Proportional-integral Control Scheme Research Articles

PD and PI Control for the Lag Consensus of Nonlinear Multiagent Systems With and Without External Disturbances.

In this article, we investigate the lag consensus and lag H∞ consensus problems for second-order nonlinear multiagent systems (MASs) by utilizing the proportional-derivative (PD) and proportional-integral (PI) control methods. On the one hand, a criterion is developed for ensuring the lag consensus of the MAS by choosing an appropriate PD control protocol. Moreover, a PI controller is also provided to guarantee that the MAS can achieve lag consensus. On the other hand, several lag H∞ consensus criteria are also given for the case in which external disturbances appear in the MAS; these criteria are developed by exploiting the PD and PI control strategies. Finally, the devised control schemes and the developed criteria are verified by employing two numerical examples.

Performance improvement of predictive voltage control for interlinking converters of integrated microgrid

Voltage quality in Renewable Energy System may be harmed by varying power demand and fluctuating energy source outputs. The model predictive voltage (MPV) control approach is suggested in this study. The proposed control strategy enables appropriate power interactions between the AC and DC subgrids while sharing power fluctuations in a coordinated way. Both the AC and DC subgrids support each other in accordance with their normalized relative changes in AC frequency and DC voltage, respectively. A typical photovoltaic (PV) wind-battery-based hybrid AC/DC microgrid is modelled and investigated, and the usefulness of the proposed scheme is validated under the renewable energy variations and load perturbations. MATLAB/Simulink has validated the efficacy of the suggested strategy. A set of comparative tests was conducted between conventional Proportional-Integral (PI) control, Sliding Mode Control (SMC) strategies, and a novel Model Predictive Voltage Control (MPV) approach under diverse conditions. The aim was to comprehensively assess the advantages of the proposed MPV method. Results demonstrate that, in contrast to traditional PI and SMC control strategies, the MPV technique exhibits distinct benefits in terms of superior control performance and achieves exceptionally low Total Harmonic Distortion (THD) values. Ultimately, the proposed solution not only raises the performance standard but also ensures the stable operation of the Renewable Energy System (RES).

A Fast and structurally simple control scheme based on a fuzzy supervisory approach for wireless power transfer systems

AbstractWireless power transfer (WPT) is a promising technology and has attracted many researchers’ attention due to its inherent advantages such as reliability, safety, flexibility, and low maintenance costs. For WPT systems, the output voltage varies according to the variable loads and the coupling coefficient conditions, affecting system stability and efficiency. In this regard, this study is concerned with the tracking control problem of WPT systems based on fuzzy supervisory proportional–integral (PI) controller and a phase shift modulation technique, which is for the first time proposed by this paper. By introducing appropriate fuzzy rules, the proposed control scheme not only can track the desired output voltage but also has better robust behaviour to deal with the problem of variations of circuit elements and coupling coefficients. That is, the proposed fuzzy supervisory PI control scheme can perform better than the traditional PI controller. Analysis of the designed control method and simulation results validate the superiority of the proposed method over the traditional PI controller in all conditions, which can make it a suitable choice for the applications of electric vehicle charging systems. The analysis is supported by experimental measurements.

Effective speed control of brushless DC motor using cascade 1PDf-PI controller tuned by snake optimizer

This paper introduces a cascade one proportional derivative incorporating filter (1PDf)-proportional integral (PI) controller abbreviated as c-1PDf-PI to deal effectively with the speed control issue of brushless DC (BLDC) motors. Two problems exist with implementing this controller such as iterated integral overflow and derivation-based chattering owing to the noise. The former is resolved by using an equivalent expression for the integral operation, while the latter is addressed by putting a first-order filter on the derivative term. To achieve the best performance from the controller, snake optimizer (SO) is fruitfully employed for optimizing the controller parameters without need for expert knowledge/interpretation. Here, a more reasonable cost function to assess the candidate solutions is also described. Simulations and laboratory experiments using DSP of TI TMS320F28335 are performed and the results are presented which show that the reference tracking performance, torque disturbance capability and robustness of the c-1PDf-PI controller have potential. These results are also contrasted by those offered by PI and 1PDf speed control schemes individually, affirming the superior performance of our proposal. As per the results, discussion and observation of this research, we stress that good performance and simplicity are salient advantages of the c-1PDf-PI controller, rendering it a good alternative over the complicated controller designs.

Power Source Converter Based on a Variable-Domain Fuzzy PI Control

The inadequacy of conventional control strategies for multi-phase interleaved parallel circuits in terms of adaptive adjustment makes it difficult to meet the power source design requirements for transient electromagnetic detection systems. This paper introduces a novel approach, the variable-domain fuzzy proportional–integral (PI) adaptive control strategy. This strategy dynamically adjusts the fuzzy domain in real time based on the input error magnitude, ensuring improved control effectiveness. By leveraging the benefits of both the functional scaling factor and the fuzzy reasoning scaling factor, we design scaling factors for the input and output domains to enhance control precision. The focus of this study is on a four-phase interleaved parallel converter, emphasizing the design of the variable-domain fuzzy PI control strategy for the voltage outer loop within the traditional dual closed-loop structure. An experimental prototype with a 220 Vac input, 380 V output, and a power rating of 1000 W is constructed. A comparative analysis between fuzzy control and variable-domain fuzzy PI control is conducted in the voltage outer loop of the dual closed-loop control. Results reveal that dual closed-loop control with variable-domain fuzzy PI control for the voltage outer loop significantly enhances system stability. The startup time to reach the steady state is reduced to 0.632 s, with an overshoot of 28.8 V. Transitioning from 25% load to full load takes only 0.096 s, resulting in a minimal drop of 21.4 V and an overshoot of 13.4 V. Similarly, switching from full load to 25% load in 0.167 s exhibits an overshoot of only 19.6 V. The adaptive regulation capability of the converter is markedly improved, showcasing smaller overshoots and higher-level controlling effectiveness.

A Composite Super-Twisting Sliding Mode Approach for Platform Motion Suppression and Power Regulation of Floating Offshore Wind Turbine

The floating platform motion of an offshore wind turbine system can exacerbate output power fluctuations and increase fatigue loads. This paper proposes a new scheme based on a fast second-order sliding mode (SOSM) control and an adaptive super-twisting extended state observer to suppress the platform motion and power fluctuation. Firstly, an affine nonlinear model of the floating wind turbine pitch system is constructed. Then, a fast SOSM pitch control law is adopted to adjust the blade pitch angle, and a new adaptive super-twisting extended state observer is constructed to achieve total disturbance observation. Finally, simulations are conducted under two cases of wind and wave conditions based on FAST (fatigue, aerodynamics, structures, and turbulence) and MATLAB/Simulink. Compared with the traditional proportional integral (PI) control scheme and standard super-twisting control scheme, the platform roll under the proposed scheme is reduced by 13% and 4%, and pitch is reduced by 16% and 3% in Case 1. Correspondingly, the roll is reduced by 9% and 15%, and pitch is reduced by 7% and 1% in Case 2. For the tower top pitch and yaw moment, load reductions of 7% and 3% or more are achievable compared with those under the PI control scheme. It is indicated that the proposed scheme is more effective in suppressing floating platform motion, stabilizing output power of the wind turbine system, and reducing tower loads.

Comparative Analysis between Dynamic Voltage Restorer and PWM-Switched Autotransformer in Voltage Sag Mitigation

This research aims to address the problem of voltage sag, which is a significant power quality issue in power systems. Voltage sag can lead to power transmission and distribution line tripping and cause damage to electrical equipment. Various voltage sag mitigation devices have been developed to reduce the impact of voltage sag. In this study, we analyze the effectiveness of three types of voltage sag mitigation devices under different voltage sag conditions: three-phase fault, multi-phase fault, and energizing transformer inrush current. The three types of mitigations analyzed are Dynamic Voltage Restorer (DVR) with Park's transformation, DVR with Artificial Neural Network (ANN) controller, and PWM-switched autotransformer with Proportional-Integral (PI) controller. The simulation results using MATLAB demonstrate that all voltage sag conditions have been mitigated except for PWM-switched autotransformer during voltage sag caused by energizing transformer inrush current due to limitations of the PI controller scheme. Based on the analysis of input and output voltage waveforms, three-phase voltage magnitude before and after mitigation, and Total Harmonic Distortion (THD) value after mitigation, DVR with ANN controller is identified as the most effective voltage sag mitigation device, followed by DVR with Park's transformation and PWM-switched autotransformer.

Simulation and experimental validation of chaotic-billiards optimization algorithm for advanced variable speed drive

AbstractTo appropriately construct the proportional–integral control strategy for stability enhancement of the speed performance of induction motors and current waveform, this study describes a novel optimization approach for three-phase induction motor pumps speed drive. This work provides a novel chaotic-billiards optimizer (C-BO) method. These strategies were implemented in the same hardware with modifications in the control algorithm. Both simulation and experimental investigation are carried out, and a comparative analysis of the optimized parameters is presented. The system setup is prepared and simulated in the environment of MATLAB/Simulink. The same system has been presented experimentally. The laboratory experiment is carried out in the Smart Systems Co laboratory using simulators of induction motors designed and manufactured by the Lucas-Nuelle company. The asynchronous motor is controlled by 3 phase variable speed drive A1000 of Yaskawa Co. The "ActiveServo" software included ensures user-friendly operation and visualization of the measurement values. Experiment has validated the speed the effectiveness of C-BO. The speed control and flux control using C-BO have a fast response even under pump load and improvement in rising time by 41.65% as compared to GA. The novel optimization model C-BO has improvement in integral square error (ISE) of the three controllers by 38.91% as compared to genetic algorithm systems. Furthermore, the difficulty of developing both systems is the same. The C-BO algorithm stands out for its uncomplicated design, low requirement for design specifications, high rate of convergence, and minimal computational strain.

Enhanced production of human interferon α2b in glycoengineered Pichia pastoris by robust control of methanol feeding and implications of various control strategies

Interferon α2b (IFNα2b), a prominent cytokine molecule, is bestowed with immunomodulatory, antiproliferative, and antiviral properties. Like any other recombinant protein produced in methylotrophic yeast Pichia pastoris, Human interferon α2b (huIFN α2b) production in P. pastoris depends on methanol utilization during the induction phase. This study investigates the impact of varying residual methanol concentrations on huIFNα2b productivity and assesses control strategies towards the tight control of residual methanol concentration in fed-batch experiments carried out in the fermentation calorimeter. Real-time monitoring of P. pastoris metabolism was facilitated by metabolic heat rate measurements and exhaust gas analyser. Optimal methanol concentration screening performed at different concentrations viz., 1.5, 3, 5, and 7 g/L, revealed the highest huIFNα2b productivity at 3 g/L (0.21 mg/g h). Methanol sensor provided input data for three different control strategies (on/off, proportional integral (PI), and model-based adaptive PI) to maintain optimal methanol levels (3 g/L). Model-based adaptive control resulted in the highest huIFNα2b yield, productivity (244.34 mg/L, 0.31 mg/g h), surpassing on/off (188.7 mg/L, 0.16 mg/g h) and PI (202.3 mg/L, 0.28 mg/g h) control strategies. The model-based adaptive PI control framework developed in this study could be employed for other heterologous protein production in P. pastoris.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

Distributed fault-tolerant PI load frequency control for power system under stochastic event-triggered scheme

This article investigates the load frequency control problem of interconnected power systems by proposing a distributed fault-tolerant proportional integral (PI) control strategy. Firstly, a unified actuator fault model is established, and a distributed fault-tolerant PI control strategy is proposed on the basis of the actuator fault model. Then, a stochastic event-triggered scheme (SETS) based on stochastic sampling period is developed to relieve the redundant occupation of network communication resources. Further, an asymptotical stability criterion with H∞ performance is established by means of the Lyapunov method. Finally, illustrative examples are presented from two cases, isolated power system and interconnected power systems, to demonstrate the effectiveness of the designed control approach and the superiority of SETS compared with periodic event-triggered scheme.

Simulation and optimization of hydrogen consumption in a fuel cell/battery hybrid vehicle

This paper presents a physics-based mathematical model for a fuel cell hybrid vehicle system. The performance design of fuel cell hybrid electric vehicles (FCHEVs) is an area of interest in transportation applications. FCHEV is a combination of a proton exchange membrane fuel cell (PEMFC), with a battery and associated DC/DC and DC/AC converters. Suitable batteries are used in FCHEV acts as a backup system with efficient energy management. The battery is designed for fast power transfer during transient response and constant performance without hydrogen. The power management schemes discussed are consistent with the most common modern power management techniques used in fuel cell vehicle applications, including: the state engine control strategies, rule-based fuzzy logic strategies, classical proportional integral control strategies, frequency fuzzy logic decoupling/control minimization strategies. Therefore, we tested and evaluated the reliability of the model using the MATLAB/Simulink environment, with mixed results.

Event-Triggered Adaptive Fuzzy PI Control of Uncertain Fractional-Order Nonlinear Systems With Full-State Constraints

This article is devoted to the adaptive fuzzy proportional-integral (PI) control of uncertain fractional-order (FO) strict-feedback nonlinear systems subject to full-state constraints (FSCs) and external disturbances via an event-triggered (ET) control technique. First, fuzzy logic systems are introduced to approximate unknown nonlinear functions. Second, a novel adaptive FOPI controller with a simple structure and low calculation cost is developed, and it is able to determine its PI gains adaptively and automatically without involving manual operation or trial and error procedures, which is an improvement compared with the traditional PI control scheme. Third, a relative-threshold-based ET controller is constructed so that the communication load from the controller to the actuator can be significantly decreased, and there is no Zeno behavior. Then, a barrier Lyapunov function is introduced into the backstepping recursive process to avoid FSC violations. Moreover, all closed-loop signals are bounded, and the output is driven to follow the desired output through the FO Lyapunov stability analysis. In particular, the proposed approach does not require a priori knowledge of the FO derivative of the reference signal. Finally, the feasibility of the proposed scheme is verified by numerical simulations.

Hybrid Controlled Multi-Input DC/DC Converter for Electric Vehicle Application

The use of energy sources for electric vehicle (EV) applications relies heavily on the power electronic interlinking and its successful control mechanism. A hybrid adaptive neuro-fuzzy inference system (ANFIS) proportional-integral (PI) based control strategy for a multi-input DC-DC converter is investigated in-depth for this purpose. At steady state, the proposed hybrid ANFIS PI controller uses the standard PI controller, and during the transient state, the ANFIS PI control strategy is used. Furthermore, the proposed control scheme aids in the tracing of a predetermined speed pattern in order to achieve total EV. A detailed simulation analysis of the proposed control strategy is carried out and its performace was compared with traditional controllers. The result indicated that the designed control strategy is reliable since it offers bidirectional power management, high gain with quick reaction, and low steady-state error. It enables rapid tracking and achieves improved dynamic response through increased flexibility and energy efficiency. The proposed scheme reduces component stress, as demonstrated by a reliability assessment using the MIL-HDBK-217F military handbook. At nominal power, the converter achieves 96.954% efficiency. An 80 W converter prototype was created and tested in the laboratory to monitor the real-time system’s response time.

Research on control strategy of photovoltaic hydrogen generation system based on Fuzzy PI control

Under the goal of carbon peak and neutrality, the search for efficient and clean green energy has attracted widespread attention. Photovoltaic (PV) hydrogen production has become a research hotspot because of its clean and near-zero carbon emissions. However, due to the influence of natural light, PV power generation is volatile and intermittent, reducing the system’s stability. In order to improve the stability of the system, a large number of power electronic devices and control strategies are applied to the PV hydrogen production system. Among them, the Buck circuit using proportional–integral (PI) control is the most widely used. However, the PI control has a long response time and large ripple, which affects the energy efficiency of PV hydrogen production. This paper proposes a PV hydrogen production system control strategy based on Fuzzy PI control. In the Matlab/Simulink environment, a simulation model of a PV hydrogen production system composed of PV cells, proton exchange membrane electrolyzers, and DC/DC units is built. The DC/DC unit uses a Buck circuit. Simulation analysis compares the output voltage and current ripple of the PV hydrogen production system model under different working conditions using the Fuzzy PI control and classical PI control strategies. Finally, the simulation results verify that the proposed control strategy reduces the system output voltage and current ripple.

Improving the performance of grid connected wind generator with a PI control scheme based on the metaheuristic golden eagle optimization algorithm

• A novel Golden Eagle optimization algorithm (GEOA) is proposed. • GEOA is used to improve the Transient and Dynamics stability of wind farm. • Transient and Dynamics stability of grid-tied PMSG-VSWT is investigated during symmetrical, unsymmetrical faults and Stochastics wind variation at that site. • The results of GEOA are compared with the results of NR, PSO and GOA. • The results shown that the GEOA is robust and competitive. Wind power installations are globally established thoroughly, resulting in significant electrical power penetration into power grids. Consequently, tremendous efforts are expended to improvise the performance of the grid-connected Permanent Magnet Synchronous Generator driven by a Variable-Speed Wind Turbine (PMSG-VSWT). In this work, eight cascaded Proportional Integral (PI) controllers in the machine side and grid side converters are optimally tuned by the golden eagle optimization algorithm with the purpose of enhancing the dynamics and transient stability performances of the grid connected PMSG-VSWT.The integration of the square error criterion was utilised as a fitness function in the optimization process, which was processed using the simulation-based optimization technique. The proposed control technique's efficacy was compared to various optimal PI controllers under symmetrical and unsymmetrical fault conditions. This study also employed the measured real-time events collected at the Coimbatore wind farm in Tamil Nadu (India) to produce realistic results and aid in the pursuit of higher performance. The wind farm modelling and simulation were processed using the DIgSILENT power factory/MATLAB Simulink environment and was utilized to test the proposed control approach's achievability. Finally, the study demonstrates that the golden eagle optimization technique improves the wind farm's performance when compared to observed events and other techniques.

Integrated DC/DC Converter Topology Study for Fuel Cell Hybrid Vehicles with Two Energy Sources

Conventional hybrid vehicles with two energy sources require two separate on-board DC/DC converters to connect the battery and the fuel cell, which have the disadvantages of large size, high cost, high losses and few applicable operating conditions. To address this situation, this paper proposes an optimized on-board integrated DC/DC converter with a non-isolated multi-port scheme that integrates a unidirectional port for the fuel cell and a bidirectional port for the battery and load. This can achieve a combined energy supply and recovery with a single integrated converter, effectively overcoming the above disadvantages. The optimized converter topology is relatively simple, and the magnetic losses of the transformer are removed. Furthermore, the switched capacitor is introduced as a voltage doubling unit to achieve high-gain output, so the fuel cell and battery voltage demand levels are reduced under the same load conditions. In addition, it has superior performance in system energy management for hybrid vehicles, which can distribute power and switch operating states by controlling the on/off of switching devices to make it suitable for five driving conditions. This paper discusses in detail the operating principles of the converter and analyzes its steady-state performance under five operating modes, derives its dynamic model, and proposes a proportional-integral control scheme. Finally, the simulation model of the topology is built by Matlab/Simulink software to verify the converter operation in each driving state, and the simulation experimental results verify the applicability of the proposed integrated DC/DC converter topology.

An improved energy management strategy for fuel cell/battery/supercapacitor system using a novel hybrid jellyfish/particle swarm/BAT optimizers

In this paper, a fuel cell (FC) hybrid emergency system is presented. It contains fuel cells, batteries, and supercapacitors which are effective for different conveyance applications. Abbreviating the hydrogen consumption and incrementing the lifetime of power sources are the key factors of each energy management strategy (EMS). An incipient proposed EMS depending on an optimized proportional-integral (PI) controller is presented considering FC efficiency to ensure the operation of the FC stack within its maximum efficiency region and reduce the stress on it which in turn leads to reducing its hydrogen consumption. Regarding tunning the PI controller gains (Kp,Ki), a proposed hybrid metaheuristic optimization technique named JSPSOBAT has been presented which merges jellyfish (JS) optimizer, particle swarm optimizer (PSO) and BAT optimizer to achieve the balance between exploration and exploitation phases. To firstly validate the effectiveness of the JSPSOBAT technique, a comparative study with other single and hybrid metaheuristic optimization techniques is presented by testing on 50 complex benchmark functions. Then, the JSPSOBAT is used to select the controller gains of the proposed PI controller. A 30 min load profile of a More Electric Aircraft (MEA) is used. The performance of the proposed EMS using the proposed optimization technique is compared with other EMS such as state machine control strategy, classical PI control strategy, equivalent consumption minimization strategy, external energy maximization strategy, and the results show that the proposed technique produces the best performance.

Design and comparison of model-based controllers for an automotive air conditioning system in an electric vehicle

The air conditioning system in an electric vehicle has to fulfill multiple tasks, which are ensuring thermal comfort for the passengers and tempering the battery. In this paper four model-based control approaches for the air conditioning system are developed. The approaches are compared regarding their ability to track desired references, reject disturbances and avoid windup effects. Two approaches are feedback controllers, a decentralized proportional-integral control strategy and a centralized linear-quadratic-integral control strategy. The other two approaches combine the feedback controllers with an inversion based feedforward controller in a two degree of freedom control structure. In addition, the four concepts are complemented by the Hanus-Conditioned anti-windup mechanism.Three of the four proposed controllers explicitly consider the couplings of the multiple-input-multiple-output system, which allows for a high-performance control. Additionally, all approaches have the benefit of being based on a physical model of the system to be controlled. The development and identification of this physical model is part of the paper. The physical basis for the controllers ensures a high level of reusability and therewith an efficient controller design process. The proposed control concepts are validated and compared by measurement data from a testbench.

Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation

The present research produces a new technique for the optimum operation of an isolated microgrid (MGD) based on an enhanced block-sparse adaptive Bayesian algorithm (EBSABA). To update the proportional-integral (PI) controller gains online, the suggested approach considers the impact of the actuating error signal as well as its magnitude. To reach a compromise result between the various purposes, the Response Surface Methodology (RSMT) is combined with the sunflower optimization (SFO) and particle swarm optimization (PSO) algorithms. To demonstrate the success of the novel approach, a benchmark MGD is evaluated in three different Incidents: (1) removing the MGD from the utility (islanding mode); (2) load variations under islanding mode; and (3) a three-phase fault under islanding mode. Extensive simulations are run to test the new technique using the PSCAD/EMTDC program. The validity of the proposed optimizer is demonstrated by comparing its results with those obtained using the least mean and square root of exponential method (LMSRE) based adaptive control, SFO, and PSO methodologies. The study demonstrates the superiority of the proposed EBSABA over the LMSRE, SFO, and PSO approaches in the system’s transient reactions.