Classical Proportional Integral Research Articles

Adaptive Controller PI-Fuzzy Logic Speed for Brushless DC Motor Drive Supplied by PEMFC Cell Optimized by P&O

Brushless Direct Current (BLDC) motors have recently gained momentum. In this study, a fuel cell stack, namely, a Proton-Exchange Membrane Fuel Cell (PEMFC), one of the promising renewable energy technologies, is chosen for a brushless DC motor. To improve the performance of PEMFC, an efficient maximum power point tracking (MPPT) algorithm was applied to the DC/DC boost converter. To this end, the perturbation and observation (P&O) algorithm were developed. This work proposes an adaptive controller proportional-integral (PI)-fuzzy logic speed for the BLDC. To evaluate its performance, the proposed controller was simulated under several conditions: load disturbance and reference speed variation. This controller is analyzed and compared with the classical PI controller. Therefore, the control performance parameters, such as rise time, settling time, steady-state error, and overshoot, were determined and compared. This system is analyzed and simulated using MATLAB/Simulink software.

Development and Experimental Implementation of Optimized PI-ANFIS Controller for Speed Control of a Brushless DC Motor in Fuel Cell Electric Vehicles

This paper compares the performance of different control techniques applied to a high-performance brushless DC (BLDC) motor. The first controller is a classical proportional integral (PI) controller. In contrast, the second one is based on adaptive neuro-fuzzy inference systems (proportional integral-adaptive neuro-fuzzy inference system (PI-ANFIS) and particle swarm optimization-proportional integral-adaptive neuro-fuzzy inference system (PSO-PI-ANFIS)). The control objective is to regulate the rotor speed to its desired reference value in the presence of load torque disturbance and parameter variations. The proposed controller uses a dSPACE platform (MicroLabBox controller board). The experimental prototype comprises a PEMFC system (the Nexa Ballard FC power generator: 1.2 kW, 52 A) and a brushless DC motor BLDC of 1 kW 1000 rpm. The PSO-PI-ANFIS controller presents better performance than the PI-ANFIS and classical PI controllers due to its ability to optimize the PI-ANFIS controller’s parameters using the particle swarm optimization (PSO) algorithm. This optimization results in improved tracking accuracy and reduced overshoot and settling 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.

Model predictive control of power plant cycling using Industry 4.0 infrastructure

This work involves the Industry 4.0 infrastructure developed at West Virginia University (WVU) for process systems applications. This infrastructure emulates an interconnected environment, enabling communication and data sharing among different components for use in academic and industrial settings. The current infrastructure encompasses a power plant model interacting with online load demand, distributed control systems, and data analytics components. The developed model of a sub-critical coal-fired power plant is employed to evaluate classical and advanced control strategies using this infrastructure under different operating conditions. Specifically, the control strategies evaluated include classical proportional–integral–derivative (PID) and advanced model predictive control (MPC) structures, focusing on the dynamic matrix control (DMC) approach with an in-house modified sequential quadratic programming (SQP) solver. The MPC approach is developed and simulated in closed loop to address setpoint tracking and load-following scenarios under power plant cycling conditions. In this infrastructure, the PI System centralizes all the information received from the power plant model and the online power demand and sends the control actions calculated by the MPC back to the power plant model for implementation. Results of the implementation of these control strategies are discussed focusing on power plant operating regions associated with cycling.

Capability Exploration of Extended-State Observer-Based Control Under the Uncertain Case of Disturbance and Actuator Saturation

Considering a class of nonlinear systems subject to actuator saturation and uncertain control input, this article explored the control capability of an extended-state observer-based active disturbance rejection controller in the presence of disturbances and uncertainties. First, the actuator saturation is handled with the convex hull in the extended-state observer-based control scheme, and an ellipsoid invariant set inside the domain of attraction for control systems is obtained using the Lyapunov method. Second, the enlargement problem of the invariant set is also studied, which illustrates that the convex hull of the ellipsoid invariant sets is also an enlargement invariant set. Finally, comprehensive comparisons with existing methods on a brushless dc motor demonstrate the stronger capability of the designed controller on disturbance rejection and actuator saturation. The results show that the disturbance offset and performance index (integrated time and absolute error and integral absolute error) are about half of the classical proportional–integral–derivative(PID) and double-integral sliding-mode control, or the disturbance rejection capability is about twice in this case.

Comparative Performance of UPQC Control System Based on PI-GWO, Fractional Order Controllers, and Reinforcement Learning Agent

In this paper, based on a benchmark on the performance of a Unified Power Quality Conditioner (UPQC), the improvement of this performance is presented comparatively by using Proportional Integrator (PI)-type controllers optimized by a Grey Wolf Optimization (GWO) computational intelligence method, fractional order (FO)-type controllers based on differential and integral fractional calculus, and a PI-type controller in tandem with a Reinforcement Learning—Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3) agent. The main components of the UPQC are a series active filter and an Active Parallel Filter (APF) coupled to a common DC intermediate circuit. The active series filter provides the voltage reference for the APF, which in turn corrects both the harmonic content introduced by the load and the VDC voltage in the DC intermediate circuit. The UPQC performance is improved by using the types of controllers listed above in the APF structure. The main performance indicators of the UPQC-APF control system for the controllers listed above are: stationary error, voltage ripple, and fractal dimension (DF) of the VDC voltage in the DC intermediate circuit. Results are also presented on the improvement of both current and voltage Total harmonic distortion (THD) in the case of, respectively, a linear and nonlinear load highly polluting in terms of harmonic content. Numerical simulations performed in a MATLAB/Simulink environment demonstrate superior performance of UPQC-APF control system when using PI with RL-TD3 agent and FO-type controller compared to classical PI controllers.

Shunt active power filter with a three-phase neutral-point-clamped inverter for power quality improvement based on fractional order proportional integral control

The quality of the electrical wave is far from being perfect, due to the use of nonlinear loads that generate current harmonics and consume reactive power. Knowing the origins and the effects of pollution harmonics on the electrical networks, this study is to implement a system of active filters with three levels inverter hysteresis-based control which injects into the network a current equal to those absorbed by the polluting load and out of phase, thus leading the supply current to be sinusoidal. The regulation and the stability of the power supply of the filter during a variation of the load is ensured by a classical proportional integral (PI) controller and then by a fractional order PI controller (FOPI). Several cases of simulation accompanied by a temporal analysis and a spectral analysis were carried out starting from the model of the supply network, nonlinear load and parallel active filter. The simulation results of the proposed methods show that the parallel active filter can effectively suppress the current harmonic to force the supply current to be sinusoidal and compensate the reactive power generated by the nonlinear load.

Improved Performance in the Control of DC-DC Three-Phase Power Electronic Converter Using Fractional-Order SMC and Synergetic Controllers and RL-TD3 Agent

In this article, starting from a benchmark represented by a Direct Current-to-Direct Current (DC-DC) three-phase power electronic converter used as an interface and interconnection between the grid and a DC microgrid, we compare the performances of a series of control structures—starting with the classical proportional integrator (PI) type and continuing with more advanced ones, such as sliding mode control (SMC), integer-order synergetic, and fractional-order (FO) controllers—in terms of maintaining the constant DC voltage of the DC microgrid. We present the topology and the mathematical modeling using differential equations and transfer functions of the DC-DC three-phase power electronic converter that provides the interface between the grid and a DC microgrid. The main task of the presented control systems is to maintain the DC voltage supplied to the microgrid at an imposed constant value, regardless of the total value of the current absorbed by the consumers connected to the DC microgrid. We present the elements of fractional calculus that were used to synthesize a first set of FO PI, FO tilt-integral-derivative (TID), and FO lead-lag controllers with Matlab R2021b and the Fractional-order Modeling and Control (FOMCON) toolbox, and these controllers significantly improved the control system performance of the DC-DC three-phase power electronic converter compared to classical PI controllers. The next set of proposed and synthesized controllers were based on SMC, together with its more general and flexible synergetic control variant, and both integer-order and FO controllers were developed. The proposed control structures are cascade control structures combining the SMC properties of robustness and control over nonlinear systems for the outer voltage control loop with the use of properly tuned synergetic controllers to obtain faster response time for the inner current control loop. To achieve superior performance, this type of cascade control also used a properly trained reinforcement learning-twin delayed deep deterministic policy gradient (RL-TD3) agent, which provides correction signals overlapping with the command signals of the current and voltage controllers. We present the Matlab/Simulink R2021b implementations of the synthesized controllers and the RL-TD3 agent, along with the results of numerical simulations performed for the comparison of the performance of the control structures.

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.

Cascaded Controller for Controlling DC Bus Voltage in Mismatched Input Powers

The lack of uniform production of panels in photovoltaic (PV) systems and snowball effects in fuel cell (FC) systems led us to use a separate dc–dc converter. In addition, in the PV and FC systems, the output voltage is low. Hence, dc–dc boost converters are commonly used in a series connection. Each small unit of a PV or FC operates independently. Therefore, another problem arises in the system that uses the series connection of the converter's output. Owing to the unequal electricity production of the cells, a voltage imbalance occurs at the output terminals. To create voltage balance, a modular structure grounded on a three-level boost converter was investigated. The main objective of this article is to design the control of a modular topology, allowing perfect control of the dc-bus voltage, even with mismatched input powers. The proposed control strategy ensures dynamical properties independent of the operating point, voltage balance, and robust control of the dc output voltage with respect to load perturbations. To ensure the balance of voltages and the control of currents, a sliding mode controller based on indirect synthesis was used. To control the dc–bus voltage, an energy controller is proposed that ensures dynamical performance independent of the operating conditions. The performance in terms of tracking and regulation, as well as its effect on the voltage balance of the output capacitors, was validated through simulations and experimental results. A comparison with a classical proportional integral (PI) approach for controlling the dc-bus voltage was performed.

Simulation of adaptive gain control via 2-D lookup table for isolated hybrid micro-grid system

This paper presented a smartness 2-D lookup table (2-DLT) control by means of adaptation gain to develop a frequency controller and facilitate the power-sharing requirements in an isolated micro-grid system. This intelligence of an expert controller adopts the scale of adaptation gain for estimating control design. Synchronous power generators are commonly used to provide power to distant and isolated regions where grid expansion is expensive due to economic and technical constraints. Load frequency control (LFC) technology challenges to guarantee the reliability and stability regarding the system. It is known that conventional control methods are unreliable due to frequency variation and sudden changes in the load or failure generation. Traditional control and criteria may not be appropriate for the new structural networks, such as micro-grid. In this work, the performance of the proposed 2-DLT controller is examined and compared to the classical proportional integral (PI) controller and artificial neural network (ANN). The simulation system is implemented and tested using MATLAB/Simulink.

Optimal Tuning of FOPID-Like Fuzzy Controller for High-Performance Fractional-Order Systems

This paper addresses improvements in fractional order (FO) system performance. Although the classical proportional–integral–derivative (PID)-like fuzzy controller can provide adequate results for both transient and steady-state responses in both linear and nonlinear systems, the FOPID fuzzy controller has been proven to provide better results. This high performance was obtained thanks to the combinative benefits of FO and fuzzy-logic techniques. This paper describes how the optimal gains and FO parameters of the FOPID controller were obtained by the use of a modern optimizer, social spider optimization, in order to improve the response of fractional dynamical systems. This group of systems had usually produced multimodal error surfaces/functions that occasionally had many variant local minima. The integral time of absolute error (ITAE) used in this study was the error function. The results showed that the strategy adopted produced superior performance regarding the lowest ITAE value. It reached a value of 88.22 while the best value obtained in previous work was 98.87. A further comparison between the current work and previous studies concerning transient-analysis factors of the model’s response showed that the strategy proposed was the only one that was able to produce fast rise time, low-percentage overshoot, and very small steady-state error. However, the other strategies were good for one factor, but not for the others.

Amelioration Effectiveness of Torque and Rotor Flux Control Applied to the Asynchronous Generator for Dual-rotor Wind Turbine using Neural Third-order Sliding Mode Approaches

In this paper, a neural third-order sliding mode-direct torque control (NTOSM-DTC) for an asynchronous generator (AG) based dual-rotor wind turbine (DRWT) is proposed. The classical DTC strategy with traditional proportional-integral (PI) controllers has been widely applied to induction machines in recent years due to the high characteristics that it provides in comparison with the classical DTC switching technique. Meanwhile, it has a major drawback that are the significant current, rotor flux and torque ripples generated by the traditional PI controllers. To overcome these drawbacks, the improvement of this control technique by removing these controllers is designed in this paper. The proposed intelligenet nonlinear control technique is based on replacing the classical PI controllers with neural TOSM controllers which will have the same inputs as these controllers. The simulation was performed in Matlab software, and the results obtained make it possible to evaluate the characteristics of the proposed intelligenet nonlinear control technique over the traditional one.

Gain-Scheduled Level Controller for FESTO MPS PA Station Tank

In this article, a Gain Scheduling (GS) approach is proposed to control the FESTO MPS PA Station Tank level by controlling the centrifugal pump. The open loop transfer function for each operating condition is experimentally obtained. The data from the open-loop system is imported to MATLAB in order to get the transfer function for each condition. The data shows that each operating point has a different transfer function in terms of gain and time constant. Based on this, a gain-scheduling controller is proposed to handle the variations of process model parameters. The proposed controller parameters are adapted based on the operating conditions of the system. These parameters include the tank capacitance and the flow pipe resistance and/or the valve resistance to the flow. Two different controller techniques for FESTO MPS PA Station Tank level control are applied, verified and compared. The first controller is based on classical Proportional Integral (PI) controller, while the second one is based on the proposed gain scheduling controller. Experimental results show that the proposed gain-scheduling controller can manipulate the process at different operating conditions with parameters variations in order to give uniform performance. The classical PI controller has failed to achieve this requirement at different operation conditions.

Optimal tuning of PI controllers using adaptive particle swarm optimization for doubly-fed induction generator connected to the grid during a voltage dip

This paper proposes the adaptive particle swarm optimization (APSO) technique to control the active and reactive power produced by a variable wind energy conversion system and the exchanged power between the electric grid and the system during a voltage dip (VD). Besides, to get the variable speed wind energy maximum power, a maximum power point (MPP) methodology is utilized. The system under study is a 5 MW wind turbine connected via a gearbox to a doubly-fed induction generator (DFIG). The DFIG stator is branched directly to the electrical network, while the Back-to-Back converters couple the rotor to the grid. The decoupled vector control of the rotor side converter and the grid side converter is established primarily by a conventional proportional-integral (PI) and a second level by an intelligent PI whose gains are tuned using the proposed control. The performances and results obtained by APSO tuned PI controllers are analyzed and compared with those attained by classical PI controllers through the MATLAB/Simulink. The superiority of the advised technique is examined during a two-phase short-circuit fault condition and confirmed by the reduced oscillations.

Finite control set model predictive torque control using sliding model control for induction motors

Finite control set model predictive torque control (FCS-MPTC) has become increasingly prevalent for induction motors (IM) owing to its simple concept, easy incorporation of constraints and strong flexibility. In traditional FCS-MPTC speed controller design, a classical proportional integral (PI) controller is typically chosen to generate the torque reference. However, the PI controller is dependent on system parameters and sensitive to the load torque variation, which seriously affects control performance. In this paper, a model predictive torque control using sliding mode control (MPTC + SMC) for IM is proposed to enhance the robust performance of the drive system. First, the influence of the parameter mismatches for FCS-MPTC is analyzed. Second, the shortcomings of traditional PI controller are derived. Then, the proposed MPTC + SMC method is designed, and the MPTC + PI and MPTC + SMC are compared theoretically. Finally, experimental results demonstrate the correctness and effectiveness of the proposed MPTC + SMC. In comparison with MPTC + PI, MPTC + SMC has the better dynamic performance and stronger robust performance against parameter variations and load disturbance.

Stability Based on PI Control of Three-Dimensional Chaotic Oscillatory System via DNA Chemical Reaction Networks.

The classical proportional integral (PI) controller of SISO linear system is realized by DNA chemical reaction networks (CRNs) in the previous work. Up to now, few works have been done to realize PI controller of chaotic system through DNA CRNs. In this paper, a three-dimensional chaotic oscillatory system and a PI controller of three-dimensional chaotic oscillatory system are proposed by DNA CRNs. The CRNs of chaotic oscillatory system are made up of catalysis modules, degradation module and annihilation module then chemical reaction equations can be compiled into three-dimensional chaotic oscillatory system by the law of mass action to generate chaotic oscillatory signals. The CRNs of PI controller are designed by an integral module, a proportion module and an addition module, which can be compiled into PI controller for stabilizing chaotic oscillatory signals. The simulations of Matlab and Visual DSD are given to show our design achieving the PI control of a three-variable chaotic oscillatory system.

A control strategy for an intermittently aerated and fed bioreactor to reduce aeration costs: A simulation study

The aim of this study was to apply the ASM1 model to predict the aeration cost of an intermittently aerated and fed bioreactor (IAFB) treating domestic wastewater at dissolved oxygen (DO) set points of 1–4 mg/L. In this work, the energy efficiency from the use of a classical Proportional-Integral (PI) DO controller installed in the IAFB system was compared to that of a DO cascade controller. In comparison to classical PI DO controller, a DO cascade controller set at 3 mg/L could reduce energy consumption by up to 16 %, decreasing the nitrification rate by only 5%. Moreover, a DO cascade controller at set point of 2.5 mg/L could lead to less energy consumption compared to a set point of 4.0 mg/L (by 41 %), reducing nitrification rate by only 7%. In particular, DO concentration higher than 3 mg/L can result in a higher air flow rate, which, however, does not drastically improve nitrification rate, causing an overaerated mode of operation, thus significantly increasing the aeration cost. These results indicate that the IAFB system can be accurately simulated through the use of ASM1 model within OpenModelica software, evaluating energy saving consumption scenarios during IAFB system operation.

Modified brain storming optimization technique for transient stability improvement of SVC controller for a two machine system

Purpose Static VAR compensators (SVC) have been recognized to be one of the most important flexible AC transmission systems devices used for mitigating the low-frequency electrochemical oscillations occurring in the system and for reactive power compensation, thereby improving the overall dynamic stability and efficiency of the system. The purpose of this paper is to optimize and dynamically tune the control parameters of the classical proportional integral and derivative (PID) controller of the SVC for a two-machine system by designing a new robust optimization technique. Design/methodology/approach The angular speed deviation between the two machines is used as an auxiliary signal to SVC for generation of the required damping output. To justify the efficacy of the system undertaken, a light load fault at time t = 1 s is projected to the system. The simulation is carried out in MATLAB/Simulink architecture. Findings The proposed technique helps in the enhancement of system efficiency, reliability and controllability and by effectively responding to the non-linearities taking place in a power grid network. The results obtained are indicative of the fact that the proposed modified brain storming optimization (MBSO) technique reduces system disturbances very quickly, increases the system response in terms of better rise time, settling time and peak overshoot and improves the efficiency of the system. Originality/value A detailed comparison of the MBSO technique is compared with the conventional brain storming optimization (BSO) and PID technique. Total harmonic distortion through fast Fourier transform is also compiled to prove that the values of the proposed MBSO method found out to be confined well within the prescribed IEEE-514 boundaries.

A Novel Control Strategy for PV/Wind/Battery Hybrid System Using High Gain Boost Converter

In this paper, a classic proportional–integral (PI) control strategy as an energy management strategy (EMS) and a microgrid stand-alone power system configuration are proposed to work independently out of grid. The proposed system combines photovoltaics (PVs), and Battery. The system supplies a dump load with its demand power. The system includes DC/DC and DC/AC converters, as well as a maximum power point tracking (MPPT) to maximize the harvested energy from PV array. The classic PI control strategy is used to control the main system parameters like state-of-charge (SOC) for the battery. The corresponding energy management and control strategy are proposed to realize the power balance among three ports in different operating scenarios, which comprehensively takes both the maximum power point tracking (MPPT) benefit and the battery charging/discharging management into consideration. The simulations are conducted using the Matlab/Simulink software to verify the operation performance of the proposed PV/battery hybrid distributed power generation system with the corresponding control algorithms, where the MPPT control loop, the battery charging/discharging management loop are enabled accordingly in different operating scenarios.