Gain PI Research Articles

Adaptive neural network control of a centrifugal chiller system

In this paper, a neuro-adaptive PI control strategy for a water-cooled centrifugal chiller system is developed, and its control performance is examined. The system model consists of a flooded evaporator, a flooded condenser, a centrifugal compressor, and an electronic expansion valve. The overall system consists of three control loops: a compressor speed control loop, an inlet guide vane (IGV) control loop, and a condenser liquid level control loop. A neuro-adaptive control strategy for compressor speed control was designed. The control performance of the chiller was tested by carrying out simulation runs using an integrated building and HVAC (IB-HVAC) system model. The major details of the IB-HVAC model are described in this paper. Results show that the neuro-adaptive controller can adapt to new system dynamics of the IB-HVAC system and give good setpoint tracking responses under a wide range of operating conditions. The results show that the neuro-adaptive controller performs better than the constant gain PI controller in terms of speed of response and setpoint tracking properties.

Optimized PI Gain in UPQC Control Based on Improved Zero Attracting Normalized LMS

Optimized PI Gain in UPQC Control Based on Improved Zero Attracting Normalized LMS

Artificial intelligence and PI gain optimisation for sensorless indirect vector control of induction motor-based electric vehicle drives

This paper aims to develop indirect vector-controlled induction motor (IM) drives for EV applications. In this paper, controllers are used in two places. The first is the speed controller, used to reduce the error between reference speed and actual EV speed. And the second one is the speed estimator used to reduce the error between the adaptive model and reference model during sensorless operation. The performance of these controllers is compared by using ANFIS, ANN and PI controllers. The real-time performance of the IM-based EV drive system is dependent on the PI gain value. Hence in this paper, seven human and nature-inspired metaheuristic optimisation techniques such as JFO, GWO, ABC, PSO, GA, TLBO and DE are used to obtain the optimised value of the PI controller.

Certain performance analysis of Islanded microgrid systems stability through biologically activated engineering optimization technique

The primary goal is to enhance the PSN by maintaining stable and consistent MGS operation and reestablishing stable operating conditions after generational interruptions. The artificial neural network is created using a bio-inspired optimization algorithm, such as particle swarm optimization, second generation particle swarm optimization, and new model particle swarm optimization., which directs the evolutionary learning process to determine the most optimal solution. For the best result, the ANN and bio-inspired algorithm (BIANN) are coupled. The suggested BIANN-based controller is made comprised of an internal current and an external power loop. The proper PI gain parameter is tuned using BIANN, allowing the MGS to be stable. Three PSOs are used to investigate the suggested method, and the Matlab Simulink platform is used to create the fitness functions. The results are examined and contrasted. The new model’s particle swarm optimization provides MGS functioning and stability that is largely accurate and reliable.

Auto tuning of PI Gains using Cuttlefish Optimization for DC Link Voltage Control in a 5-level HB MMC D-STATCOM

The performance of a Half Bridge (HB) Modular Multilevel Converter (MMC) for D-STATCOM application is studied in this article. For the control of MMC switches, the Carrier-Based (CB) Pulse Width Modulation (PWM) scheme has been selected as the appropriate method. There are two types of CB PWM schemes such as phase shift and level shift. Phase Disposition (PD), Phase Opposition Disposition (POD), and Alternate Phase Opposite Disposition (APOD) are the three different types of PWM that are used in level shifts. The PD CB PWM is projected in this work due to its simplicity and accuracy. The conventional PI Controller (PIC) has its disadvantages, including that it is time-consuming and comes with complexity in the estimation of gain values. Hence, the Cuttlefish Optimization Scheme (COS) is proposed for the online tuning of the PI gain values. The verification of performance parameters, including active power, reactive power, DC voltage balance, and Total Harmonic Distortion (THD), is effectively conducted using the COS method and subsequently compared with the conventional PIC. The suggested research is executed using the Matlab/Simulink software package.

Enhanced Cascaded Converters for Switched Reluctance Motor-Fed Electric Vehicles

This manuscript proposes an optimization technique for electric vehicles (EVs) operated through solar photovoltaic (PV) panels by enabling the drive of a switched reluctance motor (SRM) and an enhanced cascaded converter (ECC). The proposed method is an improved Tunicate Swarm Algorithm (ITSA). The Lévy flight distributions are emerging at the conventional Tunicate Swarm Algorithm (TSA) in the modified Tunicate Swarm Algorithm, which enhances the capabilities of diversification searching and avoids the potential for stagnation. The major objective of the proposed method is to minimize the ripple present in the supply current and give optimal photovoltaic power extraction to the solar module. The proposed enhanced cascaded converter method consists of a single main converter and one auxiliary converter combined as a series structure for the distribution of minimal voltage across the switch to restrict the switch stress of dv/dt. These quantities of error are lessened by the controller of Proportional–integral; the PI gain controller is tuned to the error and provides a reference current signal by using the proposed method. An optimum voltage gain and continuous input current minimize the impact of less solar irradiation in EV applications. Finally, the performance of the proposed method is implemented in the MATLAB platform and compared with existing methods.

Current Control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

Current Control of EAST Fast Control Power Supply Based on Improved Grey Prediction Variable Gain PI

DESIGN OF A VARIABLE GAIN NONLINEAR FUZZY CONTROLLER AND PERFORMANCE ENHANCEMENT DUE TO GAIN VARIATION

In this paper, variable gain nonlinear PD and PI fuzzy logic controllers are designed and the effect of the variable gain characteristic of these controllers is analyzed to show its contribution in enhancing the performance of the closed loop system over a conventional linear PID controller. Simulation results and time domain performance characteristics show how these fuzzy controllers outperform the conventional PID controller when used to control a nonlinear plant and a plant that has time delay.

PI gain optimisation and artificial intelligence based direct torque control of induction motor equipped electric vehicle drives

PI gain optimisation and artificial intelligence based direct torque control of induction motor equipped electric vehicle drives

Robust Iteration-dependent Least Mean Square-based Distribution Static Compensator Using Optimized PI Gains

A robust iteration-dependent least mean square (RIDLMS) algorithm-based fundamental extractor is developed to estimate the fundamental components of the load current for a four-wire DSTATCOM with a nonlinear load. The averaging parameter for calculating the variable step size is iteration dependent and uses variable tuning parameters. Rather than using the current value, the previous learning rate was used in this method to achieve a more adaptive solution. This additional control factor aids in determining the exact learning rate, resulting in reliable and convergent outcomes. Its faster convergence rate and the avoidance of local minima make it advantageous. The estimation of the PI controller gains is achieved through a self-adaptive multi-population algorithm. The adaptive change in the group number will increase exploration and exploitation. The self-adaptive nature of the algorithm was used to determine the subpopulation number needed according to the fitness value. The main advantage of this self-adaptive nature is the multi-population spread throughout the search space for a better optimal solution. The estimated gains of the PI controllers are used for the DC bus and AC terminal voltage error minimization. The RIDLMS-based control with PI gains obtained using the proposed optimization algorithm showed better power quality performance. The considered RIDLMS-supported control was demonstrated experimentally using d-SPACE-1104.

Performance Enhancement of the DPC Control Based on a VGPI Controller Applied to a Grid Connected PV System

Even though the PI controller is among the most widespread controllers in photovoltaic (PV) systems, it presents overshoots and undershoots in the tracking trajectory mode. This major disadvantage remains an open problem. In the current paper, a Direct Power Control (DPC) of a three-phase grid-connected PV system based on a Variable Gain PI (VGPI) controller is developed to remove the overshoots and undershoots in the classical PI controller. The proposed control system has been tested in the Matlab/Simulink environment. The simulation results demonstrate the feasibility and robustness of the proposed method in terms of overshoots, undershoots, and current quality.

Development of a Simple Robotic Driver System (SimRoDS) to Test Fuel Economy of Hybrid Electric and Plug-In Hybrid Electric Vehicles Using Fuzzy-PI Control

Over the past decade, new models of hybrid electric vehicles have been released worldwide, and the fuel efficiency of said vehicles has increased by more than 5%. To further improve fuel efficiency, vehicle manufacturers have made efforts to design modules (e.g., engines, motors, transmissions, and batteries) with the highest efficiency possible. To do so, the fuel economy test process, which is conducted primarily using a chassis dynamometer, must produce reliable and accurate results. To accurately analyze the fuel efficiency improvement rate of each module, it is necessary to reduce the test deviation. When the test conducted by human drivers, the test deviation is somewhat large. When the test is conducted by a physical robot driver, the test deviation is improved; however, these robots are expensive and time-consuming to install and take up considerable amount of space in the driver’s seat. To compensate for these shortcomings, we propose a simple, structured robot system that manipulates electrical signals without using mechanical link structures. The controller of this robot driver uses the widely used PI controller. Although PI controllers are simple and perform well, since the dynamics of each test vehicle is different (e.g., acceleration response), the PI controller has a disadvantage in that it cannot determine the optimal PI gain value for each vehicles. In this work, the fuzzy control theorem is applied to overcome this disadvantage. By using fuzzy control to deduce the optimal value of the PI gain, we confirmed that our proposed system is available to conduct tests on vehicles with different dynamics.

19 Dietary Supplementation of Botanicals Enhanced Growth Performance and Disease Resistance of Weaned Pigs Experimentally Infected with a Pathogenic E. coli

Abstract The objective of this study was to investigate the effects of dietary botanical supplementation on growth performance and frequency of diarrhea of weaned piglets experimentally infected with a pathogenic F18 Escherichia coli (E. coli). Sixty weaned piglets (around 21 days old; 7.15 ± 0.97 kg) were individually housed and randomly allotted to one of five dietary treatments (n = 12): negative control (NC), positive control (PC), high dose of botanicals blend 1 (BB1, 100 ppm), low dose of botanicals blend 2 (BB2, 50 ppm), and high dose of botanicals blend 2 (BB2, 100 ppm). The experiment lasted 28 days: from day -7 to +21 relative to E. coli inoculation. All piglets except the pigs in the NC group were orally inoculated with F18 E. coli (1010 cfu per dose, 3 doses) for 3 consecutive days. Growth performance was recorded throughout the experiment and diarrhea scores were recorded daily. Data were analyzed by ANOVA using PROC MIXED of SAS with a randomized complete block design. E. coli challenge reduced (P < 0.05) pig body weight and growth rate throughout the experiment. Pigs supplemented with high dose BB1 or BB2 tended (P < 0.10) to have greater body weight (19.52 and 19.10 vs. 18.00 kg) on d 21 PI and greater average daily gain from d 0 to 21 PI (554 and 557 vs. 515 g/d) than PC. No differences were observed in pig performance between high dose BB1 or BB2 in comparison with NC. Supplementation of high dose BB1 or BB2 also reduced (P < 0.05) frequency and severity of diarrhea of challenged pigs during the entire experimental period. In conclusion, dietary supplementation of botanicals reduced diarrhea and tended to improve growth performance of weaned pigs infected with E. coli.

Simply structured controllers for vibration suppression in long rotors

In this paper suppression of the transient flexural vibrational disturbances in long rotors, with fluid film bearings, is investigated. The rotor is described by a series of distributed shafts connected by the lumped discs, and the system is mounted on lumped fluid film bearings. Upon determination of the dynamic stiffness matrix of the system, the best approximate transfer function matrix description of the rotor, is determined. Initially vibration suppression by simple diagonal Proportional + Integral (PI) controllers is studied and via direct search optimisation techniques the PI parameters which exhibit fast vibration suppression is found. The resulted high integration rate, and low proportional gain PI controller, theoretically provided fast suppression time. However, it is shown that due to the strong coupling effect in the rotor system, and high rate of integration, the closed loop relative stability is weak, and feasibility of controller is questionable. Therefore, an alternative simple first order controller without integration action, that is named “attenuation filter “is suggested that can produce stronger stability and produces significant (not full) vibration suppression. The closed loop multivariable control of the rotor system comprising two vibration sensors and two magnetic actuators using such attenuation filter, is then simulated. The response to step disturbances, has provided 95% suppression with significantly fast response. It is concluded that although the attenuation filter may not provide 100% suppression, but it more reliable since the integration of the error, that results weak stability is avoided.

An Algorithm for DSTATCOM with Optimized Values of PI Gain Using Adaptive Internal Model

In this article, a control algorithm with Adaptive Internal Model (AIM) frequency identifier has been discussed for three-phase Distribution Static Compensator (DSTATCOM). An AIM motivated method is implicated in the control scheme to extract fundamental in each phase. The reduction of reactive burden on the supply using DSTATCOM and making the supply current sinusoidal is the main theme behind this work. The fundamental extraction and PI controller gain estimation are the two things that bring variant in the proposed work. The fundamental frequency estimation, rejection of harmonics and its components are the main features of this algorithm. The convergent property of the algorithm is not much influenced by the actual disturbed signal and design parameters principally provide the robustness to the algorithm. The PI controller gains are estimated using Grasshopper Optimization Algorithm (GOA). The suggested control scheme with estimated gain values from the GOA optimization technique has controlled issues like reactive power compensation and mitigation of grid current harmonics caused due to an identified harmonic producing non-linear load that is a three-phase ac voltage controller with resistive load. This work provides implementation performance using dSPACE-1104 of the proposed control scheme for DSTATCOM.

Experimental investigation of model-based IMC control of severe slugging

Severe slugging formation has been investigated experimentally in a two-phase flow-loop composed of a downwardly inclined pipe and a vertical riser. In this study, a bifurcation diagram was made to more clearly identify which flow the current system has at each valve opening. A model was made by inputting the dimensions of the flow loop using OLGA, a multiphase flow simulation program, to compare the experimental results. The model-based control logic was constructed by estimating the transfer function utilizing the OLGA model. The estimated transfer function is used to tune the gain of the PI controller, using the method called the Internal Model Control (IMC). Through this method, PI gain can be easily obtained through only one tuning parameter. When the PI controller gain is obtained through the transfer function of the flow loop system, mitigation of severe slugging was achieved while increasing the flow rate than the case with manual choking.

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.

WITHDRAWN: A novel methodology to capture the maximum power from variable speed wind turbines using fuzzy proportional integral Controllers, modified PSO-GSA algorithm

This article provides a modified technique for controlling generator torque efficiently in wind turbines (WT). The generator torque is generally used at low wind speeds to obtain extreme power. In this study, we introduce a method for torque of the WT generator is controlled by using a fuzzy proportional integral controller (FPI). To upsurge the PI gain, by using a radial base function neural network (RBF-NN). And another hand we conducted the optimal dataset for training this neural network has been delivered by the techniques of modified procedure of gravity search algorithm and particle swarm optimization (GSA-PSO) algorithm. Thus, the benefits of enhanced technique performance and the reliability of the FPI can be completely utilized in the control course. By showing the simulation of WT experiments with help of Matlab, the performance of a modified GSA-PSO based FPI controller to meet the high accuracy and quick response requirements of permanent magnet direct drive (PMDD) is better than that of a PI controller, Without the use of modified PSO in situations where the wind speed changes abruptly.

Combining LIDAR and LADRC for intelligent pitch control of wind turbines

At present, most of the pitch control methods are based on PI controller, the pitch control system has poor disturbance resistance, and the research of variable parameter feedforward based on Light detection and ranging (LIDAR) and the Linear Active Disturbance Rejection controller (LADRC) composite control is rarely studied to reduce the blade root load, so this paper conceives a hybrid intelligent and adaptive pitch control approach to reduce a wind turbine generator speed fluctuation and its blade root load. Specifically, we combine the Radial Basis Neural Network and Finite Impulse Response filter (RBFNNFIR) based on LIDAR wind measurement. We then use a variable bandwidth of LADRC controller. Overall the approach enables and facilitates self-adaption and self-adjustment. We use Matlab s-function to call the multi-freedom mathematical wind turbine model based on FAST code, the composite intelligent control algorithm is established in Simulink. Initial results from the statistical analysis of the experiments under different turbulent wind conditions shows that the hybrid intelligent pitch control approach can reduce the generator speed fluctuation by about 40.8%, and the blade root max value of load moment by about 13.1%, compared with the baseline values of the traditional variable gain PI control algorithm.

Adaptive Voltage Control of Distribution Network with High Proportion PV

With the increase of grid-connected PV capacity, voltage regulation at point of common coupling by controlling the reactive power injected into the grid is available. This paper presents an adaptive voltage control strategy for distribution network with high proportion PV system. The PI gain of the voltage controller is automatically adjusted by the extremum seeking algorithm to dynamically respond to the changes of the network. The PI gains are updated online through the minimization of a cost function, which represents the voltage controller performance. Finally, a distribution network model of 5 MW photovoltaic power generation system is built in MATLAB / Simulink to verify the effectiveness of the proposed control strategy.