Controller Tuning Research Articles

Physics‐Based Design of Efficient Metasurface Temperature‐Adaptive Radiative Coatings

AbstractThis work introduces a physics‐based model and detailed examination of how efficient VO2‐based, metasurface thermally‐adaptive radiative coatings (TARCs) can be designed through careful selection of the dielectric spacer material and thickness as well as tailoring of the tungsten‐doped vanadium dioxide (WxV1‐xO2 where x = 0 and 0.015) patch dimensions. This study explores how the phase and magnitude of transmission through the nanopatterned layer of the metasurface TARC are impacted by these parameters and subsequently influence the resulting contrast in emissivity between the low‐ and high‐temperature states. When these parameters are optimized, the emissive contrast (8–13 µm) is predicted to be as high as 0.725 and 0.607 respectively for the undoped and doped TARCs. Additionally, this study investigates the use of alumina (Al2O3), a dielectric spacer with significant mechanical stability, and experimentally demonstrate the realization of metasurface TARCs with notably high emissive contrast. The findings describe the fundamental limits of emissivity contrast that can be achieved with a metasurface TARC composed of a WxV1‐xO2 layer deposited atop an Al2O3 coated aluminum ground plane. Ultimately, the study provides a deeper understanding of the underlying physics of TARCs and demonstrates that metasurface engineering can be used to achieve enhanced control in tuning their emissive contrast.

Design and optimal tuning of fractional order PID controller for paper machine headbox using jellyfish search optimizer algorithm

This manuscript proposes the Jellyfish Search Optimization (JSO) algorithm-based Fractional Order Proportional-Integral-Derivative (FOPID) controller tuning for a paper machine headbox. The novelty of this method lies in integrating the JSO technique for optimizing the parameters of the FOPID controller to monitor and control headbox pressure and stock level efficiently and effectively. The JSO algorithm ensures optimal tuning of controller parameters by minimizing error indices such as Integral of Squared Error (ISE), Integral of Time Absolute Error (ITAE), and Integral of Absolute Error (IAE). Simulations conducted on the MATLAB/Simulink platform demonstrate that the FOPID controller tuned using JSO achieves superior performance compared to conventional PI (Proportional-Integral) and PID (Proportional-Integral-Derivative) controllers. Specifically, the JSO-tuned FOPID controller exhibited a 25% reduction in rise time, a 30% improvement in settling time, and a 20% decrease in overshoot when compared to the PID controller. Furthermore, comparative analyses with other optimization techniques, including Moth Flame Optimization (MFO), Ant Lion Optimization (ALO), and Elephant Herding Optimization (EHO), reveal that the JSO algorithm provides higher accuracy and stability in diverse operating conditions. This study underscores the efficacy of the JSO-tuned FOPID controller as a robust solution for complex industrial applications, such as paper machine headbox systems, and highlights its potential to enhance process efficiency and control precision.

DYNAMIC SIMULATION INTEGRATION IN PROCESS CONTROL EDUCATION: A CASE STUDY ON METHANOL-WATER SEPARATION USING AVEVA PROCESS SIMULATION

This study explores implementing a novel teaching methodology for process control education using AVEVA Process Simulation software. The methodology integrates interactive simulations and practical problem-solving exercises, focusing on the dynamic simulation of an ethanol-water distillation process. In this scenario, students are tasked with evaluating the performance of controllers integrated into the simulation, allowing them to engage with dynamic process behaviors in a virtual environment. The results indicate a significant improvement in students' understanding and assimilation of process control concepts. Feedback gathered through questionnaires, with six participants, highlighted the effectiveness of the adopted methodology. All six participants reported an enhanced understanding of PID controller tuning (average score of 4.83), demonstrating the positive impact on both practical and theoretical learning. This innovative teaching strategy demonstrates its potential to modernize process control education and bridge the gap between academic instruction and industrial applications.

A rule of robust optimal parameter tuning of Active Disturbance Rejection Control based on second order plus dead time model approximation

A rule of robust optimal parameter tuning of Active Disturbance Rejection Control based on second order plus dead time model approximation

Robust and Adaptive Tuning of PI Current Controllers for Grid-Forming Inverters

Robust and Adaptive Tuning of PI Current Controllers for Grid-Forming Inverters

Adaptive Parameter Tuning and Virtual Impedance Injection Control for Coupled Harmonic Mitigation of Photovoltaic Converter

Adaptive Parameter Tuning and Virtual Impedance Injection Control for Coupled Harmonic Mitigation of Photovoltaic Converter

4D-printed programmable sample-/eluent-actuated solid-phase extraction device for trace metal analysis.

To integrate valves, manifolds, and solid-phase extraction (SPE) columns into a compact device is technically difficult. Four-dimensional printing (4DP) technologies, employing stimuli-responsive materials in three-dimensional printing (3DP), are revolutionizing the fabrication, functionality, and applicability of stimuli-responsive analytical devices that can show time-dependent shape programming to enable more complex geometric designs and functions. However, 4D-printed stimuli-responsive actuators and valves utilized to control flowing streams in SPE applications remain rare. The tunable stimuli-responsive 4D-printed flow control actuators and valves with the capability of the programmable actuation can be used to manipulate flowing streams and simplify the automation of a conventional SPE scheme. We employed digital light processing three-dimensional printing (3DP), bisphenol A ethoxylate dimethacrylate-based photocurable resins, and 2-carboxyethyl acrylate (CEA)-incorporated flexible photocurable resins to fabricate an SPE column positioned between two [H+]-responsive flow-actuated needle valves. The two valves sequentially close after loading an alkaline sample (pH>pKa of CEA) due to swelling of the stem. Subsequently, they sequentially open upon loading an acidic eluent (pH<pKa of CEA; deswelling of the stem). The optimized device enabled a semi-automatic SPE scheme for Mn, Co, Ni, Cu, Zn, Cd, and Pb ions and showed competitive performance when coupled with inductively coupled plasma mass spectrometry-with the method detection limits ranging from 0.5 to 5.9ngL-1. We validated the reliability and applicability of this method by analyzing the metal ions in reference materials (CASS-4, SLRS-5, 1643f, and Trace Elements Urine L-2) and spike analyses of natural water and human urine samples. To the best of our knowledge, our device is the first demonstration of an SPE scheme performed by the programmable actuation of 4D-printed stimuli-responsive flow control valves, highlighting the analytical applicability of the tunable stimuli-responsive 4D-printed parts and the promising capability of 4DP technologies in advancing conventional sample pretreatment devices.

Tailoring Robust 2D Nanochannels by Radical Polymerization for Efficient Molecular Sieving.

Two-dimensional (2D) nanochannels have demonstrated outstanding performance for sieving specific molecules or ions, owing to their uniform molecular channel sizes and interlayer physical/chemical properties. However, controllably tuning nanochannel spaces with specific sizes and simultaneously achieving high mechanical strength remain the main challenges. In this work, the inter-sheet gallery d-spacing of graphene oxide (GO) membrane is successfully tailored with high mechanical strength via a general radical-induced polymerization strategy. The introduced amide groups from N-Vinylformamide significantly reinforce the 2D nanochannels within the freestanding membranes, resulting in an ultrahigh tensile strength of up to 105MPa. The d-spacing of the membrane is controllably tuned within a range of 0.799-1.410nm, resulting in a variable water permeance of up to 218 L m-2h-1 bar-1 (1304% higher than that of the pristine GO membranes). In particular, the tailored membranes demonstrate excellent water permeance stability (140 L m-2h-1 bar-1) in a 200-h long-term operation and high selectivity of solutes under harsh conditions, including a wide range of pH from 4.0 to 10.0, up to a loading pressure of 12bar and an external temperature of 40°C. This approach comprehensively achieves a balance between sieving performance and mechanical strength, satisfying the requirements for the next-generation molecular sieving membranes.

A hybrid optimization scheme for tuning fractional order PID controller parameters for a DC motor

This paper on a hybrid optimization scheme for tuning fractional order PID (FOPID) controller parameters for a DC motor explored the effectiveness of a hybrid GA-PSO approach in optimizing FOPID controller parameters. The DC motor is faced with the following challenges: brush wear and maintenance, limited speed control range, limited lifespan, size and weight, efficiency complexity of control systems, and spark generation. This problem arises from the poor tuning of the PID controller used in the systems. First, a DC motor was modelled along with an FOPID controller in a MATLAB environment. Secondly, a hybrid scheme (GA-PSO) was designed in MATLAB environment. Furthermore, the performance of the hybrid scheme (GA-PSO) was compared with GA and PSO used distinctly. The GA-PSO hybrid algorithm outperforms other algorithms in terms of rise time of 0.30, settling time of 2.90, overshoot of 5.29, peak to peak of 1.05, and peak values of 0.59. The results show improvements in key performance indicators, offering insights for improving DC motor control.

Harnessing the Electronic Spin States of Single Atoms for Precise Electromagnetic Modulation.

By manipulating their asymmetric electronic spin states, the unique electronic structures and unsaturated coordination environments of single atoms can be effectively harnessed to control their magnetic properties. In this research, the first investigation is presented into the regulation of magnetic properties through the electronic spin states of single atoms. Magnetic single-atom one-dimensional materials, M-N-C/ZrO2 (M = Fe, Co, Ni), with varying electronic spin states, are design and synthesize based on the electronic orbital structure model. The SAs 3d electron spin structure of the composite M-N-C modulates the magneto physical properties and triggers a unique natural resonance loss, which achieves a controllable tuning of the effective absorption band under low-frequency conditions. The minimum reflection loss (RLmin) of M-N-C can reach -69.71dB, and the effective absorption bandwidth (EAB) ratio is as high as 91% (2-18GHz). The current work provides a path toward achieving controllable modulation of low-frequency electromagnetic wave bands by exploring the mechanism through which atomic and even electronic level interactions influence magnetic modulation.

A tunable and reversible thermo-inducible bio-switch for streptomycetes.

Programmable control of bacterial gene expression holds great significance for both applied and academic research. This is particularly true for Streptomyces, a genus of Gram-positive bacteria and major producers of prodigious natural products. Despite that a few inducible regulatory systems have been developed for use in Streptomyces, there is an increasing pursuit to augment the toolkit of high-performance induction systems. We herein report a robust and reversible thermo-inducible bio-switch, designated as StrepT-switch. This bio-switch enables tunable and reversible control of gene expression using physiological temperatures as stimulation inputs. It has been proven successful in highly efficient CRISPR/Cas9-mediated genome engineering, as well as programmable control of antibiotic production and morphological differentiation. The versatility of the device is also demonstrated by thermal induction of a site-specific relaxase ZouA for overproduction of actinorhodin, a blue pigmented polyketide antibiotic. This study showcases the exploration a temperature-sensing module and exemplifies its versatility for programmable control of various target genes in Streptomyces species.

MATHEMATICAL MODELING AND ANALYSIS OF DYNAMIC CHARACTERISTICS OF A BRUSHLESS MOTOR IN AUTOMATED CONTROL SYSTEMS

The article provides a detailed analysis of brushless motors (BM) within the context of automated control, focusing on dynamic modes and mathematical modeling of the electric drive. It addresses the dynamic characteristics, transfer function development, and specific operational aspects of BM in systems with regulated rotational speed. The article presents experimental data and computer simulation results that validate the effectiveness of the selected models. Experimental studies conducted on a laboratory model confirm the accuracy of the developed mathematical model, particularly demonstrating the feasibility of approximating the motor's transfer function in a linear form to simplify control system analysis and tuning. Computer simulations are conducted in MATLAB using SIMULINK, which enables precise and illustrative visualization of transient processes in the BM. The article also introduces a subordinate control principle within a closed-loop control system, which ensures control stability and accuracy by dividing into speed and current loops. The simulation results demonstrate the potential of utilizing the developed models for further research and the design of efficient and reliable control systems for brushless motors in industrial settings. The proposed modeling approach paves the way for the creation of energy-efficient electric drives with high-precision control.

Optimizing s-p Orbital Overlap Between Sodium Polysulfides and Single-Atom Indium Catalyst for Efficient Sulfur Redox Reaction.

P-block metal carbon-supported single-atom catalysts (C-SACs) have emerged as a promising candidate for high-performance room-temperature sodium-sulfur (RT Na-S) batteries, due to their high atom utilization and unique electronic structure. However, the ambiguous electronic-level understanding of Na-dominant s-p hybridization between sodium polysulfides (NaPSs) and p-block C-SACs limits the precise control of coordination environment tuning and electro-catalytic activity manipulation. Here, s-p orbital overlap degree (OOD) between the s orbitals of Na in NaPSs and the p orbitals of p-block C-SACs is proposed as a descriptor for sulfur reduction reaction (SRR) and sulfur oxidation reaction (SOR). Compared to NG and NG-supported InN4 (NG-InN4) SACs, the nitrogen-doped graphene-supported InN5 (NG-InN5) SACs show the largest s-p OOD, demonstrating the weakest shuttle effect and the lowest reaction energy barriers in both SRR and SOR. Accordingly, the designed catalysts allow the Na-S pouch batteries to retain a high capacity of 490.7 mAh g-1 at 2 A g-1 with a Coulombic efficiency of 96 % at a low electrolyte/sulfur (E/S) ratio of 4.5 μl mg-1. This work offers an s-p orbital overlap descriptor describing the interaction between NaPSs and p-orbital-dominated catalysts for high-performance RT Na-S batteries.

Optimizing s‐p Orbital Overlap between Sodium Polysulfides and Single‐Atom Indium Catalyst for Efficient Sulfur Redox Reaction

P‐block metal carbon‐supported single‐atom catalysts (C‐SACs) have emerged as a promising candidate for high‐performance room‐temperature sodium‐sulfur (RT Na‐S) batteries, due to their high atom utilization and unique electronic structure. However, the ambiguous electronic‐level understanding of Na‐dominant s‐p hybridization between sodium polysulfides (NaPSs) and p‐block C‐SACs limits the precise control of coordination environment tuning and electro‐catalytic activity manipulation. Here, s‐p orbital overlap degree (OOD) between the s orbitals of Na in NaPSs and the p orbitals of p‐block C‐SACs is proposed as a descriptor for sulfur reduction reaction (SRR) and sulfur oxidation reaction (SOR). Compared to NG and NG‐supported InN4 (NG‐InN4) SACs, the nitrogen‐doped graphene‐supported InN5 (NG‐InN5) SACs show the largest s‐p OOD, demonstrating the weakest shuttle effect and the lowest reaction energy barriers in both SRR and SOR. Accordingly, the designed catalysts allow the Na‐S pouch batteries to retain a high capacity of 490.7 mAh g‐1 at 2 A g‐1 with a Coulombic efficiency of 96% at a low electrolyte/sulfur (E/S) ratio of 4.5 μl. This work offers an s‐p orbital overlap descriptor describing the interaction between NaPSs and p‐orbital‐dominated catalysts for high‐performance RT Na‐S batteries.

Computationally aided design of single‐ion‐conducting block copolymer electrolytes to boost lithium‐ion conductivity

AbstractIn this work, we implement a combinatory approach that integrates molecular dynamics (MD) simulation and a Gaussian process regression (GPR) algorithm to explore optimal design strategies for a generic single‐ion‐conducting block copolymer electrolyte (SIC‐BCPE) model system that covers a rich design parameter space inspired by recently established poly(ethylene oxide)‐based block copolymer electrolytes. The GPR algorithm is employed to efficiently reveal the relationships between the desired lithium‐ion conductivity and four design parameters reflecting both chain architectural control and specific tuning of molecular chemistry. Guided by the GPR results, an optimal combination of four parameter values is inferred, and MD simulation confirms that the corresponding SIC‐BCPE system produces relatively higher lithium‐ion conductivity. To further understand the influence of each molecular parameter on the trends of lithium‐ion conductivity, we analyse the proportion variation of different types of lithium‐ion coordination and identify a strong correlation between the ion coordination pattern and ionic conductivity. Due to its generality, we expect that the MD‐GPR combinatory approach reported in this work is applicable to a broad range of other polymer‐based ion transport systems. © 2024 Society of Chemical Industry.

A Novel Approach to Robust PID Autotuner for Overdamped Systems: Case Study on Liquid Level System

This paper proposes and validates an automatic control tuning methodology based on initial frequency response. This approach facilitates the design of robust PID controllers for overdamped systems characterized by S-shaped step responses. In the prior autotuner, which is inherently robust, the critical frequency value is determined via the relay test, while process frequency response and its derivative at this frequency are found using the sine test. Our novel approach offers a fully automated calculation for these parameters based solely on the step response test (i.e., minimal information from the process), eliminating the need for additional calculations by relay and sine tests. For this aim, it estimates these values employing the first order plus dead time models. Firstly, five step response methods are introduced to ascertain the model parameters. Secondly, three alternative estimation methods for the critical frequency of the system are proposed through (i) solving a nonlinear equation, (ii) employing a linear approximation, and (iii) using a power regression. Lastly, the model parameters are then employed to calculate the system frequency response and its derivative at the critical frequency. The remaining design steps are the same as in the initial robustness-based autotuner. In the simulation studies, two types of overdamped systems are considered. The critical frequency estimation values obtained from three estimation methods are compared to each other. Additionally, the impact of the step response methods and the proposed estimation methods within the novel approach on system response and control signal are investigated. The designed controllers use a new approach to the initial autotuner and are implemented on a two-tank liquid-level system. The experimental outcomes are in accordance with the simulation results, confirming the validity and compatibility of the findings. Quantitative performance metrics are used to evaluate the effectiveness of the designs comprehensively.

Concurrent AI Tuning of a Double-Loop Controller for Multi-Phase Drives

The control of electric drives is an important topic due to the wide-spread use of such devices. Among these, multi-phase induction machines are gaining momentum in variable-speed applications. The usual control practice is the use of a speed Proportional–Integral loop that sets the current reference for an inner controller. This inner controller decides the voltage to be applied, which is realized by an electronic power converter. This paper presents an Artificial Intelligence (AI) scheme for tuning. It aims to optimize the usual figures of merit for drives. Moreover, tuning for both loops is tackled concurrently. The adjustment is performed relying on the operating region to address non-linear behavior. The results obtained using a five-phase induction motor illustrate that the proposed method can work in the entire operating range of the drive with improved results.

Vibration control of a base-excited rotor supported by active magnetic bearings using the linear active disturbance rejection controller

Rotor systems supported on a moving base, such as turbines on a ship, by active magnetic bearings are commonly subjected to additional excitations, namely base excitations. As a result, rotor vibration could be increased dramatically and even endanger the normal operation of the rotating machinery. This paper extends the linear active disturbance rejection control (LADRC) technique to the field of suppressing the base-excited effect. According to the characteristics of the LADRC, the decentralized control structure is adopted, and the control principle of employing the LADRC is illustrated. To improve the performance of the LADRC, using assisted parameters to facilitate its designation is a common method. Based on the control principle, the method for acquiring the assisted parameters is designed. Afterward, the effect of the controller parameters, including bandwidth parameters and assisted parameters, on the LADRC performance is analyzed systematically in terms of stability, noise rejection, and base excitation rejection. Based on the analyzed results, the steps for the controller tuning are suggested. On the designed test rig of an AMBs-rotor system with base excitations, the vibration suppression tests are performed to demonstrate the effectiveness of the LADRC technique and validate the conclusions of theoretical analysis.

Rehabilitation Technologies by Integrating Exoskeletons, Aquatic Therapy, and Quantum Computing for Enhanced Patient Outcomes.

Recent advancements in patient rehabilitation integrate both traditional and modern techniques to enhance treatment efficacy and accessibility. Hydrotherapy, leveraging water's physical properties, is crucial for reducing joint stress, alleviating pain, and improving circulation. The rehabilitation of upper limbs benefits from technologies like virtual reality and robotics which, when combined with hydrotherapy, can accelerate recovery. Exoskeletons, which support and enhance movement, have shown promise for patients with neurological conditions or injuries. This study focused on implementing and comparing proportional-integral-derivative (PID) and fuzzy logic controllers (FLCs) in a lower limb exoskeleton. Initial PID control tests revealed instability, leading to a switch to a PI controller for better stability and the development of a fuzzy control system. A hybrid strategy was then applied, using FLC for smooth initial movements and PID for precise tracking, with optimized weighting to improve performance. The combination of PID and fuzzy controllers, with tailored weighting (70% for moderate angles and 100% for extensive movements), enhanced the exoskeleton's stability and precision. This study also explored quantum computing techniques, such as the quantum approximate optimization algorithm (QAOA) and the quantum Fourier transform (QFT), to optimize controller tuning and improve real-time control, highlighting the potential of these advanced tools in refining rehabilitation devices.

Evaluation of Hysteresis Models for Estimating the Characteristics of High Pressure Solenoid Valves for Mechanical Ventilation

Abstract The flow control of inspiratory valves for mechanical ventilation is a crucial element of the overall functionality as it mainly governs precision and safety. However, control tuning can be challenging as the widely used magnetic actuation principle comes with a hysteresis shaped characteristic. As a result, the production of ventilators is highly dependent on specific valves and therefore vulnerable if supply chains are interrupted as during the pandemic. An approach which is capable of providing a reliable control for a variety of standard solenoid valves would be beneficial. Therefore, this work examines different models of hysteresis and their precision when being applied to actual measurement data of high pressure solenoid valves as a foundation for a following model-based control approach which can be either feedback or feedforward. Regarding the precision, the Prandtl-Ishlinskii (PI) model outperformed alternative approaches (MAEbelow2.5 L/ min) for the chosen parameters which is why the model is described for fitting both the forward and inverse case.