PI Controller Research Articles

Verification and Validation of Advanced Control Systems for a Spinal Joint Wear Simulator.

Wear simulation aims to assess wear rates and their dependence on factors like load, kinematics, temperature, and implant orientation. Despite its significance, there is a notable gap in research concerning advancements in simulator control systems and the testing of clinically relevant waveforms. This study addresses this gap by focusing on enhancing the conventional proportional-integral-derivative (PID) controller used in joint simulators through the development of a fuzzy logic-based controller. Leveraging a single-input multiple-output (SIMO) fuzzy logic control system, this study aimed to improve displacement control, augmenting the traditional proportional-integral (PI) tuning approach. The implementation and evaluation of a novel Fuzzy-PI control algorithm were conducted on the Leeds spine wear simulator. This study also included the testing of dailyliving (DL) profiles, particularly from the hip joint, to broaden the scope of simulation scenarios. While both the conventional PI controller and the Fuzzy-PI controller met ISO tolerance criteria for the spine flexion-extension (FE) profile at 1 Hz, the Fuzzy-PI controller demonstrated superior performance at higher frequencies and with DL profiles due to its real-time adaptive tuning capability. The Fuzzy-PI controller represents a significant advancement in joint wear simulation, offering improved control functionalities and more accurate emulation of real-world physiological dynamics.

Enhancing PI controller performance in grid-connected hybrid power systems

The optimal operation of a microgrid was buildup of both uncontrollable (solar, wind) and controllable (batteries, diesel generators) electrical energy sources are enclosed in this paper. By replacing controllers, the variations in wavering power supply caused by load fluctuations are managed. The objective of the research paper is to optimize these controller gain settings for effective use of electrical energy. In this paper integral time square error principle is combined along with the Cuckoo search algorithm (CSA) and particle swarm algorithm (PSA) to obtain the accurate, precise and appropriate results. It enhances the microgrid's steady-state sensitive responsiveness in comparison to trial-and-error techniques, assuring a stable supply of electricity to the load.

Static output feedback LFC for semi‐Markov type interconnected multi‐area power systems: A non‐fragile PI strategy

AbstractThis article addresses the static output feedback load frequency control (LFC) problem for semi‐Markov type interconnected multi‐area power systems (IMAPSs) through non‐fragile proportional‐integral (PI) strategy. Primarily, by roundly exploiting the dynamical properties and considering random mutations of the IMAPSs, the semi‐Markov jump process (MJP) is elaborately scheduled for establishing the dynamical model with precision. Subsequently, on account of the characteristic of geographical isolation, a memory‐like sampled‐data LFC mechanism considering time‐varying transmission delay for the semi‐Markov jump IMPASs is formulated accordingly. Additionally, the non‐fragile issue is explored for the PI controller gain fluctuations to meet the actual operation scenario. After that, a mode‐dependent two‐side polynomial‐type looped Lyapunov functional is constructed for enhancing flexibility. Then, in virtue of the stochastic analysis technique, some relaxed conditions guaranteeing stochastic stability with prescribed performance are thereby derived in the shape of linear matrix inequalities (LMIs). Ultimately, a numerical example is carried out to validate the efficacy of the developed control methodology.

A Novel LQI Control Technique for Interleaved-Boost Converters

Hybrid electric vehicles (HEVs) and fuel cell electric vehicles (FCEVs) utilize boost converters to gain a higher voltage than the battery. Interleaved boost converters are suitable for low input voltage, large input current, miniaturization, and high-efficiency applications. This paper proposes a novel linear quadratic integral (LQI) control for the interleaved boost converters. First, the small-signal model of the interleaved-boost converter is derived. In the proposed method, an output voltage and a current signal error between two-phase input currents are selected to control not only the output voltage but also a balance between two-phase input currents. Furthermore, steady-state characteristics in terms of the output voltage and the input current are demonstrated by experiments and simulations using an experimental apparatus with a rated power of 700 W. The validity of the proposed method’s tracking performance and load response is demonstrated by comparing it with that of the conventional PI control. The tracking performance of the LQI control for the 40 V step response has a ten times faster response than that of the PI control. Also, the experimental results demonstrate that the proposed method maintains a constant output voltage for a 300 W load step while the PI control varies by 10 V during 70 ms. Additionally, the proposed method has an excellent disturbance rejection.

Simulation of anaerobic biodegradation process in a tubular bioreactor with a biofilm layer: Steady-state and unsteady-state conditions

In this paper, anaerobic biodegradation process in a tubular bioreactor with an inner biofilm layer for steady-state and unsteady-state conditions are simulated. The effects of various parameters including bioreactor diameter, fraction of active biomass transferred to liquid phase, and residence time of the liquid on bioreactor performance are examined. Simulations indicate that decreasing diameter of bioreactor leads to increasing degree of conversion of the substrate in liquid phase and decreasing dimensionless concentration of the substrate in biofilm. With an increase in the fraction of active biomass transferred to liquid, substrate concentrations in liquid and biofilm slightly vary. Increased residence time of the liquid phase results in the degree of conversion of substrate goes up, but substrate concentration in biofilm lowers a little. In addition, it is found that biomass concentration of liquid phase is boosted with decreased bioreactor diameter and increased residence time of liquid. A proportional-integral controller is designed and the tuned parameters of KP=−0.131 and KI=0.02 are obtained using genetic algorithm. It is observed that controller regulate well the degree of conversion of the substrate within 120 s for both servo and regulatory modes.

Design of a Multi-loop PI Controller for Minimum Phase System Level Regulation in a Quadruple Tank: A Method for Constraint Optimization

Design of a Multi-loop PI Controller for Minimum Phase System Level Regulation in a Quadruple Tank: A Method for Constraint Optimization

Designing a hydrogen generation system through PEM water electrolysis with the capability to adjust fast fluctuations in photovoltaic power

—Hydrogen can be produced through water electrolysis using photovoltaic (PV) power in a way that is both pollution-free and friendly to the environment, rendering hydrogen a clean energy source. Proton Exchange Membrane (PEM) water electrolysis is an efficient hydrogen generation process because of its compact design, fast response, high efficiency and high current density. The fast fluctuations in solar power, however, are difficult for PEM water electrolysers to control effectively. To address the problems caused by intermittent power, a system for producing hydrogen consisting of a solar panel, battery, and commercial PEM electrolyser, alongside an electrical control strategy, is proposed. The Maximum Power Point Tracking (MPPT) Perturb and Observe (P&O) technique is employed for PV systems, while a bidirectional charge controller manages the charging and discharging of the battery. A Proportional-Integral (PI) controller is also utilised for the PEM electrolyser to ensure efficient operation. This setup provides a practical solution for hydrogen production. It enhances efficiency and reduces the risk of damage to the electrolyser, thereby making the process more sustainable and reliable. The proposed approach has been validated by simulation under different conditions. A comparative analysis has also demonstrated that the proposed system can perform well despite having less PV power. When comparing systems with and without batteries for energy storage, the system with the battery for energy storage has a 10% higher energy efficiency under the same operating conditions. This suggests that adding energy storage can increase energy utilization, maintain the DC link voltage constant, and achieve stable hydrogen production.

Design and Preliminary Experiment of Track Width Adjustment System for Sprayer Based on Integral Separated Fuzzy Proportional Integral Derivative Control Strategy

Design and Preliminary Experiment of Track Width Adjustment System for Sprayer Based on Integral Separated Fuzzy Proportional Integral Derivative Control Strategy

Modeling and control of a novel mechatronic model of a 750 kW-FSWT with power-splitting transmission using RBF neural network: a bond graph approach

This work addresses issues that appear from wind turbines equipped with frequency converters and presents a new fixed-speed wind turbine (FSWT) concept based on a split power transmission, along with a Mechatronic Control Model. The wind turbine rotor drives the first transmission shaft, while a servomotor with variable speed powers the second input. The differential gear transmission output is linked to the electric grid through an asynchronous generator. To optimize the power extracted from the wind energy while minimizing excessive dynamic loads on the wind turbine, a mechatronic control model of a 750 kW-FSWT is applied using a proportional and integral controller (PI). The paper suggests employing neural networks and evolutionary algorithms for determining appropriate PI gains. For collective servomotor control of a 750 kW-FSWT, a radial basis function (RBF) neural networks based on PI controller is proposed. To acquire an optimum dataset for RBF training, the particle swarm optimization (PSO) evolutionary algorithm is harnessed. The robustness and effectiveness of the proposed model and controller are verified and confirmed through simulation results. Hands-on experience is conducted using the 20-sim software package.

An enhanced proportional resonance controller design for the PMSM based electric vehicle drive system

Permanent magnet synchronous machine (PMSM) has proven to be a more economical traction drive system for electric vehicle (EV) applications owing to increased efficiency and high-power density. However, the drive system requires more efficient control schemes to deliver better dynamic performance irrespective of dynamic changes in the motor speed, machine parameters and disturbances. Hence, to tackle the dynamic changes, to enhance the wider operating speed, to achieve precise speed tracking capability, and improved efficiency, a novel control algorithm for the PMSM based EV is proposed in this paper. The control algorithm is implemented by adopting the merits of conventional proportional resonance (PR) and proportional integral (PI) controller. The proposed control strategy is designed with an outer PI speed regulator and the inner enhanced PR (EPR) current regulator. The uniqueness of the proposed EPR controller is that the controller is designed to damp the torsional mode oscillation owing to dynamic changes such as speed and torque regulation evading the additional control loop. The effectiveness of the control scheme is tested in MATLAB Simulink and hardware-in-loop (HIL) real time simulator RT5700. To validate the effectiveness of the proposed control scheme the results are compared with the conventional control schemes. The results presented show that the proposed control technique successfully enhances the static and dynamic performance, and resilience of the EV system. Also, the proposed scheme significantly reduces the flux ripples, torque ripples, current jitter, peak overshoot, undershoot compared to the conventional current controllers.

Personalized heart rate management through data-driven dynamic exercise control

Maximizing healthy life expectancy is essential for enhancing well-being. Optimal exercise intensity is crucial in promoting health and ensuring safe rehabilitation. Since heart rate is related to exercise intensity, the required exercise intensity is achieved by controlling the heart rate. This study aims to control heart rate during exercise by dynamically adjusting the load on a bicycle ergometer using a proportional-integral (PI) control. The choice of PI parameters is very important because the PI parameters significantly affect the performance of heart rate control. Since the dynamic characteristics of heart rate relative to work rate vary widely from subject to subject, the PI parameters for each subject must be determined individually. In this study, PI parameters are optimized directly from exercise data using a data-driven design approach. Thus, the proposed method does not require excessive exercise of the subject to model heart rate dynamics. Using the proposed method, the heart rate can be controlled to follow a designed reference model so that the heart rate is safely increased to the desired value. The quantitative evaluation of the control results of fifteen healthy volunteers confirmed that the proposed method improved the control error of the target heart rate trajectory by approximately 40%, regardless of gender or age. In addition, it was shown that control parameters from the exercise experiment also indicate that females are more likely than males to have an elevated heart rate at the same load.

Optimal design of fractional-order proportional integral derivative controllers for structural vibration suppression

In designing control systems, it is known that fractional-order proportional integral derivative (FOPID) controllers often provide greater flexibility than conventional proportional integral derivative (PID) controllers. This higher level of flexibility has proven to be extremely valuable for various applications such as vibration suppression in structural engineering. In this paper, we study the optimization of FOPID controllers using twelve well-established algorithms to minimize structural responses under seismic excitations. The algorithms include crystal structure algorithm (CryStAl), stochastic paint optimizer, particle swarm optimization, krill herd, harmony search, ant colony optimization, genetic algorithm, grey wolf optimizer, Harris hawks optimization, sparrow search algorithm, hippopotamus optimization algorithm, and duck swarm algorithm. In addition to highlighting the benefits of fractional calculus in structural control, this study provides a detailed analysis of FOPID controllers as well as a brief description of the algorithms used to optimize them. To evaluate the efficiency of the proposed techniques, two building models with different numbers of stories are examined. FOPID controllers are designed based on oustaloup’s approximation and the El Centro earthquake data. Using five well-known metrics, the performances of the developed methods are evaluated against five earthquake scenarios, including the recent earthquake in Turkey. A non-parametric (Friedman) test is also employed to compare the algorithms based on their corresponding vibration reduction. The findings of this analysis show that CryStAl consistently performs better than the other algorithms for both building models, thus resulting in superior vibration suppression.

Improved control strategy and designed control parameters of pitch system for wind turbine considering blade load reduction

As wind turbines increase in the power, the longer the blade, the more severe the blade load. Traditional control strategies and control parameters do not consider to reduce blade load, which seriously threatens the safe operation of wind turbines. Therefore, this study proposes a pitch control strategy based on blade active damping control and a multistage control parameter design method. Firstly, this study proposes a pitch control strategy based on blade active damping control to reduce the blade load based on the establishment of a mathematical model of the pitch system considering the blade load and a dynamic model of blade active damping control. Secondly, in the control parameter initial values design of Stage Ⅰ, this study proposes a variable forgetting factor recursive least squares to identify a simplified mathematical model of the pitch system and a Low–overshoot Chien–Hrones–Reswisk to solve the proportional integral control parameter initial values. Thirdly, in the control parameter optimal values design of Stage Ⅱ, this study proposes an adaptive genetic algorithm with improved hyperbolic tangent function to optimize control parameter at the equilibrium points and proposes an adaptive control to calculate control parameter at the non–equilibrium points. Finally, based on the Bladed software for 2 MW and 5 MW wind turbines, the superiority of the proposed methods, effectiveness and applicability in improving the control accuracy of the output power and generator speed, and reducing the load situation at the blade root position are verified.

Enhancing DC Motor Speed Control Performance Using Heuristic Optimization and Comparative Analysis of Control Methods

Direct Current (DC) motors are an important component that converts electrical energy into mechanical energy, used in a wide range of applications from industrial applications to home appliances. DC motor speed control has an important role in industrial processes to increase efficiency, realize precise movements and optimize energy consumption. In this study, various control methods and parameter optimization techniques for speed control of DC motors, which have a wide range of applications, have been systematically analyzed. The aim of the study is to develop an effective control strategy to ensure that DC motors reach the determined target speed by monitoring them in real time at different speeds and to minimize fluctuations caused by variable loads or external factors. In our study, Proportional-Integral-Derivative (PID), Proportional-Integral (PI), and Proportional-Derivative (PD) control methods were used. The parameters of these controllers were tuned using Matlab Tuned, The Cheetah Optimizer (CO) Algorithm, a new generation heuristic optimization method, and Particle Swarm Optimization (PSO), a widely accepted optimization method. The performances of the controllers were determined using criteria such as Integral of Absolute Error (IAE), Integral Squared Error (ISE), and Integral of Time multiplied by Absolute Error (ITAE). According to the results obtained, it was found that the PID, PI and PD control parameters determined using the CO Algorithm performed better than the controllers created using Matlab Tuned and PSO methods. New optimization methods, such as the CO Algorithm, have been found to have significant potential to improve the performance of control systems. Thanks to this study, it offers a practical approach for optimizing DC motor speed control in industrial processes. As a result, it has been found that the control parameters determined by the CO Algorithm have significant potential in improving the performance of DC motor speed control and control systems compared to other optimization methods.

Advanced Controllers for Heat Transfer Fluid Mass Flow Rate Control in Solar Tower Receivers

Efficient control strategies for managing the mass flow rate (MFR) of heat transfer fluids (HTF) during cloud transients in Solar Tower receivers play a pivotal role in optimizing plant profitability and receiver durability. This study focuses on the performance and durability of Solar Tower receivers during cloud transients. It evaluates adaptive feedback and feedforward control methods, which adjust the flow rate of heat transfer fluids based on real-time measurements of direct normal irradiance (DNI), receiver outlet and receiver panel outlet temperatures. The effectiveness of an aggressive all-sky and conservative clear-sky control strategy is explored against a conventional PI controller, emphasizing energy efficiency and receiver longevity. Simulations using a thermal Modelica model resembling a 100 MWel Crescent Dunes-like solar tower plant reveal that both advanced controllers provide precise setpoint tracking, while the PI controller struggles. The conservative controller which has a cloud standby mode prevents overheating during cloud transients by using a clear sky mass flow rate, while the aggressive controller uses the receiver panel outlet temperatures to correct for upstream tube temperature variations allowing for fast tracking correction and disturbance rejection, albeit with slight overshoots. Furthermore, the controllers significantly decrease the creep-fatigue damage accumulated in the receiver panels during cloudy days, due to limiting the increase in wall temperature spikes when cloud events end. Overall, this study underscores the pivotal role of HTF mass flow rate control systems in influencing receiver system failure modes and longevity and offers a new tool in controller design and operation assessment.

Study on novel control method for small wind – solar hybrid power system using photovoltaic cooperating system in parallel connection mode

Currently, a small-scale wind turbine can be connected to the Power Conditioning System (PCS) of the solar power system by simulating the technical characteristics of the solar panels to enhance the efficiency of the PCS on cloudy or rainy weather days. However, the working efficiency of the entire system has not been optimized because the use of traditional control techniques. P-I control has several problems of high starting overshoot, and slow response to sudden disturbances. Besides, P&O MPPT control has fluctuations in output power and slow response time at peak solar cell power due to weather effects. Therefore, this research proposes a novel control system including Artificial Neural Networks (ANN) MPPT control and digital slide mode control (DMSC) for the power conversion circuit to connect small-scale wind turbine power with the PCS of the solar power system in the parallel mode. The novel control solution in this study can minimize the disadvantages of PI control and P&O MPPT control. The study results showed that DSMC controller has better performance than the PI controller when controlling the PVCS in parallel mode with more 17.5 % power, no static error, 20 % less chattering of the power value of PVCS in the steady state, and 20 % less static error of power. Besides, the ANN MPPT controller also works better when changing solar radiation and has a harvested power of about 0.6 % higher than the power generated by the P&O MPPT controller.

DTC-SVM strategy of double star induction motor using sliding mode controller enhanced by fuzzy logic

Extensive research and numerous articles on Direct Torque Control (DTC) systems have consistently highlighted various deficiencies associated with the employment of hysteresis control units. Specifically, these issues include significant torque and stator flux ripples, as well as distortions in stator current. Additionally, the sensitivity to parametric variations stemming from Proportional-Integral (PI) controllers poses a challenge due to the complexities involved in their adjustment. To address these drawbacks, this article explores the implementation of Space Vector Modulation DTC (SVM-DTC) technology integrated with a Sliding Mode Controller (SMC). The proposed control scheme for DTC amalgamates the benefits derived from SMC control and the SVM algorithm. The SMC controller is utilized to oversee PI controllers, employing this nonlinear technique ensures superior dynamic performance and robust resistance against external disturbances. Notwithstanding its advantages, one notable issue with SMC is chattering. To mitigate this phenomenon, we adopt an approach that integrates Fuzzy Logic with SMC. By replacing the saturation function (Sat) with a fuzzy inference system, we effectively minimize chattering. The simulation is conducted using MATLAB SIMULINK. Comparative analysis between DTC-SVM strategy and traditional methods including standard SMC and Fuzzy-enhanced Sliding Mode Controller (FSMC), demonstrates that our hybrid nonlinear controller exhibits high efficiency and remarkable robustness.

Pairing voltage-source converters with PI tuning controller based on PSO for grid-connected wind-solar cogeneration

This paper introduces a novel method to improve the efficiency of grid-connected wind-solar cogeneration systems. It involves the integration of Voltage-Source Converters (VSCs) with a Proportional-Integral (PI) tuning controller that has been optimized using Particle Swarm Optimization (PSO). Integrating renewable energy sources into power grids presents a set of challenges in terms of ensuring stability and optimizing power flow. VSCs are essential for effectively managing power flow between renewable sources and the grid. The PI controller is vital in maintaining voltage levels and ensuring stable operation. Conventional PI controller tuning methods commonly rely on heuristic approaches, which might only partially optimize performance across various operational conditions. In order to tackle this issue, PSO is utilized to fine-tune the parameters of the PI controller automatically. This results in a significant reduction in system deviations and a notable improvement in efficiency, even when faced with fluctuating wind and solar conditions. The simulation results conducted in MATLAB/Simulink confirm the effectiveness of the proposed approach in enhancing system stability and reducing response times. The PI controller optimized using PSO showcases exceptional adaptability to varying environmental and grid conditions, resulting in minimized power fluctuations and improved grid reliability. This study makes a valuable contribution to the field of renewable energy integration. It provides valuable insights into enhancing the performance of grid-connected wind-solar cogeneration systems through advanced control techniques and optimization algorithms such as PSO.

Machine learning-based predictive control of an electrically-heated steam methane reforming process

Hydrogen plays a crucial role in improving sustainability and offering a clean and efficient energy carrier that significantly reduces greenhouse gas emissions. However, the primary method of industrial hydrogen production, steam methane reforming (SMR), relies on the combustion of hydrocarbons as the heating source for the reforming reactions, resulting in significant carbon emissions. To address this issue, an experimental setup of an electrically-heated steam methane reformer (e-SMR) has been constructed at UCLA, and a lumped first-principle dynamic process model was built based on parameters estimated from the experimental data in a previous study. Subsequently, the first-principle dynamic process model was implemented into the computational model predictive control (MPC) scheme, successfully driving the hydrogen production rate to the desired setpoint. While these works are important and pave the way for developing MPC for large-scale e-SMR processes, the first-principle process model may not accurately reflect the actual process behavior, particularly as the process behavior changes with time. Therefore, the development and establishment of an adaptive data-driven approach for implementing model predictive control in the e-SMR process is necessary. To address this need, the present work investigates the construction of recurrent neural network (RNN) models for an e-SMR process in-depth, utilizing data from an experimentally-validated first-principle model. Specifically, a long short-term memory (LSTM) layer was utilized in the RNN model to effectively capture the complex correlations present in long-term sequential data. Subsequently, this LSTM-based RNN process model was employed to design an MPC, and its performance was evaluated through comparison with proportional–integral (PI) control. To address potential disturbances and variability in a typical e-SMR process, three distinct approaches were developed: MPC with an integrator, MPC with real-time online retraining (transfer learning), and offset-free MPC. These approaches effectively eliminated the offset caused by disturbances. Overall, this study underscores the effectiveness of utilizing RNN models to capture process dynamics in an experimental e-SMR process. It also outlines strategies for employing RNN-based control and multiple approaches to address disturbances in general processes with partially infrequent and delayed measurement feedback. This approach is particularly valuable in scenarios where developing first-principle models for a new process may be challenging.

Application of DPC to improve the integration of DFIG into wind energy conversion systems using FOPI controller

While the direct power control (DPC) approach has proven effective in improving the efficiency of wind energy conversion systems (WECS) using doubly fed induction generators (DFIG), its applicability is currently confined to a single usage and has not been extended to meet numerous applications. This work aimed to modify the implementation of DPC in WECS-DFIG for several objectives. This is accomplished by updating the reference power of the conventional DPC method into an adapted one to achieve two goals independently. The first objective is to track the maximum power during wind speed variations. This tracking is performed by updating the reference power to match the maximum available power at the current wind speed. The second purpose is to ensure that the WECS remains connected to the grid and continues to operate smoothly even in the event of faults; supporting fault-ride through (FRT) capability. That is achieved by reducing the reference power during these faults. The discrimination between these two objectives is based on the voltage level at the point of connecting WECS to the grid. The controller provided is an improved fractional order PI controller developed using arithmetic optimization technique (AOA). A comparison between the AOA and cuckoo search is presented. The results demonstrate the efficacy of the suggested configuration and regulator in enhancing the performance of integrating DFIG into the WECS in the presence of wind fluctuations and short circuit faults occurring. It is worth noting that AOA is better than cuckoo search in fine-tuning the settings of the FOPI controller.