Proportional-integral Control System Research Articles

A Novel Feedforward Scheme for Enhancing Dynamic Performance of Vector-Controlled Dual Active Bridge Converter with Dual Phase Shift Modulation for Fast Battery Charging Systems

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based system is proposed, replacing the traditional two-loop Proportional-Integral (PI) control system. The extra loop performs a decoupled dq vector control of the inductor current, which is typically not used in single-phase Dual Active Bridge (DAB) systems. This additional loop not only facilitates the optimal determination of both internal and external phase shift angles of a Dual-Phase Shift (DPS) modulator but also lowers the peak input current of the converter, allowing for lower-rated switches. Numerical simulations using MATLAB/Simulink demonstrate the robustness of the proposed control strategy against both input voltage disturbances and load disturbances during the transition from CC to CV charging modes. Hence, the dynamic performance of the charging system is significantly improved with minimal controller effort.

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.

Adaptable robotics for disaster response and search & rescue: Integration of deformable smart car design and pi control

In the context of disaster response and search and rescue operations, the need for adaptable and efficient robotic systems has become increasingly evident. This research paper addresses the evolving challenges in this domain by introducing a novel DIY deformable robot equipped with advanced PI (Proportional-Integral) control. The background of this study emerges from the growing urgency to enhance the capabilities of robotics in post-disaster scenarios, where navigation through complex terrains and the swift delivery of supplies are paramount.The primary objective of this paper is to develop and assess a versatile robotic platform using a multidisciplinary approach encompassing mechanical engineering, electronics, and control systems. The core of the investigation is a DIY deformable car consisting of two sensor-equipped containers and a linking module, making it well-suited for search and rescue missions. This paper contains the implementation of a PI control system to govern the robot's mobility and adaptability. This includes a detailed examination and demonstration of the PI control mechanism, encompassing its proportional and integral components. The actual results also indicate that the smart car controlled by the PI controller has better performance in both stability and speed.

Quantized Proportional-Integral Tracking Control for Two-Stage Chemical Reactor Systems

Abstract This paper is concerned with the problem of proportional-integral tracking control of a two-stage chemical reactor system subject to time delays, disturbances, uncertainties, and input quantization. In this work, an improved equivalent-input-disturbance estimator is incorporated into the proportional-integral tracking control system to compensate for the disturbances in the addressed model. Moreover, to minimize the communication congestion in the control networks, the quantized control input signals are considered while designing the controller. Further, a robust stability condition for the addressed system is established in the form of linear matrix inequalities by employing asymmetric Lyapunov–Krasovskii functional and Jensen's integral inequalities. Moreover, in accordance with the derived conditions, the control and observer gain matrices are determined. Finally, a numerical example is provided to demonstrate the validity of the proposed control scheme.

Dynamic performance enhancement of nonlinear AWS wave energy systems based on optimal super-twisting control strategy

This paper presents a Super-Twisting Algorithm (STA) sliding mode control technique as an alternative to the conventional Proportional-Integral (PI) control system. This application is specifically in the context of a nonlinear Archimedes Wave Swing (AWS) device, which functions as a wave energy converter system (WECS) connected to the power grid. The main goal is dynamic performance enhancement of such wave energy systems under network disturbances. Two STA controllers areessential for the rectifier control system in the grid-connected system to capture the most power from waves while limiting losses in the AWS linear generator. In addition, four STA controllers are incorporated into the inverter control loop to preserve the DC link and common coupling voltages at the preset values. The Honey-Badger Algorithm (HBA) is applied for the optimization of gain parameters of the STA controllers, which are compared to the PI gains adjusted using the coot, the hybrid augmented grey wolf optimizer and cuckoo, and PSO search techniques to assess the worthiness of adopting this new control approach. The grid-connected AWS experiences a variety of faults, includingsymmetrical and unsymmetrical faults with successful and unsuccessful breaker reclosures. Finally, the system is experimentally validated using the OP4510 real-time simulator. The experimental results reveal that the HBA-STA controllers outperformPI controllers in omitting fluctuations in the regulated variables, making the STA a strong contender as a control method.

Cognitively Driven Autonomous Flow Chemistry for Producing On-Demand Perovskite Quantum Dots Via Advanced Closed-Loop Feedback Control.

Recent developments in the synthesis of hybrid organic-inorganic halide perovskite quantum dots (HP-QDs) through compositional adjustments have highlighted their potential applications in the fields of photovoltaics and light sources due to their unique optoelectronic properties. However, traditional methods to fine-tune their composition involve repetitive, labor-intensive, and costly processes. Herein, the utilization of a continuous flow chemistry approach is developed, in combination with a Proportional-Integral (PI) feedback control system as an effective method for producing on-demand methylammonium lead bromoiodide (MAPbBrx I3-x ) HP-QDs. The PI feedback control allows for real-time optimization of the flow rates of halide precursor solutions (halide PSs), enabling the precise tuning of the emission wavelength of HP-QDs. HP-QDs having an emission wavelength of 550 and 650nm are synthesized through a blue-shifted and red-shifted algorithm, respectively, from any arbitrary reaction condition within 400 s. The iterative process through the PI feedback control produces the target HP-QDs with short rise time and low overshoot. The proposed automatic flow chemistry system integrated with a universal and accessible control algorithm of PI can generate the target HP-QDs with high accuracy, stability, and robustness, demonstrating a significant advancement in constructing an autonomous flow chemistry synthetic system.

PI Control Technology Principal Analysis and Simulation

As the requirements for control accuracy and speed increase, proportional-integral (PI) control systems can better achieve stable control. Proportional-integral (PI) control is widely used in modern industry because it has the characteristics of block speed and high accuracy. However, the performance of proportional control systems with different parameters varies greatly in different usage scenarios, and the behavior of PI control systems can be observed and adjusted in advance through computer simulation and modeling. This process can be optimized in advance by observing the response performance of the PI control system, thus reducing the development time and helping to improve the development efficiency in actual engineering. This paper explains the basic principle of PI control from the basic mathematical principle, and simulates the specific behavior of PI control system under different regulation parameters and shows the zero-pole distribution and step response of PI controller under different states and analyzes the application of PI control system in real engineering.

Stand-Alone Hybrid PV System by Using The Bypass Converter Topology to Optimize the Reliable of Device

One of the applications of DC-DC converters in photovoltaic-battery hybrid systems is a two-way converter used for battery charging. The power supplied to the two-way converter experiences twice the power lost during the battery charging process. Therefore, it is necessary to arrange a phovoltaic-battery hybrid system topology so that only one stage of power conversion occurs when charging the battery so that the power loss is smaller. The proposed topology of this system uses three DC-DC converters connected to a battery and a DC bus. The one-way converter used in this system allows cross-connection between other DC-DC converters. Two one-way converters connect the phovoltaics to the DC bus and the photovoltaics to the battery, and the other one-way converter connects the battery to the DC bus. The purpose of using a one-way converter for charging the battery is called a bypass converter so that charging the battery does not go through two power conversions which cause twice the lost power. To make the proposed system more reliable, an inverter is added to the self-contained photovoltaic-battery hybrid system. Therefore, a control system is needed to maintain the DC bus voltage as input to the inverter using a double-loop control system. The two-loop Proportional-Integral control system is used for the DC electricity section while the Proportional-Resonant control system is used as an inverter control to maintain the balance of AC power. The results of system testing using Simulink/Matlab simulations show that the proposed topology uses a one-way converter capable of carrying out the battery charging process and can control the flow of power while maintaining DC and AC voltages.

Nested Magnetic Field Compensation With Regulated Coefficients for Bio-Magnetic Field Measurement

For a spin-exchange relaxation free (SERF) optically pumped magnetometer (OPM) that obtains its best performance in a zero magnetic field, an operation environment with approximately zero magnetic field is desired. This article proposes a nested magnetic field compensation system for a magnetic shielding room (MSR) to suppress the magnetic field. Two sets of coils, one set located outside of the MSR and the other one placed inside of the MSR, are combined in the system. The magnetic fields outside of the MSR are measured by tunneling magnetoresistance (TMR) sensors, while the fields inside the MSR are monitored by SERF OPMs. The two kinds of sensors complement each other in terms of measurement range and sensitivity. A nested two-stage proportional-integral (PI) closed-loop control strategy is employed to compensate for the magnetic field inside the MSR. The control parameters are regulated by the magnitude of the magnetic field to take advantage of the two kinds of sensors. The effect of the proposed system is experimentally tested. The three-axis field fluctuations are reduced by 97.52%, 98%, and 98.64% by the proposed method, respectively. The proposed method is compared with a conventional two-stage series PI control system. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> -axis magnetic field fluctuations of the conventional PI control system and the proposed system are 75.81 and 11.71 pT, respectively. The magnetic fluctuation of the proposed system is about 15.5% of the fluctuation of the conventional PI system. To validate the feasibility of the proposed magnetic field compensation system for ultra-weak bio-magnetic field measurement, magnetocardiography (MCG) and magnetoencephalography (MEG) signals are measured.

Renewable DNA Proportional-Integral Controller with Photoresponsive Molecules.

A molecular robot is an intelligent molecular system. A typical control problem of molecular robots is to maintain the concentration of a specific DNA strand at the desired level, which is typically attained by a molecular feedback control mechanism. A molecular feedback system can be constructed in a bottom-up method by transforming a nonlinear chemical reaction system into a pseudo-linear system. This method enables the implementation of a molecular proportional-integral (PI) controller on a DNA reaction system. However, a DNA reaction system is driven by fuel DNA strand consumption, and without a sufficient amount of fuel strands, the molecular PI controller cannot perform normal operations as a concentration regulator. In this study, we developed a design method for a molecular PI control system to regenerate fuel strands by introducing photoresponsive reaction control. To this end, we employed a photoresponsive molecule, azobenzene, to guide the reaction direction forward or backward using light irradiation. We validated our renewable design of the PI controller by numerical simulations based on the reaction kinetics. We also confirmed the proof-of-principle of our renewable design by conducting experiments using a basic DNA circuit.

Deep Reinforcement Learning Based Dynamic Proportional-Integral (PI) Gain Auto-Tuning Method for a Robot Driver System

To meet the growing trend of stringent fuel economy regulations, automakers around the world are designing modules such as engines, motors, transmissions and batteries to be as efficient as possible. In order to verify the effect of these designs on the overall fuel efficiency of the vehicle, the vehicle equipped with each module is placed on the chassis dynamometer, driven to follow the target vehicle speed, and actual fuel efficiency is measured. These tests are traditionally performed by human operators, but are now being replaced by robots (physical or software) to ensure the accuracy and reliability of test results. Although the conventionally proposed proportional integral (PI)-based controller has a simple structure and is easy to implement, it requires the process of finding the optimal gain whenever the test conditions such as vehicle or drive cycle change, which is difficult and time consuming. In this study, we propose a proportional integral controller gain adjustment algorithm using deep reinforcement learning. The reinforcement learning agent learns to dynamically modify the PI gain value of the acceleration/deceleration pedal to better follow the target vehicle in a simulation environment. The perturbation is used in each training episode to reduce the difference between the simulation and real testing environment. Upon completion of the training process, the trained agent performs an adjustment process that generates a reference gain table. We then use this reference gain table to perform a real test. The performance of the proposed system was evaluated using Hyundai Tucson HEV (NX4) on an AVL chassis dynamometer. We also compared the performance of our proposed algorithm to traditional fuzzy logic-based PI controllers. The obtained experimental results show that the proposed control system achieved a performance improvement of aounrd 46.8% compared to the conventional PI control system in terms of root mean square error.

Adaptive FOPI controller based on the fuzzy supervisory for wind power conversion system equipped by a doubly fed induction generator

Wind power systems have non-linear dynamics and contain many uncertainties such as the parametric uncertainty and the unknown external disturbances. For these reasons, it is a crucial task to design the robust control systems to assure a robust response of wind systems during uncertainties without deteriorating the supplied power quality or stressing the static power converters. This paper designs an adaptive fractional-order proportional-integral (FOPI) control system for a wind power conversion system (WPCS) equipped by a doubly fed induction generator (DFIG) in the electric power grid (EPG) connected mode. The designed adaptive control system combines the robust and intelligent nature of the fuzzy supervisory system (FSS) and simple structure of FOPI controller, where the fuzzy rules are utilized to adjust the FOPI controller parameters based on the error and its rate, resulting in an adaptive fuzzy FOPI (AFFOPI) control system, which can keep the control action touch of the standard FOPI controller and, at the same time, provides the robustness during uncertainties without effect on the power quality supplied to the EPG. Pursuant to the vector control technology of the DFIG, the designed adaptive fuzzy FOPI controllers are applied in both the external loop of power control and in the internal loop of rotor current control, simultaneously. The feasibility of the presented adaptive FOPI control is validated through the numerical results obtained, under different running conditions, using the Matlab/Simulink software.

Self-Tuning Two Degree-of-Freedom Proportional–Integral Control System Based on Reinforcement Learning for a Multiple-Input Multiple-Output Industrial Process That Suffers from Spatial Input Coupling

Proportional–integral–derivative (PID) control remains the primary choice for industrial process control problems. However, owing to the increased complexity and precision requirement of current industrial processes, a conventional PID controller may provide only unsatisfactory performance, or the determination of PID gains may become quite difficult. To address these issues, studies have suggested the use of reinforcement learning in combination with PID control laws. The present study aims to extend this idea to the control of a multiple-input multiple-output (MIMO) process that suffers from both physical coupling between inputs and a long input/output lag. We specifically target a thin film production process as an example of such a MIMO process and propose a self-tuning two-degree-of-freedom PI controller for the film thickness control problem. Theoretically, the self-tuning functionality of the proposed control system is based on the actor-critic reinforcement learning algorithm. We also propose a method to compensate for the input coupling. Numerical simulations are conducted under several likely scenarios to demonstrate the enhanced control performance relative to that of a conventional static gain PI controller.

Performance optimization of doubly-fed induction generator (DFIG) equipped variable-speed wind energy turbines by using three-level converter with adaptive fuzzy PI control system

The present paper aims at optimizing the performance of a Doubly Fed Induction Generator (DFIG) equipped variable-speed wind energy turbine in utility grid-connected mode. Since maximum power extraction and output power quality improvement are considered the main challenges for enormous scale variable-speed wind turbines to meet the worldwide demand for clean energy, this work has two contributions; the first concerns the use of the three-level converters to upgrade the output power quality of the wind energy turbine through reducing the total harmonic distortion (THD) of the currents delivered to the power grid, whereas the second contribution intends to design an adaptive proportional-integral (PI) control system for the rotor side converter (RSC) control. The proposed adaptive PI control system is designed by combining the intelligent and adaptive nature of fuzzy logic control (FLC) and the simpleness of the PI controller framework to effectively optimize the DFIG wind energy turbine performance. In the proposed control system, the PI controller's gains are adjusted in real-time by a supervisory system based on FLC according to the wind turbine running conditions. The adopted control scheme to controlling the RSC comprises two cascaded regulation loops for reaching the maximum power point tracking (MPPT), that intends to extract obtainable maximum power of the wind energy turbine. The internal regulation loop is applied to control the rotor current while the external regulation loop is employed to regulate the generator speed. To prove the efficiency of the three-level converter and performance of the proposed control system, simulation studies were performed utilizing MATLAB/Simulink software. The obtained results through simulation clearly show that research paper purposes are achieved and the performance optimization of the wind turbine is well done.

Data-Driven Model-Free Sliding Mode and Fuzzy Control with Experimental Validation

The paper presents the combination of the model-free control technique with two popular nonlinear control techniques, sliding mode control and fuzzy control. Two data-driven model-free sliding mode control structures and one data-driven model-free fuzzy control structure are given. The data-driven model-free sliding mode control structures are built upon a model-free intelligent Proportional-Integral (iPI) control system structure, where an augmented control signal is inserted in the iPI control law to deal with the error dynamics in terms of sliding mode control. The data-driven model-free fuzzy control structure is developed by fuzzifying the PI component of the continuous-time iPI control law. The design approaches of the data-driven model-free control algorithms are offered. The data-driven model-free control algorithms are validated as controllers by real-time experiments conducted on 3D crane system laboratory equipment.

Robust ANFIS Vector Control of Induction Motor Drive for High-Performance Speed Control Supplied by a Photovoltaic Generator

This paper presents the vector control of Induction Motor (IM) supplied by a photovoltaic generatorwhich is controlled by an adaptive Proportional-Integral (PI) speed controller. The proposed solution is used toovercome the induction motor rotor resistance variation problem, which can affect negatively the performanceof the speed control. To overcome the rotor resistance variation, an adaptive Proportional-Integral controller isdeveloped with gains adaptation based on Adaptive Neuro-Fuzzy Inference System (ANFIS) in order to guaranteea high performances of electric drive systems against the parametric variations. The proposed control algorithmis tested by Matlab-Simulink. Analysis of the obtained results shows the characteristic robustness to disturbancesof the load torque and to rotor resistance variation compared to the classical PI control and Model ReferenceAdaptive System (MRAS) rotor resistance observers.

Optimal design of an Autonomous Emergency Braking (AEB) system for a passenger vehicle

This paper models the performance of a passenger vehicle equipped with Autonomous Emergency Braking (AEB) using a feedback control system in MATLAB Simulink. Two types of AEB control systems are simulated and compared; proportional-integral (PI) control and fuzzy logic control (FLC). Performance of both control systems simulated are compared using AEB City collision scenario defined in Euro New Car Assessment Protocol (NCAP). Results show that the proposed PI control system is able to meet Euro NCAP AEB City set requirements.

Automatic Control of Prosthetic Socket Size for People WithTranstibial Amputation: Implementation and Evaluation.

The purpose was to design, implement, and test a control system for a motor-actuated, cable-panel prosthetic socket that automatically maintains socket fit by continuous adjustment of the socket size. Sockets with motor-driven adjustable panels were fabricated for participants with transtibial amputation. A proportional-integral control system was implemented to adjust socket size based on Socket Fit Metric (SFM) data collected by an inductive sensor embedded within the socket wall. The sensed distance was representative of limb-to-socket distance. Testing was conducted with participants walking on a treadmill to characterize the system's capability to maintain a set point and to respond to a change in the set point. Test results from 10 participants with transtibial amputation showed that the Integral of Absolute Error (IAE) to maintain a set point ranged from 0.001 to 0.046 mm with a median of 0.003 mm. When the set point was changed, IAE errors ranged from 0.001 to 0.005 mm, with a median of 0.003 mm. An IAE of 0.003 mm corresponded to approximately a 0.08% socket volume error, which was considered clinically acceptable. The capability of the control system to maintain and respond to a change in set point indicates that it is ready for evaluation outside of the laboratory. Integration of the developed control system into everyday prostheses may improve quality of life of prosthesis users by relieving them of the burden of continually adjusting socket size to maintain fit.

Adaptive Backstepping Control for Synchronous Reluctance Motor Based on Intelligent Current Angle Control

The design and analysis of a novel current angle-based adaptive backstepping (ABS) speed control system for a synchronous reluctance motor (SynRM) drive system is presented in this article. First, a proportional-integral control system with field-oriented control is described. Owing to the unmodeled dynamics and magnetic saturation effects of the SynRM, currently, there is no predominant way to design the command of the d-axis current for the SynRM. Therefore, an ABS based on the current angle control (ABS-CAC) system is designed for the speed tracking of SynRM. The ABS speed tracking control is proposed to generate the stator current command, and a lookup table of the results of maximum torque per ampere (MTPA) analysis by using the finite element analysis method is proposed to provide the current angle commands. Moreover, to improve the transient dynamic response of SynRM under MTPA operating conditions, an intelligent speed transient control system using a recurrent Hermite fuzzy neural network is developed to generate the compensated current angle command. The proposed intelligent ABS-CAC is implemented in a 32-bit floating-point digital signal processor TMS320F28075. Finally, some experimental results are provided to demonstrate the robustness and effectiveness of the proposed control system.

Identification and Tuning Methods for PI Control Systems Based on Symmetric Send-on-delta Sampling

Two procedures for the definition of an autotuning algorithm for event-based proportional-integral (PI) control systems are proposed in this contribution. Firstly, a method for the identification of the most common transfer functions used for tuning PI controllers is explained. The rationale of the identification method is based on the limit cycles that the event-based sampler and a convenient tuning of the PI controller can produce in the closed loop. The identification method is designed for controller tuning, that is, fitting the behaviour of the process in the range of frequencies between ω-180° and ω-135°. Secondly, a new method for tuning the event-based PI controller is explained. The main control objective of the new tuning rule is to generate a set of parameters that introduces the process in a limit cycle at a user-specified inter-event time. However, by introducing an oscillation margin in the tuning rule, the method generalizes to produce controller parameters that avoid limit cycles with a certain robustness margin. Simulations demonstrate the effectiveness of the tuning rule to force the system to oscillate at a specified frequency as a consequence of the events triggered by the event-based sampler.