Reduction Of Overshoot Research Articles

Optimal Selection of Switch Model Parameters for ADC-Based Power Converters

Real-time hardware-in-the-loop-(HIL) simulation integration is now a fundamental component of the power electronics control design cycle. This integration is required to test the efficacy of controller implementations. Even though hardware-in-the-loop-(HIL) tools use FPGA devices with computing power that is rapidly evolving, developers constantly need to balance the ease of deploying models with acceptable accuracy. This study introduces a methodology for implementing a full-bridge inverter and buck converter utilising the associate-discrete-circuit-(ADC) model, which is optimised for real-time simulator applications. Additionally, this work introduces a new approach for choosing ADC parameter values by using the artificial-bee-colony-(ABC) algorithm, the firefly algorithm (FFA), and the genetic algorithm (GA). The implementation of the ADC-based model enables the development of a consistent architecture in simulation, regardless of the states of the switches. The simulation results demonstrate the efficacy of the proposed methodology in selecting optimal parameters for an ADC-switch-based full-bridge inverter and buck converter. These results indicate a reduction in overshoot and settling time observed in both the output voltage and current of the chosen topologies.

A novel cat swarm optimization‐based fuzzy PI controller for improving dynamic response of converter

AbstractNowadays, the use of fuel cells in various applications has become very widespread. Some of their most important applications are in electric vehicles (EVs) and local off‐grid power systems. An EV works as an electric motor that burns a mixture of fuel and gases to generate electricity. One of the challenges with fuel cells is the slow dynamic response to load power changes. In the past few decades, the modeling and control of DC‐DC converters have undergone extensive research and development. However, the error, settling time, and peak overshoot performance are not reduced by the robust nonlinear controller and current mode controller. To overcome these problems, quantile regressive extreme seeking cat swarm optimized Mamdani fuzzy PI controller (QRESCSO‐MFPIC) approach is developed. The goal of the QRESCSO‐MFPIC approach is to reduce the integral time absolute error (ITAE) for tuning the fuzzy PI controller. Initializing the fuzzy PI controller parameter is an input in the QRESCSO‐MFPIC approach. For every parameter value, the fitness function is determined and quantile regression analysis is carried out. The efficiency of the QRESCSO‐MFPIC approach is measured by evaluating the settling time and peak overshoot. Tuning of the PI controller is carried out with cat swarm optimization (CSO) and particle swarm optimization (PSO) based on the objective function of ITAE. The result analysis shows that the QRESCSO‐MFPIC approach improves the efficiency of an optimized Fuzzy PI controller compared to the existing methods. Peak overshoot reduction and settling time are improved through the QRESCSO‐MFPIC approach than the proportional integral particle swarm optimization controller (PI‐PSO).

The relevance of reducing Veress needle overshooting

Safe insertion of the Veress needle during laparoscopy relies on the surgeons’ technical skills in order to stop needle insertion just in time to prevent overshooting in the underlying organs. To reduce this risk, a wide variety of Veress needle systems were developed with safety mechanisms that limit the insertion speed, insertion depth or decouple the driving force generated by the surgeon’s hand on the needle. The aim of this study is to evaluate current surgeons’ perceptions related to the use of Veress needles and to investigate the relevance of preventing overshooting of Veress needles among members of the European Association of Endoscopic Surgery (EAES). An online survey was distributed by the EAES Executive Office to all active members. The survey consisted of demographic data and 14 questions regarding the use of the Veress needle, the training conducted prior to usage, and the need for any improvement. A total of 365 members residing in 58 different countries responded the survey. Of the responding surgeons, 36% prefer the open method for patients with normal body mass index (BMI), and 22% for patients with high BMI. Of the surgeons using Veress needle, 68% indicated that the reduction of overshoot is beneficial in normal BMI patients, whereas 78% indicated that this is beneficial in high BMI patients. On average, the members using the Veress needle had used it for 1448 (SD 3031) times and felt comfortable on using it after 22,9 (SD 78,9) times. The average years of experience was 17,6 (SD 11,1) and the surgeons think that a maximum overshoot of 9.4 (SD 5.5) mm is acceptable before they can safely use the Veress needle. This survey indicates that despite the risks, Veress needles are still being used by the majority of the laparoscopic surgeons who responded. In addition, the surgeons responded that they were interested in using a Veress needle with an extra safety mechanism if it limits the risk of overshooting into the underlying structures.

Optimization of Third Order System with a Novel Fuzzy-based Fractional PID Controller

The conventional Integer Order-based Proportional Integral Derivative (IO-PID) control has been widely employed in process control and manufacturing industries. However, upon thorough review, it is evident that traditional IO-PID control falls short when applied to non-linear and higher-order systems. This paper addresses the challenge of controlling higher-order systems prevalent in numerous industrial plants. The primary goal of controllers in intricate systems is to minimize settling time, overshoot, and related parameters for higher-order systems. To tackle these issues, a novel Fuzzy fractional order-based PID (FFOPID) controller is introduced through this work. This controller aims to provide solutions for the significant challenges encountered in control systems. By subjecting both the traditional controller and the novel controller to the same unit step input, simulations are carried out using MATLAB. The comparative analysis of the output responses highlights the superior performance of the designed novel controller works over the traditional IO-PID controllers. There is a reduction of overshoot by 15% and settling time by more than one second by the FFOPID controller compared to the IO-PID controller.

Design and performance analysis of cascaded pseudo derivative feedback plus pseudo derivative feedforward controller for PMSM drive

PurposeThis paper aims to present a cascaded pseudo derivative feedback (PDF) plus pseudo derivative feedback plus pseudo derivative feedforward (PDFF) controller for a permanent magnet synchronous motor (PMSM) to improve the transient response of the system.Design/methodology/approachProportional integral (PI) plus PI controller and the proposed PDF plus PDFF controller are designed, stability analysis is performed using the extended root locus method, and the effect of the damping coefficient is also extensively studied to validate the robustness of the proposed controller.FindingsWhen compared to a cascaded PI plus PI controller, the proposed control approach has a much shorter settling time for the entire system and a 50% reduction in overshoot in stator current under extensive variations in speed with load disturbance.Originality/valueThe proposed controller is programmed into an FPGA Altera Cyclone II and applied to a 1.5 kW laboratory prototype PMSM drive. The effectiveness of the proposed methods has been demonstrated experimentally throughout a wide variable speed range, from 0 to 157 rad/s at different load conditions.

Advanced Flowrate Control of Petroleum Products in Transportation: An Optimized Modified Model Reference PID Approach

Efficient flowrate control is paramount for the seamless operation and reliability of petroleum transportation systems, where precise control of fluid movement ensures not only operational efficiency but also safety and cost-effectiveness. The main aim of this paper is to develop a highly effective modified model reference PID controller, tailored to ensure optimal flowrate control of petroleum products throughout their transportation. Initially, the petrol transportation process is analyzed to establish a suitable mathematical model based on vital factors like pipeline diameter, length, and pump attributes. However, using a basic first-order time delay model for petrol transportation systems is limiting due to inaccuracies, variable delay issues, safety oversights, and real-time control complexities. To improve this, the delay portion is approximated as a third-order transfer function to better reflect complex physical conditions. Subsequently, the PID controller is synthesized by modifying its structure to address flowrate control issues. These modifications primarily focus on the controller’s derivative component, involving the addition of a first-order filter and alterations to its structure. To optimize the proposed controller, the genetic, black hole, and zebra optimization techniques are employed, aiming to minimize an integral time absolute error cost function and ensure that the outlet flow of the controlled system closely follows the response of an appropriate reference model. They are chosen for their proficiency in complex optimization to enhance the controller's effectiveness by optimizing parameters within constraints, adapting to system dynamics, and ensuring optimal conditions. Through simulations, it is demonstrated that the proposed controller significantly enhances the stability and efficiency of the control system, while maintaining practical control signals. Moreover, the proposed modifications and intelligent tuning of the PID controller yield remarkable improvements compared to previous related work, resulting in a 36% reduction in rise time, a 63% reduction in settling time, an 80% reduction in overshoot, and a 98% reduction in cost value.

Application of Particle Swarm Optimization (PSO) Algorithm for PID Parameter Tuning in Speed Control of Brushless DC (BLDC) Motor

With the advancement in the technology of power electronics and microelectronics, the use of brushless DC motor has been increasingly expanded. The numerous benefits of brushless DC motor are one of the prime factors that has helped in the rapid expansion in the use of BLDC motor. Simple operation and maintenance, compact volume, light weight, energy conservation, high efficiency, easy regulation of speed and high reliability are some the merits that promotes the increasing use of BLDC motor. Its current application is observed in almost all the sectors of industrial control. BLDC motor drivers are most widely used in motion control applications. With the increasing necessity of designing effective control strategy, the use of electronically commuted brushless DC motors is increasing rapidly in various industrial applications. For this purpose, cheap and efficient speed regulator is needed for the motor. Proper tuning of PID can also be one of the ways of controlling BLDCM. This paper adopts intelligent controlling technique for the reduction of torque ripple, settling time and overshoot through the use of MATLAB and SIMULINK.

Baby Incubator with Overshoot Reduction System using PID Control Equipped with Heart Rate Monitoring Based on Internet of Things

Baby Incubator with Overshoot Reduction System using PID Control Equipped with Heart Rate Monitoring Based on Internet of Things

Overshoot Reduction Using Adaptive Neuro-Fuzzy Inference System for an Autonomous Underwater Vehicle

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques

In this study, a method for optimizing membership function tuning for fuzzy control of robotic manipulators using PID-driven data techniques is presented. Traditional approaches for designing membership functions in fuzzy control systems often rely on the experience and knowledge of the system designer, which can lead to suboptimal performance. By utilizing data collected from a PID control system, the proposed method aims to enhance the precision and controllability of robotic manipulators through improved fuzzy logic control. A Mamdani-type fuzzy logic controller was developed and its performance was simulated in Simulink, demonstrating the effectiveness of the proposed optimization technique. The results indicate that the method can outperform conventional P control systems in terms of overshoot reduction while maintaining comparable transient response specifications. This research highlights the potential of the PID-driven data-based approach for optimizing membership function tuning in fuzzy control systems and offers valuable insights for the development and evaluation of fuzzy logic control in robotic manipulators. Future work may focus on further optimization of the tuning process, evaluation of system robustness under various operating conditions, and exploring the integration of other artificial intelligence techniques for improved control performance.

A Sliding Mode Control Algorithm with Elementary Compensation for Input Matrix Uncertainty in Affine Systems

This paper aims to develop a sliding mode control (SMC) approach with elementary compensation for input matrix uncertainty in affine systems. As a multiplicative uncertainty regarding the control inputs, input matrix uncertainty adversely modifies the control effort and even further causes the instability of systems. To solve this issue, a sliding mode control algorithm is developed based on a two-step design strategy. The first step is to design a general sliding mode controller for the system without input matrix uncertainty. In the second step, a control term is specially designed to compensate for input matrix uncertainty. In order to realize the elementary compensation for input matrix uncertainty, this term is obtained by solving a nonlinear vector equation which is derived from the Lyapunov function inequality. Theorems and lemmas based on the convex cone theory are proposed to guarantee the existence and uniqueness of the solution to the vector equation. Additionally, an algorithmic process is proposed to solve the vector equation efficiently. In the simulation part, the proposed controller is applied to two systems with different structures and compared with two state-of-the-art SMC algorithms. The comprehensive simulation results demonstrate that the proposed method is able to provide the closed-loop system with a competitive performance in terms of convergence level, overshoot reduction and chattering suppression.

Assessing the Effectiveness of Fuzzy-PID Controller under Temperature Setpoint Changes During Coffee Roasting Process

Roasting coffee is an essential step in the production of coffee because it affects the flavour, aroma, and overall quality of the final product. Temperature control during the roasting process is essential to achieve a consistent and desired taste. This study seeks to assess the performance of a Fuzzy PID (FPID) controller during coffee roasting when the temperature setpoint varies. The temperature range in this study is from 34-200℃. The study employs an SR500 coffee roaster as the process plant, a FOPDT model structure, a PID-AMIGO benchmark controller, FPID-3 and FPID-5 controllers as the proposed controller. The study includes both simulation and real-time testing for the FPID-5 controller. The results for the FPID-5 controller indicated that the overshoot was reduced to 0.24% and 4.04% in simulation and 2.9% and 8.6% in real-time testing, representing an improvement of more than 90% in overshoot reduction compared to the PID controller. Thus, the proposed FPID-5 controller has been proven to effectively suppress overshoot during temperature setpoint changes in the coffee roasting process. Keywords— Arduino, FOPDT, Process control, temperature controller, real-time control.

Linear Active Disturbance Rejection Control for a Laser Powder Bed Fusion Additive Manufacturing Process

Functional metal parts with complicated geometry and internal features for the aerospace and automotive industries can be created using the laser powder bed fusion additive manufacturing (AM) technique. However, the lack of uniform quality of the produced parts in terms of strength limits its enormous potential for general adoption in industries. Most of the defects in selective laser melting (SLM) parts are associated with a nonuniform melt pool size. The melt pool area may fluctuate in spite of constant SLM processing parameters, like laser power, laser speed, hatching distance, and layer thickness. This is due to heat accumulation in the current track from previously scanned tracks in the current layer. The feedback control strategy is a promising tool for maintaining the melt pool dimensions. In this study, a dynamic model of the melt pool cross-sectional area is considered. The model is based on the energy balance of lumped melt pool parameters. Energy coming from previously scanned tracks is considered a source of disturbance for the current melt pool cross-section area in the control algorithm. To track the reference melt pool area and manage the disturbances and uncertainties, a linear active disturbance rejection control (LADRC) strategy is considered. The LADRC control technique is more successful in terms of rapid reference tracking and disturbance rejection when compared to the conventional PID controller. The simulation study shows that an LADRC control strategy presents a 65% faster time response than the PID, a 97% reduction in the steady state error, and a 98% reduction in overshoot. The integral time absolute error (ITAE) performance index shows 95% improvement for reference tracking of the melt pool area in SLM. In terms of reference tracking and robustness, LADRC outperforms the PID controller and ensures that the melt pool size remains constant.

Assessment of the benefits of 3D printing of advanced thermosetting composites using process simulation and numerical optimisation

3D printing of continuous fibre reinforced thermosetting matrix composites is set to revolutionise composite manufacturing practice. The potential of curing additively is anticipated to bring significant improvement in terms of increasing process speed, producing geometries that are inaccessible with current processing routes and eliminating detrimental exothermic effects during the process. This study presents a comparison between the curing stage of the 3D printing and standard batch processing for carbon fibre/epoxy components of varying thickness and size. An optimisation methodology links simulation of the cure using Finite Element solver Abaqus with a Genetic Algorithm capable of dealing with multi-objective problems. Optimal cure cycles to minimise both process time and temperature overshoot in 3D printing and batch processing are identified and the optimal trade-offs compared. The results highlight that temperature overshoot reduction up to 85 % is possible and that the intrinsic additive nature of the 3D printing allows eliminating the dependence of temperature overshoot on thicknesses and producing components with thicknesses that are very difficult to manufacture conventionally. A simplified procedure for the estimation of 3D printing process duration is proposed based on the results of finite element simulation. This is used for exploration of the limits of the process with respect to part size and for a generic comparison of process applicability against batch processing. The analysis shows that 3D printing is highly advantageous for small components, is efficient for mid-size components and can – on the basis of its scalability – offer a feasible route for producing large and very large components.

Coupled Inductors With an Adaptive Coupling Coefficient for Multiphase Voltage Regulators

As modern microprocessors continuously advance, a high-efficiency, high-power-density voltage regulator design with fast transient response is a must. Compared with the non-coupled-inductor-based solution, a multiphase buck converter with an indirect-coupled inductor (ICL) can dramatically improve the circuit transient response without increasing the inductor current ripple and sacrificing efficiency. However, a design conflict between the steady-state performance and transient response exists for the conventional ICL structure with a given inductor size. This article first analyzes the benefits and limitations of the conventional ICL based on a detailed modeling process. Then, an adaptive-coupled inductor structure is proposed to solve the issue. This is realized by applying a variable inductor in the extra winding loop of an ICL. By self-adjusting the coupling coefficient, the proposed structure can further improve the circuit transient response without increasing the phase inductor current ripple. A four-phase buck converter with the proposed inductor structure is experimentally tested, and it shows that a 25.6% output voltage overshoot reduction or a 21.8% output capacitance reduction can be realized by the proposed coupled inductor structure.

Ultrafast Mode Reversal Coriolis Gyroscopes

Mode reversal is an advanced technique that can effectively improve the performance of the Coriolis vibratory gyros (CVGs). In this article, we propose and demonstrate a modified mode reversal scheme to reduce the measurement dead zone limitations of state-of-the-art mode switching in CVGs. The dynamic model and working principle of the mode reversal gyro are first reviewed. Theoretical analysis and experimental verification are conducted to describe nonlinearity, mode coupling, and time-varying characteristics. A reversal scheme based on iterative learning control (ILC) is then presented. The experimental results showed 145% improvement in reversal speed and 90% reduction in overshoot when compared to other popular controllers and robustness to environmental variations.

Seamless Transition and Fault-Ride-Through by Using a Fuzzy EO PID Controller in AVR System

One of the most crucial control aspects in electric power networks is ensuring constant voltage levels throughout different circumstances. To overcome this problem, an automatic voltage regulator (AVR) is installed in the electrical power networks to preserve the voltage at its allowable range. In this paper, a fuzzy-based PID controller was used to enhance the AVR dynamic performance under several operating conditions. Moreover, an Equilibrium Optimizer (EO) algorithm was used to initialize the controller gains. The validation of the proposed controller was proven through three different systems. The first was a simple AVR system under fixed and dynamic references. The proposed fuzzy EO PID controller proved its superiority in this case through the reduction of voltage overshoot by around 3–28% compared with the reported methods in the literature. Then, the fault-ride through capability of the proposed controller was proven through the second system, which was the Kundur two-area system suffering from a 3-phase fault condition, where the overshoot with the proposed controller was reduced by 4–7% compared with the default controller and modern weighted method reported in the literature. Lastly, an IEEE 9-bus system performance was tested with the proposed controller under normal, faulty, and dynamic loading conditions. Again, the proposed controller succeeded in reducing the maximum overshoot by around 5% compared with the default controller in the system. Moreover, the proposed controller achieved a seamless transition between the islanding and grid-connected mode of operation.

Leveraging fuzzy Logic PID controllers for accelerating chemical reactor CFD

A control technique comprising three Fuzzy Logic Proportional Integral Derivative (FLPID) controllers was evaluated to determine their ability to shorten convergence times for the computationally expensive globally unsteady model of a low-density polyethylene reactor. The numerical reactor contained millions of computational elements, a rotating stirrer shaft, intricate near-wall geometry, and highly exothermic polymerization kinetics. Catalyst feed rates were instantaneously increased by 50% to evaluate the FLPID’s performance during a hypothetical process excursion. FLPID achieved quasi-steady state (QSS) 54% faster than conventionally tuned PID controllers. Reducing the percent overshoot of the error and rise time of the controller output, the FLPID demonstrated its ability to lower computational cost. FLPID in CFD offers the potential to improve control methods on actual plant scale processes. In particular, the reduction in error overshoot could reduce the likelihood of reactor overheat events as the temperatures are kept within a tighter operational window.

A Neuro-Predictive Controller Scheme for Integration of a Basic Wind Energy Generation Unit with an Electrical Power System

Developing control methods that have the ability to preserve the stability and optimum operation of a wind energy generation unit connected to power systems constitutes an essential area of recent research in power systems control. The present work investigates a novel control of a wind energy system connected to a power system through a static VAR compensator (SVC). This advanced control is constructed via integration between the model predictive control (MPC) and an artificial neural network (ANN) to collect all of their advantages. The conventional MPC needs a high computational effort, or it can cause difficulties in implementation. These difficulties can be eliminated by using Laguerre-based MPC (LMPC). The ANN has high performance in optimization and modeling, but it is limited in improving dynamic performance. Conversely, MPC operation improves dynamic performance. The integration between ANN and LMPC increases the ability of the Neuro-MPC (LMPC-ANN) control system to conduct smooth tracking, overshoot reduction, optimization, and modeling. The new control scheme has strong, robust properties. Additionally, it can be applied to uncertainties and disturbances which result from high levels of wind speed variation. For comparison purposes, the performance of the studied system is estimated at different levels of wind speed based on different strategies, which are ANN only, Conventional MPC strategy, MPC-LQG strategy, ANN- LQG strategy, and the proposed control. This comparison proved the superiority of the proposed controller (LMPC-ANN) for improving the dynamic response where it mitigates wind fluctuation effects while maintaining the power generated and generator terminal voltage at optimum values.

6-phase DFIG for wind energy conversion system: A hybrid approach

This research presents a novel wind power system based on a six-phase doubly-fed induction generator (DFIG). Optimization approaches are required to improve the efficiency of the traditional controllers. This study introduces a blended method for DFIG-based wind power transformation systems that combines quantum process and deep reinforcement learning (QPDRL) to improve control efficiency. It will be driven by using online control algorithms to eliminate the optimizing step and upgrade online control strategies. The proposed QPDRL can prevent local optimum solutions, forecast the future essential phase, and update DFIG-based wind power plants' regulation methods online. For two distinct scenarios, the QPDRL was contrasted with the proportional integral derivative (PID) controller, fractional-order PID, and reinforcement learning (for changeable air velocity, there are two types of arbitrary and step amplitudes). Matlab software was used to experiment. As air velocity variations exist, the findings revealed a 62% reduction in the DC link voltage ripples and a 99% reduction in speed overshoot with wind velocities overrun. Finally, comparing PID controls revealed a 42.15 percent reduction in grid current THD and an 11.38 percent reduction in the generator current.