Closed-loop System Research Articles

An adaptive backstepping sliding mode control strategy for the magnetorheological semi-active suspension

This paper proposes an adaptive backstepping sliding mode control strategy for addressing nonlinear issues in the semi-active suspension systems, such as uncertainties and external disturbances. Firstly, a hyperbolic tangent model is chosen for parameters identification of the magnetorheological (MR) damper, and a model for the semi-active suspension system is established. Secondly, a control strategy is designed by combining the backstepping and sliding mode control strategies, and adaptive methods are employed to mitigate external disturbances, enhance the robustness of the controller, and estimate system uncertainties. Finally, the stability and controllability of the closed-loop system are verified using Lyapunov theory. Under the road excitations of A-Class, B-Class, and speed bump, the dynamic characteristics of the passive control, backstepping sliding mode control, and adaptive backstepping sliding mode control strategies applied to the MR semi-active suspension are analyzed. The vertical acceleration of vehicle body, suspension dynamic deflection, and tire dynamic load are selected as evaluation indexes, the results indicate that this control strategy significantly improved the ride comfort and handling stability of the vehicle.

Event-Triggered Neural Adaptive Distributed Cooperative Control for the Multi-Tug Towing of Unactuated Offshore Platform with Uncertainties and Unknown Disturbances

An event-triggered neural adaptive cooperative control is proposed for the towing system (TS) with model parameter uncertainties and unknown disturbances. Different from ordinary multi-vessel formation control, the tugs and unactuated offshore platform in the TS are connected together by towlines, and the resultant tension of the towlines serves as the actual drag force for the platform. Initially, based on the radial basis function neural network (RBFNN), an adaptive RBFNN is designed to compensate unknown disturbances and model parameter uncertainties of the TS, and we use minimal learning parameter (MLP) algorithm to reduce the online learning parameters of adaptive RBFNN. Combined with dynamic surface technology and event-triggered control (ETC) mechanism, an event-triggered neural adaptive virtual controller is designed to obtain the desired drag force of the platform. According to the quadratic programming algorithm, the desired drag force is allocated as the desired tensions of towlines. Subsequently, the desired towline length and the desired position information of the tugs are obtained sequentially through the towline model and the position relationship between the tugs and the platform. Then, according to the desired positions of tugs, an event-triggered neural adaptive distributed cooperative controller is designed for achieving the multi-tug towing of the offshore platform. The ETC mechanism is introduced to reduce the communication burden within the TS and the execution frequency of the tugs’ thrusters. Finally, the stability of the closed-loop system is proven using the Lyapunov theory, and the ETC mechanism proves that no Zeno behavior occurs. The effectiveness of the ETC mechanism and the MLP-based adaptive RBFNN on the controllers of TS is verified through simulations and comparison analysis.

Artificial intelligence-driven transformations in diabetes care: a comprehensive literature review

Artificial intelligence (AI) has been applied in healthcare for diagnosis, treatments, disease management and for studying underlying mechanisms and disease complications in diseases like diabetes and metabolic disorders. This review is a comprehensive overview of various applications of AI in the healthcare system for managing diabetes. A literature search was conducted on PubMed to locate studies integrating AI in the diagnosis, treatment, management and prevention of diabetes. As diabetes is now considered a pandemic now so employing AI and machine learning approaches can be applied to limit diabetes in areas with higher prevalence. Machine learning algorithms can visualize big datasets, and make predictions. AI-powered mobile apps and the closed-loop system automated glucose monitoring and insulin delivery can lower the burden on insulin. AI can help identify disease markers and potential risk factors as well. While promising, AI’s integration in the medical field is still challenging due to privacy, data security, bias, and transparency. Overall, AI’s potential can be harnessed for better patient outcomes through personalized treatment.

Adaptive event-triggered finite-time prescribed performance control of PMSM stochastic system considering time-varying delays

This paper investigates the finite-time prescribed performance tracking control problem of permanent magnet synchronous motor (PMSM) considering stochastic disturbances and time-varying delays under event-triggered mechanism. A quintuple polynomial finite-time prescribed performance function (FPPF) is introduced to ensure the transient and steady-state performance of the system output, and a nonlinear transformation function is employed to convert the constrained error into an unconstrained one. The Lyapunov-Krasovskii function is constructed to address time delays. And the system uncertainties are approximated by the radial basis function neural networks (RBFNN). For the “explosion of complexity” caused by backstepping method, a tracking differentiator (TD) is employed. By combining finite time control, command filtering backstepping control, and event-triggered mechanism, the effect of the filter errors is decreased, and the update frequency of the control signals are reduced. It is shown that the proposed controller can guarantee finite time convergence bounded of all signals in the closed-loop system, and the tracking error can converge in finite time. Finally, simulation results are presented to illustrate the effectiveness of the proposed controller.

A new distributed robust H∞ control strategy for a class of uncertain interconnected large-scale time-delay systems subject to actuator saturation and disturbance

In the realm of large-scale systems, the complexity of controller design has long been exacerbated by the proliferation of decision variables and inherent conservatism. This study introduces a novel approach to address these challenges, presenting a new distributed robust controller design methodology tailored for large-scale systems grappling with disturbances, uncertainties, and actuator saturations. The primary objectives include reducing conservatism, minimizing decision variables, and significantly curtailing computation time. To surmount these hurdles, the research leverages descriptive and reciprocally convex methods, formulating the design procedure using linear matrix inequalities. This enables the adjustment of uncertain parameters and robust disturbance rejection, thereby ensuring stability in large-scale systems. Additionally, a feedback control law is proposed to accommodate saturation constraints and ensure the closed-loop system’s stability. Notably, the effectiveness of the proposed control scheme is demonstrated through the evaluation of a full-car active suspension system, which is partitioned into interconnected subsystems to a large-scale system. Comparative analyses underscore the superior performance and technical advancements offered by the proposed methodology over existing approaches.

Shaping closed-loop supply chain dynamics: Mitigating the bullwhip effect and improving customer satisfaction in production systems with material reuse

Modern societies are currently transitioning towards more circular economic models to reduce environmental impacts and leverage economic opportunities. Managing the emerging supply chains, more complex and uncertain, poses a considerable challenge for many organisations. This work examines the dynamic behaviour of these closed-loop systems under different replenishment policies and varying levels of product returns and lead times. We model a generalised multi-level, closed-loop supply chain of a production system with material reuse, drawing inspiration from the real-world operations of cider makers in Spain. We investigate its ability to effectively and efficiently satisfy the consumer through four performance metrics: fill rate, bullwhip ratio, inventory cover, and reuse demand share. Through experimentation techniques, we observe that the highest performance is generally obtained when the retailer applies a pure pull policy while the manufacturer adopts a level scheduling policy. This strategy, built on lean principles, can achieve much lower operational costs while providing similar, or even better, service levels compared to more popular and complex alternatives. Based on our findings, we provide a set of actionable managerial implications that would help supply chain professionals successfully implement and manage these supply chains.

Decoding EEG for optimizing naturalistic memory

Background:Spectral features of human electroencephalographic (EEG) recordings during learning predict subsequent recall variability. Methods:Capitalizing on these fluctuating neural features, we develop a non-invasive closed-loop (NICL) system for real-time optimization of human learning. Participants play a virtual navigation and memory game; recording multi-session data across days allowed us to build participant-specific classification models of recall success. In subsequent closed-loop sessions, our platform manipulated the timing of memory encoding, selectively presenting items during periods of predicted good or poor memory function based on EEG features decoded in real time. Results:We observed greater memory modulation (difference between recall rates when presenting items during predicted good vs. poor learning periods) for participants with higher out-of-sample classification accuracy. Comparison with Existing Methods:This study demonstrates greater-than-chance memory decoding from EEG recordings in a naturalistic virtual navigation task with greater real-world validity than basic word-list recall paradigms. Here we modulate memory by timing stimulus presentation based on noninvasive scalp EEG recordings, whereas prior closed-loop studies for memory improvement involved intracranial recordings and direct electrical stimulation. Other noninvasive studies have investigated the use of neurofeedback or remedial study for memory improvement. Conclusion:These findings present a proof-of-concept for using non-invasive closed-loop technology to optimize human learning and memory through principled stimulus timing, but only in those participants for whom classifiers reliably predict out-of-sample memory function.

End-Tidal Control Versus Manual Control of Inhalational Anesthesia Delivery: A Randomized Controlled Noninferiority Trial.

Precise anesthesia delivery helps ensure amnesia, analgesia, and immobility. Conventionally, the end-tidal anesthetic concentration is maintained through manual adjustment of the fresh gas flow and anesthetic vaporizer output. Some anesthesia delivery systems can deliver and maintain clinician-selected end-tidal anesthetic agent (EtAA) concentration using a modified closed-loop system. We evaluated the performance of an End-tidal Control (EtC) system on the Aisys CS2 anesthesia machine (GE HealthCare). We hypothesized EtC anesthetic delivery would be noninferior to manually controlled anesthetic delivery. The Multi-site Anesthesia randomized controlled STudy of End-tidal control compared to conventional Results (MASTER) Trial evaluated anesthetic delivery in 210 adult patients receiving inhaled anesthesia. Patients were randomized to either EtC or manual control (MC) anesthetic delivery. The primary objective was to determine whether, compared to conventional anesthesia practice, EtC achieves and maintains clinician-specified EtAA and end-tidal oxygen (Eto2) concentrations within defined noninferiority limits. Noninferiority was concluded if the lower limit of the 95% confidence interval (CI) of the difference between the percent duration within the acceptable range (5% of steady state or a margin of ~10% of each agent's minimum alveolar concentration) for EtC and MC was ≥ -5% for both EtAA and Eto2. Secondary objectives included performance measures: response time: time required to attain 90% of the first desired EtAA, overshoot: amount the controller (or vaporizer delivery) exceeded the desired EtAA, and accuracy: average deviation from the desired EtAA. EtC achieved and sustained targeted EtAA and Eto2 concentrations within the noninferiority threshold. The EtAA was within 5% of the desired value 98% ± 2.05% of the time with EtC compared to 45.7% ± 31.7% of the time with MC (difference 52.3% [95% CI, 45.9%-58.6%], P < .0001). For Eto2, EtC was within the noninferiority limit 86.3% ± 22.8% of the time compared with MC at 41% ± 33.3% (P < .0001, difference 45.3% [95% CI, 36.1%-54.5%]). The median response time for achieving 90% of the initial EtAA desired value was 75 seconds with EtC and 158 seconds with MC (P = .0013). EtC exhibited a median overshoot of 6.64% of the selected EtAA concentration, whereas MC often failed to reach the clinician's desired value. The difference in median percent deviation from desired EtAA value was 15.7% ([95% CI, 13.5%-19.0%], P < 0001). EtC achieves and maintains the EtAA and Eto2 concentration in a manner that is noninferior to manually controlled anesthesia delivery.

Static anti-windup compensator based on BMI optimisation for discrete-time systems with directional change in controls avoidance

In the paper, a design method of a static anti-windup compensator for systems with input saturations is proposed. First, an antiwindup controller is presented for system with cut-off saturations, and, secondly, the design problem of the compensator is presented to be a non-convex optimization problem easily solved using bilinear matrix inequalities formulation. This approach guarantees stability of the closedloop system against saturation nonlinearities and optimizes the robust control performance while the saturation is active.

A generic error-related potential classifier based on simulated subjects.

Error-related potentials (ErrPs) are brain signals known to be generated as a reaction to erroneous events. Several works have shown that not only self-made errors but also mistakes generated by external agents can elicit such event-related potentials. The possibility of reliably measuring ErrPs through non-invasive techniques has increased the interest in the brain-computer interface (BCI) community in using such signals to improve performance, for example, by performing error correction. Extensive calibration sessions are typically necessary to gather sufficient trials for training subject-specific ErrP classifiers. This procedure is not only time-consuming but also boresome for participants. In this paper, we explore the effectiveness of ErrPs in closed-loop systems, emphasizing their dependency on precise single-trial classification. To guarantee the presence of an ErrPs signal in the data we employ and to ensure that the parameters defining ErrPs are systematically varied, we utilize the open-source toolbox SEREEGA for data simulation. We generated training instances and evaluated the performance of the generic classifier on both simulated and real-world datasets, proposing a promising alternative to conventional calibration techniques. Results show that a generic support vector machine classifier reaches balanced accuracies of 72.9%, 62.7%, 71.0%, and 70.8% on each validation dataset. While performing similarly to a leave-one-subject-out approach for error class detection, the proposed classifier shows promising generalization across different datasets and subjects without further adaptation. Moreover, by utilizing SEREEGA, we can systematically adjust parameters to accommodate the variability in the ErrP, facilitating the systematic validation of closed-loop setups. Furthermore, our objective is to develop a universal ErrP classifier that captures the signal's variability, enabling it to determine the presence or absence of an ErrP in real EEG data.

Event-triggered dynamic output-feedback control for LPV systems

This paper addresses the co-design of event-triggered dynamic output-feedback (DOF) control for discrete-time linear parameter-varying (LPV) systems. Two independent event-triggering schemes are introduced to determine when to transmit data from the sensor to the controller and from the controller to the actuator channels. Enhanced conditions employing linear matrix inequalities (LMIs) are derived to establish constructive criteria for simultaneously designing the gain-scheduled DOF controller and the triggering mechanisms, ensuring the asymptotic stability of the closed-loop LPV system's origin. Additionally, a convex optimisation problem is proposed to enlarge the inter-event sampling on the independent channels of sensor-to-controller and controller-to-actuator. Numerical examples are provided to illustrate the effectiveness of the proposed method.

Design of Adaptive Finite-Time Backstepping Control for Shield Tunneling Systems with Constraints

This paper focuses on the finite-time tracking control problem of shield tunneling systems in the presence of constraints on the states and control input. By modeling the system based on the LuGre friction model, an effective method of tracking control in finite time is designed to overcome these actual constraints at the same time. First, the constraint on the system state is transformed into a symmetric constraint on the tracking error, and the constraint on control input is handled by designing an auxiliary differential equation. Then, radial basis function (RBF) neural networks are introduced to approximate the uncertainties. Next, using an adaptive finite-time backstepping method and choosing a logarithmic barrier Lyapunov function (BLF), a finite-time controller is designed to realize the finite-time stability of the closed-loop system. Finally, a simulation example is given to verify the correctness and validity of the theoretical results.

Evaluation of time in tight range and the glycaemia risk index in adults with type 1 diabetes using an advanced hybrid closed loop system: A 1-year real-world assessment.

To assess the long-term glycaemic outcomes, with additional metrics, in adults with type 1 diabetes (T1D) using the Tandem t:slim X2 with Control-IQ technology advanced hybrid closed-loop (AHCL) system. This was a single-centre, retrospective study involving 56 T1D patients who transitioned to the Tandem t:slim X2 with Control-IQ system. The primary and secondary endpoints consisted of variations in time in tight range (TiTR; 70-140 mg/dL) and the glycaemia risk index (GRI), respectively. Additional standardized continuous glucose monitoring (CGM) metrics, mean sensor glucose, coefficient of variation, the glucose management indicator (GMI), HbA1c and insulin daily dose, were also evaluated. Variables were measured at baseline and at 15 days, 3 months, 6 months and 1 year after Tandem t:slim X2 Control-IQ initiation. Glucose outcomes are expressed as mean (standard deviation). Use of Tandem t:slim X2 with Control-IQ over 1 year was associated with an increase in mean TiTR, from 38.11% (17.05%) to 43.10% (13.20%) (P = .059), and with a decline in the GRI, from 41.03 (25.48) to 28.55 (16.27) (P = .008). CGM metrics, including time in range and time above range, showed consistent improvements. Mean sensor glucose, the GMI and HbA1c decreased significantly over time. After an initial increase, insulin daily dose remained stable throughout the 12 months. The results highlight the sustained effectiveness of Tandem t:slim X2 with Control-IQ in improving glycaemic outcomes over 1 year and support the use of this technology for the management of T1D.

An Overview on Bioeconomy in Agricultural Sector, Biomass Production, Recycling Methods, and Circular Economy Considerations

This review examines the essential components of a circular economy (CE) in relation to the agricultural sector. The bioeconomy and circular economy are crucial for sustainable global industrial growth, focusing on closed-loop systems. The sustainability debate centers on intergenerational equity and natural capital. The CE requires new environmental technologies and global coordination in order to combat climate change and biodiversity loss. In addition, efficient food production and waste reduction are essential due to population growth. However, biomass is vital for a bio-based economy, impacting food waste and climate change. Grasslands support sustainable dairy production and carbon sequestration. Thus, effective waste and wastewater management are critical, with biomass energy providing renewable alternatives. Nonetheless, biofuels remain key for sustainability, focusing on pollution control and Green Chemistry. It is well known that sustainable transportation relies on bioenergy, with ongoing research improving processes and discovering new fuels. One notable challenge is managing heavy metals in biofuel production, and this underscores the need for eco-friendly energy solutions. The main purpose for this review paper is to create a connection between circular economy aspects and the agricultural system, with focus on the following: bioeconomy research, biomass utilities, and biofuel production. Extensive research was performed on the specialized literature by putting in common the main problems. Key subjects in this paper include the use of biomass in agriculture, the problems of plastic recycling, and the function of the CE in mitigating climate change and biodiversity loss. Efficient food production and waste minimization are highlighted due to their relevance in a growing population. The study’s detailed research and discussion aim to give important insights into how these practices might promote economic development and sustainability. Furthermore, the study covers important waste management issues such as food waste, plant composting, and chemical waste neutralization. These topics are critical to understanding the circular economy’s broader implications for minimizing environmental damage and implementing sustainable waste management strategies.

Predefined-Time Adaptive Neural Tracking Control for a Single Link Manipulator with an Event-Triggered Mechanism.

This paper introduces an adaptive trajectory-tracking control method for uncertain nonlinear systems, leveraging a time-varying threshold event-triggered mechanism to achieve predefined-time tracking. Compared to conventional time-triggering approaches, the employment of a time-varying threshold event-triggered mechanism significantly curtails communication resource wastage without compromising the system's performance. Furthermore, a novel adaptive control algorithm with predefined timing is introduced. This method guarantees that tracking errors converge to within a small vicinity of the origin within a predefined timeframe, ensuring all signals in the closed-loop system remain bounded. Moreover, by adjusting a controller-related parameter, we can predefine the upper bound of the convergence time. Finally, the efficacy of the control scheme is corroborated by simulation results obtained from a nonlinear manipulator system.

Impact on Glycemia Risk Index and other metrics in type 1 adult patients switching to Advanced Hybrid Closed-Loop systems: a one-year real-life experience

BackgroundAdvanced Hybrid Closed-Loop system (AHCL) has profoundly changed type 1 diabetes therapy. This study primarily aimed to assess the impact on Glycemia Risk Index (GRI) and other continuous glucose monitoring (CGM) metrics when switching from one of four insulin strategies to AHCL in type 1 adult patients.MethodsA single-center, retrospective pre/post observational study; 198 patients (age 44.4 ± 12.7 years, 115 females/83 males, diabetes duration 24.7 ± 11.6 years, HbA1c 7.4 ± 1%), treated with different insulin therapies (MDI, CSII, SAP with PLGS, HCL) were assessed before and after switching to an AHCL (MiniMed 780G, Diabeloop Roche, Tandem Control-IQ) at 1, 3, 6, and 12 months. Mixed-effects multivariable regression models were used to estimate the mean pre/post variations at different time points, adjusted for potential confounders.ResultsA month after the switch, there was an improvement in CGM metrics and HbA1c for all patients: GRI −10.7, GMI −0.27%, CV −2.1%, TAR>250 −3.7%, TAR180-250 −5.6%, TIR + 9.7%, HbA1c −0.54% (all p < 0.001). This improvement was maintained throughout the observational period (at 3, 6, and 12 months, with all p-values < 0.001). When improvements across the 780, Diabeloop, and Tandem CIQ devices were compared: Diabeloop demonstrated significantly better performance in terms of GRI, GMI, CV, TAR>250 at T1 (for all p < 0.01); 780 recorded highest average decrease in TAR180-250 (p = 0.020), while Tandem achieved the most significant reduction in TBR54-69 (p = 0.004).ConclusionsAdopting an AHCL leads to a rapid and sustained improvement in GRI and other parameters of metabolic control for up to a year, regardless of prior insulin therapies, baseline conditions or brands.

Numerical Simulation of Geothermal Energy Development at Mount Meager and Its Impact on In Situ Thermal Stress

The Meager Mountain Geothermal Project stands as one of the pioneering geothermal energy initiatives in its early stages of resource development. Despite its abundant geothermal heat resources, no prior studies have systematically evaluated the potential of implementing coaxial borehole heat exchangers on site. This study addresses this research gap by presenting a comprehensive heat transfer model for an underground closed-loop geothermal system utilizing a single coaxial well. Finite element analysis incorporated fluid and solid heat transfer, as well as solid mechanics. The results obtained facilitated the construction of the temperature and thermal stress profiles induced by the cooling effects resulting from years of heat extraction. After 25 years of operation, the outlet temperature has reached approximately 74 °C, and the maximum radial tensile thermal stress amounts to ~47 MPa. Furthermore, the analysis demonstrates that higher fluid velocities contribute to more perturbed temperature and stress distributions. The study attained maximum thermal and electric power outputs of 208 kW and 17 kW, respectively. This research also underscores the significant impact of geothermal gradient and well length on BHE design, with longer wells yielding more power, especially at higher injection velocities.

Decentralized adaptive tracking control for interconnected nonlinear systems with unmodeled dynamics and input delays

This paper focuses on the decentralised adaptive tracking control problem for nonstrict-feedback interconnected nonlinear systems with unmodeled dynamics and input delays. To identify the unstructured uncertainties, a novel broad learning system (BLS) approximation technique is introduced in this study to solve it. On this basis, a nonlinear state observer is designed to estimate the unmeasurable state variables. A dynamic signal is introduced to deal with the effect of unmodeled dynamics, and the appropriate error transformation is utilised to handle the effect of input delays. The decentralised adaptive tracking control strategy is proposed to the considered interconnected nonlinear systems using the design procedures of backstepping control with command filter. The semi-globally uniformly ultimately boundedness (SGUUB) stability and the tracking performance of the whole closed-loop systems could be guaranteed by using the Lyapunov approach. Finally, simulation results are provided to validate the present theoretical claims.

Local exponential stabilisation of the delayed Fisher's equation

Exponential stabilisation is undoubtedly an essential property to study for any control system. This property is often difficult to establish for nonlinear distributed systems and even more difficult if the state of such a system is subject to a time-delay. In this context, this paper is devoted to the study of the well-posedness and exponential stabilisation problems for the one-dimensional delayed Fisher's partial differential equation (PDE) posed in a bounded interval, with homogeneous Dirichlet condition at the left boundary and controlled at the right boundary by Dirichlet condition. Based on the infinite dimensional backstepping method for the linear and undelayed corresponding case, a feedback law is built. Under this feedback, we prove that the closed-loop system is well-posed in L 2 ( 0 , 1 ) and locally exponentially stable in the topology of the L 2 ( 0 , 1 ) -norm by the use of semigroup theory relating to differential difference equations and Lyapunov–Razumikhin analysis, respectively. A numerical example that reflects the effectiveness of the proposed approach is given.

Event-Triggered Adaptive Finite-Time Control Using a Fuzzy State Observer for Nonlinear Cyber-Physical Systems Under Deception Attacks.

This article presents a new event-triggered adaptive finite-time control strategy using a fuzzy state observer for a class of nonlinear cyber-physical systems (CPSs) under malicious deception attacks with a more general form. Compared with the traditional assumptions on the deception attacks in the existing results, a more general assumption on deception attacks is given in this article. During the design process, real system states are initially estimated by developing an improved state observer, which effectively addresses the problem of state unavailability. Then, a coordinate transformation technology, in which the estimated states of observer are considered, is presented to stabilize the studied system. By constructing the singularity-free finite time virtual controls, the singularity problem in the traditional finite time design algorithms is cleverly avoided. Furthermore, to minimize communication overhead, a final finite-time controller is established by using a relative threshold event-triggered scheme. The developed event-triggered adaptive finite-time control strategy guarantees that all signals in the closed-loop system are semi-globally bounded in finite time without Zeno behavior. Finally, the correctness of the proposed control strategy is validated through two simulation results.