Self-tuning PI Research Articles

Correction: Self-tuning PI controller using PSO algorithm to control active and reactive power of VSCs in microgrids

Correction: Self-tuning PI controller using PSO algorithm to control active and reactive power of VSCs in microgrids

Self-tuning PI controller using PSO algorithm to control active and reactive power of VSCs in microgrids

Self-tuning PI controller using PSO algorithm to control active and reactive power of VSCs in microgrids

Adaptive PI control of temperature with natural ventilation in greenhouses using a bat algorithm variant

This paper presents a self-tuning PI controller to control the temperature of a greenhouse using natural ventilation. The PI controller parameters are adapted according to the changing dynamics of the process, identified with a simplified greenhouse temperature model based on first principles. The time-varying model parameters are estimated online using the random scaling-based bat algorithm. The model is linearized to obtain a first-order transfer function which facilitates the design of the PI controller using well-known tuning methods. Simulated results with real greenhouse data show that the proposed solution could be applied to keep controllers tuned throughout different agri-seasons.

Pressure control for pneumatic pressing roller in automated fiber placement

In automated fiber placement (AFP), the contact pressure between the roller and the mold is an important process parameter. Too small or too large contact pressure will result in unacceptable laying defects. This paper focuses on the control method of contact pressure in AFP. Firstly, the dynamic model of the pressing foot mechanism is established, and a mathematical relationship of input air pressure and output force is developed. Then, a self-tuning PI control algorithm is used to implement servo control of the roller output force based on the Slowly Time-Varying Recursive Identification Algorithm. Also, using Abaqus/CAE, the mapping relationship between the contact area and measurements from displacement sensors is established to predict the contact area in real time. Finally, the contact pressure control method is established by combining the servo control method of contact force and the prediction method of contact area. Experimental results show that the constant pressure control method can effectively stabilize the contact pressure, with a pressure fluctuation of less than 4% under the experimental conditions set in this paper.

Design and experimental implementation of DC-DC converter based self-tuning fuzzy PI controller

Bu çalışmada kendinden ayarlı bulanık PI (SFPI) kontrolör tabanlı DA-DA yükselten dönüştürücü topolojisi sunulmuştur. Dönüştürücünün kapalı çevrim transfer fonksiyonu elde edilmiş ve kararlılık analizi için küçük işaret modeli incelenerek tasarımı gerçekleştirilmiştir. Giriş-çıkış ilişkisi kara kutu modelleme ile doğrulanmıştır. Sistemin farklı anahtarlama frekanslarında 20 kHz ile 100 kHz aralığındaki davranışları incelenmiştir. Sistemin dinamik tepkisini test etmek için geleneksel PI kontrolör uygulanmış ve sistemde giriş gerilimi ve yük değişimleri gibi durumlarda gerilim regülasyonunun yetersiz olduğu gözlenmiştir. Bu nedenle dönüştürücünün çıkış gerilim regülasyonunu iyileştirmek için SFPI kontrolör önerilmiştir. Önerilen SFPI ile PI kontrolörler yükselme zamanı, yerleşme zamanı ve aşma gibi performans kriterleri ile karşılaştırılmıştır. Analiz sonuçlarına göre önerilen kontrolör ve sönümleme devrelerinin tasarlanması ile güç dönüşümündeki kayıpların azaltılarak sistem veriminin iyileştirildiği gözlenmiştir. Prototipi gerçekleştirilen dönüştürücünün nominal gerilimi 48 V, gücü 100 W olup sistemin genel verimi %92,9 olarak bulunmuştur. Sistemin analiz ve benzetim sonuçları deneysel sonuçlarla doğrulanmıştır.

DRNN Robust DTC for Induction Motor Drive Systems Using FSTPI

In this paper, a self-tuning PI speed controller based on diagonal recurrent neural network is (DRNN) investigated and simulated to increase the robustness of the direct torque control (DTC) scheme for three-phase low-power IM drive system using a Four Switch Three-Phase Inverter (FSTPI). The drive is subjected to different system inputs and disturbances, step changes in speed under different load conditions, abrupt loading at high speed and speed reversal. Furthermore, the robustness of the controller is evaluated by varying motor parameter, stator resistance and moment of inertia. A comparison of classical and self-tuning PI speed controllers was presented to determine the effectiveness of the proposed controller. It is concluded based on simulation results using Matlab/Simulink. that the self-tuning PI speed controller provides the best performance by reacting rapidly and adaptively.

Automatic Indoor Air Quality Control of COVID -19 Patient Facilities Using Type-2 Fuzzy Controller

Big facilities can be used to set up cohort wards for severe and critical COVID-19 patients. The purpose of carbon dioxide and humidity control is to provide healthy air for breathing by both diluting the pollutants originating in the building and removing the pollutants from it. In this paper, for proper mechanical ventilation to maintain indoor air quality (IAQ), a Self-tuned Interval Type-2 Fuzzy PI controller (SIT2FPIC) is proposed. 49 fuzzy if-then rules are used to tune the Interval Type-2 Fuzzy PI controller (IT2FPIC) for proper functioning and to reduce the parameter variations. Correlations of CO2 concentration and air humidity are studied to maintain the air quality in COVID-19 facilities. Mathematical models of CO2 concentration and air humidity in hospitals or temporary COVID-19 facilities are developed considering the presence of COVID-19 patients, doctors, nurses, etc in the room. The control and monitoring of room air quality are very much essential for any infectious diseases like coronavirus. To maintain the air quality in terms of humidity and carbon dioxide, the proposed SIT2FPIC is applied to the mathematical model of humidity and carbon dioxide for the simulation study. The effectiveness of the proposed controller in mechanical ventilation in the treatment facilities is shown by a comparative study of different transient parameters. Effects of load variations in the model are also studied to check the usefulness of the proposed scheme.

Analytical Study of the Robustness of the Different Variants of Fractional-Order Self-Tuned Fuzzy Logic Controllers

Due to the evolution in technologies, it has become necessary for the systems to work in real time and for efficient output they have to be adaptive. Since the conventional Proportional-Integral-Derivative (PID) controller lacks to control the less informative non-linear systems and has fixed parameters, therefore, artificial techniques with the inclusion of adaptability has been studied in this paper. It has been observed that by implementing an extra layer of fuzzy controller in parallel, the gains of the controller could be change in real time and can highly reduce the external effects on the system as compared to the non-adaptive controller. In addition to this, different variants of fuzzy controllers like self-tuned fractional-order fuzzy PD (STFOFPD), self-tuned fractional-order fuzzy PI (STFOFPI) and self-tuned fractional-order fuzzy PID (STFOFPID) are compared and it was found that the STFOFPID outperforms all the controllers in the servo-regulatory mechanism.

An Overall System Delay Compensation Method for IPMSM Sensorless Drives in Rail Transit Applications

In rail transit application, the multimode pulsewidth modulation (PWM) is used to take full advantage of the dc-bus voltage in wide speed range. To improve the sensorless control performance of interior permanent magnet synchronous motor (IPMSM) drives in this condition, an overall system delay compensation method is proposed for a hybrid position observer that combines a simple square-wave voltage injection with a nonsingular terminal sliding mode observer. Due to the delay effect, the currents and position estimation performance are deteriorated, such as more severe dq-axis currents coupling and fluctuation of estimated position error. In order to solve these problems, the analysis of delay characteristics in the IPMSM sensorless drives is first presented. For carrier-based modulation, the compensation time is obtained through a self-tuning PI regulator based on predictive q-axis voltage error. Then, the three-phase reference currents are utilized to judge the direction near zero-crossing point and the parameters robustness is also analyzed. For optimal PWM modulation, the voltage vector angle and modulation depth are predicted in a simple way, and the delay effect can be eliminated. Furthermore, the harmonics distribution in sensorless control is described by power spectral density. Finally, the effectiveness of proposed strategy is verified by experimental results with a 3.7-kW IPMSM sensorless drive platform.

Design of artificial pancreas based on the SMGC and self-tuning PI control in type-I diabetic patient

Design of artificial pancreas based on the SMGC and self-tuning PI control in type-I diabetic patient

Design of artificial pancreas based on the SMGC and self-tuning PI control in type-I diabetic patient

Optimal closed loop control of blood glucose (BG) level has been a major focus for the past so many years to realise an artificial self-regulating insulin device for type-I diabetes mellitus (TIDM) patients. There is urgency for controlled drug delivery system to design with appropriate controller not only to regulate the blood glucose but also for other chronic clinical disorders requiring continuous long-term medication. As a solution to the above problem, a novel optimal self-tuning PI controller is proposed whose gains dynamically vary with respect to the error signal. The controller is verified with a nonlinear model of the diabetic patient under various uncertainties arises in various physiological conditions and wide range of disturbances. A comparative analysis of self-tuning PI controller performance has been done with the sliding mode Gaussian control (SMGC) and other optimal control techniques. Obtained results clearly reveal the better performance of the proposed method to regulate the BG level within the normoglycaemic range (70-120 mg/dl) in terms of accuracy, robustness and handling uncertainties.

Active and Reactive Power Control for DFIG Using PI, Fuzzy Logic and Self-Tuning PI Fuzzy Controllers

Active and Reactive Power Control for DFIG Using PI, Fuzzy Logic and Self-Tuning PI Fuzzy Controllers

Variable frequency drive optimization using torque ripple control and self-Tuning PI controller with PSO

Drive’s output power must be restricted for the prevention of stresses over higher components in the input power system while utilizing a three-phase Variable Frequency Drive (VFD) which has powered from a single-phase AC source. To resolve this problem, we introduced a novel motor q-axis current (M-QAC) with torque ripple control (TRC) of an induction motor and self-tuning PI controller with particle swarm optimization (STP-PSO) for mitigating the stress over induction motor by the torque ripple elimination and controlling. Our proposed approach has an information related to the different parts stresses of the VFD which includes the terminal block and the diodes in the input side, DC bus capacitors, torque ripple, harmonics in the current and active performance for sudden changes in the speed and load. Our proposed model is simulated in MATLAB/Simulink environment. In this paper the standard dc-bus voltage ripple-based fold- back, q-axis average current fold-back and q-axis ripple current fold-back methods are utilized for the comparative analysis. Also the comparative analysis of proposed M-QAC, TRC and STP-PSO methodologies are provide with respect to steady-state values of peak-to-peak dc voltage (Vdc), peak-to-peak input current (IINPUT), input RMS current (IRMS), motor speed and the output power. Extensive simulated performance show that the STP-PSO obtained superior results over conventional standard dc-bus voltage ripple-based fold-back method, M-QAC and TRC schemes.

FPGA-realization of an RBF-NN tuning PI controller for sensorless PMSM drives

The estimation and control of the rotor position for sensorless permanent magnet synchronous motor (PMSM) drives based on extended Kalman filter (EKF) and artificial neural network (ANN) is presented in this paper. The EKF is a rotor position estimator which is a full-order stochastic observer for the recursive optimum state estimation of a nonlinear dynamic system in real time by using signals that are in the noisy environment. An ANN constructed by radial basis function neural network (RBFNN) and a parameter adjustable mechanism is applied to speed control loop of the PMSM drives to cope with the effect of the system dynamic uncertainty and external load. In this paper, firstly, a mathematical model for PMSM is derived, and a sensorless FOC is built up. Secondly, the rotor position and rotor speed which are estimated by using EKF is described. These estimated values are feed-backed to the current loop for FOC and to the speed loop for RBFNN-based self-tuning PI control. Thirdly, a very high-speed IC hardware description language (VHDL) is presented to describe the behavior of the adopted control and estimation algorithm. Fourthly, to verify the correctness of the designed VHDL code of the control and the estimation algorithm, based on electronic design automation (EDA) simulator link, a co-simulation work is constructed by Simulink and ModelSim. And some simulation results verify the correctness and effectiveness. Finally, an experimental system with a PMSM, a motor driver circuit, and a field programmable gate array (FPGA) board are set up to implement the proposed rotor position estimation and speed control algorithm.

A novel self-tuning PI controller for a DFIG-based wind turbine system

A novel self-tuning PI controller for a DFIG-based wind turbine system

A novel self-tuning PI controller for a DFIG-based wind turbine system

This article presents a novel self-tuning proportional integral (ST-PI) controller for a grid connected wind turbine based on doubly-fed induction generator (DFIG) to regulate the active and reactive power under variable wind speed conditions. The gains of the controller are dynamically adjusted according to the error variation during transient conditions to damp the system oscillations faster with better settling time and lesser overshoot. They are designed to vary continuously according to the error based on tanh function logic within a pre-determined range. The proposed self-tuning controller is able to circumvent the problems usually associated during application of conventional PI controller with constant gain such as robustness, adaptability and wide range of operation. Simulation analysis and comparative studies with conventional PI controller reveal the efficacy of the proposed control scheme. Simulation is done for the system model to test the proposed approach using MATLAB.

Steam temperature control of essential oil extraction system using Fuzzy-Fopi controller

This research proposed a closed-loop temperature control using a self-tuning fuzzy fractional-order PI (FOPI) controller to overcome the problem. The controller will regulate the steam temperature at a desired level to protect the oil from excessive heat. Self capability of fuzzy rules was found to facilitate the tuning using only information about output error and rate of output error. The proposed controller was found to be more in terms of energy consumption and produce better response compared to self-tuning fuzzy PI. Essential oil quality assessment was also performed for citronella oil samples that were extracted at 85ºC and 100ºC (uncontrolled). Significant improvement had been observed in the sample extracted at 85ºC based on physical and chemical evaluations using refractive index measurement and GC-MS analysis.Keywords: fuzzy control; fractional-order PID; essential oil extraction; hydro-steam distillation.

A Comparative Study of Fuzzy Logic Controllers for Wind Turbine Based on PMSG

This paper gives a complete design and comparative study of vector control based on Proportional Integral, fuzzy logic, and self-tuned PI by the fuzzy logic controllers of a Permanent Magnet Synchronous Generator. The direct-quadrature currents components will be controlled by the proposed controllers in order to achieve the desired performance. A complete modeling of control strategies of the overall system is presented. In order to analyze the dynamic and transient performance of the control system, some simulations are done by Matlab-Simulink.

Study on improved Ip-iq APF control algorithm and its application in micro grid

In order to enhance the tracking velocity and accuracy of harmonic detection by ip-iq algorithm, a novel ip-iq control algorithm based on the Instantaneous reactive power theory is presented, the improved algorithm adds the lead correction link to adjust the zero point of the detection system, the Fuzzy Self-Tuning Adaptive PI control is introduced to dynamically adjust the DC-link Voltage, which meets the requirement of the harmonic compensation of the micro grid. Simulation and experimental results verify the proposed method is feasible and effective in micro grid.

Seeker optimization approach to dynamic PI based virtual impedance drooping for economic load sharing between PV and SOFC in an islanded microgrid

Nowadays Load Sharing has been an emerging trend, necessary for the integration of multiple distributed generators (DGs) into the microgrid system through sophisticated power electronics converters. Proper sharing of load ascertains the utilities with increased energy cost efficiency, reliability, stability and protects the system from being overloaded. This work puts forth a novel control technique for proportional load sharing among parallel VSCs connected to an islanded microgrid in a distributed generating system consisting of Photovoltaic (PV) and Solid Oxide Fuel Cell (SOFC) as two micro sources. For tracking maximum solar energy a Mamdani Fuzzy Logic Controller (MFLC) based self-tuned Dynamic PI Controller (DPIC) is implemented for the Modified Perturb and Observe MPPT method. Also for the optimum cost management of the microgrid system, DPIC based communication less Virtual Impedance Drooping (VID) technique is implemented for suitable load sharing between the two hybrid micro sources. In order to further enhance the performance of the DPIC, the Membership function parameters are optimized using a heuristic computational algorithm named Seeker Optimization Approach (SOA). SOA computes the optimum value of the Membership function parameters for the MFLC and thus avoids the use of constant input valued parameters. The dynamic response and stability of the system with proposed method is compared and contrasted with DPIC during load sharing for ensuring robust control under conditions of linear and nonlinear loads. Validation and justification of the improvements achieved by the proposed controller are realized using MATLAB/Simulink environment.