Optimal PI Controller Research Articles

Optimal iterative learning PI controller for SISO and MIMO processes with machine learning validation for performance prediction

The multivariable process plays a significant role in industrial applications, and designing a controller for the Multi-Input Multi-Output process is challenging due to dynamic process changes and interactions between system variables. Traditionally, the PI family of controllers has been used for its simple design, easy tuning, and quick deployment. However, these processes require complex control actions due to multiple loops in process plants. Thus, this paper proposes an Iterative Learning Controller Dead-time compensating PI, which utilizes the newly developed hybrid Simulated Annealing-Ant Lion Optimization algorithm for Single-Input Single-Output process simulation and real-time experimentation on the Quadruple Tank System. To validate the effectiveness of the developed controller, Machine Learning techniques such as regression and ensemble trees are used to accurately predict the actual system response using error values from respective processes. The simulation and experimental results demonstrate that the proposed controller achieved better performance. The regression and ensemble tree algorithm models effectively predicted the actual response. The obtained data shows that the proposed controller improved system stability and robustness by minimizing nearly half of the overshoot and improving settling time, with an average of 29.9596% faster than the other controller in the SISO process and 14.6116% in the MIMO process.

Hybrid PV/fuel cell based system using integrated SEPIC-Cuk converter with crow search optimized PI controller

Hybrid PV/fuel cell based system using integrated SEPIC-Cuk converter with crow search optimized PI controller

High speed BLDC motor for grid tied PV based EV system using hybrid PSO-spotted hyena optimized PI controller

High speed BLDC motor for grid tied PV based EV system using hybrid PSO-spotted hyena optimized PI controller

Improved Harmonic Mitigation and Power Injection Technique for Solar-fed DG System Using GA and PSO

This paper deals with Distributed Generation (DG) system requiring power quality features such as current harmonics, reactive power compensation, and power factor improvement in grid tied operation. The motivation is to combine DG system with the capabilities of shunt active filter to perform real power injection, current harmonics mitigation and power factor improvement. The control algorithm employed in this setup utilizes a reference current generator based on p-q theory together with a pulse width modulation controller for switching pulse generation. The control strategy utilizes PI controller with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for finding optimal PI controller parameters. The effectiveness of the proposed method is verified using MATLAB/Simulink and experimental model developed for 3kWp inverter. The PI-PSO controller injects 0.588kW active power into the grid results in sharing 54% of load power demand and the current harmonics is reduced from 23.70% to 1.63%. The obtained results demonstrate that the grid tied inverter maintains harmonic standards in accordance with the IEEE-519 standard while injecting maximum possible active power to the line.

FAULT-TOLERANT CONTROL ALGORITHM FOR AUTOMOTIVE ELECTRONIC MECHANICAL BRAKING RAMP ASSIST

The electronic mechanical braking system directly affects the driving safety of automobiles, but there are auxiliary control methods. Therefore, a fault-tolerant control algorithm for automotive electronic mechanical braking ramp assistance is proposed. The faults of the electronic mechanical braking ramp assistance system and select sensor faults are analyzed as the main research object. By introducing a BP neural network, the longitudinal speed of the vehicle, the wheel speed, the difference between the target slip rate and the current slip rate, and the rate of change of the difference are taken as the network input. Online learning is used to adjust and update the weight coefficients of each layer of the network, output fault diagnosis results and optimized PI controller parameters, and achieve fault-tolerant control. Three different types of sensor fault signals were artificially added in the experimental testing. After the proposed fault-tolerant control method, the controlled signals were used to replace the fault signals, ensuring the normal driving of the car.

Enhancing power quality with optimized PI controller in three-phase four-wire wind energy system

Enhancing power quality with optimized PI controller in three-phase four-wire wind energy system

Efficient and Reliable Fast Charging Station for Electric Vehicles: Integrating PV System and Optimized Control

In this study, an innovative electric vehicle (EV) charging station that integrates multiple energy sources for efficient EV charging is introduced. It combines photovoltaic (PV) panels, a battery storage system, and grid connectivity to ensure reliable and sustainable charging. The PV panels act as the primary source of clean and renewable energy, prioritizing PV energy for EV charging to reduce grid reliance and promote eco-friendly charging. A modified bidirectional Zeta-KY converter is utilized to maintain a constant output voltage, with a chaotic dragonfly optimized PI controller for precise regulation. During periods of insufficient PV power, the bidirectional converter steps down grid power for EV charging. The flow of grid power to the EV station’s battery is managed by a dynamic power source management algorithm for uninterrupted power availability. The efficacy of the design is demonstrated through comprehensive simulation and experimental outcomes. The suggested converter outperforms other counterparts, achieving peak efficiencies of 97% in boost mode and 96.8% in buck mode. A comparative analysis of controllers reveals that the Chaotic Dragonfly optimized PI controller excels with a low settling time of 0.10 sec, zero overshoot, rapid rise time, and a low steady-state error of 0.1%.

Hybrid Transient Search Algorithm With Levy Flight for Optimal PI Controllers of Islanded Microgrids

Hybrid Transient Search Algorithm With Levy Flight for Optimal PI Controllers of Islanded Microgrids

PV-Based Grid Integrated EV with GWO Optimized PI Controller for Boost Integrated Luo Converter

PV-Based Grid Integrated EV with GWO Optimized PI Controller for Boost Integrated Luo Converter

Analysis of Distribution Static Compensator Control Strategies for Mitigating Voltage Dip Impact on Distribution Network

Voltage dip, over-voltage, load unbalanced, and current and voltage harmonics distortions are the key characteristics of poor power quality (PQ) issues on the power distribution network with a significant negative impact. The performance of a custom power device, distribution static compensator (D-STATCOM), in reducing current total harmonic distortion (THD) during the mitigation process of voltage dip with fault is investigated in this study. To control the load side voltage, the D-STATCOM utilizes a three-phase voltage source converter and is coupled at the point of common coupling (PCC). To mitigate voltage dip effects, this study implements and compares the effectiveness of the conventional PI controller with an intelligent optimization-based PI controller using the dynamic gravitational search algorithm (DGSA). The performance of these controllers is validated by the MATLAB/Simulink simulation results obtained. Analysis of the results demonstrates that D-STATCOM operates flawlessly with an intelligently optimized PI control strategy reducing the current THD from 11.68% to 3.74%.

Optimizing Electric Vehicle Charging with Moth Flame Control Algorithm of Boost-KY Converter

Electric vehicles have assumed a prominent role in future transport system due to the diminishing availability and escalating costs of fossil fuels, coupled with growing concerns about the impact of global warming. The purpose of the work consists in addressing the pressing need for efficient and sustainable solutions in the realm of electric vehicles and renewable energy integration. The tasks solved in the article to achieve the given goal are the following: an improved Boost-KY converter has been introduced to counter the inherent limitation of low PV panel voltage output. This converter effectively mitigates voltage and current ripples, thereby ensuring a stable power supply for EV charging. Additionally, the Moth Flame Optimized Proportional Integral (MFO-PI) controller has been implemented to regulate converter operation, demonstrating exceptional proficiency in mitigating PV output unpredictability. MATLAB simulation is done to validate the proposed system's performance. The most important results are the achievement of impressive maximum efficiency of 96.21% and remarkably low Total Harmonic Distortion (THD) value of 1.04%. The system maintains consistent voltage and current levels for PV panels and EV battery, ensuring dependable energy supply. The significance of the results obtained consists in their potential to revolutionize the intersection of renewable energy integration, electric vehicle (EV) adoption, and sustainable transportation practices.The PV-based EV charging system not only reduces dependence on finite fossil fuel resources but also contributes to environmental preservation, aligning with global efforts to combat climate change. Furthermore, system adheres to stringent requirements of IEEE 519 standards, positioning it as a catalyst for the adoption of clean energy solutions within the future transport system. Keywords: PV system, electric vehicle, improved Boost-KY converter, Moth Flame optimized PI controller.

Research on Two-stage Isolated AC–DC Converter with PSO Optimized PI Control

Research on Two-stage Isolated AC–DC Converter with PSO Optimized PI Control

Virtual Inertia Control in Autonomous Microgrids via a Cascaded Controller for Battery Energy Storage Optimized by Firefly Algorithm and a Comparison Study with GA, PSO, ABC, and GWO

Modern (micro) grids host inverter-based generation units for utilizing renewable and sustainable energy resources. Due to the lack of physical inertia and, thus, the low inertia level of inverter-interfaced energy resources, the frequency dynamic is adversely affected, which critically impacts the stability of autonomous microgrids. The idea of virtual inertia control (VIC), assisted by battery energy storage systems (BESSs), has been presented to improve the frequency dynamic in islanded microgrids. This study presents the PD-FOPID cascaded controller for the BESS, a unique method for enhancing the performance of VIC in islanded microgrids. Using the firefly algorithm (FA), the settings of this controller are optimally tuned. This approach is robust to disruptions due to uncertainties in islanded microgrids. In several scenarios, the performance of the suggested approach is compared with those of other control techniques, such as VIC based on an MPC controller, VIC based on a robust H-infinite controller, adaptive VIC, and VIC based on an optimized PI controller. The simulation results in MATLAB show that the suggested methodology in the area of VIC is better than previous methods.

Compensation of the Current Imbalance of an Interleaved DC–DC Buck Converter, Sensorless Online Solution Based on Offline Fuzzy Identification and Post-Linearization

This paper presents a new approach to compensate for the current imbalance of an interleaved DC–DC buck converter (IBC), in which the current sensors are not involved in the operation of the converter when it is connected to an invariable load. The current sensors are only used during the offline identification process that builds the universal fuzzy model of the converter’s steady states. Model building involves an upstream identification phase, followed by further dimensionality reduction of the model and error minimization. The method presented here discusses the mathematical complexity of the analytical modelling of hybrid systems and opposes it with a complexity-reduced identification by learning from data. An offline rendered model of the stable and steady states of the IBC is used as a mapping of the required inverter output current to n-fold asymmetric duty cycles, which are distributed among the IBC phases to allow arbitrarily accurate load sharing. The mapping is carried out in the mathematically normalized space of variables or in the physical sense RMS values, achieving the desired robustness in a noisy environment and stability. The final and canonical feedback control is built from the standard and optimized PI controller, which is compensated by the identified IBC model correction. The only measured feedback of the whole controller is the output voltage. Even when applied to the simulation model (physical MATLAB platform) of a two-phase IBC with the built-in system asymmetry, the presented methodology is also applicable to the n-phase IBC without loss of generality.

PSO based optimized PI controller design for hybrid active power filter

This research study presents the design and simulation of a hybrid active power filter (HAPF) for reducing harmonics. The reference currents have been determined using the synchronous reference frame technique. To achieve its goals, the proposed HAPF employs AI algorithm known as particle swarm optimization (PSO) to fine-tune the proportional-integral PI controller's parameters. With the help of PI-PSO controller the DC link voltage is regulated in the HAPF-inverter. A non-linear current control strategy based on hysteresis employed here to construct the pulse gate by comparing the retrieved reference and real currents necessitated by the HAPF. Simulations were carried out in MATLAB and shown that the proposed method is extremely adaptable and efficient in reducing harmonic currents caused by non-linear loads.

DC motor control using model reference adaptive control

Despite having higher maintenance costs than AC motors, DC motors had been widely employed in the industry due to their outstanding speed control capabilities. This employment increased due to the DC output of some renewable sources recently. This article introduces the speed control of DC motors using model reference adaptive control (MRAC). This control is achieved through regulating the armature voltage at different load changes. A comparison between the proposed adaptive controller and optimized PI controller using the elephant herding optimization (EHO) is presented. The PI controller parameters were optimality adjusted to minimize the integral absolute error, minimum overshoot, and minimum settling time. Computer simulations show that the suggested MRAC is preferable to a traditional optimized PI controller. In addition, the proposed controller is effective in regulating the DC motor over a broad range of operating speeds.

DC motor control using model reference adaptive control

Despite having higher maintenance costs than AC motors, DC motors had been widely employed in the industry due to their outstanding speed control capabilities. This employment increased due to the DC output of some renewable sources recently. This article introduces the speed control of DC motors using model reference adaptive control (MRAC). This control is achieved through regulating the armature voltage at different load changes. A comparison between the proposed adaptive controller and optimized PI controller using the elephant herding optimization (EHO) is presented. The PI controller parameters were optimality adjusted to minimize the integral absolute error, minimum overshoot, and minimum settling time. Computer simulations show that the suggested MRAC is preferable to a traditional optimized PI controller. In addition, the proposed controller is effective in regulating the DC motor over a broad range of operating speeds.

Optimal control design and management of complete DC- renewable energy microgrid system

This paper presents an optimal control design for a DC-renewable Microgrid (DC-MG) system optimized by applying the Arithmetic Optimization Algorithm(AOA). This approach is proposed in this study to get the optimal PI controller design parameters for the DC-MG when connecting and disconnecting from the grid. The suggested system comprises a solar photovoltaic system(PVS), an Energy Storage System (ESS), a DC-AC inverter, DC load, LC filter, variable AC load, and a single-phase induction motor. The ESS is used to store energy in hydrogen form and is made up of a fuel cell system (FCS), an electrolyser, and a hydrogen tank module. The results are compared with those obtained from control system design using Particle Swarm Optimization(PSO), Cuckoo Search(CS), and Gray Wolf Optimizer (GWO) methodologies for a variety of test situations. The simulated results using AOA are more accurate and have less settling time, rise time, and overshoot compared to other techniques.

Meta Heuristic Optimization Algorithm based Rectifier Control in Switched Reluctance Generator

A simple, robust, economical and highly efficient 8/6 switched reluctance (SR) generator is connected to a wind turbine to increase the amount of power extracted from the wind. Output power of the generator is fed to the PWM rectifier via a four-phase asymmetric half-bridge (AHB) converter and maximum power point tracking (MPPT) of the rectifier is assisted with torque ripple minimization to present an effective control as it allows torque ripple suppression arising from the SR generator nature while capturing maximum wind power. The wide range of speed variation of the SR generator is minimized using the combination of PI and hysteresis current controller. Further, the output of the AHB converter is directed to the conventional PWM rectifier. The purpose of this research is to deliver ripple-free dc power to a dc grid/load or battery. Hence, in this paper, the GWO-based optimized PI controller is implemented as an MPPT method to maintain the constant dc bus voltage. The inherent ability of GWO optimization-based control of the rectifier system ensures better dynamic control. The simulation is performed in a MATLAB environment for a power rating of 1000W standalone system and tested using an FPGA controller. This paper carries out a stability analysis of the system by varying the wind velocity.

Active hybrid energy storage management in a wind-dominated standalone system with robust fractional-order controller optimized by Gases Brownian Motion Optimization Algorithm

Accessibility to sustainable and reliable electrical energy for remote area power supply (RAPS) systems under high penetration levels of wind energy requires adopting appropriate technologies and controllers. In recent years, energy storage has gained significant attention for smoothing inherent voltage and power fluctuations of standalone systems. This paper presents a filtration-based fractional-order PI (FOPI) controller optimized by Gases Brownian Motion Optimization (GBMO) algorithm to manage an active battery-supercapacitor hybrid energy storage system (HESS) in a wind-dominated RAPS system. The GBMO algorithm has a high accuracy and convergence rate among optimization algorithms introduced in recent years. Moreover, a fuzzy logic controller (FLC) guarantees the wind turbine generator's maximum power point tracking (MPPT) at any wind condition while mitigating its fluctuating torque. The proposed RAPS system uses the advantages of the FOPI controller, the GBMO optimization algorithm, the FLC-based MPPT algorithm, and a high-pass filter to manage precise coordination between different components of the RAPS system. The high-pass filter decouples high and low-frequency voltage oscillations to modify the HESS performance and relieve battery stress, thereby extending its lifespan. The proposed controller's performance and robustness are verified in comparison with an optimal PI controller based on GBMO under turbulent wind speed and load step changes in a detailed model of the system. The results demonstrate the superiority of the optimal FOPI controller over the optimal PI controller.