Conventional Proportional Integral Research Articles

Super Twisting Sliding Mode Control of Four-Phase Interleaved Boost Converter

This paper presents a novel control method that integrates super-twisting sliding mode (STSM) voltage control with proportional-integral (PI) current control for a four-phase interleaved boost converter (IBC) in fuel cell applications. The STSM control, employed in the outer voltage loop, provides robust voltage regulation by generating precise reference currents for each phase. The conventional PI control in the inner current loop utilizes these reference currents to generate pulse width modulation (PWM) signals for each phase. The effectiveness of the proposed control strategy is evaluated through comprehensive simulation studies in MATLAB/Simulink, demonstrating an improvement in dynamic performance and enhanced robustness compared to conventional methods. Quantitative analysis shows that the output voltage quickly rises to the reference voltage within approximately 0.25 seconds in the proposed STSM-PI control method and improves transient response by 16 times compared to the conventional PI-PI method. This integrated STSM-PI control strategy offers significant advancements in reliability and efficiency making it a promising solution for high-performance fuel cell power systems.

Revolutionizing PV grid integration: Metaheuristic optimization of fractional PI controllers in T-type neutral point piloted inverters for enhanced performance

This study investigates the efficacy of FOPI regulators as a substitute for conventional proportional integral (PI) controllers in grid-connected PV inverters. The research's objective is to improve the dynamic efficiency of these systems by integrating intelligent optimization techniques that utilize both PI and FOPI controllers and employing different metaheuristic optimization procedures. The study introduces four new optimization algorithms: the Tyrannosaurus Optimization Algorithm (TROA), the Nutcracker Optimization Algorithm (NOA), the Golden Eagle Optimizer (GEO), and the Jellyfish Search Optimizer (JSO). These are made to meet the needs of multi-objective optimization. The proposal suggests using two PI/FOPI regulators to regulate both voltage and amperage provided by the multilayer inverter. The proposal employs a T-type three-level inverter, known for its superior conversion efficiency over conventional inverters. Matlab-Simulink simulations demonstrate that the Nutcracker optimization algorithm (NOA) outperforms other metaheuristic techniques for optimizing dynamic behavior, such as overshoot, settling time, execution, and rising time. Comparisons with existing methods, such as the Manta Rays Foraging Optimization (MRFO) and Grey Wolf Optimizer (GWO), show that all four new algorithms consistently outperform these existing algorithms. The data suggest that using NOA improves stability, efficiency, and power factor while reducing the inverter's THD.

A new approach to instantaneous power based control of DFIG using active disturbance rejection control

This paper presents the design and control of a wind energy converter system that uses a doubly fed induction generator. It introduces a model that incorporates instantaneous active and reactive power as state variables to represent both the stator and rotor sides of the doubly fed induction generator. Subsequently, a linear active disturbance rejection-based control regulator is proposed for the control loops. The parameters of the linear active disturbance rejection controllers (ADRC) are adjusted by two methods: the single parameter tuning approach and the genetic algorithm (GA) method. The performance of the suggested controller is ultimately compared with a tuned proportional-integral (PI) control regulator in the MATLAB environment. In order to examine the performance of the system, various test scenarios are taken into account. These scenarios include different percentages of load change in both sub-synchronous and super-synchronous modes of operation, faults with different fault periods are considered in both modes of operation. The conventional PI controller-based approach requires 35 control cycles to reach steady state under load change conditions and 50 control cycles under symmetrical fault conditions, regardless of whether the system is operating in sub synchronous or super synchronous mode. The single parameter-based tuned ADRC approach only requires 15 control cycles for load change and 20 control cycles for symmetrical fault scenarios. In contrast, for both the load change and symmetrical fault scenarios, the ADRC with GA-based parameter tuning reaches steady state in just 5 control cycles. Furthermore, a case involving wind speed variation is presented to demonstrate the maximum power point tracking capability of the proposed control structure. Similarly, for the robustness test of the control regulator, variations in resistance and inductance are introduced to observe their impact on the response of the control regulators. Finally, a real-time hardware in loop setup is presented to validate the control strategy during the load change in the sub-synchronous mode of operation of the doubly fed induction generator.

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Pairing voltage-source converters with PI tuning controller based on PSO for grid-connected wind-solar cogeneration

This paper introduces a novel method to improve the efficiency of grid-connected wind-solar cogeneration systems. It involves the integration of Voltage-Source Converters (VSCs) with a Proportional-Integral (PI) tuning controller that has been optimized using Particle Swarm Optimization (PSO). Integrating renewable energy sources into power grids presents a set of challenges in terms of ensuring stability and optimizing power flow. VSCs are essential for effectively managing power flow between renewable sources and the grid. The PI controller is vital in maintaining voltage levels and ensuring stable operation. Conventional PI controller tuning methods commonly rely on heuristic approaches, which might only partially optimize performance across various operational conditions. In order to tackle this issue, PSO is utilized to fine-tune the parameters of the PI controller automatically. This results in a significant reduction in system deviations and a notable improvement in efficiency, even when faced with fluctuating wind and solar conditions. The simulation results conducted in MATLAB/Simulink confirm the effectiveness of the proposed approach in enhancing system stability and reducing response times. The PI controller optimized using PSO showcases exceptional adaptability to varying environmental and grid conditions, resulting in minimized power fluctuations and improved grid reliability. This study makes a valuable contribution to the field of renewable energy integration. It provides valuable insights into enhancing the performance of grid-connected wind-solar cogeneration systems through advanced control techniques and optimization algorithms such as PSO.

Enhanced Adaptive Dynamic Surface Sliding Mode Control for Optimal Performance of Grid-Connected Photovoltaic Systems

This study presents an enhanced, adaptive, and dynamic surface sliding mode control (SMC), a cutting-edge method for improving grid-connected photovoltaic (PV) system performance. The suggested control approach uses dynamic SMC and adaptive approaches to enhance the robustness and efficiency of a system. Proportional–integral (PI) and SMC, two control systems for maximum power point tracking (MPPT) in PV systems, are compared in this paper. This study finds that the SMC system is a more effective and efficient MPPT approach for PV systems compared to the conventional PI control system. The SMC system’s unique feature is the capacity to stabilize grid voltage and attain a modulation index of less than one. An important component of power electronic system control is the index, which acts as a parameter representing the relationship between the output signal’s amplitude and the reference signal’s amplitude. The SMC method demonstrates improved robustness, efficiency, and stability, especially in dynamic operating settings with load and solar radiation changes. Compared to the PI control, the SMC exhibits a noteworthy 75% reduction in voltage fluctuations and an improvement in the power output of 5% to 10%. Regarding output power optimization, voltage stability, and accurate current tracking, the SMC system performs better than the PI control system. Furthermore, the SMC technique maintains a modulation index below one and guarantees grid voltage stability, both of which are essential for the efficiency and stability of power electrical systems.

Adaptive Control for Suspension System of In-Wheel Motor Vehicle with Magnetorheological Damper

This study proposes two adaptive controllers and applies them to the vibration control of an in-wheel motor vehicle’s (electric vehicle) suspension system, in which a semi-active magnetorheological (MR) damper is installed as an actuator. As a suspension model, a nonlinear quarter car is used, providing greater practical feasibility than linear models. In the synthesis of the controller design, the values of the sprung mass, damping coefficient and suspension stiffness are treated as bounded uncertainties. To take into account the uncertainties, both direct and indirect adaptive sliding mode controllers are designed, in which the principal control parameters for the adaptation law are updated using the auto-tune method. To reflect the practical implementation of the proposed controller, only two accelerometers are used, and the rest of the state values are estimated using a Kalman observer. The designed controller is applied to a quarter car suspension model of an in-wheel motor vehicle featuring an MR damper, followed by a performance evaluation considering factors such as ride comfort and road holding. It is demonstrated in this comparative work that the proposed adaptive controllers show superior control performance to the conventional proportional–integral–derivative (PID) controller by reducing the vibration magnitude by 50% and 70% for the first and second modes, respectively. In addition, it is identified that the second mode (wheel mode) of the in-wheel motor vehicle is more sensitive than the first body mode depending on the mass ratio between the sprung and unsprung mass.

A new analytical method for designing centralised PI controllers for unstable systems using a direct synthesis approach

This paper introduces a new analytical technique using a direct synthesis strategy to develop centralised proportional–integral (PI) controllers for multivariable processes. The current method design controller is focused on attaining the desired closed-loop response for multi input multi output (MIMO) processes that involve multiple time delays. The conventional multivariable PI controller is obtained by approximating the ideal multivariable controller by the Maclaurin series expansion. This is accomplished by choosing the desired closed-loop response order as the system order plus two. Subsequent analysis investigates the proposed method’s efficacy in designing multivariable PI controllers. Servo and regulatory problems studies were conducted to find the effectiveness of the proposed method and compared it with other methods recently reported in the literature, as well as assessment of performance indices like integral absolute error (IAE) and integral square error (ISE). The proposed controller's robustness is assessed by plotting the inverse maximum singular value against frequency, both input and output multiplicative uncertainties.

ANFIS-based optimisation for achieving the maximum torque per ampere in induction motor drive with conventional PI

This research presents an innovative approach to controlling the speed of an induction motor drive by utilizing a combination of neural networks and fuzzy inference systems (ANFIS). The study focuses on computing the rotor's magnetic flux while considering different overshoot and settling criteria for torque and motor speed. The goal is to optimize torque per ampere and generate the necessary torque. The proposed ANFIS-based torque-per-ampere control technique offers a distinctive method applicable to a static induction motor model. This method allows for an increase in stator current while maintaining flexibility and individuality in motor control strategies. It compares various motor vector control methods, specifically focusing on strategies to reduce torque ripple. These strategies include adaptive ANFIS, fuzzy logic control (FLC), and proportional-integral (PI) control. The research highlights the effectiveness of an adaptive ANFIS controller in achieving the most significant reduction in torque ripple within the induction motor system. This proposed problem identification sets the stage for exploring and developing solutions to enhance the performance and efficiency of induction motor drives.

Power system load frequency management using fuzzy logic

A power system is a sizable, intricately linked network that has multiple sources and loads. The population growth causes variations and unpredictability in load demand. The utilities are responsible for meeting load demand while abiding by operational restrictions on the power system. Control of load frequency is alone in charge of providing the customers with sustainable electrical power. Its primary purpose is to control system frequency and power flow in additional areas within advised bounds by controlling tie-line power and the utility generator's output power in reaction to changes in load without compromising system frequency. The paper's recommended concept illustrates the load frequency control of two area systems using fuzzy logic, fuzzy tuned proportional integral, conventional proportional integral, and proportional integral derivative controllers. SIMULINK is used for simulation research.

Performance Improvement of an Electric Vehicle Charging Station Using Brain Emotional Learning-Based Intelligent Control

Electric vehicle (EV) charging facilities are essential to their development and deployment. These days, autonomous microgrids that use renewable energy resources to energize charging stations for electric vehicles alleviate pressure on the public electricity grid. Nevertheless, controlling and managing such charging stations’ energy is difficult due to the nonlinearity and irregular character of renewable energy sources. The current research recommends using a Brain Emotional Learning Intelligent Control (BELBIC) controller to enhance an autonomous EV charging station’s performance and power management. The charging station uses a battery to store energy and is primarily powered by photovoltaic (PV) solar energy. The principles of BELBIC are dependent on emotional cues and sensory inputs, and they are based on an emotion processing system in the brain. Noise and parameter variations do not affect this kind of controller. In this study, the performance of a conventional proportional–integral (PI) controller and the suggested BELBIC controller is evaluated for variations in solar insolation. The various parts of an EV charging station are simulated and modelled by the MATLAB/Simulink framework. The findings show that, in comparison to the conventional PI controller, the suggested BELBIC controller greatly enhances the transient responsiveness of the EV charging station’s performance. The EV keeps charging while the storage battery perfectly saves and keeps steady variations in PV power, even in the face of any PV insolation disturbances. The suggested system’s simulation results are provided and scrutinized to confirm the concept’s suitability. The findings validate the robustness of the suggested BELBIC control versus parameter variations.

Model predictive control (MPC) applied to a simplified model, magnetic nanoparticle hyperthermia (MNPH) treatment process

Magnetic nanoparticle hyperthermia (MNPH) has emerged as a promising cancer treatment that complements conventional ionizing radiation and chemotherapy. MNPH involves injecting iron-oxide nanoparticles into the tumor and exposing it to an alternating magnetic field (AMF). Iron oxide nanoparticles produce heat when exposed to radiofrequency AMF due to hysteresis loss. Minimizing the non-specific heating in human tissues caused by exposure to AMF is crucial. A pulse-width-modulated AMF has been shown to minimize eddy-current heating in superficial tissues. This project developed a control strategy based on a simplified mathematical model in MATLAB SIMULINK® to minimize eddy current heating while maintaining a therapeutic temperature in the tumor. A minimum tumor temperature of 43 [°C] is required for at least 30 [min] for effective hyperthermia, while maintaining the surrounding healthy tissues below 39 [°C]. A model predictive control (MPC) algorithm was used to reach the target temperature within approximately 100 [s]. As a constrained MPC approach, a maximum AMF amplitude of 36 [kA/m] and increment of 5 [kA/m/s] were applied. MPC utilized the AMF amplitude as an input and incorporated the open-loop response of the eddy current heating in its dynamic matrix. A conventional proportional integral (PI) controller was implemented and compared with the MPC performance. The results showed that MPC had a faster response (30 [s]) with minimal overshoot (1.4 [%]) than PI controller (115 [s] and 5.7 [%]) response. In addition, the MPC method performed better than the structured PI controller in its ability to handle constraints and changes in process parameters.

Analysis of interval type-2 fuzzy controller based interleaved sepic PFC converter for PQ enhancement in power solutions

The Power solutions have become indispensable for all the devices in recent years with an appropriate power conversion circuitries and control methods to ensure good dynamic response, improved stability, reliability and efficiency. The main intent of this article is to impart the designing of interval type-2 fuzzy logic controller (IT2FLC) based interleaved Sepic power factor correction (PFC) converter. This work also involves the careful design of the robust controller with enhanced precision and good power quality (PQ) performance at the AC mains. In addition, the development of IT2FLC based power solution improves the overall power conversion with stabilized output in the perspective of its quick rise time, less overshoot and fast settling time in comparison to other traditional controllers. Further, the uncertainties and issues associated with the conventional proportional integral (PI) and fuzzy logic controllers (FLCs) are handled effectively by the proposed IT2FLC controller. Moreover, this preferred converter is modeled with an internal parasitics and its performances are evaluated and compared with other conventional Zeigler Nicholas (ZN) tuned PI controller and FLC by dint of MATLAB/Simulink platform. Finally, the experimental test bench set up of 250 W, 48 V power circuitry is devised and the test outcomes confirm the excellent transient behavior and PQ performances of the modeled power solution.

Decoupled incremental nonlinear dynamic inversion control for aircraft autonomous landing with ground-effect

Due to motion coupling and increased model dependence, the use of nonlinear controllers in real-time applications for aircraft is extremely constrained. This explains why proportional, derivative, and integral (PID) controls are frequently used in practice. However, fast changes in the commanded path and the requirement for high accuracy and tracking performance limit use of PID in landing. This paper presents a design of a decoupled incremental nonlinear dynamic inversion (INDI) controller for performing autonomous landing of a fixed-wing aircraft. The investigation is carried out using the ground effect model and is compared without it. A carrot chasing guidance algorithm is implemented for waypoint navigation to generate the command for the proposed controller's outer loop. The navigational state estimate issue has been resolved using the Cubature Kalman Filter (CKF) approach. Two decoupled CKF algorithms named complementary Cubature Kalman Filter (CCKF) and Integrated Cubature Kalman Filter (I-CKF) are proposed to estimate aircraft navigational states comprised of attitudes, navigational position in terms of latitude, longitude altitudes, and North-East-down velocities. The mathematical model for flare and approach trajectory is designed and commanded to follow the same by the proposed INDI controller. The effectiveness of INDI have been evaluated in a simulation environment by landing the aircraft in the same runway position at arbitrary wind conditions. The performance of the proposed INDI controller is juxtaposed with a conventional proportional integrator and derivative (PID) based landing controller. The simulation findings demonstrate that the INDI has a competitive edge over traditional PID approaches due to its robust performance, less dependence on model dynamics, and fast command tracking capability required during the flare phase.

Performance Comparison of a DC–DC Boost Converter With Conventional and Non-linear SMC Controller in Dual Loop Structure

Generally, the control system confronts various control techniques in the dual-loop control concept. Most of the studies deal with two different controllers in their loops, which increases the problem of designing two distinct procedures with lengthened calculations. In this article, different control techniques like conventional proportional integral (PI), proportional integral derivative (PID), and nonlinear sliding mode control (SMC) have been discussed, and the implementation of SMC in the Dual loop control of a DC–DC Boost converter is presented. The converter’s dynamic behavior is observed by driving their analytical equations. The effective controller for the proposed converter is ascertained by their comparative analysis. The proposed concept is validated with MATLAB/Simulink and a hardware prototype. The controller’s response is compared for various disturbances. Here, dual-loop SMC is put forward in both loops and its competence is confirmed with a minimal settling period of 0.005 sec and a percentage overshoot of 0.2% during the startup conditions, whereas during the line disturbances a minimum peak time of 0.0001 sec is achieved with a settling time of 0.025 sec. Additionally, a nil percentage overshoot and a settling time of 0.02 sec with a 0.001 sec peak time are attained during the load variations. Furthermore, a settling time of 0.01 sec and a percentage overshoot of 0.8% is reached during the reference voltage variations. From the numerical analysis, it is contemplated that an appropriate selection of SMC controllers in both the outer and inner loop results in optimal performance of the converter during the closed loop operation. Finally, the SMC + SMC controller exhibits superior controlling action compared to PI + PI and PID + PID controllers.

Improved frequency control performance of restructured multi-area power system through optimized controller design

This paper presents an optimal cascaded three degree of freedom proportional integral (3DOF PI) controller and compares its performance with conventional proportional integral (PI) controller for load frequency control of two area deregulated power system. The 3DOF PI controller enhances the degree of freedom and gives better dynamic response. Genetic algorithm (GA) is used to get the optimal gains of the proposed controllers. In the considered two area thermal-gas-fuel cell system, the thermal system consists of a reheat turbine with appropriate generation rate constraint non linearity. Battery storage and unified power flow controller (UPFC) are also assumed to be present. The performance of the proposed controllers is evaluated under different operating conditions that take place in deregulated power market. To tackle any contract violation this article emphasizes on the ancillary services provided by electric vehicles (EVs) and distributed generations. A robustness analysis is also presented by varying parameters from their nominal values.

Modular Open Source Solar Photovoltaic-Powered DC Nanogrids with Efficient Energy Management System

Initially the concept of a DC nanogrid was focused on supplying power to individual homes. Techno-economic advances in photovoltaic (PV) technology have enabled solar PV stand-alone nanogrids to power individual devices using device-specific architectures. To reduce costs and increase accessibility for a wider range of people, a modular open-source system is needed to cover all applications at once. This article introduces a modular PV-powered nanogrid system, consisting of a do it yourself (DIY) PV system with batteries to allow for off-grid power. The resultant open-source modular DC nanogrid can deliver DC power to loads of different voltage levels, which is possible because of the efficient and parametric energy management system (EMS) that selects modes of operation for the grid based on DC bus voltage and state of charge of batteries. Simulation results verify the coordination between the EMS and the PV-battery system under varying PV power generation and load conditions. This EMS has potential to enable easy personalization of a vast area of applications and expand appropriate technology for isolated communities. A thorough stability analysis has been conducted, leading to the development of an LQR (Linear Quadratic Regulator) controller as a replacement for the conventional PI (Proportional - Integral) controllers for better transient stability of the system.

Honey Badger Algorithm Based PI Controller for DC Link Voltage Control of Solar Photovoltaic System Connected to Grid for Enhanced Power Quality

The parameters of the proportional integral (PI) controller used for DC link voltage control play a vital role in regulating the DC link voltage of grid-connected solar photovoltaic system that experiences heavy DC voltage fluctuation and various power quality issues due to integration of non-linear loads, varying irradiance and loading conditions. The existing strategies might fail to respond to the above complications due to fixed PI gains which restrict their scope of controlling domain. Therefore, adaptive PI gain values should be selected to quickly adjust to the varying conditions and dynamically obtain the optimal control settings. This article employs the honey badger algorithm (HBA), developed in Matlab to adaptively tune the PI controller gains value by minimizing the DC link voltage deviation. The advantages of using this technique over conventional PI controllers are its quick response, adaptability, stability, and optimal self-tuning process. The proposed algorithm has been tested under steady-state and dynamic conditions for different irradiation and loading conditions. Further, results of the proposed algorithm have been compared with PI controller and particle swarm optimization-PI controller to validate the efficacy of the proposed algorithm for grid-connected solar photovoltaic systems. It has been observed that the proposed algorithm maintains THD <3% in all the tested conditions.

Hybrid ANFIS‐PI‐Based Robust Control of Wind Turbine Power Generation System

This paper introduces a novel hybrid controller designed for a wind turbine power generation system (WTPGS) that utilizes a permanent magnet synchronous generator (PMSG). This hybrid controller combines the adaptability of an adaptive neuro‐fuzzy inference system (ANFIS) with the simplicity of a proportional‐integral (PI) controller. The PI controllers are traditionally used for stability and noise handling. ANFIS adds adaptability, making it more suitable to cope with the variable nature of wind energy. The primary objective of this hybrid strategy is to augment the overall control performance and reliability of PMSG‐based WTPGS when encountered with continuous variable wind conditions. However, implementing the PI controller alone with the WTPGS often suffers from high overshoot and sluggish response in nonlinear systems like WTPGS. In contrast, ANFIS controllers offer superior performance to PI and other artificial intelligence controllers but are still susceptible to noise issues. In this paper, the proposed WTPGS system is designed in MATLAB/Simulink software where a hybrid controller (ANFIS‐PI) is implemented in the machine‐side converter (MSC) and grid‐side converter (GSC) of a variable speed PMSG‐based wind turbine to enhance its performance subjected to wind variations. The hybrid controller is implemented in such a way that the ANFIS controller is implemented in the outer layers while the PI controller is applied in the inner layers of both MSC and GSC. The simulation results for this hybrid controller in the MSC outperform those of the conventional PI controller. They demonstrate minimal overshooting and settling time, maintaining consistent stability even when subjected to various test signals at different intervals. Similarly, the GSC also surpasses conventional PI controllers, achieving a significant 6.4% reduction in maximum overshoot and a decrease of 4.36 seconds in settling time. This highlights its strong suitability for wind turbine applications.

Performance analysis of FEC based SR motor drive using fuzzy tuned PI controller

PurposeIn this paper, a new front end converter (FEC) topology has been proposed for the switched reluctance (SR) motor drive. This study aims to present the performance analysis of FEC-based SR motor drive using various types of control schemes like conventional proportional integral (PI) controller, fuzzy logic controller (FLC) and fuzzy-tuned proportional integral controller (Fuzzy-PI).Design/methodology/approachThe proposed FEC-based SR motor drive with various control strategies is derived for the torque ripple minimization and speed control.FindingsThe steady state and the dynamic response of the FEC-based SR motor drive are analyzed using three different controllers under change in speed and loading conditions. The Fuzzy-PI-based control scheme improves the dynamic response of the system when compared with the FLC and the conventional PI controller.Originality/valueThe hardware prototype has been implemented for the FEC-based SR motor drive by using the Xilinx SPARTAN 6 FPGA processor. The experimental verification has been conducted and the results have been measured under steady state and dynamic conditions.