization and the particle swarm optimization. To complete the study, the developed controller is implemented on a DSP board and used to control a three-degrees-of-freedom robot manipulator; its results are compared to the neural network-based model predictive control based on the teaching–learning-based optimization and the particle swarm optimization. The simulation and the experimental results demonstrate that the proposed controller provides satisfactory robustness and accuracy, can handle constraints, and can be used to control systems with fast dynamics. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach for tuning Proportional Integral Derivative (PID) controllers, utilizing experimental data obtained from an open-loop step input. We propose to use the measurements of the times taken to reach 5%, 35.3% and 85.3% of the final output, as well as the process static gain, to tune the controller. The tuning equations are applicable for a wide range of stable and over damped systems. Starting from an approximate model with three real poles and time delay (obtained from the measurements), the tuning equations approximate the controller that minimizes the Integral of Absolute Error (IAE) of the disturbance response. The designer can freely decide the Sensitivity Margin (M s) to fix a desired robustness, as a difference with respect to other known methods where the user chooses among few predefined robustness values. The user can also select freely the derivative filter parameter, N , to find the required compromise between speed response and measurement noise amplification. For N = 0 a PI controller is chosen. As N increases, a faster PID controller is selected, with higher noise amplification. We have also developed a web-based application and an Android application, downloadable for free, that implement the tuning equations as well as the whole required procedure. • We present a new method for PID tuning PID using measurements from a step input experiment. • Need to measure the gain, and the times taken to reach the 5%, 35.3% and 85.3% of the final output. • Tuning equations allow to compute K p , T i and T d that minimize IAE as a function of M s and N. • Robustness (M s) and derivative filter (N) can be freely selected to reach any perfoemance-noise compromise. • A free application (in the web or downloadable for Android), implements the tuning procedure. [ABSTRACT FROM AUTHOR]

In this short note, the relative stability results (based on root locus and Bode analyses) reported in "Optimal tuning of sigmoid PID controller using nonlinear sine cosine algorithm for the automatic voltage regulator system" (Suid and Ahmad, 2022) are analyzed. It is shown that the use of closed-loop transfer functions (CLTFs) should be avoided in relative stability analysis since they may lead to less accurate stability results. The present study affirms that the results of relative stability analysis obtained using loop transfer functions (LTFs) are more reliable and accurate. • Relative stability results reported in [ISA Trans 128(B) (2022) 265–86] are analyzed. • It is found that instead of LTF, CLTF was used for relative stability analysis. • The use of CLTF led to less accurate relative stability results. • Relative stability analysis is more reliable and accurate with LTF. [ABSTRACT FROM AUTHOR]

The characteristic effects of a permanent magnet brushless dc (PMBLDC) motor which is propelled by a quadratic DC–DC boost converter was investigated in this paper. The boost converter applied inductor coupling with semi-conductor switches which operate concurrently to produce a high conversion voltage ratio. Modeling of the PMBLDC motor was presented while simulation was performed using a closed loop control system with different controllers for the enhancement of torque–speed performance at varied load. Simulation results show that an optimum voltage gain with minimum voltage stress was achieved at 0.9 duty cycle while varying the inductor turns ratio (n) from 1 to 6 and inductor coupling coefficient (K) from 0.1 to 1.0 so as to ensure that the entire flux generated in one coil is fully linked to the other. A simplified PI controller was designed and experimented through simulation for excellent drive performance at varying load. The results from the spectral display show that the usual speed oscillation and torque ripples produced by the machine were attenuated using different speed controllers. The Total Harmonic Distortion (THD) values indicate that the PID controller achieved a minimal value of 24.06% for speed and 23.16% for torque as compared to 28.73% and 29.82% obtained from the PI controller, while the P controller achieved 41.88% and 35.67% as shown in the graphical abstract. [Display omitted] • Star-connected trapezoidal back-EMF brushless DC motor propelled by a semi-quadratic DC–DC boost converter (SQBC) was modeled and simulated. • For an effective proof of concept, a computer simulation was carried out to demonstrate the level of torque ripples. • The PID-controller provided a minimum value of 24.06% for the speed and 23.16% for the torque, according to the THD data. • 41.88% and 35.67% were reached using the P-Controller, while 82% was achieved using the PI-Controller. [ABSTRACT FROM AUTHOR]

The integration of a PID controller into the A* algorithm presents a novel approach to enhance water boat path planning efficiency. This fusion leverages the precision of the PID controller to fine-tune the navigation decisions made by the A* algorithm, optimizing trajectory adjustments and overcoming challenges posed by dynamic water environments. The PID controller dynamically adjusts the boat's heading based on real-time feedback, ensuring smoother path execution and faster convergence towards the optimal route. This innovative synergy between a classical pathfinding algorithm and a feedback control system addresses the complexities of water-based scenarios, where unpredictable currents, obstacles, and varying conditions necessitate adaptive strategies. The proposed PIDenhanced A* algorithm not only enhances path planning accuracy but also exhibits improved resilience in the face of environmental uncertainties, making it a promising solution for efficient and reliable autonomous watercraft navigation in diverse and challenging aquatic settings. the results show that the A* algorithm with PID controller is superior to the original A* without PID controller with respect to mean path length and standard deviation with a reduction of up to 23% which leads to improved path planning for proposed environment. [ABSTRACT FROM AUTHOR]

The design of autopilots is often enabled by simulating the behavior of the aircraft when it is steered by these systems. The automatic pilot of an airplane consists of relieving the workload of the crew by allowing it to display an altitude, speed and/or heading instruction and letting the control system manage the trajectory to reach this instruction. The autopilot uses control laws to compare the pilot's flight parameters with the predetermined instructions, then adjusts the control system accordingly. These control laws are parametrized as a function of the stability and precision performances to be respected. The objective of our proposed approach is to demonstrate the usage of Model-Free Control (MFC) using an intelligent controller to check the autopilots altitude based on retention of the aircrafts attitude. The proposed MFC approach is applied in the aircraft system and numerical results have presented good performances in terms that the autopilot altitude regulation error using the MFC controller is more better than the error used by the PID controller. [ABSTRACT FROM AUTHOR]

The main issue in electrical system is providing a stable voltage values in order to obtain best devices performance for this reason controlling the automatic voltage regulation (AVR) system becoma more helpful to achieve this requirments, in this paper three controllers are suggested for maintaining the terminal voltage level value of the generator part in AVR system that supplied to custumers, these controllers are (Conventional PID, ArcTan PID and Nonlinear PID), all gains of these controllers is tuned by using an intelligent sun flower optimization (SFO) algorithm. The objective of the design is finding suitable values of these gains that give a stable response based on minimizing error value and testing it using the Integral Time Absolute Error (ITAE) fitness function, the numerical results exhibited that the ArcTan PID controller give the best results values with a lower settling time (0.698) and its faster than conventional PID 5.034 % and faster than nonlinear PID by 5.163% for 5 second simulation time also its reach its peak value in 0.513 sec. and an expectable overshoot value equal to 0.513 with a small error value (0.000645) at its steady state case and then small error value when working at normal state without any disturbance or any uncertainty cases applied but when a disturbances signal is applied with a value equal to ± 0.3 to the system, the NLPID and the conventional PID presents a best response by returning the system to its desired value in just 2 second in the two cases applied and when change the gain values of two parts of AVR system(amplifier & sensor) the NLPID behaves as a the more suitable one and give a superior robust performance in facing this unwanted signals and then a desired response is achieved after small period time with satisfied and acceptable values for woring in this enviroment. [ABSTRACT FROM AUTHOR]

A modified Smith predictor (MSP) with IMC based tuning is proposed for second order delay dominated processes. These processes often exhibit unstable behaviour due to right half poles (RHP) with positive or negative zeros. The control scheme incorporates forward path PI/PID controller with a lead–lag filter and a PD controller in the feedback path. IMC technique having single tuning parameter is utilized to design the forward path controller, while the feedback path controller is tuned by Routh stability criterion. The proposed scheme ascertains quick set point following without overshoot and smooth disturbance rejection, resulting superior closed-loop performance compared to recent MSP schemes. Validation is made based on performance indices and stability margins. • IMC based MSP design for second order time delayed processes with RHP. • Easy tuning of controllers with sole tuner λ. • Set point tracking with null overshoot and faster load recovery. [ABSTRACT FROM AUTHOR]

In this paper, the energy efficiency of the widespread application of backstepping control to a class of nonlinear motion systems is investigated. A Switched Step Integral Backstepping Control (SSIBC) scheme is introduced to improve immunity to measurement noise and to increase the energy efficiency of conventional backstepping in practice. The SSIBC is realized by switching between two candidate controllers obtained at different steps of the iterative backstepping design process. A bi-state dependent hysteresis rule is developed to supervise stable switching between the different regimes in the presence of noise. The proposed method is experimentally verified on a MIMO twin rotor laboratory helicopter involving coupled nonlinear dynamics, inaccessible states and uncertainties. Experimental results show that in addition to a reduction in power consumption, the SSIBC reduces saturation of the control signal and visible motor jerking in contrast with conventional backstepping. Additional comparisons with a previously proposed optimized decoupling PID controller also show significant improvement in precision achieved with higher energy efficiency. Experimental results obtained with the introduction of an external disturbance into the system also show the robustness of the proposed SSIBC. • We propose a switched step integral backstepping control for nonlinear motion systems. • Experiments on a laboratory helicopter with time varying references show good system performance and energy efficiency. • The controller is robust to an external disturbance and reduces visible motor jerking. [ABSTRACT FROM AUTHOR]

As the impacts of global warming intensify, the automotive industry is increasingly emphasizing the development of eco-friendly vehicles with superior range and performance compared to conventional ones. Electric vehicles have emerged as a promising solution to reduce harmful emissions in the transportation sector. This study focuses on creating a nonlinear dynamic model for electric vehicles by integrating kinetic and electrical components. The key criterion in EV speed control is robustness, prompting the construction of various controllers to ensure resilience and disturbance rejection. These controllers include both traditional ones like proportional-integral-derivative (PID) and fractional order PID (FOPID) controllers. Fractional calculus has gained significant attention in control systems engineering due to the fractional orders of the integral and derivative terms, offering enhanced robustness and optimal control. This thesis employs multiple optimization strategies to design an FOPID controller, ensuring the optimal performance of a robust control system for electric vehicles. Initially, the controller is developed using intelligent swarm optimization techniques, such as particle swarm optimization (PSO) and grey wolf optimization (GWO), through simulations in MATLAB R2022b. The results demonstrate the effectiveness of PSO and GWO algorithms in reducing the objective integral of time multiplied by absolute error (ITAE) function in the speed control system utilizing an FOPID controller. The performance of a conventional PID controller is compared with that of a FOPID controller, highlighting the superiority of the GWO-FOPID strategy, presented as a novel methodology. The outcomes underscore the remarkable performance of the GWO-FOPID controller, ensuring rapid responsiveness in controlling EV speed, with a rise time of 0.008978 seconds, a settling time of 0.01 seconds, and zero absolute time error (ITAE). [ABSTRACT FROM AUTHOR]

The Fluid Catalytic Cracking Unit (FCCU) serves as the lifeblood of a modern refinery, playing a crucial role in ensuring superior product quality. This paper delves into the design of a nonlinear Proportional-Integral-Derivative (PID) control mechanism aimed at regulating the temperature of both the reactor and the regenerator within the FCCU. A total of four nonlinear PID controllers have been meticulously designed, two each for controlling the temperature of the reactor and the regenerator. Utilizing the cross-coupling method, these controllers are proficient in compensating for the effects of cross-coupling and the nonlinearities inherent in the model. The Firefly algorithm, renowned for its capability in finding optimal solutions to intricate optimization problems, has been employed for tuning the parameters of the controllers. The outcomes discussed in this paper underscore the transient responses of the reactor temperature, featuring an overshoot of 0.239% and a rise time of 24.87631 sec. For the regenerator temperature, the overshoot stands at 0.703% with a rise time of 25.636 sec. These results highlight significant improvements in product quality, achieved by enhancing and closely monitoring the temperatures of both the reactor and the regenerator within the FCCU--an aspect of paramount importance in the oil industry. [ABSTRACT FROM AUTHOR]

In recent years, electric vehicles have garnered significant attention due to their environmental and economic advantages compared to conventional vehicles, including reduced emissions and lower fuel costs. This study proposes an optimal fractional-order PID (FOPID) controller to regulate electric vehicle (EV) speed. The FOPID controller is advantageous due to its ability for stabilizing the system, managing parameter variations, and mitigating potential disturbances. The tuning of this controller's gains is achieved through an intelligent Ant Colony Optimization (ACO) algorithm. The selection of the gain values is strategically based on minimizing error, thereby ensuring a robust system response without overshoot or undershoots. The performance of the proposed controller is analyzed and compared to a classical PID controller for demonstrating its superior performance. Simulation results illustrate the efficiency of the proposed controller, which exhibits no fluctuation or oscillation in its response (zero overshoot) and fast settling and rise times of 0.0476 and 0.0297, respectively. By using the optimal gains determined by the smart ACO, the proposed controller achieves a satisfactory and robust system response in controlling EV speed. [ABSTRACT FROM AUTHOR]

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Tuning of PID/PIDD2 controllers are proposed in the paper for integrating processes with time delay via the state space pole placement method. The tuning formulas give the controller parameters in terms of a given maximum sensitivity. An observer-based PID structure is proposed to implement the ideal PID or PIDD2 controllers. The structure utilizes a model-independent observer to estimate the various order of the derivatives of the plant output, thus the sensitivity of the derivatives on the measurement noise can be reduced. Simulation results show that the tuning formulas can achieve good compromise among robustness, disturbance rejection, and noise attenuation for the integrating processes. • Tuning of PID/PIDD2 controller is proposed for integrating plants (IPTD, DIPTD, and IFOPTD) using based on state-space pole placement. • Tuning formulas ensure desired robustness by incorporating maximum sensitivity (M s) and plants parameters. • Ideal PID/PIDD2 controller is implemented with observer-based PID structure to reduce effect of measurement noise. [ABSTRACT FROM AUTHOR]

In the case of hybrid sustainable energy power systems made up of Solar Photovoltaic, wind, and an integrated energy storing system connected to a grid network, this research suggests a better control technique for optimal Power Quality improvement. The Unified Power Quality Conditioner with Real and Reactive Power are built-in with the proposed Hybrid Sustainable Energy Systems, and they include Type-1 and Interval Type-2 Fuzzy-based controllers. The main objective is to simultaneously control voltages while minimizing power losses and Total Harmonic Distortion. The Unified Power Quality Conditioner is utilized to reduce Power Quality concerns such as sag, swell, real power, reactive power, and harmonic distortion related to voltage/current by utilizing a proposed controller. The innovative method is displayed in various ways, such as simultaneous PQ replenishment and RES power injections, RESP > 0, and RESP = 0. In addition, the recommended methodology outperforms earlier literature approaches, such as PID controllers. The system is built on the MATLAB Simulink platform, where various performance studies are conducted. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a Coefficient Diagram Method (CDM) to design a conventional PID controller. This controller is used to decrease the frequency fluctuations of a microgrid system composed of two renewable energy sources (WTG and STPG) and four controlled elements (UC, FESS, BESS and DEG). The method compares two characteristic polynomials of the same order:, the coefficients of the first polynomial are a function of microgrid parameters and the unknown gains of the PID controller. The second is called the target polynomial; its coefficients are calculated by choosing the stability indices and the equivalent time constant to satisfy the desired performances of the closed-loop system. Mathematically, the order of the polynomial controller determines the type of linear system of equations to solve: undetermined or overdetermined. In our application, the least squares method is used to find an approximate solution to the overdetermined system resulting from this comparison. Digital simulation is performed to test the performance of the microgrid controlled by the CDM-PID controller. The obtained results are compared with two recently published works where the parameters of the PID controllers are tuned by DE and chaotic PSO algorithms. The results show that the CDM-PID controller gives better performance. [ABSTRACT FROM AUTHOR]

In this paper, the stabilization and set point tracking of the utility boiler is achieved via the Proportional-Integral-Derivative (PID) controller, tuned using the versions of metaheuristic Particle Swarm Optimization (PSO) algorithms. The utility boiler model dynamics are based on the Bell and Astrom boiler turbine system. Initially, the non-linear mathematical model is obtained using the first principles data from the plant model. Further, Taylor series approximation is applied to linearize the non-linear equations around the equilibrium point to implement a stable controller for the plant. Finally, the PID controller gains for the plant model are determined using the PSO algorithms to achieve the desired response. For tuning the PID controller, three PSO algorithms namely standard PSO, Adaptive PSO (APSO) and Improved Adaptive PSO (IAPSO) are proposed. The performance of the proposed PSO based PID control algorithms are evaluated using error metrics such as Integral Absolute Error (IAE), Integral Time Absolute Error (ITAE) and Integral Square Error (ISE). The simulation results show that the proposed APSO and IAPSO algorithms have improved performance when compared to regular PSO algorithms and are capable of optimizing the control parameters. [ABSTRACT FROM AUTHOR]

In this paper, an optimized Genetic Algorithm (GA) based internal model controller-proportional integral derivative (IMC-PID) controller has been designed for the control variable to output variable transfer function of dc-dc boost converter to mitigate the effect of non-minimum phase (NMP) behavior due to the presence of a right-half plane zero (RHPZ). This RHPZ limits the dynamic performance of the converter and leads to internal instability. The IMC PID is a streamlined counterpart of the standard feedback controller and easily achieves optimal set point and load change performance with a single filter tuning parameter ?. Also, this paper addresses the influences of the model-based controller with model plant mismatch on the closed-loop control. The conventional IMC PID design is realized as an optimization problem with a resilient controller being determined through a genetic algorithm. Computed results suggested that GA-IMC PID coheres to the optimum designs with a fast convergence rate and outperforms conventional IMC PID controllers. [ABSTRACT FROM AUTHOR]

In this study, an intelligent PID (i-PID) controller is designed for position control of a nonlinear electro-hydraulic system with uncertain valve characteristics and supply pressure variations. The proposed controller uses estimation of ultra-local model of the system. To exhibit the capability of the proposed method, the controller parameters are optimized via the particle swarm optimization method through a nominal nonlinear model of the system. Then, the performance of the i-PID controller, parameters of which are optimized by using the nominal model, is examined under uncertainties caused by valve characteristics and supply pressure variations. Moreover, the friction between the piston and the hydraulic cylinder is also considered in experiments. A PID controller whose parameters are also optimized based on the same performance criteria, is used for comparison purposes with i-PID control both in simulations and experiments. Performance metrics of the controllers are examined and presented by employing two separate reference signals: Sine wave and ramp. The results show that the i-PID controller shows significantly better results than the classical PID controller in tracking the test signals under various supply pressures and valve modes. • Intelligent PID (i-PID) controller excels in industrial hydraulics. • It is easy to design and implementation. • i-PID control is implemented experimentally in an electro-hydraulic system. • Parameters of i-PID controller are optimized via particle swarm optimization. • The controller tested against uncertainties (supply pressure and valve variations). • i-PID outperforms optimized PID for sine and ramp tracking in diverse conditions. [ABSTRACT FROM AUTHOR]

The appearance of limit cycle oscillations in control systems with fixed threshold based samplers degrades the performance of the control loop, accelerates the wear out of actuators, and introduces an unnecessary communication overhead in distributed control systems. In this paper, the role of the input signals to the control loop is taken into account when analyzing the existence of limit cycles induced by fixed threshold samplers. With this analysis, a methodology to re-tune PID controllers while running to avoid limit cycle oscillations generated by ramp-like excitation signals is presented. Implementation guidelines and several examples to illustrate the usefulness of the method are provided. • Biased limit cycle oscillations induced in a kind of fixed sampled loops are studied. • A methodology to retune the controller to correct the bias effect is proposed. • The robustness assured by the original controller is granted after the retuning. • The method is applicable to the most commonly found controllers in industry. [ABSTRACT FROM AUTHOR]

A rubber bellows has certain creep characteristics, and its role has often been ignored in previous air suspension research. To describe the motion state of a vehicle more accurately during driving, this paper uses amplitude correlation and frequency correlation to describe the mechanical characteristics of the rubber bellows in air spring mechanical characteristics research, establishes an air suspension simulation, and explores the role of the rubber bellows in the suspension. In addition, an adaptive neural network controller is designed to simulate and analyze the Body Acceleration (BA), Sprung Mass Displacement (SMD), Unsprung Mass Displacement (UMD) and Dynamic Wheel Load (DWL) of the suspension. Simulation results show that the rubber bellows has a certain impact on suspension SMD and has a great impact on the performance of the active suspension. The root mean square (RMS) can be increased by 4.55 % but little impact is found on other performance indicators. In addition, compared with the conventional PID control strategy, the adaptive neural network control strategy designed in this paper has better control performance, reducing the RMS of BA and SMD by 27.58 % and 16.67 %, respectively, and improving the control effect by about 4.48 % and 4.17 %, respectively compared with PID. Thus, it can better maintain the stability of the car during driving. [ABSTRACT FROM AUTHOR]

In this paper, a new topology with two shunts flexible AC transmission system (FACTs) devices, thyristor binary switched capacitors (TBSC), and thyristor binary switched reactors (TBSR) based SVC has been developed, which are working in parallel. Both TBSC and TBSR are designed, and simulation results are obtained for the dynamic loading condition. Switching of capacitor and reactor banks with thyristor as a switch is obtained at transient-free conditions so that the significant problem of switching harmonics is eliminated. The coarse control of reactive power is obtained by selecting switchable capacitor banks in binary mode. For fine control, the TBSR bank is designed with a total value equal to the lowest step value of the TBSC bank. It is discrete stepwise compensation at a period of one cycle, and this reactive power compensation is almost continuous as per requirement. It has near-to-zero switchings and zero steady-state harmonics. The mathematical model of TBSC + TBSR has been identified with the system identification toolbox. Different control strategies are implemented as PID controller, Model predictive control, and Model reference adaptive control. The proposed SVC topology performance is discussed based on the performance parameters such as rise time, settling time, and peak overshoot using adaptive controllers. [ABSTRACT FROM AUTHOR]

The Brushless DC Motor drive systems are used widely with renewable energy resources. The power converter controlling technique increases the performance by novel techniques and algorithms. Conventional approaches are mostly focused on buck converter, Fuzzy logic control with various switching activity. In this proposed research work, the QPSO (Quantum Particle Swarm Optimization algorithm) is used on the switching state of converter from the generation unit of solar module. Through the duty cycle pulse from optimization function, the MOSFET (Metal-Oxide-Semiconductor Field-Effect Transistor) of the Boost converter gets switched when BLDC (Brushless Direct Current Motor) motor drive system requires power. Voltage Source three phase inverter and Boost converter is controlled by proportional-integral (PI) controller. Based on the BLDC drive, the load utilized from the solar generating module. Experimental results analyzed every module of the proposed grid system, which are solar generation utilizes the irradiance and temperature depends on this the Photovoltaics (PV) power is generated and the QPSO with Duty cycle switching state is determined. The Boost converter module is boost stage based on generation and load is obtained. Single Ended Primary Inductor Converter (SEPIC) and Zeta converter model is compared with the proposed logic; the proposed boost converter achieves the results. Three phase inverter control, PI, and BLDC motor drive results. Thus the proposed grid model is constructed to obtain the better performance results than most recent literatures. Overall design model is done by using MATLAB/Simulink 2020a. [ABSTRACT FROM AUTHOR]

In this paper, a combination of the interval type-2 fuzzy logic controller (IT2FLC) with the fractional-order proportional–integral–derivative (FOPID) controller, namely optimal interval type-2 fractional-order fuzzy proportional–integral–derivative controller (OIT2FOFPIDC), is developed for enhancing the seismic performance and robustness in seismic structural control applications. Based on the energy concepts, a decentralized framework of the OIT2FOFPIDC is proposed for easy and simple implementation in structures during earthquakes. For this purpose, a coot optimization algorithm (COA), as a powerful optimization algorithm, is also applied to adjust the membership functions (MFs), scaling factors, and the main controller parameters. Three controllers, namely optimal type-1 fuzzy proportional–integral–derivative controller (OT1FPIDC), optimal interval type-2 fuzzy proportional–integral–derivative controller (OIT2FPIDC), and optimal proportional–integral–derivative controller (OPIDC), are also proposed for comparison purposes. The seismic performances of the suggested controllers are examined with the evaluation of nine seismic performance indices and different ground accelerations in a 6-story smart structure equipped with two dampers. The robustness of the four controllers in the presence of the stiffness uncertainties is also compared in this study. On average, a reduction of 25.0%, 18.8%, and 18.5% in peak displacement, inter-story drift, and acceleration of stories is obtained for the OIT2FOFPIDC over the OT1FPIDC, respectively. Similarly, these reductions in comparison with the OIT2FPIDC are 16.3%, 13.3%, and 12.0%. Also, these reductions, in comparison with the OPIDC, are 33.3%, 27.8%, and 25.8%. Furthermore, simulation results show that the OIT2FOFPIDC is more robust than the other proposed controllers against uncertainties due to structural stiffness. • A new framework controller namely OIT2FOFPIDC is developed for implementation in seismic-excited structures. • Based on the energy concepts, a decentralized optimal design framework of the OIT2FOFPIDC is proposed. • A COA is utilized for optimal tuning of the MFs, scaling factors, and the order of the integral and derivative operators. • The OIT2FOFPIDC represents superior seismic performance and robustness over the proposed OPIDC, OT1FPIDC, and OIT2FPIDC. • The proposed optimal design procedure of the OIT2FOFPIDC can be implemented for a wide range of seismic-excited structures. [ABSTRACT FROM AUTHOR]

This paper proposes an event triggered (ET) multi-rate output feedback sliding mode control technique for load frequency control (LFC) in multi-area interconnected power systems. Uncertainties and disturbances are commonly found in LFC problem. The designed controller attains quasi sliding mode in discrete time (DT) for linear time invariant (LTI) interconnected power executing system with limited matched variability. Event-triggered control is used to minimalize the control updates resulting in reduced utilization of resources. The ET rule is designed using Lyapunov stability analysis. The efficacy of the presented controller in the presence of generation rate constraint (GRC) and governor dead band (GDB) is authenticated through simulation results. A comparative study of the proposed controller and the reported ET control scheme for power executing system is also presented for a peer-reported two-area power executing system. • Use of Event triggered MOF-DTSMC to study the frequency deviation problem in multi-area power system. • States of the system are computed from the output of the system circumventing the need of observer/estimator. • System responses in two area power transacting system when subjected to load disturbances and system non-linearities such as GRC and GDB are shown. • For a discrete-time power execution system, minimization of a defined linear quadratic cost function. • Amount of data/signals to be transmitted is minimized using event triggered mechanism. • A comparison of the proposed control technique with reported event trigger sliding mode controller (Su et al., 2017) for unified transacting system is performed to establish the efficacy of design controller. • The performance of the proposed control technique is compared with conventional PID controllers. [ABSTRACT FROM AUTHOR]

This paper optimizes the frequency response of parallel operation of a grid connected Virtual Synchronous Generators (VSG) using a Gray-Wolf Optimization (GWO). The frequency response is achieved when the VSG is synchronized with the grid. The load demand is covered by using only VSGs (Eliminating the existence of conventional generators). The control scheme includes the active power loop aided with the proportional-integral-derivative (PID) controller with optimized parameters, proportional gain Kp, integral gain Ki, and the derivative gain kd. The PID controller gains are optimized using Grey Wolf Optimization. The control scheme resulted in increasing the stability of the power system. The simulation results show the effectiveness of using GWO to reduce the overshoot and steady state deviation of the frequency through the provided damping torque that enhances the VSG inertia. The overshot in the grid frequency due to synchronization is reduced from 8% to 0% with GWO. Therefore, the effect is a more stable system with less overshoot in the frequency (nearly zero). Moreover, the settling time of optimized response has no changed compared with the original frequency response. The system rated is supposed to be 4 kVA for better indication which simulated using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

This paper describes a methodology to obtain a neuro-fuzzy model of a hydro-generator unit (HGU) and its PID controller from the identification of its linear time-invariant (LTI) model. The study performs a cause-effect record, which allows a continuous time identification of the LTI type, then obtaining a classic PID controller capable of complying with a performance specification given by a pole of a second order system, which enables the training of the proposed neuro-fuzzy model. Applying the cost function of the root mean square error and the root of the percentage relative mean square error, the system parameters were adjusted using the decreasing gradient method. By means of linear models, the initialization of the singletons of the neuro-fuzzy models was done in two stages using the decreasing gradient and a cost function. The first stage was carried out without dynamics and the second stage with the dynamics of the simulated system. A case study of the Hydro-Agoyán HGU was selected and the results showed that the development of the LTI model allowed the development of the neuro-fuzzy model able to represent the behavior of the power plant and its response under variations of the power setpoint of 5, 10, 15 and 20%. [ABSTRACT FROM AUTHOR]