Conventional Tuning Methods Research Articles

Optimized PID controller and model order reduction of reheated turbine for load frequency control using teaching learning-based optimization

Load frequency control (LFC) systems in power grids face challenges in maintaining stability while managing computational complexity. This research presents an optimized approach combining model order reduction techniques with Teaching Learning-Based Optimization (TLBO) for PID controller tuning in single-area LFC systems. Three reduction methods—Routh Approximation, Balanced Truncation, and Hankel Norm Approximation—were implemented to reduce system order from 4th to 2nd order, achieving a 47.3% reduction in computational time. The TLBO-optimized PID controller was compared with conventional tuning methods (Ziegler-Nichols, AMIGO, S-IMC, and CHR), demonstrating superior performance with a 38.2% decrease in settling time and 42.7% reduction in peak overshoot. The Routh Approximation method exhibited optimal performance with minimum settling time (2.8s) and peak overshoot (8.4%). Sensitivity analysis revealed stable system behavior with phase margin maintained at 84.25 degrees across parameter variations. The proposed approach achieved a 56.8% reduction in Integral Square Error compared to conventional methods, establishing its effectiveness for modern power grid applications. This research provides a robust framework for implementing efficient load frequency control in power systems while maintaining system stability and performance.

Advances and Prospects of PID Controller in Mechanical Field: From Traditional Tuning to Intelligent Optimization

Abstract. The extensive use of PID controllers in the mechanical fieldparticularly in robots and other mechanical systemsis covered in this study. In these sectors, PID controllers have become the most popular control technology due to their straightforward design and simplicity of use. PID controller performance in nonlinear complex systems is explored, along with stability and adaptive adjustment, by comparing the conventional and intelligent PID tuning methods. According to the research, control performance can be greatly enhanced by merging PID controller with intelligent technologies like fuzzy logic and genetic algorithms. These results point to the possible use of PID-based control in numerical control systems in the future. PID controllers' automatic tuning capabilities have garnered significant attention in the industrial sector. Future research will focus on the automatic tuning of PID controller to further optimize its performance and broaden its application range.

PI gain tuning for pressure-based MFCs with Gaussian mixture model

A vast number of mass flow controllers (MFCs) are used in semiconductor industry. For the stable supply, an efficient production method of MFC is required. The gain tuning of the proportional-integral (PI) control to realize a setting flow rate is essential for efficient mass production. The gains are tuned to meet the specifications required for evaluation indices of response time and overshoot amount in a step response waveform. The tuning is complicated especially for the case of pressure-based MFCs. In this paper, we propose a simple method for the PI gain tuning using the Gaussian mixture model (GMM) and the direct inverse analysis applicable to the pressure-based MFCs’ production. The relationship between the gains and evaluation indices for a standard unit of the MFC is modeled as the GMM. The direct inverse analysis calculates the difference between the standard and a test unit. Under the assumption that the difference can be compensated by a simple shift, gains likely to meet the specifications for the test unit are searched. We applied the method to seven test units. The result showed that the gains of all the test units were tuned within only a few iterations whose numbers were much less than the conventional manual tuning method, and there was no untunable unit.

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.

Simultaneous optical power insensitivity and non-volatile wavelength trimming using 2D In4/3P2Se6 integration in silicon photonics

In integrated photonic circuits, microring resonators are essential building blocks but are susceptible to phase errors due to fabrication imperfections and optical power fluctuations. Conventional active phase tuning methods are power-intensive and challenging to integrate into densely packed photonic chips. This study proposes a solution by integrating a thin 2D layer of In4/3P2Se6 (InPSe) onto silicon microring resonators (Si-MRR). This approach mitigates sensitivity to laser power and achieves non-volatile wavelength trimming. Under bias voltage, the device exhibits electro-optic behavior, offering a non-volatile phase trimming rate of −2.62 pm/V to −4.62 pm/V, corresponding to InPSe thicknesses of 45 nm to 120 nm. Low optical losses of 0.0091 to 0.0361 dB/μm were also measured, corresponding to thicknesses of 30 nm to 120 nm. The devices demonstrate stable in-situ resonance wavelength stabilization and bidirectional trimming, ensuring cyclic stability for non-volatile phase control. This advancement enhances the performance of silicon photonics across diverse applications, facilitating high-capacity, high-power operation in compact designs.

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Chirality manipulation of ultrafast phase switches in a correlated CDW-Weyl semimetal

Light engineering of correlated states in topological materials provides a new avenue of achieving exotic topological phases inaccessible by conventional tuning methods. Here we demonstrate a light control of correlation gaps in a model charge-density-wave (CDW) and polaron insulator (TaSe4)2I recently predicted to be an axion insulator. Our ultrafast terahertz photocurrent spectroscopy reveals a two-step, non-thermal melting of polarons and electronic CDW gap via the fluence dependence of a longitudinal circular photogalvanic current. This helicity-dependent photocurrent reveals continuous ultrafast phase switches from the polaronic state to the CDW (axion) phase, and finally to a hidden Weyl phase as the pump fluence increases. Additional distinctive attributes aligning with the light-induced switches include: the mode-selective coupling of coherent phonons to the polaron and CDW modulation, and the emergence of a non-thermal chiral photocurrent above the pump threshold of CDW-related phonons. The demonstrated ultrafast chirality control of correlated topological states here holds large potentials for realizing axion electrodynamics and advancing quantum-computing applications.

Two Feedback PID Controllers Tuned with Teaching–Learning-Based Optimization Algorithm for Ball and Beam System

The proportional integral derivative (PID) controller continues to be the most popular and widely used in the industry despite the development of various artificial intelligence-based controllers due to its simplicity and ease of use. Setting PID parameters, especially in non-linear systems, is still a significant problem. This study presents a cascade PID tuning technique based on the teaching–learning-based optimization (TLBO) algorithm. The proposed method is tested for the position control of a non-linear ball and beam system. A comparative analysis of the proposed method is done with the conventional tuning method and particle swarm optimization-tuned cascade PID controller. The optimization was carried out using integral time absolute error, integral time error, and integral square error as objective functions. It was observed that the evolutionary algorithm-based controller tuned by TLBO gives a much better response regarding rise time, settling time, and overshoot. To test the effectiveness and validity of the proposed controller robust analysis of the proposed controller is carried out with a step disturbance applied at t = 3 s. The comparative study proves that TLBO-tuned response is better than other controllers. Sensitivity analysis of the proposed controller is also performed by varying system parameters.

An Auto-Tuned Robust Dispatch Strategy for Virtual Power Plants to Provide Multi-Stage Real-Time Balancing Service

To fully exploit the flexible potential of distributed energy resources (DERs) in providing balancing service to the power system, Virtual Power Plants (VPPs) act as control centers to conduct the optimal real-time dispatch of their managed DERs. This study investigates a VPP’s auto-tuned robust policy based on a multi-stage distributionally robust optimization model (DRO) in response to the uncertainties from both the setpoint of the top-level system operator (SO) and the outputs of renewable DERs. We propose a concise paradigm to reduce the complexity of the original large-scale optimization task. Specifically, we first cast the multi-stage DRO problem into a dynamic programming (DP) formulation and further simplify it to derive a single-stage convex optimization control policy (COCP) at each time stage. Further, an automatic update method based on implicit differentiation is employed to tune the parameters of COCP. Case studies show that this method ensures higher solution quality and faster convergence during training than conventional tuning methods. The proposed COCP outperforms other stochastic optimization techniques in terms of robustness, efficiency, and computational speed.

Hyper-parameter optimization for improving the performance of localization in an iterative ensemble smoother

This work aims to help improve the performance of an iterative ensemble smoother (IES) in reservoir data assimilation problems, by introducing a data-driven procedure to optimize the choice of certain algorithmic hyper-parameters in the IES. Generally speaking, algorithmic hyper-parameters exist in various data assimilation algorithms. Taking IES as an example, localization is often useful for improving its performance, yet applying localization to an IES also introduces a certain number of algorithmic hyper-parameters, such as localization length scales, in the course of data assimilation. While different methods have been developed in the literature to address the problem of properly choosing localization length scales in various circumstances, many of them are tailored to specific problems under consideration, and may be difficult to directly extend to other problems. In addition, conventional hyper-parameter tuning methods determine the values of localization length scales based on either empirical (e.g., using experience, domain knowledge, or simply the practice of trial and error) or analytic (e.g., through statistical analyses) rules, but few of them use the information of observations to optimize the choice of hyper-parameters. The current work proposes a generic, data-driven hyper-parameter tuning strategy that has the potential to overcome the aforementioned issues. With this proposed strategy, hyper-parameter optimization is converted into a conventional parameter estimation problem, in such a way that observations are utilized to guide the choice of hyper-parameters. One noticeable feature of the proposed hyper-parameter tuning strategy is that it iteratively estimates an ensemble of hyper-parameters. In doing so, the resulting hyper-parameter tuning procedure receives some practical benefits inherent to conventional ensemble data assimilation algorithms, including the nature of being derivative-free, the ability to provide uncertainty quantification to some extent, and the capacity to handle a large number of hyper-parameters. Through 2D and 3D case studies, it is shown that when the proposed hyper-parameter tuning strategy is applied to tune a set of localization length scales (up to the order of 103) in a parameterized localization scheme, superior data assimilation performance is obtained in comparison to an alternative hyper-parameter tuning strategy without utilizing the information of observations.

Virtual reference setting and its application in thermal power unit control

In thermal power units, superheater outlet temperature is a very important parameter, but because of its long delay time and large inertia, it is difficult to obtain good control effect by traditional control algorithm. In this paper, taking a supercritical once-through boiler as the research object, combined with the established superheated steam temperature object model, a two-degree-of-freedom superheater outlet temperature control system based on VRFT is designed, and compared with the conventional tuning method, it is proved that the virtual reference feedback tuning (VRFT) method has many advantages, such as simple design, less computation, no need to select initial parameters and good convergence, is an application-oriented direct control system design method.

A novel strategy to design lattice structures with zero Poisson’s ratio

Cellular metamaterials are man-made structures that have unusual mechanical properties. Microlattice structures belong to a category of cellular metamaterials formed by periodic arrangement of interconnected struts or plates. The Poisson’s ratio of a microlattice structure can be tuned for a specific application by tailoring the unit cell parameters. In this work, lattice structures exhibiting zero Poisson’s ratio were designed by mimicking composite materials in which a positive Poisson’s ratio exhibiting lattice was incorporated as reinforcements in the matrix of a negative-Poisson’s-ratio lattice structure. Numerical simulations were conducted to study the quasi-static mechanical behavior of the proposed zero-Poisson’s-ratio structures, and the results were corroborated with experiments. The elastic moduli as well as Poisson’s ratios obtained from numerical and experimental results were within the limits of the conventional rule of mixture for composite materials. These composite-materials inspired lattices with zero Poisson’s ratio resulted in superior mechanical properties than three commonly studied zero Poisson’s ratio structures found in previously published literature. Thus, the proposed strategy of improving the mechanical properties of zero Poisson’s ratio lattice structures using a reinforcement lattice can be more promising than the conventional method of tuning the unit cell geometry of a zero Poisson’s ratio lattice.

Optimum Setting Algorithm Based PI Controller Tuning for SRF-PLL Used Grid Synchronization System

Synchronous reference frame-phase lock loop (SRF-PLL) is most widely used for grid synchronization applications. PLL structure typically consists of a phase detector (PD), loop filter (LF) and voltage control oscillator (VCO). Design and tuning of proportional-integral (PI) controller based LF is a changeling task under distorted grid voltage conditions. Conventional tuning methods such as symmetrical optimum (SO) and Ziegler-Nichols techniques are used to tune the PI controller, resulting in increased phase margin (PM) of the system. However, high bandwidth develops harmonics in the extracted signals under non-ideal grid conditions and affects the stability. In this research article, a new optimum setting algorithm is proposed to tune the PI controller and effective synchronization is achieved. The plant is reduced as a first-order plus dead time delay (FOPDT) model and critical gain; critical frequency of the open loop system transfer function is used to model the LF of the SRF PLL system. The controller parameters are derived such that minimization of time-weighted integral performance criteria is achieved and phase angle, frequency of the grid voltage is accurately extracted. The performance of the proposed optimum setting algorithm for the SRF-PLL grid synchronization technique is discussed in detail and simulation results are presented.

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters.

Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction studies have generally focused on home-to-school TMC. Hence, LIGHT GRADIENT BOOSTING MACHINE (LGBM), as a robust machine learning method, is applied to predict children's TMC and detect its determinants since it can present the relative influence of variables on children's TMC. Nonetheless, the use of machine learning introduces its own challenges. First, these methods and their performance are highly dependent on the choice of "hyperparameters". To solve this issue, a novel technique, called multi-objective hyperparameter tuning (MOHPT), is proposed to select hyperparameters using a multi-objective metaheuristic optimization framework. The performance of the proposed technique is compared with conventional hyperparameters tuning methods, including random search, grid search, and "Hyperopt". Second, machine learning methods are black-box tools and hard to interpret. To overcome this deficiency, the most influential parameters on children's TMC are determined by LGBM, and logistic regression is employed to investigate how these parameters influence children's TMC. The results suggest that MOHPT outperforms conventional methods in tuning hyperparameters on the basis of prediction accuracy and computational cost. Trip distance, "walkability" and "bikeability" of the origin location, age, and household income are principal determinants of child mode choice. Furthermore, older children, those who live in walkable and bikeable areas, those belonging low-income groups, and short-distance travelers are more likely to travel by sustainable transportation modes.

On-line method for optimal tuning of PID controllers using standard OPC interface

Introduction− The controlled PID is the most widely used mathematical algorithm as a regulatory control strategy in industrial environments. The applications are varied; however, its answer depends on the proper calculation of its three parameters: the proportional, the derivative, and the integral. Analytical tuning and experimental methods solve the problem, but new tuning possibilities are now enabled within the digital and process integration context. Objective− Automatically and remotely obtain the optimal parameters of the PID controller, taking advantage of an online connection via the OPC communication protocol to analyze the transient response of the system. Methodology− The study is carried out in three main phases; it begins with a PD3 SMAR thermal process with connection via OPC; in this phase, the mathematical model of the process is built analytically based on fundamental laws. In the second phase, using an analytical tuning method, the PID control architecture is created on which the online experimentation is carried out. In the third phase, the genetic algorithms for automatic tuning are implemented, extracting performance measures from the PID controller through the transient response of the process and optimally determining the values for the proportional, derivative, and integral parameters. Results− The automatic tuning method was tested with two properly instrumented industrial processes. The potential for application can be seen due to its good result and because it does not require specific mathematical knowledge compared to conventional tuning methods. Conclusions− The automatic tuning method can be used remotely to calculate the optimal parameters of a PID controller. The parameters are calculated from the transient response and the definition of design criteria adaptable to any need for control, response, and process.

RETRACTED: Cuckoo Search Optimization based PI Controller Tuning for Hopper Tank System

The paper work focuses on soft computing and Conventional controller tuning approach to design of PI controller, for a nonlinear hopper tank liquid level control system process Industries. The automation industries provide conventional techniques where it is impossible to maintain its settling time which motivates to do the research in this field. The system processes the combination of a conical and cylindrical tank for providing Multi-region based mathematical modelling to obtain the first order with delay time (FOPDT) process transfer function model. The Ziegler Nichols, Cohen-coon, Tyreus Luben, CHR (Chien, Hrones, and Reswick), IMC (Internal Model Control), Direct Synthesis, FOPI(Fractional Order PI) Conventional tuning formulae and Cuckoo Search Optimization (CSO) algorithm are used to optimize the servo regulatory responses of PI controller. The integral and proportional gain of the PI controller is said to produce the fastest settling time and reduces the error using performance indices and achieves Liquid Level control in hopper tank. Comparison is made for the various conventional controller tuning methods with Cuckoo Search Optimization tuning responses and identified to CSO-PI method offers enhanced Optimized Performance of settling time which was about 105.58 s which is relatively less while comparing to Conventional PI controller tuning methods for a different region based system.

Implementation of Water Cycle Optimization for Parametric Tuning of PI Controllers in Solar PV and Battery Storage Microgrid System

In this article, a nature-inspired optimization method, based on the water cycle, is implemented for optimal control of a solar photovoltaic microgrid with battery storage. The water cycle optimization is used to tune the parameters of the proportional-integral controllers in the regulation of the battery current and the dc-link voltage. In order to implement the optimization algorithm, the microgrid model is developed and simulated to carry out the cost function to be minimized in the optimization process. The optimization is conducted using different performance indices. The optimized control parameters are validated on a simulated microgrid and an experimental setup. Simulation and experimental results are provided and compared to genetic algorithms and a conventional tuning method to demonstrate the effectiveness of the water cycle technique.

Automated QFT-Based PI Tuning for Speed Control of SynRM Drive with Analytical Selection of QFT Control Specifications

The gains of PI controllers, used in the cascaded speed control of synchronous reluctance motors (SynRMs), are synthesized using quantitative feedback theory (QFT). A systematic design approach is employed to quantitatively determine the PI controller gains in terms of speed and current loops, using a mathematical model of the SynRM. Further, to make the QFT design a more transparent method, an analytical procedure using the frequency domain is attempted to design the QFT bounds as well as the initial search space of the optimization algorithm used in automatic loop shaping. The effectiveness of the proposed PI tuning method is verified with the extensive MATLAB/Simulink simulation environment. The results illustrate the supremacy of the proposed PI tuning method, in terms of control performance, over the conventional PI tuning method, using the analytical procedure.

Design of voltage and current controller parameters using small signal model-based pole-zero cancellation method for improved transient response in microgrids

Microgrids are supposed to provide stable power for seamless utility-grid interaction under all conditions as stated by IEEE-1547 standard. But, the use of power electronic inverter makes the microgrid sensitive to transients than synchronous generator-based plants. This degrades the voltage/frequency responses during transients, which can lead to transient stability problem if not controlled properly. Hence, the design of effective closed-loop voltage and current (V/I) controllers is highly desired to control the inverter output against the disturbances. The V/I controllers are based on PI (proportional-cum-integral) formulas. Thus, the effectiveness of V/I controllers relies on how accurate that their gain parameters are tuned. Many PI-tuning methods have been developed in the literature, but, it is yet difficult to identify a suitable method for an application. Also, only a few researchers have focused on the microgrids due to the complexity involved in its controller design by the presence of V/I cascaded dual-loop. Hence, to address this problem, this paper proposes a novel way of designing V/I controller parameters by using pole-zero cancellation method. This method is implemented by deriving the microgrid’s small-signal model. This improves the transient response through reduced system order and/or alleviated sluggish/marginal-stable/unstable poles by adding zeros at same places where those poles are laid, to in effect cancel them. The efficacy of the proposed method over existing methods is assessed by plotting frequency and voltage responses under different test conditions. From the simulation results, it is witnessed that the proposed method relatively improved the transient characteristics of microgrids.Article HighlightsAnalyzes the applicability of conventional PI tuning methods for microgrid controllers’ design.Proposes a novel small signal model based pole-zero cancellation method for the design of microgrid controllers.Enhances the gain margin, which improves the stabilization capacity of the system when subjected to disturbances.Improves the transient behavior of frequency and voltage responses, which ensure the safety of sensitive loads.

Application of Iterative Feedback Tuning Method in Superheated Steam Temperature Control System

Taking a 600 MW supercritical once-through boiler as the study object, this paper designed an IFT-based two-degree-of-freedom (DOF) PID superheated steam temperature control system. Besides, the IFT-PID controller was formed and compared with the conventional tuning method. The simulation results show that this method can be successfully applied to great-inertia and large-lag controlled objects, with evident superiority.