Control Approach Research Articles

Model predictive control of switched nonlinear systems using online machine learning

This work introduces an online learning-based model predictive control (MPC) approach designed for the modeling and control of switched nonlinear systems featuring scheduled mode transitions. Initially, recurrent neural network (RNN) models are constructed offline, utilizing sufficient historical operational data to capture the nominal system dynamics. Subsequently, we employ real-time process data to develop online learning RNN models, aiming to approximate the dynamics of switched nonlinear systems under the influence of bounded disturbances. In cases where the initial RNN models are unavailable for a specific switching mode due to limited historical data, we use real-time data from closed-loop operations under a proportional-integral (PI) controller to build online learning RNN models. To evaluate the predictive performance of online learning RNNs, a theoretical analysis on their generalization error bound is developed using statistical machine learning theory. Additionally, considering the presence or absence of initial RNN models, two MPC schemes are developed. These schemes employ RNNs as prediction models to stabilize switched nonlinear systems, ensuring closed-loop stability by accounting for the generalization error bound derived for online learning RNNs. Finally, the effectiveness of the proposed MPC schemes is demonstrated through a nonlinear process example with two switching modes.

The alleviative effect of Bacillus subtilis-supplemented diet against Vibrio cholerae infection in Nile tilapia (Oreochromis niloticus).

Bacterial illness causes detrimental impacts on fish health and survival and finally economic losses for the aquaculture industry. Antibiotic medication causes microbial resistance, so alternative control strategies should be applied. In this work, we investigated the probiotic-medicated diet as an alternative control approach for antibiotics in treating Vibrio cholerae infection in Nile tilapia (Oreochromis niloticus). One hundred eighty fish (50 ± 2.5g Mean ± SD) were allocated into six groups in glass aquariums (96L) in triplicate for 10 days. Groups 1 (G1), G2, and G 3 were intraperitoneally (IP) injected with 0.5 mL sterilized tryptic soy broth and fed on a basal diet, basal diet contained B. subtilis (BS) (1 × 10 5 CFU/ kg-1 diet), and basal diet contained trimethoprim-sulfamethoxazole (TMP-SMX) (1.5g/kg-1 diet), respectively. Additionally, G4, G5, and G6 were IP challenged with 0.5 mL of V. cholerae (1.5 × 107 CFU) and received the same feeding regime as G 1 to 3, respectively. The results exhibited that the V. cholera-infected fish exhibited skin hemorrhage, fin rot, and the lowest survival (63.33%). Additionally, lowered immune-antioxidant biomarkers (white blood cells count, serum bactericidal activity, phagocytic activity, phagocytic index, and lysozymes) with higher lipid peroxidation marker (malondialdehyde) were consequences of V. cholerae infection. Noteworthy, fish-fed therapeutic diets fortified with BS and TMP-SMX showed a substantial amelioration in the clinical signs and survival. The BS diet significantly improved (P < 0.05) the immune-antioxidant indices of the infected fish compared to the TMP-SMX diet. The current findings supported the use of a BS-enriched diet as an eco-friendly approach for the control of V. cholerae in O. niloticus.

A digital twin-based traffic light management system using BIRCH algorithm

Urban transportation networks are vital for the economic and environmental well-being of cities and they are faced with the integration of Human-Driven Vehicles (HVs) and Connected and Autonomous Vehicles (CAVs) challenge. Most of the traditional traffic management systems fail to effectively manage the dynamic and complex flows of mixed traffic, mainly because of large computational requirements and the restrictions that control models of traffic lights directly based on extensive and continuous training data. Most of the times, the operational flexibility of CAVs is severely compromised for the safety of HVs, or CAVs are given high priority without taking into account the efficiency of HVs leading to lower performance, especially at low CAV penetration rates. On the other hand, the existing adaptive traffic light approaches were usually partial and could not adapt to the real-time behaviors of the traffic system. Some systems operate with inflexible temporal control plans that cannot react to variations in traffic flow or use adaptive control strategies that are based on a limited set of static traffic conditions. This paper presents a novel traffic light control approach utilizing the BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering algorithm combined with digital twins for a more adaptive and efficient system. The BIRCH is effective in processing large datasets because it clusters data points incrementally and dynamically into a small set of representatives. The suggested method does not only enable better simulation and prediction of traffic patterns but also makes possible the real-time adaptive control of traffic signals at signalized intersections. It also improves traffic flow, reduces congestion, and minimizes vehicle idling time by adjusting the green and red light durations dynamically based on both real-time and historical traffic data. This approach is assessed under different traffic intensities, which include low, moderate, and high, while efficiency, fuel consumption, and the number of stops are being compared with the traditional and the existing adaptive traffic management systems.

Modeling, Analysis, and Control of Autonomous Mobility-on-Demand Systems: A Discrete-Time Linear Dynamical System and a Model Predictive Control Approach

Modeling, Analysis, and Control of Autonomous Mobility-on-Demand Systems: A Discrete-Time Linear Dynamical System and a Model Predictive Control Approach

Rapid identification of back-flow in three distributaries of Jingjiang Reach, middle Yangtze River, China

Unusual back-flow events occurred in 2015 and 2017 at the near-mainstream ends of the three distributaries of the Jingjiang Reach, middle Yangtze River, China. It is presently challenging to precisely quantify back-flow discharges at river distributary ends where normal-flow discharges are measured chronically. This problem is more complicated at the ends of the three Jingjiang distributaries, each of which is more than 100 km long, and thus favors the generation of different back-flow forms. We use empirical formulae based on the Saint-Venant Equation and the Fall Index Method to quantify impacts on flow discharges at the two ends of each distributary of three key hydrodynamic variables: (1) water discharge at Zhicheng hydrological station, (2) total discharge from the four southwest tributary rivers, and (3) water levels at the two ends of the distributaries. We use the variable control approach to identify the contribution ratios of natural and human driving factors to flow discharges at the two ends of the distributaries during five historical stages. The empirical formulae provide reasonable results, with high values of correlation coefficients (R2 = 0.84 ∼ 0.98) obtained, demonstrating that the total water discharge from the four southwest tributary rivers is negligible. These formulae perform well in reproducing observed flow discharges at the two ends of the distributaries, especially the maximum discharges and cumulative volumes for normal-flow and back-flow events. Three charts are devised for different flow conditions at the two ends, from which back-flow events can be rapidly identified. Before impoundment of the Three Gorges Dam (TGD), riverbed adjustment caused by the lower Jingjiang Cutoff Projects and the Gezhou Dam contributed the most to flow discharge changes (≥50 %) at the near-mainstream ends of the distributaries. Afterwards, precipitation dominated (>60 %). In the future, back-flow events are expected only to occur at Mituosi (i.e., Taipingkou) and Nanxian hydrological stations, for which optimal reservoir operation and water resource utilization could remedy the back-flow crisis. Use of climate models and more advanced technologies such as remote sensing and GIS are recommended to enhance the reliability of predictions.

A Sampled-Data-Based Secure Control Approach for Networked Control Systems Under Random DoS Attacks.

This article aims to analyze H∞ stability of a class of networked control systems (NCSs) under random denial of service (DoS) attacks and design a sampled-data-based state feedback security controller to mitigate the influence of attacks. Different from the existing random attacks, the information about the maximum duration time of DoS attacks can be captured by introducing a predesigned logical processor. Then, based on the periodic sampling technique, the probability of attack occurrence and the resultant number of maximum allowable consecutive packet dropouts can be calculated, which is quite significant to investigating the security problem of NCSs. A DoS-dependent security controller which makes full use of the attack probability information and the number of attack-induced packet dropouts is designed. A novel networked sampled-data system model is first established that enables us to deal with the random DoS attacks phenomena and the time-varying delay induced by attacks under a uniform framework. By structuring a suitable Lyapunov-Krasovskii functional, the relationship between mean square asymptotic stability and attack characteristics is obtained. Finally, the reliability and applicability of the presented control strategy in eliminating the influence of DoS attacks are validated by two practical engineering applications.

Performance assessment of VSC-based HVDC system in asynchronous grid interconnection: Offline and real-time validation of control design with symmetric optimum PI tuning

Asynchronous interconnection is essential for integrating AC networks operating at different frequencies, typically 50 Hz and 60 Hz. This need arises from distributed power generation methods, including offshore renewable sources and diverse regional grid configurations. Advanced strategies are required to overcome these frequency differences and ensure uninterrupted power transfer.High-Voltage Direct Current (HVDC) transmission systems facilitate efficient power exchange, enhancing grid reliability and stability. This study focuses on optimizing the Proportional-plus-Integral (PI) controller parameters within a 20 MVA Voltage Source Converters (VSC)-based HVDC system to enable asynchronous interconnection between offshore and onshore AC networks. The offshore VSC regulates active and reactive power, while the onshore VSC controls DC voltage and reactive power. A vector control approach with symmetric optimum PI tuning is proposed for a comprehensive performance assessment of the VSC-based HVDC transmission system.The effectiveness of the tuned PI controller parameters is evaluated through four test cases using MATLAB/Simulink for offline simulation and Typhoon HIL604 for real-time validation. These cases involve abrupt changes in reference active and reactive power for the offshore VSC; and in reference reactive power and DC voltage for the onshore VSC. Results demonstrate rapid and satisfactory dynamic performance across all test cases, as evidenced by offline simulations and real-time validation.The validation highlights the effectiveness of the proposed control design with symmetric optimum PI tuning, confirming its ability to enhance the overall performance of the HVDC transmission system for efficient asynchronous interconnection.

Repetitive Impedance Learning-Based Physically Human-Robot Interactive Control.

Model-based impedance learning control can provide variable impedance regulation for robots through online impedance learning without interaction force sensing. However, the existing related results only guarantee the closed-loop control systems to be uniformly ultimately bounded (UUB) and require the human impedance profiles being periodic, iteration-dependent, or slowly varying. In this article, a repetitive impedance learning control approach is proposed for physical human-robot interaction (PHRI) in repetitive tasks. The proposed control is composed of a proportional-differential (PD) control term, an adaptive control term, and a repetitive impedance learning term. Differential adaptation with projection modification is designed for estimating robotic parameters uncertainties in the time domain, while fully saturated repetitive learning is proposed for estimating time-varying human impedance uncertainties in the iterative domain. Uniform convergence of tracking errors is guaranteed by the PD control and the use of projection and full saturation in the uncertainties estimation and is theoretically proved based on a Lyapunov-like analysis. In impedance profiles, the stiffness and damping are composed of an iteration-independent term and an iteration- dependent disturbance, which are estimated by repetitive learning and compressed by the PD control, respectively. Therefore, the developed approach can be applied to the PHRI where iteration-dependent disturbances exist in the stiffness and damping. The control effectiveness and advantages are validated by simulations on a parallel robot in a repetitive following task.

Optimal fiscal policy in times of uncertainty: a stochastic control approach

Optimal fiscal policy in times of uncertainty: a stochastic control approach

Wage patterns in West Berlin: a synthetic control approach after the fall of the wall

ABSTRACT We investigate the impact of the Berlin Wall’s fall on West Berliners’ salaries using the Synthetic Control Method and regional-level data (NUTS-2) from the German Socio-Economic Panel (SOEP). Our study shows that the collapse of the Berlin Wall led to a sudden stagnation in salaries for West Berliners, compared to a scenario where the Wall had remained intact.

Active fault-tolerant control for asynchronous machines using EKF-based fault estimation and 3-H-bridge inverter mitigation of ITSCs

This paper introduces an active fault-tolerant control system designed to effectively detect and mitigate inter-turn short-circuit (ITSC) faults in asynchronous machines. Utilizing the extended Kalman filter, the system precisely estimates electrical parameters, including resistances and inductances, crucial for identifying such faults. By integrating these estimations into the control mechanism, the system dynamically detects, isolates, and adjusts control laws based on fault severity assessments. The proposed controller, built upon an accurate three-phase faulty model, not only addresses torque ripple issues but also incorporates a 3-H-bridge inverter to manage current imbalances, particularly in degraded operating conditions. To evaluate its efficacy and robustness, comprehensive numerical simulations were conducted using MATLAB/SIMULINK. Results validate the effectiveness of the proposed control approach in ensuring the uninterrupted safe operation of asynchronous motors, even in the presence of ITSC faults, presenting a promising solution for enhancing fault tolerance in industrial settings. Further exploration is warranted to assess its real-world applicability and potential limitations across diverse operational scenarios. This system’s implementation promises practical benefits by bolstering operational reliability and safety in industrial machinery, contributing to improved workforce safety and reduced downtime, thus offering significant social advantages.

Predefined-Time Consensus Tracking Control for Multiagent System With Channel Fading.

This article proposes a predefined-time leader-following consensus control scheme for a second-order multiagent system (MAS) whose communication network is subject to channel fading. New distributed observers are designed to achieve prescribed-time leader's states estimation under undirected graph and digraph over faded communication channel. Then, a new adaptive dynamic surface predefined-time control is developed for the elimination of the mismatched disturbances stemmed from estimation error and achieving practical predefined-time leader-following consensus. It is proved that the developed control approach achieves predefined-time consensus tracking. This article's contribution is to propose novel distributed observers to remove the influence of channel fading and estimate leader's states under undirected graph and digraph within prescribed time and develop a novel predefined-time control to achieve predefined-time consensus tracking over fading channel. A simulation example verifies that the designed control scheme is effective.

Performance of Robust Type-2 Fuzzy Sliding Mode Control Compared to Various Conventional Controls of Doubly-Fed Induction Generator for Wind Power Conversion Systems

This paper presents a novel hybrid type-2 fuzzy sliding mode control approach for regulating active and reactive power exchanged with the utility grid by a doubly-fed induction generator in a wind energy conversion system. The main objective of this hybridization is to eliminate the steady-state chattering phenomenon inherent in sliding mode control while improving the transient delays caused by type-2 fuzzy controllers. In addition, the proposed control approach has proven to be successful in coping with varying generator parameters and exhibited good reference tracking. An in-depth comparative study with state-of-the-art advanced control techniques is also the focus of the present paper. The comparative study has three objectives, namely: a qualitative comparative study that aims to compare response times and reference tracking capabilities; a quantitative evaluation that takes into account time-integrated performance criteria; and finally, robustness capabilities. The simulation results, carried out in the Matlab/Simulink environment, have demonstrated the effectiveness and best performance of the proposed hybrid type-2 fuzzy sliding mode control with respect to other advanced techniques included in the comparison study.

Plasma-modified Co3O4 nanosheets for efficient CO detection: Insight into the structure-activity relationship

Exploring the correlation between crucial reactive sites and surface electron structure in the sensitive layer of gas sensors can achieve simultaneous optimization of kinetics and thermodynamics. In this work, Ar and O2 plasma sputtering are used to study how varying levels of Co3+ and oxygen vacancy affect the gas-sensitivity of Co3O4. The gas-sensing performance of Co3O4-O2PT (O2 plasma sputtering) is better than that of Co3O4-ArPT (Ar plasma sputtering), which exhibits a high response value (100 ppm-4.16) and quick response-recovery time (1.5 s/38 s). The enhancement of gas-sensing performance benefits from a high Co3+/Co2+ ratio, which provides strong CO-selective adsorption and oxidation capabilities. Therefore, the interfacial oxidation reaction reaches equilibrium rapidly. This work provides a straightforward and controllable modification approach and enhances the understanding of how oxygen vacancies and Co3+ affect Co3O4 gas sensors, which promotes the design and development of high-performance cobalt-based gas sensors.

A new adaptive Perturb & Observe controller via robust approach for PV grid-connected

Given the importance of renewable energies, several countries are integrating these energies into their energy strategies, such as their connections to the grid, in particular photovoltaic energy. In this paper, a PV grid-connected system in a two-stage configuration is studied. In this configuration, an improvement to a control MPPT is proposed. To achieve this, we propose a novel concept for MPPT control that consists of generating an adaptive step size for P&O control that is based on sliding mode control in order to minimise ripple phenomena and chattering phenomena. The proposed control approach was then tested using MATLAB/Simulink simulations under varying irradiation conditions. We compared this approach to the conventional P&O method as well as the adaptive step size of P&O and hybrid P&O-sliding mode control. The results of the test indicate that the proposed control strategy significantly minimises the two phenomena and improves overall efficiency.

Combined Speed and Distance Control for Optimized Vehicle Platooning

Abstract This paper presents a novel approach to vehicle platooning using hybrid formation-regulation control, aiming to enhance the operation of Connected and Autonomous Vehicles (CAVs). By integrating advanced control algorithms with precise positioning and synchronization tools, our methodology enables CAVs to autonomously navigate while maintaining minimal inter-vehicle distances, thus optimizing convoy synchronization. The proposed system consists of two main controllers: a speed consensus controller to synchronize vehicle speeds and a following distance controller to ensure safety. Our hybrid control approach combines these controllers dynamically based on real-time vehicle spacing, employing exponential functions to smooth the transition between control modes, thereby preventing abrupt changes in vehicle behavior. This method not only increases energy efficiency and traffic throughput but also significantly reduces the likelihood of accidents caused by sudden braking, contributing to decreased fuel consumption and enhanced road safety. Our work not only advances the technology of vehicle platooning but also promises broader implications for the overall transportation ecosystem.&amp;#xD;

Performance Comparison and Analysis of Four- and Six-Switch Inverter Controls of BLDC Motor Employing Deep Learning

Robust and effective control of brushless dc (BLDC) motors is paramount in modern-day motion control. The BLDC motor is known for its high speed, high torque, small size, low noise, and equally low maintenance requirements compared to the brushed DC motor. Nowadays, it can be found in the areas of robotics, aerospace, military, and industrial machines, among others. In this paper, two inverter topologies, the threephase six-switch driver circuit (TPSSDC) and the three-phase four-switch driver circuit (TPFSDC), are used to drive and control the speed of the BLDC motor. For the control technique, however, a fitting neural network from the deep learning (DL) toolbox is employed to train and improve the speed performance of the motor. Both TPSSDC and TPFSDC are simulated and tested in MATLAB Simulink, and the resultant output is analyzed graphically and analytically. Graphical observation shows that the TPSSDC control approach is superior in terms of reference tracking and has less ripple, and better rise and settling times when compared to the TPFSDC control approach, however, the TPFDC is considered for its low cost and simple circuitry. Numerically, the TPSSDC also outperforms at 1000 rpm with a 1.685 ms rise time and a 1.420 ms settling time compared to the TPFSDC, which rises at 6.526 ms and settles at 5.237 ms. At 3000 rpm motor speed, the TPSSDC is better, having a rise time of 5.815 ms and settling time of 4.048 ms, when compared to the TPFSDC, which rises at 11.277 ms and settles at 10.067 ms.

Observer‐based adaptive neural networks optimal control for spacecraft proximity maneuver with state constraints

AbstractThis article proposes an adaptive neural network (NN) optimal control approach for autonomous relative motion control of non‐cooperative spacecraft in proximity. The proposed method aims to minimize fuel consumption under various challenges including model uncertainty, state constraints, external disturbances, and input saturation. To account for uncertain parameters of non‐cooperative target and external disturbances, we start by designing a NN disturbance observer. Subsequently, a novel optimal control index function is presented. An adaptive NN based on the actor‐critic (A‐C) framework and backstepping theory is then utilized to approximate the solution of Hamilton–Jacobi–Bellman (HJB) equation and obtain an optimal control law. The Lyapunov framework is leveraged to establish the stability of the closed‐loop control system. Finally, numerical simulations are conducted to assess the feasibility and effectiveness of the proposed control scheme in comparison with an existing approach.

Calculation Method for Particle Self-Regulation Servo Based on Discrete Element Method

Particle Flow Code, as noncontinuous media calculation software, has found extensive applications in geotechnical engineering research. To simulate real physical processes, the commonly utilized servo-computing methods involve adjusting the boundaries to modify the model’s stress state. However, this approach alters the model’s initial boundaries, thereby compromising computational accuracy. Addressing this issue, this paper introduces a particle self-regulation servo-computing method. This method controls the internal particle self-adjustment and completes the servo through error analysis. On the basis of the error analysis of the average stress within the particle system and the servo stress during the iterative process, the method automatically calculates the change in particle size within the system, achieving rapid convergence to servo stress tolerance after finite number of iterations. Validation through uniaxial compression tests and complex slope cases indicates that this particle self-regulation servo method yields an error of less than 5% compared with conventional rigid servo methods. That is, it accurately achieves numerical simulation state control without altering the outer contours of the boundary model. The proposed computational method is applicable to servo calculations for irregular models in two or three dimensions, providing a viable approach for stress control in models under complex conditions.

Vaping habits and respiratory symptoms using a smartphone app platform

BackgroundWidespread use of e-cigarette (EC) or vaping products causes respiratory disorders including the nationwide outbreak of e-cigarette or vaping product use-associated lung injury (EVALI) in 2019. Chronic adverse health effects are now being reported as well. To address this important public health issue, an innovative approach of epidemic control and epidemiologic study is required. We aimed to assess the association between short-term and long-term use of EC products and respiratory health in adults using smartphone app data.MethodsA population-based, repeated measures, longitudinal smartphone app study that performed 8-day survey participation over 60 days for each participant from August 2020 to March 2021, including 306 participants aged 21 years and older in the US. The participants were asked to complete the respiratory health questionnaire daily, weekly, and monthly on their smartphone app. We analyzed the association between vaping habits and respiratory health using generalized linear mixed models (GLMMs).ResultsEC use in the previous 7 days was associated with frequent cough (OR: 5.15, 95% CI: 2.18, 12.21), chronic cough (OR: 3.92, 95% CI: 1.62, 9.45), frequent phlegm (OR: 3.99, 95% CI: 1.44, 11.10), chronic phlegm (OR: 3.55, 95% CI: 1.41, 8.96), episodes of cough and phlegm (OR: 4.68, 95% CI: 1.94, 11.28), mMRC grade 3–4 dyspnea (OR: 3.32, 95% CI: 1.35 to 8.13), chest cold (OR: 3.07, 95% CI: 1.29, 7.33), eye irritation (OR: 2.94, 95% CI: 1.34, 6.47) and nose irritation (OR : 2.02, 95% CI: 0.95, 4.30). Relatively long-term effects of the past 90 days EC use was associated with an increased risk of wheeze (OR: 3.04, 95% CI: 1.31, 7.03), wheeze attack (OR: 2.78, 95% CI: 1.07, 7.24), mMRC grade 3–4 dyspnea (OR: 2.54, 9% CI: 1.05 to 6.18), eye irritation (OR: 3.16, 95% CI: 1.49, 6.68), and eye irritation during the past month (OR: 3.50, 95% CI: 1.52, 8.04).ConclusionsIn this smartphone app-based repeated measures study, short-term and relatively long-term use of EC increased the risk of respiratory symptoms.