PI Controller Tuning Research Articles

PI Optimized Cuckoo Search Algorithm for Single Phase Grid Tied PV Inverter with SEPIC Converter

A new approach for single phase grid interconnected PV module with SEPIC converter is introduced in this paper. As the outcome of PV is low voltage DC, a suitable converter is needed to boost the voltage. Hence SEPIC converter is used as it has high voltage gain with same polarity in the input as well as output side. A closed loop control is executed with a PI controller tuned with Cuckoo search (CS) algorithm. As the traditional tuning of PI controller results in peak overshoot problems, Cuckoo search (CS) based Optimization is utilized as it is simple with has a smaller number of tuning parameters. This output is being fed to the grid through a single phase VSI and the grid synchronization is accomplished by PI controller by analogizing the actual and reference values of power. Thus, the proposed control strategy is verified through MATLAB and it is observed that the source current THD is minimized, which satisfies the IEEE standard.

Grid power quality enhancement using an ANFIS optimized PI controller for DG

Grid frequency variation causes phase angle deviation in current with respect to voltage. This is sensed at the phase-locked loop in the controller. In past studies the effect of grid frequency variation is neglected while designing the controller for power quality restoration. When modern grids are connected to large numbers of non-linear loads and various types of distributed generation (DG), it results in continuous variation in grid frequency. Thus it is necessary to consider the grid frequency variation for effective power quality restoration. However, tuning of conventional PI controller gains considering frequency variation is very difficult. Thus it is necessary to develop an adaptive intelligent nonlinear controller to tackle the effects of frequency variation, voltage distortion and non-linear load simultaneously. This paper presents the importance of considering the effects of the frequency variation, grid voltage distortion and non-linear load, while designing and deploying a controller for power quality restoration. The proposed controller supplies power to local load as well as transferring surplus power to the grid from DG along with the additional benefit of improving grid power quality. A DG with an ANFIS optimized PI current controller for power quality enhancement is proposed. The method is economical as it requires no additional hardware. Results are compared with PI, PI-RC and fuzzy current controllers to validate the effectiveness of the proposed controller.

Novel MAF-Fuzzy Based IR-SRF Controller for DVR to Improve PQ Under Dynamic Weak Grid Conditions

This paper presents the development of advanced adaptive controller for dynamic voltage restorer (DVR) to regulate the power quality connected to a distribution system. Various voltage quality issues have been generated as distorted grid conditions are responsible for abnormal operation of the consumer electronics and domestic household appliances. Therefore dynamic voltage restorer (DVR) is employed for elimination of voltage quality issues in the distribution lines. To improve the performance and efficiency of DVR, an improved robust SRF (IR-SRF) controller has been developed in this paper for the mitigation of voltage quality issues under weak grid conditions. In the developed IR-SRF controller, moving average filter (MAF) has been utilized for better extraction of ripples from the sensed voltage. For better grid voltage signal synchronization with the proposed controller under highly dynamic condition, an enhanced Phase Locked Loop (PLL) scheme has been utilized instead of conventional PLL. For the regulation of DC link voltage under dynamic environmental conditions, MAF based fuzzy tuned PI controller has been utilized with the ability to adapt the situation. The developed controller has been simulated and results for various grid voltage conditions have been analyzed. The obtained simulation results are compared with conventional control algorithm to validate the efficacy. The efficiency of the proposed controller has been validated using the experimental results.

Coot Bird Algorithms-Based Tuning PI Controller for Optimal Microgrid Autonomous Operation

This paper develops a novel methodology for optimal control of islanded microgrids (MGs) based on the coot bird metaheuristic optimizer (CBMO). To this end, the optimum gains for the PI controller are found using the CBMO under a multi-objective optimization framework. The Response Surface Methodology (RSM) is incorporated into the developed procedure to achieve a compromise solution among the different objectives. To prove the effectiveness of the new proposal, a benchmark MG is tested under various scenarios, 1) isolate the system from the grid (autonomous mode), 2) islanded system exposure to load changes, and 3) islanded system exposure to a 3 phase fault. Extensive simulations are performed to validate the new method taking conventional data from PSCAD/EMTDC software. The validity of the suggested optimizer is proved by comparing its results with that achieved using the LMSRE-based adaptive control, sunflower optimization algorithm (SFO), Ziegler-Nichols method and the particle swarm optimization (PSO) techniques. The article shows the superiority of the suggested CBMO over the LMSRE-based adaptive control, SFO, Ziegler-Nichols and the PSO techniques in the transient responses of the system.

Optimal PI Parameters Tuning for a DC-DC Boost Converter

This paper designs a nonlinear PI-type controller for the robust control of a boost DC-DC converter using a particle swarm optimization (PSO) algorithm for increasing the output voltage of the photovoltaic (PV) system. In addition, the PI controller is used to tracking the maximum power from the PV panel, at different atmospheric condition. For tuning PI controllers is a tedious work and it is difficult to tune the PI gains optimally due to the nonlinearity. This paper presents an approach to use the particle swarm optimization algorithm to design the optimal PI controllers. The Simulations results show that the proposed controller exhibits better behavior in terms of settling time and overshoot and better performance of the converter.

A multilayer affective computing model with evolutionary strategies reflecting decision-makers’ preferences in process control

Many industrial control problems related to multi-objective optimization, such as controller parameters tuning, often require operators to perform multiple-step interactions without considering the changes of decision-makers’ affective states and quantitative description of decision-makers’ preferences during the interactive decision. Regarding this problem, we developed a multilayer affective computing model (MACM), including three factors: human personality, emotional space, and affective states, to demonstrate the iterative affective computing during the interactions. First, a concise model of affective computing-driven interactive decision-making was built before three submodules involved were described in detail. (1) An affective state recognition method based on facial expressions was presented, providing the basis for obtaining expert affective states during decision-making. (2) An identification method of affective parameters was given, providing an approach to describing personalized affective state-changing rules of different persons. (3) A definition of decision-makers’ preferences in interactive decision-making was specified. In addition, a decision-makers’ preferences mining method was developed by the MACM and an iterative learning control (ILC) strategy. Thus, we proposed affective computing-driven interactive decision-making method, which provided a simplified approach to converting the interactive decision problems based on decision-makers’ preferences to decision issues based on incremental decision vector, along with assisting computers to learn from human experts and perform decision-making automatically in a general sense. Then, two typical process control cases—PI controller tuning for decoupling problem and manipulate vector optimization for batch processes were used to show the correctness and effectiveness of the contributions. Compared with other traditional optimization algorithms without affective state tracking and recognition (fuzzy control, ILC, reinforcement learning, and so on), experimental results indicated that the proposed method could achieve good performance. Finally, this study presented the efficiency and limitations of using this technique for a specific application

Control Method for Flexible Joints in Manipulator Based on BP Neural Network Tuning PI Controller

With the development of robot technology, integrated joints with small volume and convenient installation have been widely used. Based on the double inertia system, an integrated joint motor servo system model considering gear angle error and friction interference is established, and a joint control strategy based on BP neural network and pole assignment method is designed to suppress the vibration of the system. Firstly, the dynamic equation of a planetary gear system is derived based on the Lagrange method, and the gear vibration of angular displacement is calculated. Secondly, the vibration displacement of the sun gear is introduced into the motor servo system in the form of the gear angle error, and the double inertia system model including angle error and friction torque is established. Then, the PI controller parameters are determined by pole assignment method, and the PI parameters are adjusted in real time based on the BP neural network, which effectively suppresses the vibration of the system. Finally, the effects of friction torque, pole damping coefficient and control strategy on the system response and the effectiveness of vibration suppression are analyzed.

TORQUE RIPPLE REDUCTION OF A BRUSHLESS DC MOTOR USING Y-SOURCE CONVERTER

The brushless DC motor (BLDC) is a low cost, reliable and efficient motor for low power applications. In general, the speed, torque and current of the BLDC motor are controlled using a well tuned PI controller in the inner and outer control loops. This controller will be effective in reducing the dynamic speed error, but will produce large current ripples. This reference current when given to the inner control loop and controlled using hall-effect position sensing technique, leads to comparatively large ripples in the torque. Because of large dynamic behavior of dc link voltage when nominal rating capacitor is used, there will be torque ripples and reduction in rotor speed from the reference current value. Hence, to mitigate this torque ripples in BLDC motor a fast acting adjustable dc link voltage like chopper is generally used. The effective dc link voltage control with voltage boosting and controlling action is observed with Y-source converter and is compared with a Z-source converter in this paper. The Y-source converter is designed in such a way that, it will effectively control the speed and also produces lesser current ripples reference. Further, the inverter topology uses a six switch basic configuration but with a new switching strategy. The results are compared with a Z-source converter with the proposed Y-source converter under variable load torque and variable speed cases in MATLAB/ SIMULINK environment. It is found that, the torque ripples are reduced effectively without much change in the reference speed. Also, even at higher rotor speeds, the torque ripples and surges are also lesser.

Making the PI and PID Controller Tuning Inspired by Ziegler and Nichols Precise and Reliable.

This paper deals with the design of a DC motor speed control implemented by an embedded controller. The design is simple and brings some important changes to the traditional Ziegler–Nichols tuning. The design also includes a novel anti-windup implementation of the controller and an integrated noise-reduction filter design. The proposed tuning method considers all important aspects of the control, such as pre-processing of the measured signals and filtering (to attenuate the measurement noise), time delays of the process, modeling and identification of the process, and constraints on the control signal. Three important aspects of designing PI and PID controllers for processes with noisy output on Arduino-type embedded computers are considered. First, it deals with the integrated design of the input filter and the controller parameters, since both are interdependent. Secondly, the method of setting the controllers from step responses by Ziegler and Nichols is modified for the case of digital signal processing (without drawing the tangent), while it recommends the suitability of its modification in terms of the use of both integral and static models. Third, the most suitable anti-windup solution for the given controller structure is proposed. In summary, the paper shows that an appropriate design of the embedded controller can achieve excellent closed-loop performance even in a noisy process environment with limited control signals.

Double closed-loop PI controller tuning for motor control

This paper demonstrates an algebraic algorithm performed in the control loop of a Brushless DC Motor (BLDCM) speed controller by computing the PI controller parameter that can be accepted. Using methodology of PI controller stability analysis, accurate stability conditions can be derived for the BLDC Motor speed controller. The results of applying the new method ensure the system stability and fast response by tuning the PI controller in the order of the motor and mechanical constant.

SOFC-Supported Hybrid PSOGSA-Optimized Dynamic Voltage Restorer for Power Quality Enhancement

A novel optimal method of a Dynamic Voltage Restorer (DVR) supported by solid oxide fuel cell (SOFC) and its simplified topological structure are proposed. DVR is a power-electronic converter-based device, and the objective of the DVR control system is to minimize supply voltage variations at the load terminals. This is attained by generating a compensating voltage at the series injection transformer. Conventional controllers are mathematical model-based; also, the particular system varies widely, and nonlinear factors make the PI controller tuning more challenging to some extent. As a result, an intelligent PI optimization control method is essential. This paper proposes Hybrid PSOGSA to search for optimal values of two PI control parameters for the [Formula: see text]–[Formula: see text]-axis components by considering a novel bi-objective function. The performance of the test system is analyzed for five test scenarios using the proposed PI controller with SPSO-optimized and Ziegler–Nichols tuning methods. DVR system provides an excellent control performance in the transient and steady states for compensating the sensitive load voltages with almost zero steady-state errors. Simulation results show that the proposed approach can provide improved performance than PSO-optimized and classical PI controllers for the power quality indices measured.

Hopfield Neural Network-Based Average Current Mode Control of Synchronous SEPIC Converter

ABSTRACT Average Current Mode (ACM) controlled synchronous DC–DC converters are increasingly required for computer power supplies and electric vehicle battery chargers. In this study, Generalized Hopfield Neural Network (GHNN) tuned PI controllers has been proposed for ACM control of synchronous single ended primary inductance converter (SEPIC). The dynamic converter model, required for designing the controllers, is derived using the state-space averaging technique considering all the converter non-idealities. In addition, modified Hankel matrix method is applied to reduce the derived converter model to its first-order equivalent to minimize the design complexity. Using the reduced-order model, the control loop errors are formulated as objective functions in terms of their PI controller parameters. These objective functions are then solved using GHNN to obtain the optimal PI controller parameters. The transient and steady-state performance of the converter with the proposed controller is studied and compared with controllers tuned using Zeigler Nicholas (ZN) method, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Reduced-Order Linear Quadratic Regulator (ROLQR) control scheme for variations in the input voltage, reference voltage and load using MATLAB/ Simulink R2015b software tool. Results revealed that the proposed GHNN based controller has fast dynamic performance with less settling time, % overshoot, and steady-state error than existing control methods. A 100W converter prototype is also constructed and tested using TMS320C2000TM microcontroller to verify the effectiveness of the GHNN control algorithm.

Design of Microelectronics Microsystem Neural Learning Controller Based on Subspace State Recognition

Aiming at the large delay and large inertia in cone storage and irrigation, this paper adopts machine learning and predictive control, proposes a closed-loop N4SID (numerical subspace state system identification) algorithm, which is adjusted by IMC based on Smith predictor PI controller and parameter tuning are given, and the steps designed in cascade control are given. The experimental results verify the effectiveness of the proposed method.Compared with closed-loop SVA and open-loop methods, the method proposed in this paper has better verification results and pole assignment. According to this model, the PI controller is adjusted using IMC based on Smith Smith predictor. Compared with the traditional ZN adjustment method, this method shows good results in a conical system. In addition, the PI parameters can be adjusted to obtain robust control using the H∞-based mixed sensitivity algorithm. The contribution of this paper is the use of closed subspace system identification to identify the MIMO state space. The developed model validates the IMC tuned PI controller based on the Smith predictor in the factory.

Advanced Controllers Implementation for Speed Control Analysis of Two Mass Drive System

The speed control of two mass drive systems is presented in this paper. For the purpose of analysis, PI, Linear Matrix Inequality controller and Internal Model Controller are chosen. Tuning of PI controller is achieved by Zeigler Nichols method, whereas the LMI and IMC are adapted with the conventional method of tuning. The various simulations are carried out in order to analyze the performance index of these controllers and to fix the best controller, and the results are compared

Performance analysis of real-time PSO tuned PI controller for regulating voltage and frequency in an AC microgrid

<p>In this study, a control strategy based on the self-tuning method and synchronous reference frame (SRF) with PI regulator is proposed to achieve optimum quality of power in an autonomous micro grid (MG). The MGS is based on multiple distributed generation (DG) connected with 120 kV power grid. The proposed system is first simulated with fixed gain values for PI controller which are not optimal for sudden changes in the system i.e. transition of MG to islanding mode, load variations. So, the particle swarm optimization (PSO) has been utilized for tuning of PI controller parameters which ensure flexible performance and superior quality of power. The principal parameters considered in this study are, regulation of voltage and frequency, steady-state and dynamic response and harmonic distortion, mainly when microgrid is islanded. The performance of PI and PI-PSO is compared in this study by simulating AC microgrid in the MATLAB/Simulink environment. Summarized results of the system are provided to authenticate viability of proposed arrangement. The proposed controller performs intelligently while regulating voltage and frequency of the MGS and utility system.</p><div> </div>

Modulated MPC for Arm Inductor-Less MVDC MMC With Reduced Computational Burden

A modulated model predictive controller is designed for an inductor-less modular multilevel converter targeting an MVDC solid-state transformer application. The underlying optimization problem is formulated such that a unique closed-form solution is derived from a highly accurate dynamic system model, which significantly reduces computation time. Unlike other closed-form methods, the proposed controller achieves inductor-less current control, has a reduced sampling speed enabled by a novel model-based circulating energy compensator, and does not require tuned PI controllers to generate current references. Simulation and experimental results demonstrate that the proposed controller achieves transient power flow control and steady state circulating energy control despite the low system inertia.

Performance Analysis and Comparison of Optimally Tuned Hybrid Filter Controller for Arc Furnace Load

Power quality enhancement is a concept based on the modern power electronics devices designed at enhancing the quality and reliability of power in distribution networks. This work focuses on modeling and analysis of custom power quality enhancement devices. The modeling approach involves the design of shunt hybrid filter using current control techniques. The application of artificial intelligence is recently growing in power electronic systems. Particle swarm optimization, cuckoo search algorithm, gravitational search algorithm, genetic algorithm, harmony search algorithm based control methods are developed. The aim is to enrich the power quality in distribution side by decreasing the level of harmonics generated by the nonlinear loads. This paper reveals an innovating shunt hybrid filter accompanied by tuned PI controller for termination of harmonic. Soft computing algorithms based PI controller is verified for minimization of current harmonic distortion.

Realization and Control Of Multilevel Inverter With GSA Tuned Pi Controller

Realization and Control Of Multilevel Inverter With GSA Tuned Pi Controller

Optimal rotor blade control using Grey Wolf Optimizer for small signal stability of SCIG Wind Turbine

The pitch control of Wind Turbine needs to be tuned for optimal pitch control performance. In this paper, the Grey Wolf Optimizer (GWO) is proposed to tune the PI controller in the pitch control of the Squirrel Cage Induction Generator (SCIG) Wind Turbine. The goal is to show that the slow convergence of the Particle Swarm Optimization (PSO), and the trapping into a local optimum of Genetic Algorithm (GA) tuning methods can be overcome by applying the GWO tuning method. The GWO, PSO and GA were applied online to tune the PI controller in pitch control. The tuning results were executed in the PI controller of pitch control of simulated SCIG Wind Turbine connected to a double circuit distribution line. Equally, the Ziegler Nichols (ZN) tuned PI controller was executed in the rotor blade control of the same Wind Turbine. From the simulation results, the GWO tuned PI controller executed in the pitch control of the Wind Turbine provided the least overshoot and the settling time in the aerodynamic power and torque compared to the PSO, GA and ZN tuned PI controllers. The significance of the proposed GWO tuning method for PI controller in the rotor blade control of the SCIG Wind Turbine, it can reduce the stress on the pitch actuator of the Wind Turbine. This is because it reduced overshoot and settling time in the trajectories of blade pitch demand of the Wind Turbine compared to PSO, GA and ZN tuning methods.

Intelligent hybrid technique for cascaded multilevel inverter based three phase grid-tie hybrid power system: a WPSNN technique

This dissertation introduces an intelligent hybrid technique for the design of modelling and controller of the three-phase hybrid network connection system based on CMI. The hybrid technique is the Recurrent Neural Network (RNN) assisted by Wolf Pack Search (WPS) and therefore it is known as WPSNN technique. Here, the three stage CMI control scheme is considered as two stages and these are analyzed with the help of the proposed intelligent controller. In first stage, the WPS is developed to demonstrating the PV total and wind voltages and the WPS algorithm of the second stage assisted to RNN is used to extracting the reference currents of network. In first stage, input of WPS algorithm is PV and wind reference voltages and the gain parameters is generated as the output for optimal tuning of PI controller. In the second phase, the RNN is trained with on line process; the output of RNN is given to the WPS algorithm. It provides the corresponding tuning parameters to achieve the network current. To regulate the dc link voltage and the extraction of network current is developed with established technique. CMI based on the established technique is synchronizes the firing work relationship and reduces the modulation load. By then, the established technique is evaluated in MATLAB/Simulink working stage and the execution is assessed to existing techniques.