Proportional-integral Parameters Research Articles

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.

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.

CONTRÔLE DIRECT DU COUPLE AVEC MODULATION VECTORIELLE SPATIALE UTILISANT DES RÉGULATEURS À LOGIQUE FLUUSE DE TYPE 2 À INTERVALLE D'UNE MACHINE À INDUCTION À DOUBLE ÉTOILE ALIMENTÉE PAR DEUX ONDULEURS À POINT NEUTRE À TROIS NIVEAUX SERRÉS

This paper introduces a direct torque control (DTC) method with space vector modulation (SVM) using interval type-2 fuzzy logic regulators for a dual-star induction machine fed by two three-level neutral point clamped inverters. The conventional DTC drive utilizes a pair of hysteresis comparators, which can lead to issues such as high torque ripple and variable switching frequency. As a result, we introduce several alternative methods, including DTC-SVM. We replace the hysteresis comparators with two Proportional-Integral (PI) regulators to enhance DTC performance, which generates the direct and quadrature voltage components in the d-q frame. The intricacy of the control system can make adjusting the PI regulator parameters difficult. To address this challenge, we propose using an interval type-2 fuzzy logic regulator for PI parameter adjustment in this paper. Our simulation results demonstrate the robustness and efficiency of the IT2FLC regulator.

Multi-objective optimization of PI controller for BLDC motor speed control and energy saving in Electric Vehicles: A constrained swarm-based approach

Proportional Integral Derivative (PID) controllers are widely employed in industrial applications due to their effectiveness, simplicity, and versatility. Within the automotive sector, one notable application of the Proportional Integral (PI) controller is in the realm of electric motors, particularly the Brushless Direct Current (BLDC) motors powering Electric Vehicles (EVs). The precision of speed control over BLDC motors, renowned for their efficiency and reliability, is paramount for optimizing vehicle performance and ensuring energy efficiency. PIDs must be tuned each time they are used in an application to optimize performance. A well-known empirical hit and trial tuning strategy is the Ziegler–Nichols method, which results in sub-optimal performance in the presence of locally optimal solutions of large-dimensional search spaces. Existing Artificial Intelligence methods tailored for PID controller tuning often focus on singular optimization aspects, neglecting potential performance gains achievable through multi-objective considerations. Furthermore, prevalent swarm-based PID optimization methods typically initialize the population randomly without imposing any bounds on input parameters, thus rendering them susceptible to inconsistent, unreliable, sub-optimal solutions in unconstrained search environments. In our research endeavors, we mathematically model the multi-objective optimization of BLDC motor speed control, energy usage, and efficiency using constrained Differential Evolution and Particle Swarm Optimized PID controllers. The Ziegler–Nichols tuning method is used to ascertain the initial PI parameters and define bounds within the input space. Our methodology addresses a critical gap by integrating real-time road gradient data into the EV control system, enhancing precision and minimizing energy consumption, especially during challenging road conditions. The proposed approach significantly improves motor speed control and energy efficiency in EVs. With our proposed swam-based optimization over 30 iterations, MSE decreased by approximately 95.4% (from 3.8834 to 0.1809), while energy consumption reduced by about 3.1% (from 1.015 kWh to 0.984 kWh). These results highlight the effectiveness of our method in enhancing both speed control precision and energy efficiency. In addition, code related data is available at Github.

Research on system of ultra-flat carrying robot based on improved PSO algorithm

Ultra-flat carrying robots (UCR) are used to carry soft targets for functional safety road tests of intelligent driving vehicles and should have superior control performance. For the sake of analyzing and upgrading the motion control performance of the ultra-flat carrying robot, this paper develops the mathematical model of its motion control system on the basis of the test data and the system identification method. Aiming at ameliorating the defects of the standard particle swarm optimization (PSO) algorithm, namely, low accuracy, being susceptible to being caught in a local optimum, and slow convergence when dealing with the parameter identification problems of complex systems, this paper proposes a refined PSO algorithm with inertia weight cosine adjustment and introduction of natural selection principle (IWCNS-PSO), and verifies the superiority of the algorithm by test functions. Based on the IWCNS-PSO algorithm, the identification of transfer functions in the motion control system of the ultra-flat carrying robot was completed. In comparison with the identification results of the standard PSO and linear decreasing inertia weight (LDIW)-PSO algorithms, it indicated that the IWCNS-PSO has the optimal performance, with the number of iterations it takes to reach convergence being only 95 and the fitness value being only 0.117. The interactive simulation model was constructed in MATLAB/Simulink, and the critical proportioning method and the IWCNS-PSO algorithm were employed respectively to complete the tuning and optimization of the Proportional-Integral (PI) controller parameters. The results of simulation indicated that the PI parameters optimized by the IWCNS-PSO algorithm reduce the adjustment time to 7.99 s and the overshoot to 13.41% of the system, and the system is significantly improved with regard to the control performance, which basically meets the performance requirements of speed, stability, and accuracy for the control system. In conclusion, the IWCNS-PSO algorithm presented in this paper represents an efficient system identification method, as well as a system optimization method.

Optimization of X-axis servo drive performance using PSO fuzzy control technique for double-axis dicing saw

The dicing saw is a critical piece of equipment in IC processing, primarily used to cut wafers. Due to the high spindle speed, even small errors in the cutting process can result in wafer chipping or cracking. Therefore, the dicing saw requires a high degree of accuracy and stability. In this paper, the accuracy of the X-axis servo response was simulated using an Israeli ADT-8230 dual-axis abrasive wheel dicing saw. The study introduces a novel approach by using a fuzzy controller instead of the traditional position loop proportional integral (PI) controller. In addition, a two-input, two-output fuzzy rule is used for on-line correction of the position loop PI parameters. A heuristic algorithm is used to optimise the position loop fuzzy controller parameters. The quantization and proportionality factors are rectified using Particle Swarm Optimisation (PSO) algorithm and Genetic Algorithm (GA) respectively. By comparing the performance of the PSO fuzzy and GA fuzzy controllers, the optimal control method is derived. The proposed method is validated by simulation in the MATLAB/Simulink development environment using real ADT-8230 servo data. Experimental results show that the PSO-fuzzy structured controller reduces the position control error by 11.8%, improves the tracking performance by 26% and reduces the torque pulsation by 23%. Therefore, in future research, more advanced search algorithms should be further combined to improve the servo accuracy of the dicing saw.

An Automatic PI Tuning Method for Photovoltaic Irrigation Systems Based on Voltage Perturbation Using Feedforward Input

This paper proposes a new automatic tuning method for the proportional-integral (PI) controllers of photovoltaic irrigation systems (PVIS) without the need for any other power source or batteries. It enables the optimisation of the values of the PI parameters (Kp and Ki) automatically, eliminating the requirement for skilled personnel during the installation phase of PVIS. This method is based on the system’s voltage response when a disturbance signal is introduced through the feedforward input of the PI controller. To automatically assess the response properties, two indicators are proposed: the total harmonic distortion (THD), used to evaluate the sine response, and the total square distortion (TSD), used to evaluate the square response. The results indicate that the tuning changes for different irradiance and temperature conditions due to the non-linearity of the system, obtaining the most conservative values at maximum irradiance and temperature. The robustness of the results of the new automatic tuning method to abrupt photovoltaic (PV) power fluctuations due to clouds passing over the PV generator has been experimentally tested and the results show that the obtained tuning values make the PVIS stable, even when PV power drops of 66% occur abruptly.

Parallel Multi-Layer Monte Carlo Optimization Algorithm for Doubly Fed Induction Generator Controller Parameters Optimization

This work proposes a parallel multi-layer Monte Carlo optimization algorithm (PMMCOA) that optimizes proportional–integral parameters for a doubly fed induction generator-based wind turbine controller. The PMMCOA, an improved form of the Monte Carlo algorithm, realizes the optimization process via a parallel multi-layer structure. The PMMCOA includes rough search layers, precise search layers, and re-precise search layers. Each layer of the PMMCOA adopts a multi-region and multi-granularity approach to increase the diversity and randomness of the search samples. The PMMCOA is employed to tune the controller parameters for achieving maximum power point tracking and improving generation efficiency. The controller fitness function reflects the sum of the rotor angular velocity error and the reactive power error. Compared with the five metaheuristic algorithms, the PMMCOA has a higher global convergence and more accurate power tracking ability.

Evaluation of renewable and energy storage system-based interlinked power system with artificial rabbit optimized PI(FOPD) cascaded controller

This research work addresses automatic generation control of multiple arenas and causes under various system disturbances. Sources within arena-1 comprises thermal, bio-diesel, in arena-2 sources represent thermal elements, in arena-3 sources are thermal and hydro units. An innovative endeavor has been undertaken to employ conglomerate controller with the amalgamation of proportional-integral (PI) (integer-order) in addition fractional order proportional-derivative (FOPD). Various examination manifests the fineness of PI(FOPD) overtop additional integer order controllers from perspective concerning reduced grade of crown overshoot, expanse-of-instabilities, crown undershoot in addition to subsiding period. In determination to attain the controller’s features biologically enthused meta-heuristic artificial rabbit optimization is pragmatic. The minimum cost function value has been achieved as 0.000309 for the artificial rabbit optimization in comparison to various established optimization technique along with the proposed PI(FOPD) controller. It is likewise detected that occurrence of inexhaustible sources like realistic dish-Stirling thermal system in arena-1, geothermal power plant and precise wind turbine system styles the structure meaningfully improved related to origin structure when judged exclusively or all together. Accomplishment in presence of interline power flow controller besides combination of energy stowage elements alike redox flow battery in addition solid oxide fuel cell is as well inspected using PI(FOPD) controller, that offers with notable consequence in dynamic presentation in each cases separately. It has been clearly observed that in presence of all the renewables, in case of frequency deviation crown overshoot, crown undershoot, and subsiding period are 0.0053, 0.0131, and 24.32 respectively. These values are showing improvements in comparison to the individual presence of renewables in each arena. Similarly, with the presence of both SOFC and RFB for tie line power between arena-1 and areana-3, the crown overshoot, crown undershoot, and subsiding period are 0.00018, 0.0013, and 22.63 respectively. These values are showing improvements in comparison to the individual presence of Solid Oxide Fuel Cell without battery energy storage in each arena. Also, PI(FOPD) parameters values at nominal condition are appropriate for random pattern of disturbance needs no optimization, which justified the reliability of the suggested controller.© 2017 Elsevier Inc. All rights reserved.

Bandwidth oriented approach for the design of a PI controller for a three phase two-stage grid-connected PV system

ABSTRACT The two-stage power converter is currently the most widespread topology for three-phase grid-connected photovoltaic (PV) systems. The PV power is injected to the grid through the regulation of the dc-link voltage. This method is commonly called the voltage control method, whereby the current is controlled indirectly. This paper focuses on the modelling of power converters and the parameter design of proportional-integral (PI) controllers, based on the bandwidth of the inner current loop and the outer voltage loop. It is assumed that the inner loop has a bandwidth that is ten-fold bigger than that of the outer loop, in order to fully decouple the dual loops. The PI parameters are thus determined through this relationship, and two sets of simulations are run to validate the methodology used. Results show that the controllers give highly satisfactory performance even in the presence of transients introduced by rapidly varying irradiance model.

Wax actuator’s empirical model development and application to underfloor heating control with varying complexity of controller modelling detail

This paper investigates how a simulated room’s energy and temperature performance are affected if its underfloor heating control is modelled with increasing detail. Experiments were performed to develop and calibrate an empirical model of wax motor and to calibrate the valve curve. These models were used to implement and test the On/Off and proportional-integral (PI) control processes at various levels of modelling detail. Controllers were implemented by gradually adding optimized control parameters, signal delay, calibrated valve curve, signal modulation, and actuator modelling. The On/Off control dead band and PI parameters exhibited the largest impact, reducing energy use (1%–5%) and temperature fluctuations (ca 1 K). Modulating the PI output signal increased temperature fluctuations to the same amplitude as On/Off with 0.5 K dead band, increasing space heating demand by 1.3%. The wax actuator counted for less than 1%; however, it increased time delays to maximally 7 min and remarkably changed the mass flows.

Fuzzy and PSO tuned PI controller based SAPF for Harmonic Mitigation

The development of a reliable power filter is essential for meeting the need for high-quality power. Current and voltage harmonics are a major contributor to poor power quality and must be eliminated. Shunt active power filters (SAPFs) can be installed to reduce the negative effects of harmonics. Fuzzy logic and proportional integral (PI) controllers excel at regulating DC link voltage in shunt active power filters (SAPFs). This research assessed how well Particle Swarm Optimization controls the DC link voltage in a Shunt Active Power Filter (SAPF), thereby mitigating harmonics. The appropriate PI control parameters are first determined using the Particle Swarm Optimization technique. The PSO-tuned PI parameters are combined with a fuzzy logic controller for the SAPF simulation. It has been demonstrated in simulations that a Shunt Active Power Filter (SAPF) with reference currents are generated in accordance with the instantaneous real and reactive power (p-q) theory, employing fuzzy and PSO -PI controller for diode rectifier type non-linear load. It is extremely effective for harmonic correction in distribution networks. The simulation work is carried out using MATLAB-SIMULINK.

A DC–DC Converter Topology for Optimal Utilization of Hybrid Renewable Energy Resources Using Hybrid Technique

This article proposes a DC–DC converter for the optimal use of hybrid renewable energy sources (HRES) with the hybrid control approach using the proposed technology. HRES-like photovoltaic (PV), wind turbine (WT), and battery energy storage technology are examined (BESS). The proposed hybrid control technique is a hybrid wrapper of the Marine Predators Algorithm (MPA) and recalling-enhanced recurrent neural network (RERNN). The biological interaction of MPA is improved using the crossover (C) and mutation (M) method, which is later referred to as the combined CM-MPA (C2MPA) technique. The proposed system is called as C2MPA-RERNN technique. The C2MPA approach is used in the proposed method to evaluate the exact control signals system and to build up a control signals database for offline use according to power variation of source and load sides. The collected dataset is utilized to run the RERNN technique online, allowing the control method to run in less time. Based on the error value of active and reactive power, the C2MPA-RERNN determines optimal gain parameters of a proportional integral (PI) controller. The real and reactive power values are input algorithms. The system error is diminished, and optimal power flow management is connected to tune the PI parameters. The proposed system is tested using the MATLAB/Simulink platform. The gain parameters like Kp and Ki of the proposed system refer 2.5 and 150, respectively.

Chaos Elimination for a Buck Converter Using the Multi-Objective Grey Wolf Optimiser

Abstract A wide variety of nonlinear phenomena, such as bifurcation and chaos, have been observed in power electronics converters. Much research has been conducted on these behaviours in different converter topologies. The buck converter is known to exhibit chaotic behaviour in a wide parameter range, giving rise to unstable behaviours depending on the circuit parameters values. This paper investigates this bifurcation behaviour by varying the parameters of a voltage PI (Proportional Integral) controlled buck converter operating in continuous conduction mode, using a continuous-time model and constant frequency control signal. Furthermore, a novel and improved version of the PI compensation technique, designed using the multi-objective grey wolf optimiser (MOGWO), is proposed to stabilise the buck converter from chaotic state to periodic orbit.

An Integrated Observer Framework Based Mechanical Parameters Identification for Adaptive Control of Permanent Magnet Synchronous Motor

An integrated observer framework based mechanical parameters identification approach for adaptive control of permanent magnet synchronous motors is proposed in this paper. Firstly, an integrated observer framework is established for mechanical parameters' estimation, which consists of an extended sliding mode observer (ESMO) and a Luenberger observer. Aiming at minimizing the influence of parameters coupling, the viscous friction and the moment of inertia are obtained by ESMO and the load torque is identified by Luenberger observer separately. After obtaining estimates of the mechanical parameters, the optimal proportional integral (PI) parameters of the speed-loop are determined according to third-order best design method. As a result, the controller can adjust the PI parameters in real time according to the parameter changes to realize the adaptive control of the system. Meanwhile, the disturbance is compensated according to the estimates. Finally, the experiments were carried out on simulation platform, and the experimental results validated the reliability of parameter identification and the efficiency of the adaptive control strategy presented in this paper.

MSP designing with optimal fractional PI–PD controller for IPTD processes

Abstract An effective tuning methodology of modified Smith predictor (MSP) based fractional controller designing for purely integrating time delayed (IPTD) processes is reported here. IPTD processes with pole at the origin are truly difficult to control; exhibit large oscillations once get disturbed from their steady state. Proposed MSP design consists of fractional PI (proportional-integral) and fractional PD (proportional-derivative) controllers together with P (proportional) controller. Fractional controllers are competent to provide improved closed loop responses due to flexibility of additional tuning parameters. Fractional tuning parameters of PI and PD controllers are derived through optimization algorithms where integral absolute error (IAE) is considered as cost function. Efficacy of the proposed methodology is validated for IPTD processes having wide range of time delay. Stability and robustness issues are explored under process model uncertainties with small gain theorem. Performance of the proposed MSP-FO(PI–PD) controller is validated through simulation study relating five IPTD process models. Overall satisfactory closed loop responses are observed for each case during transient as well as steady state operational phases.

Optimized speed control with torque ripple reductions of BLDC motor based on SMC approach using LFD algorithm

Brushless DC motors (BLDCM) are utilized in various applications, including electric cars, medical and industrial equipment, where highefficiency speed control is necessary to meet load and tracking reference fluctuations. In this study, the optimal parameters of proportional-integral (PI) and sliding mode controller (SMC) for BLDC motor speed control are determined using Lévy flight distribution (LFD) technique. The integral time absolute error (ITAE) is used as a fitness function with the LFD algorithm for tuning the PI and SMC parameters. The optimization algorithms' performance is presented statistically and graphically. The simulation results show the SMC based on the LFD technique has superiority over SMC without optimization and PI controller in fast-tracking to the desired value with zero overshoot with rising time (6 ms) and low-speed ripple up to (± 9 RPM) under non-uniform conditions.

Optimized Luenberger Observer-Based PMSM Sensorless Control by PSO

In the real applications, we found that it is difficult to achieve good control performance through manually tuning proportional–integral (PI) parameters of phase locked loop (PLL) and speed-loop of Luenberger observer (LO) for the PMSM sensorless control system. Therefore, this paper is to use the particle swarm optimization (PSO) algorithm to optimize the PI parameters of PLL and speed-loop of Luenberger observer of the system. Firstly, the ranges of PLL parameters are obtained by analyzing the PLL subsystem stability. Then, the ranges of PI parameters of PLL and speed-loop are set based on theoretical estimation and empirical values. The control system model is realized in MATLAB/Simulink that considers the constraints such as the saturation. The integral time absolute error is the objective function, and the PSO with different topologies is used to optimize the PI parameters. The simulation and experimental results show that the proposed method is feasible, and the optimized parameters can effectively improve the precision of position estimation and speed estimation. Moreover, the simulations and experiments are carried out to verify the robustness of the proposed method, and the results show that the optimized system can achieve good performance when there are uncertainties or disturbances.

Energy-Feedback Load Simulation Algorithm Based on Fuzzy Control

In order to solve the problem that the traditional Proportional Integral (PI) controller cannot take into account both response speed and input current overshoot and cannot adapt to complex working conditions, an energy-feedback DC load simulation control algorithm based on fuzzy control was proposed. By adding a fuzzy adaptive PI setting to the traditional load simulation control algorithm, the dynamic response performance of the system and the accuracy of load simulation were improved. A small-signal model of a load analog boost circuit was established based on the state average method. Based on the phase margin, the PI parameter setting of series correction was completed, and the corresponding fuzzy control rules were formulated to identify the response state. Simulation results show that the regulation time of constant current load simulation based on a fuzzy control strategy is shortened by 75.2%, the steady-state error is reduced by 60%, and the overshoot is reduced from 28.3% to 7.47%. The prototype test results show that the maximum relative error of input current load simulation is only 4.50%, and the system response speed can meet the requirements of variable load simulation. The results show that the load simulation control algorithm based on fuzzy control has the characteristics of high precision and excellent dynamic characteristics.

Inter-relationship between approximate dynamic inversion and MRAC augmented with proportional-integral controller

Approximate Dynamic Inversion (ADI) is basically an approximation of exact dynamic inversion or feedback linearisation, which converts a nonlinear system to an equivalent linear structure. This method can be widely applied for controlling minimum phase, nonaffine-in-control systems. For applying the ADI method, a fast dynamic subsystem for deriving explicit inversion of the nonaffine equation is required. With full state feedback, ADI may be expressed in the same way as a Proportional Integral (PI) controller with only knowledge of the sign of control effectiveness and also without any approximation. The Model Reference Adaptive Controller (MRAC) augmented with the PI method is an adaptive control technique where the PI parameters are updated/tuned as per the control methodology based on the MRAC-Massachusetts Institute of Technology (MIT) rule so that the plant is capable to follow the reference model. The main objective of this paper is to find the relationship between ADI and MRAC augmented with a PI controller.