Multivariable Systems Research Articles

Global adaptive stabilization for a class of uncertain multivariable systems with nonlinear parametrization

Global adaptive stabilization for a class of uncertain multivariable systems with nonlinear parametrization

Limit hypersurface state of the art Gaidai multivariate risk evaluation approach for offshore Jacket

Current study presents state of the art approach to risk and reliability assessment for multivariate nonlinear dynamic systems. Specifically, novel hypersurface Gaidai risks evaluation methodology has been presented for evaluating offshore structural risks. Advocated approach being particularly suitable for offshore engineering multidimensional dynamic systems, possessing large number of critical components, that have either been physically observed/measured, or numerically modeled over a representative timelapse. Advocated Gaidai hypersurface structural risks evaluation methodology is applicable for a wide range of engineering and industrial systems. This study demonstrates that given in situ environmental conditions, it is well possible to assess accurately risks of system’s failures, damages or hazards, caused by excessive structural dynamics. Multivariate dynamic systems, possessing nonlinear cross-correlations between critical system’s components often present design challenges, when utilizing classic structural risks evaluation approaches, as those are mostly only univariate or bivariate. Dynamic offshore Jacket hot spot stresses have been utilized as an example in this reliability investigation. Modeling system’s excessive dynamics is challenging because of the non-stationarity of the system and the intricacy of fluid-structural dynamic interactions, arising from in situ wave-induced loads, influencing Jacket’s structural dynamics. Nonlinearities greatly affect structural dynamics, e.g., 2nd, 3rd, higher order effects within fluid-structural interactions. Methodology presented in this work offers a straightforward, effective, yet precise means of assessing the risks of failure or hazard for multivariate, non-stationary, non-linear dynamic offshore systems. For bivariate case, verification note has been added.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Influences of thioalcohols on the release of aromas in sesame-flavor baijiu

This study investigated the interactions between 2-furylmethanethiol, benzenemethanethiol, and 18 skeletal aroma-active compounds as well as four aroma notes in sesame-flavor baijiu based on the Feller Additive Model, the Odor Activity Value (OAV) Approach, and the Sigma-Tau (σ-τ) plots. In addition, a predictive model for the interactions between 2-furylmethanethiol and esters was developed, and the determinants of the interaction results in complex systems were explored. The results reveal that both thioalcohols interacted with the skeletal aroma-active compounds in a similar trend, where 2-furylmethanethiol tends to enhance the release of fruit and acid aroma. Moreover, the intensity of the thiols and their intensity ratio to the notes were the determinants of the interaction results in the multivariate blended system, with the lower the concentration of the thiols, the closer the ratio was to 1, and the more likely that additive interactions would take place. Predictive modeling showed that 2-furylmethanethiols were more likely to have additive or synergistic effects with esters when the olfactory thresholds of the esters were between 75.86 and 199.53 μg/L. Conversely, masking effects were more likely.

Noteworthy synthesis strategies and applications of metal-organic frameworks for the removal of emerging water pollutants from aqueous environment

17 global Sustainable Development Goals (SDGs) were established through the adoption of the 2030 Agenda for Sustainable Development by all United Nations members. Clean water and sanitation (SDG 6) and industry, innovation, and infrastructure (SDG 9) are the SDGs focus of this work. Of late, various new companies delivering metal-organic frameworks (MOFs) have blossomed and moved the field of adsorption utilizing MOFs to another stage. Inside this unique circumstance, this article aims to catch recent advancements in the field of MOFs and the utilizations of MOFs relate to the expulsion of arising contaminations that present huge difficulties to water quality because of their steadiness and possible damage to environments and human wellbeing. Customary water treatment techniques regularly neglect to eliminate these poisons, requiring the advancement of novel methodologies. This study overviews engineering techniques for controlling MOF characteristics for better flexibility, stability, and surface area. A current report on MOFs gathered new perspectives that are amicably discussed in emergent technologies and extreme applications towards environmental sectors. Various applications in many fields that exploit MOFs are being fostered, including gas storage, fluid separation, adsorbents, catalysis, medication delivery, and sensor utilizations. The surface area of a wide range of MOFs ranges from 103 to 104 m2/g, which exceeds the standard permeability of several material designs. MOFs with extremely durable porosity are more significant in their assortment and variety than other classes of porous materials. The work outlines the difficulties encountered in the synthesis steps and suggests ways to make use of MOFs' value in a variety of contexts. This caters to creating multivariate systems enclosed with numerous functionalities, leading to the synthesis of MOFs that offer a synergistic blend of in-built properties and exclusive applications. Additionally, the MOF-related future development opportunities and challenges are discussed.

Manic Residual Symptoms Also Deserve Attention: A Symptom Network Analysis of Residual Symptoms in Bipolar Disorder.

Lots of patients with bipolar disorder (BD) continue to have residual symptoms after treatment in their remission, BD exhibits intricate characteristics and transformation patterns in its residual symptoms, residual symptoms of different polarities and degrees can mix with and transform to each other. There is a need for further investigation of BD as a comprehensive multivariate disease system. The current research lacks network analyses focusing on BD's residual and subsyndromal symptoms. 242 patients were included with bipolar disorder in remission. We compared demographic data and differences in symptoms between populations with and without residual symptoms using t-tests and chi-square tests, with FDR applied for multiple comparison correction. Logistic regression was used to identify influencing factors for residual symptoms. Symptom networks were compared by network analysis to analyze the relationships between different types of residual symptoms. Depressive residual symptoms (N=111) were more common than manic residual symptoms (n=29) in the patients included. The comparison between two groups with and without residual symptoms shows no difference in demographic data and medical history information. The main influencing factors related to residual symptoms were time from diagnosis to first treatment (OR=0.88), the first(OR=1.51) and second (OR=17.1)factors of the Mood Disorder Questionnaire (MDQ), the Quick Inventory of Depressive Symptomatology Self-Report (QIDS)(OR=5.28), the psychological(OR=0.68) and environment (OR=1.53) subscale of the World Health Organization Quality of Life Short Form (WHOQOL-BREF). There was a significant difference in network structure between the groups with and without residual symptoms (network invariance difference=0.4, p =0.025). At the same time, there was no significant difference between the groups with and without depressive residual symptoms. However, the symptom network in patients with depressive residual symptoms is more loosely structured than in those without, with symptoms exhibiting weaker interconnections. When there is no depressive or manic residual symptom, it can still form a symptom network and cause an impact on social function. This study underscores the complexity of bipolar disorder's residual symptoms. Although it primarily manifests as loosely structured depressive residual symptoms, manic residual symptoms should not be ignored. Future research should explore network-based interventions targeting specific symptom clusters or connections to improve residual symptom management and patient outcomes.

Optimal Design of I-PD and PI-D Industrial Controllers Based on Artificial Intelligence Algorithm

This research aims to apply Artificial Intelligence (AI) methods, specifically Artificial Immune Systems (AIS), to design an optimal control strategy for a multivariable control plant. Two specific industrial control approaches are investigated: I-PD (Integral-Proportional Derivative) and PI-D (Proportional-Integral Derivative) control. The motivation for using these variations of PID controllers is that they are functionally implemented in modern industrial controllers, where they provide precise process control. The research results in a novel solution to the control synthesis problem for the industrial system. In particular, the research deals with the synthesis of I-P control for a two-loop system in the technological process of a distillation column. This synthesis is carried out using the AIS algorithm, which is the first application of this technique in this specific context. Methodological approaches are proposed to improve the performance of industrial multivariable control systems by effectively using optimization algorithms and establishing modified quality criteria. The numerical performance index ISE justifies the effectiveness of the AIS-based controllers in comparison with conventional PID controllers (ISE1 = 1.865, ISE2 = 1.56). The problem of synthesis of the multi-input multi-output (MIMO) control system is solved, considering the interconnections due to the decoupling procedure.

Squirrel Cage Induction Motor (SCIM) Rotor Flux Estimation and Observer Using Multivariable Sliding Mode Control (MSMC)

This paper proposes a multivariable sliding mode control (MSMC) based in rotor flux estimation and observer methods for squirrel cage induction motor (SCIM). The principle of sliding mode control (SMC) adjustment can also be applied to multivariable systems. In this case, for each control quantity, there is a control member who switches the latter quickly between a maximum value and a minimum value, thus causing the sliding mode to appear. This article will discuss sliding mode tuning of multivariable systems, highlighting issues that arise in relation to mono-variable systems studied so far. In this paper, we will establish the general relationships, where we introduce the regulators of multivariable systems. To describe the behavior in sliding mode, we will call or equivalent control vector, at the end of this article we will introduce a rotor flux observer to improve the results obtained. The work is developed with a 2-level voltage source inverter. The simulation results are carried out to verify and validate the multivariable sliding mode control method proposed based rotor flux estimation and observer scheme

Multiple output response decoupling: With applications to vibration control of optical tables

This paper introduces a novel control theorem called multiple output response decoupling (MORD) and applies it to the vibration control of optical tables. Control design for multivariable systems is challenging because improving a particular performance might compromise others. Hence, this paper proposes the MORD theorem, which allows independent, simultaneous modifications of all output responses. First, we develop the output response decoupling (ORD) lemma, which can modify specified output responses while keeping others unchanged. Second, we derive the MORD theorem, which can adjust all output responses as individual ORD controls without cross-influences. Third, we apply the MORD theorem to optical table models, enabling the design of particular ORD controllers to suppress ground disturbances and other ORD controllers to minimize strut deflections. Integrating these controllers using the MORD structure provides concurrent optimization of these output responses. Finally, we perform experiments that illustrate the feasibility and effectiveness of the proposed MORD theorem.

A Partial Information Decomposition for Multivariate Gaussian Systems Based on Information Geometry.

There is much interest in the topic of partial information decomposition, both in developing new algorithms and in developing applications. An algorithm, based on standard results from information geometry, was recently proposed by Niu and Quinn (2019). They considered the case of three scalar random variables from an exponential family, including both discrete distributions and a trivariate Gaussian distribution. The purpose of this article is to extend their work to the general case of multivariate Gaussian systems having vector inputs and a vector output. By making use of standard results from information geometry, explicit expressions are derived for the components of the partial information decomposition for this system. These expressions depend on a real-valued parameter which is determined by performing a simple constrained convex optimisation. Furthermore, it is proved that the theoretical properties of non-negativity, self-redundancy, symmetry and monotonicity, which were proposed by Williams and Beer (2010), are valid for the decomposition Iig derived herein. Application of these results to real and simulated data show that the Iig algorithm does produce the results expected when clear expectations are available, although in some scenarios, it can overestimate the level of the synergy and shared information components of the decomposition, and correspondingly underestimate the levels of unique information. Comparisons of the Iig and Idep (Kay and Ince, 2018) methods show that they can both produce very similar results, but interesting differences are provided. The same may be said about comparisons between the Iig and Immi (Barrett, 2015) methods.

Robust Controller Design for Multivariable Systems under Nonstationary Parametric Variations and Bounded External Disturbances

Robust Controller Design for Multivariable Systems under Nonstationary Parametric Variations and Bounded External Disturbances

Robust Controller Design for Multivariable Systems under Nonstationary Parametric Variations and Bounded External Disturbances

Robust Controller Design for Multivariable Systems under Nonstationary Parametric Variations and Bounded External Disturbances

Optimization of rotor blind hole parameters for inner-hole rotary cavitator

Abstract In order to study the cavitation characteristics of rotary cavitator and the optimization of blind hole parameters, this paper first deduces the blind hole geometric parameters (aperture, hole depth, and number of holes) that affect the cavitation effect, then uses a numerical simulation method to optimize the blind hole parameters that affect the cavitation effect based on the control variable method and gas phase contour. Using single factor analysis as the optimization criterion and gas phase volume as the optimization basis, the response surface method was applied to the multi-parameter optimization problem with interaction. The results indicate that the intensity of cavitation can be effectively increased by increasing the geometric dimensions of the blind holes’ diameter, depth, and number appropriately. On the other hand, the response surface method can be utilised to study the multi-parameter optimization problem in the multivariable system comprised of the geometric parameters of the blind hole of the cavitator rotor. This paper’s optimization research on the rotor parameters of inner-hole rotary cavitator provides a foundation and reference for future cavitator structure optimization research.

A cooperative control method and application for series multivariable coupled system

Series multivariable coupled system is a typical controlled object in process control industry. The interaction of various state variables between multiple inputs and outputs in the system forms a complex series multivariable coupled structure. This coupled structure makes the control of a controlled object in the system affect the controlled object in the upper and lower control loop. As a result, it is difficult to control one or more control loops in the system without changing the state of other links in the system. In this paper, a cooperative control method for series multivariable coupled system is proposed. A decoupling controller is designed to remove the coupling effect caused by the interaction between stages, and the system is decoupled into several independent control loops. Differential leading PI (proportional-integral) error compensation method is introduced to ensure the following performance of the controller without static error. The proposed cooperative control method satisfies the Lyapunov stability, and has been successfully applied in the simulation experiment of cascade pumping station system of Beijing East-to-West water transfer project. The proposed method reduces the difficulty to controlling the water level of forebay of each pumping station and ensures the efficient operation of the cascade pumping station system.

Bond graph realizations of state space linear time-invariant multivariable descriptions

Given a state space description of a linear time-invariant multivariable system, a similarity transformation is applied to get a controllability canonical form. This transformation assures a Bond Graph (BG) realization and avoids the use of active bonds. Active or signal bonds do not preserve the continuity of power flows and consequently, energy conservation properties are lost. The state matrix of the system in transformed coordinates contains an antisymmetric matrix associated with the storage field and the rest is associated with the dissipative field. Thus, in general, the dissipative field is implemented with a multiport element, and in order to give physical meaning, the storage field is implemented with one-port elements. The resulting BG model does not have zero-order causal paths, that is, algebraic loops between the dissipative elements. The results are applied to the BG realization of a linear robust multivariable controller that is designed for a two mass system modelled in BG.

Multi-Variable Multi-Metric Optimization of Self-Assembled Photocatalytic CO2 Reduction Performance Using Machine Learning Algorithms.

The sunlight-driven reduction of CO2 into fuels and platform chemicals is a promising approach to enable a circular economy. However, established optimization approaches are poorly suited to multivariable multimetric photocatalytic systems because they aim to optimize one performance metric while sacrificing the others and thereby limit overall system performance. Herein, we address this multimetric challenge by defining a metric for holistic system performance that takes multiple figures of merit into account, and employ a machine learning algorithm to efficiently guide our experiments through the large parameter matrix to make holistic optimization accessible for human experimentalists. As a test platform, we employ a five-component system that self-assembles into photocatalytic micelles for CO2-to-CO reduction, which we experimentally optimized to simultaneously improve yield, quantum yield, turnover number, and frequency while maintaining high selectivity. Leveraging the data set with machine learning algorithms allows quantification of each parameter's effect on overall system performance. The buffer concentration is unexpectedly revealed as the dominating parameter for optimal photocatalytic activity, and is nearly four times more important than the catalyst concentration. The expanded use and standardization of this methodology to define and optimize holistic performance will accelerate progress in different areas of catalysis by providing unprecedented insights into performance bottlenecks, enhancing comparability, and taking results beyond comparison of subjective figures of merit.

On-board model predictive control for autonomous lane keeping with fuzzy preview distance: Design and experiment

This paper concerns with the development of computationally efficient lane keeping control method for the autonomous vehicles. To obtain autonomous lane keeping, model predictive control (MPC) scheme was intensively investigated in the previous studies owing to its inherent advantage of dealing with constrained multivariable systems. However, the tradeoff problem between the good tracking performance and the low computational complexity is inevitably raised in developing of MPC-based lane keeping technology. To alleviate the conflict between performance and cost, an on-board MPC with fuzzy preview distance is designed in this study. A linear system dynamics model is used in the MPC design to reduce the computational cost, and a fuzzy logic algorithm is developed to select an appropriate preview distance for enhancing the MPC performance. Further, hardware-in-the-loop test is adopted to explore the effectiveness and efficiency of the proposed control method. In comparison to the proportional-integral controller, the experimental results show that the MPC is more sensitive to the selective value of fixed preview distance. Since the significant impact of preview distance selection on the MPC-based lane keeping performance, the fuzzy logic algorithm is of the essence in terms of selecting the appropriate preview distance for MPC enhancement under different vehicle speed and road curvature. Eventually, experimental results validate that the proposed fuzzy preview distance algorithm can effectively improve the MPC-based lane keeping performance for autonomous vehicles subject to limited computational resource.

Enhancing energy recovery in automotive suspension systems by utilizing time-delay

This study introduces time-delay active control technology to with the aim of enhancing the system's energy harvesting potential and ride smoothness. The time-delay active suspension represents a nonlinear, multivariable system, and stability analysis in this context is intricate. The mainly challenge lies in effectively leveraging time delay to augment the system's energy collection potential while harmonizing the vehicle's ride comfort and energy efficiency. Addressing this issue, our study proposes enhancing the suspension's energy recovery capability through time-delay control. Initially, the impact of time-delay control parameters on energy collection ability under various operational conditions was analyzed, establishing that appropriate time-delay parameters can enhance energy harvesting capacity. Subsequently, to balance the relationship between vehicle comfort and energy efficiency, the research introduces a method for constructing an optimized objective function based on linear equivalent excitation, thereby quantifying the relationship between system time-domain vibrational response, time-delay control parameters, and external excitations. This approach enables the comprehensive optimization of the suspension system's comfort, safety, and energy efficiency. The control system's stability was analyzed using cluster treatment of characteristic roots method. Finally, simulations and experiments were conducted to evaluate the effectiveness of time-delay active across different scenarios, confirming the efficacy of the proposed control methodology.

Distributed identification based partially-coupled recursive generalized extended least squares algorithm for multivariate input–output-error systems with colored noises from observation data

System identification determines the model of the plant from the measurement data and has been widely used in the prediction and control. In this paper, the parameter estimation problem is studied for multivariate equation-error systems with autoregressive moving average noises. Since the considered system is high-dimensional, the distributed identification is considered in this paper. The subsystems are obtained by decomposing the original system in accordance with the number of the outputs. However, the subsystems contain the same parameter vector, resulting in many redundant estimates. By taking the average of the parameter estimation vectors, we develop a partially-coupled subsystem recursive generalized extended least squares (PC-S-RGELS) algorithm to cut down the redundant parameter estimates. In consideration of the information communication between the subsystems and the convergence of the estimation algorithm, a partially-coupled RGELS (PC-RGELS) algorithm is presented by means of the coupling identification concept. Compared with the multivariate RGELS algorithm, the two algorithms have higher computational efficiencies. Finally, an illustrative example is provided to demonstrate the effectiveness of the two proposed algorithms.

Exponential-form predefined-time convergent controller and its applications to Van-der-Pol system and 2-DOF helicopter

This paper proposes a predefined-time convergent stabilization controller for a class of nonlinear systems. First, a controller is introduced for linear systems where the convergent time is selected in advance, independent of initial conditions, and can be assigned as a parameter of a control input. The corresponding result is then obtained for a multidimensional system consisting of [Formula: see text] nonlinear differential equations. The block control technique is employed to consequently stabilize each state component by a virtual control until the real control appears. Finally, the presented result is applied to a multivariable nonlinear system to design a predefined-time convergent robust controller. To show effectiveness of the proposed convergent controller, numerical simulations have been carried out for a Van-der-Pol system and a 2-degree-of-freedom (2-DOF) helicopter model.