Time-varying Trajectory Research Articles

P0558 An Integrative Blood-Based Risk Score Predicts Development of Crohn's Disease

Abstract Background Individual blood markers have been associated with future risk of Crohn's disease (CD). However, there is a need to understand which combination of biomarkers will be most predictive to facilitate CD prevention trial recruitment. We aimed to develop and validate a blood-based integrative risk score for CD development. Methods We analyzed data from 200 patients with CD and 100 age, sex, and race-matched healthy controls (HC) from PREDICTS, a nested case-control study of US active-component service members (ACSM). Longitudinal serum samples were collected at four time points up to 6+ years prior to diagnosis. Anti-microbial antibodies (Prometheus), proteomic markers (Olink inflammation panel), and anti-granulocyte macrophage-colony stimulating factor autoantibodies (anti-GM-CSF) were assayed in each sample. Participants were randomly divided into equal sized training and testing sets. Time-varying trajectories of marker abundance were estimated for each biomarker. Logistic regression modeled disease status as a function of each marker for different time points and multivariate modeling was performed via logistic lasso regression. A risk score to predict CD onset within 2 years was developed using penalized mixed-effects logistic regression. Prediction models were fit on the testing set and predictive performance evaluated via receiver operating characteristic (ROC) curves and area under the curve (AUC). Results The AUCs of individual markers were dynamic over time, with some having stable predictive capacity and others increasing or decreasing closer to time of diagnosis (Figure 1A). The AUCs for a model combining antibodies (Prometheus and anti-GM-CSF) and proteins were consistently above 0.8 starting 4 years prior to diagnosis, primarily driven by proteomic markers (Figure 1B). An integrative model predicting CD onset within 2 years incorporated 10 biomarkers and significantly associated with CD onset (Figure 1C). The model resulted in an AUC of 0.87 with a specificity of 99% and positive predictive value of 84% at a classification threshold of 0.7 (Figure 1D). Stratifying the integrative model scores into quartiles revealed a clear gradient in CD incidence within 2 years, increasing from 2% in the first quartile to 57.7% in the fourth quartile (Figure 1E). The relative risk (RR) for developing CD among ACSM in the top quartile, compared to the lower quartiles, was 10.4 (95% CI 6.9, 15.6). Conclusion An integrative blood-based model combining anti-microbial antibodies and proteins predicts onset of CD. Serologic and proteomic markers have dynamic changes years before diagnosis, highlighting the varying preclinical phases of CD and how leveraging biomarker dynamics can predict time to diagnosis. Figure 1

Fabrication of angle-gradient echelle grating on metallic glass using shaped vibration cutting with time-varying trajectory

In order to solve the problem of narrow band coverage of traditional infrared gratings, a new type of diffraction grating is invented and named as angle-gradient echelle grating. The blaze angle of the grating gradually changes along the direction of the grating grooves, which can be regarded as etching countless sub-gratings with different blaze angles on a grating, breaking the limitation of the number of blaze angles of traditional gratings. In addition, in order to solve the processing problem of angle-gradient echelle grating, a new processing method, shaped vibration cutting (SVC) with a time-varying trajectory is developed. By exploring the influence of different processing parameters on the grating processing results, a suitable processing plan was proposed. Eventually, angle-gradient echelle grating with blaze angles varying from 30° to 55° and surface roughness of as low as 20 nm was fabricated on metallic glass, a material that is difficult to machine.

Feedback switching control of a walking piezoelectric actuator for trajectory tracking

Abstract This paper proposes a feedback switching control (FSC) scheme for a Walking Piezoelectric Actuator (WPA) to track a time-varying trajectory. A WPA has two feet with each foot integrated with a clamp and shear Piezoelectric Stack Actuators (PSAs). The first foot pushes the stage translator a step forward when clamped while the second foot is reset and retracted; subsequently, ‘foot switching’ occurs; as a result, the second foot is clamped to push the translator to make a next step forward and the first foot is reset and retracted. To make sure that the translator is always controlled in closed-loop no matter which foot is clamped, the FSC scheme is proposed. It consists of a clamping waveform generator which converts the trajectory into control signals for the clamp PSAs of the two feet and a feedback controller which provides control signals for the two shear PSAs of the feet. Experimental results show that the proposed control scheme achieves a tracking error of 0.028 µm for a constant velocity trajectory, which is a 75% reduction compared to the 0.113 µm of waveform-based control with ILC compensation in literature.

Decoupled Model-Free Adaptive Control with Prediction Features Experimentally Applied to a Three-Tank System Following Time-Varying Trajectories

In this paper, the performance of three model-free control approaches on a multi-input, multi-output (MIMO) nonlinear system with constant and time-varying references is compared. The first control algorithm is model-free adaptive control (MFAC). The second is a modified version of MFAC (MMFAC) designed to handle delays in the system by incorporating the output error difference (over two sample time steps) in the control input. The third approach, model-free adaptive predictive control (MFAPC) with a one-step-ahead forecast of the system input, is obtained by using predictions of the outputs based on the data-based linear model. The experimental device used is an MIMO three-tank system (3TS) assumed to be an interconnected system with multiple coupled single-input, single-output (SISO) subsystems with unmeasurable couplings. The novelty of this contribution is that each coupled SISO partition is assumed to be controlled independently using a decoupled control algorithm, leading to fewer control parameters compared to a centralized MIMO controller. Additionally, both parameter tuning for each controller and performance evaluation are conducted using an evaluation criterion considering energy consumption and accumulated tracking error. The results demonstrate that almost all the proposed model-free controllers effectively control an MIMO system by controlling its SISO subsystems individually. Moreover, the predictive features in the decoupled MFAPC contribute to more accurate tracking of time-varying references. The utilization of tracking error differences helps in reducing energy consumption.

A Cooperative Control Method for Wide-Range Maneuvering of Autonomous Aerial Refueling Controllable Drogue

In the realm of autonomous aerial refueling missions for unmanned aerial vehicles (UAVs), the controllable drogue represents a novel approach that significantly enhances both the safety and efficiency of aerial refueling operations. This paper delves into the issue of wide-range maneuverability control for the controllable drogue. Initially, a dynamic model for the variable-length hose–drogue system is presented. Based on this, a cooperative control framework that synergistically utilizes both the hose and the drogue is designed to achieve wide-range maneuverability of the drogue. To address the delay in hose retrieval and release, an open-loop control strategy based on neural networks is proposed. Furthermore, a closed-loop control method utilizing fuzzy approximation and adaptive error estimation is designed to tackle the challenges posed by modeling inaccuracies and uncertainties in aerodynamic parameters. Comparative simulation results show that the proposed control strategy can make the drogue maneuvering range reach more than 6 m. And it can accurately track the time-varying trajectory under the influence of model uncertainty and wind disturbance with an error of less than 0.1 m throughout. This method provides an effective means for achieving wide-range maneuverability control of the controllable drogue in autonomous aerial refueling missions.

Optimizing Search and Rescue Strategies for Deep-Sea Submersibles Using the Monte Carlo Method

Deep-sea exploration is an important field where humans challenge the limits of nature. Improving its safety and efficiency is of great significance to deep-sea scientific research and resource development. In response to this, this study established a Monte Carlo-based motion model to predict the location of the crashed submersible. To truly simulate the distress environment of the submersible, this study introduced the Monte Carlo method to randomly generate 2,000 particles to reflect the uncertainty of the initial distress location, ocean current direction, and seawater density. Finally, it was combined with a six-degree-of-freedom kinematic model to generate All possible time-varying trajectories. This study also compared the effects before and after using the corresponding equipment and found that the equipment has a good effect on reducing uncertainties. This paper expands the field focused on floating objects on the sea surface to the search for underwater targets.

Modeling and contractivity of neural-synaptic networks with Hebbian learning

This paper is concerned with the modeling and analysis of two of the most commonly used recurrent neural network models (i.e., Hopfield neural network and firing-rate neural network) with dynamic recurrent connections undergoing Hebbian learning rules. To capture the synaptic sparsity of neural circuits we propose a low dimensional formulation. We then characterize certain key dynamical properties. First, we give biologically-inspired forward invariance results. Then, we give sufficient conditions for the non-Euclidean contractivity of the models. Our contraction analysis leads to stability and robustness of time-varying trajectories — for networks with both excitatory and inhibitory synapses governed by both Hebbian and anti-Hebbian rules. For each model, we propose a contractivity test based upon biologically meaningful quantities, e.g., neural and synaptic decay rate, maximum in-degree, and the maximum synaptic strength. Then, we show that the models satisfy Dale’s Principle. Finally, we illustrate the effectiveness of our results via a numerical example.

Event-Triggered Distributed Data-Driven Iterative Learning Bipartite Formation Control for Unknown Nonlinear Multiagent Systems.

In this study, we investigate the event-triggering time-varying trajectory bipartite formation tracking problem for a class of unknown nonaffine nonlinear discrete-time multiagent systems (MASs). We first obtain an equivalent linear data model with a dynamic parameter of each agent by employing the pseudo-partial-derivative technique. Then, we propose an event-triggered distributed model-free adaptive iterative learning bipartite formation control scheme by using the input/output data of MASs without employing either the plant structure or any knowledge of the dynamics. To improve the flexibility and network communication resource utilization, we construct an observer-based event-triggering mechanism with a dead-zone operator. Furthermore, we rigorously prove the convergence of the proposed algorithm, where each agent's time-varying trajectory bipartite formation tracking error is reduced to a small range around zero. Finally, four simulation studies further validate the designed control approach's effectiveness, demonstrating that the proposed scheme is also suitable for the homogeneous MASs to achieve time-varying trajectory bipartite formation tracking.

Metabolic disturbances potentially attributable to clogging during continuous renal replacement therapy

BackgroundClogging is characterized by a progressive impairment of transmembrane patency in renal replacement devices and occurs due to obstruction of pores by unknown molecules. If citrate-based anti-coagulation is used, clogging can manifest as a metabolic alkalosis accompanied by hypernatremia and hypercalcemia, primarily a consequence of Na3Citrate infusion. An increased incidence of clogging has been observed during the COVID-19 pandemic. However, precise factors contributing to the formation remain uncertain. This investigation aimed to analyze its incidence and assessed time-varying trajectories of associated factors in critically ill patients on continuous renal replacement therapy (CRRT).MethodsIn this retrospective, single-center data analysis, we evaluated COVID-19 patients undergoing CRRT and admitted to critical care between March 2020 and December 2021. We assessed the proportional incidence of clogging surrogates in the overall population and subgroups based on the specific CRRT devices employed at our institution, including multiFiltrate (Fresenius Medical Care) and Prismaflex System (Baxter). Moderate and severe clogging were defined as Na > 145 or ≥ 150 mmol/l and HCO3− > 28.0 or ≥ 30 mmol/l, respectively, with a total albumin-corrected calcium > 2.54 mmol/l. A mixed effect model was introduced to investigate factors associated with development of clogging.ResultsFifty-three patients with 240 CRRT runs were analyzed. Moderate and severe clogging occurred in 15% (8/53) and 19% (10/53) of patients, respectively. Twenty-seven percent (37/136) of CRRTs conducted with a multiFiltrate device met the criteria for clogging, whereas no clogging could be observed in patients dialyzed with the Prismaflex System. Occurrence of clogging was associated with elevated triglyceride plasma levels at filter start (p = 0.013), amount of enteral nutrition (p = 0.002) and an increasing white blood cell count over time (p = 0.002).ConclusionsClogging seems to be a frequently observed phenomenon in critically ill COVID-19 patients. The presence of hypertriglyceridemia, combined with systemic inflammation, may facilitate the development of an impermeable secondary membrane within filters, thereby contributing to compromised membrane patency.

Appointed-time velocity-free prescribed performance control for space manipulators

This paper investigates the appointed-time velocity-free prescribed performance control for the space manipulators. A novel exponential-modified tracking differentiator is developed for estimating differential signals to address missing velocity information measurements. The original tracking problem is converted into the boundedness control, achieved through the bidirectional constraint-free mapping, which incorporates a novel performance function characterized by appointed-time convergence property. The coordinated control law is contributed with the designed velocity-free reference stable schemes. Ultimately, we achieve effective tracking of time-varying trajectories, ensuring simultaneous tracking of the desired values for the base and the manipulator. The tracking errors are constrained by the performance functions and convergence to the steady values at an appointed time. Simulation results validate the effectiveness of the proposed control scheme.

Height-Fixed UAV Enabled Energy-Efficient Data Collection in RIS-Aided Wireless Sensor Networks

Wireless sensor nodes (SNs) are usually energy-limited, a low energy efficiency of SN may be caused by the occlusion of transmission link in the unmanned aerial vehicle (UAV)-enabled data collection. Reconfigurable intelligent surfaces (RISs) provide potential communication opportunities by reflecting and enhancing the signal, thus increasing SNs’ energy efficiency. This paper investigates energy-efficient data collection aided by <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> RIS elements, where a height-fixed and flight time-constrained UAV is considered as the collector, a tractable air-to-ground channel model is established considering the cascaded RIS channel and time-varying trajectory of UAV, and energy consumption of SNs is minimized. Specifically, coupling two intractable fourth-order moment variables, the air-to-ground channel model is processed into an easily computable expression through the Gamma function. Furthermore, based on the proposed two RIS deployment schemes, maximum energy consumption of SNs minimization problem is formulated as a non-convex mixed-integer nonlinear programming (NMNP) problem with optimization of UAV trajectory and SNs wake-up schedules. Additionally, through the slack technique and Taylor expansion, the NMNP problem is decoupled as linear programming and quadratically constrained quadratic programming, and an effective algorithm is designed to obtain the sub-optimal solution. Simulations prove the validity of our algorithm and RIS deployment insights are given for SNs energy saving.

Affine Formation Maneuver Control for Multi-Heterogeneous Unmanned Surface Vessels in Narrow Channel Environments

This paper investigates the affine formation maneuver control for multi-heterogeneous unmanned surface vessels (USV), aiming to enable them to navigate through narrow channels in the near-sea environment. The approach begins with implementing an affine transformation to facilitate flexible configuration adjustments within the formation system. The affine transformation of the entire formation is achieved by controlling the leaders’ positions. Second, this article introduces an anti-perturbation formation tracking controller for the underactuated vessels, which assume the role of leaders, to accurately follow the arbitrary formation transformation. Third, the followers consist of fully actuated vessels with the same kinematic model as the leaders but different dynamic models. This paper utilizes the affine localizability theorem to derive an expected virtual time-varying trajectory based on the leaders’ trajectory. The followers achieve the desired formation maneuver control by tracking this expected virtual time-varying trajectory through an anti-perturbation formation tracking controller. Finally, the efficacy of the introduced control law is confirmed and supported by the results obtained from rigorous simulation experiments.

Neural Network-Based Cooperative Trajectory Tracking Control for a Mobile Dual Flexible Manipulator.

For a mobile dual flexible manipulator (MDFM) system, this article focuses on the problem of cooperative trajectory tracking under unknown dynamics and time-varying trajectories. The dynamic model of the wheeled mobile manipulator system in 2-D space is established. Taking into account the unmodeled dynamics of the system, unknown terms of the system are approximated by integrating the radial basis function neural network (RBFNN) structure. By introducing the servo system, the cooperative trajectory tracking control (CTTC) strategy is designed, which realizes the system's cooperative operation, time-varying trajectory tracking, and vibration suppression. The performance of the proposed control scheme is verified through theoretical analysis and numerical simulations.

Operando Electrochemical Impedance Spectroscopy and Its Application to Commercial Li-Ion Batteries

Electrochemical impedance spectroscopy (EIS) is a powerful non-invasive tool for characterising electrochemical systems. Applied to Li-ion batteries, EIS is shown to be an informative indicator for their state-of-health (SoH) [1,2,3]. However, EIS is limited by the constraints of linearity and stationarity, while Li-ion batteries inherently behave in a nonlinear and nonstationary way. Regarding linearity, the voltage over the electrodes is a nonlinear function of the current through the electrodes. Linearity is achieved by applying a small amplitude zero-mean current excitation around an operating point, such that the nonlinear function is quasi linear in this range. Regarding time-variation, the impedances of fully charged and fully discharged cells are different, the same for pristine and aged cells, or cells kept at room temperature and in freezing environments. For Li-ion batteries, this means that EIS experiments should be performed in steady-state when at a specific state-of-charge (SoC) and temperature. The impedance therefore depends on the operating point (temperature and SoC) and the constraints of linearity and stationarity are very restrictive.Lately, we have developed operando to reveal impedance data from measurements not satisfying linearity and stationarity [4,5]. This technique allows to measure the impedance of electrochemical systems along a time-varying trajectory, for instance, while charging or discharging Li-ion batteries [5,6]. For this purpose, a nonzero-mean odd random phase multisine current excitation is used, and the time-varying impedance along the trajectory is estimated from the spectrum of the voltage response. Moreover, nonlinear distortions in the measurements are detected and quantified, the noise level is estimated, and uncertainty bounds are enclosed on the time-varying impedance. For Li-ion batteries most time-variation happens at low frequencies. However, obtaining time-varying impedance data at low frequencies is a challenging problem. This on the one hand due to drift signals, for instance the increasing voltage while charging a battery, hiding the low frequency content, and on the other hand due to low-frequency noise. Operando EIS implements a drift signal suppression such that obtaining impedance data at low frequencies becomes attainable [4].In this paper operando EIS measurements while charging and discharging commercial LG M50 Li-ion batteries are presented, demonstrating that the battery’s impedance while charging, discharging and resting are different from each other. Prospectives are given for utilizing operando EIS data for smart-charging and SOH prognosis applications.[1] Pastor-Fernández, C., Uddin, K., Chouchelamane, G.H., Widanage, W.D. and Marco, J., 2017. A comparison between electrochemical impedance spectroscopy and incremental capacity-differential voltage as Li-ion diagnostic techniques to identify and quantify the effects of degradation modes within battery management systems. Journal of Power Sources, 360, pp.301-318.[2] Zhang, Y., Tang, Q., Zhang, Y., Wang, J., Stimming, U. and Lee, A.A., 2020. Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning. Nature communications, 11(1), pp.1-6.[3] Jones, P.K., Stimming, U. and Lee, A.A., 2022. Impedance-based forecasting of lithium-ion battery performance amid uneven usage. Nature communications, 13(1), pp.1-9.[4] Hallemans, N., Pintelon, R., Zhu, X., Collet, T., Havigh, M.D., Wouters, B., Revilla, R.I., Claessens, R., Ramharter, K., Hubin, A. and Lataire, J., 2022. Trend Removal in Measurements of Best Linear Time-Varying Approximations—With Application to Operando Electrochemical Impedance Spectroscopy. IEEE Transactions on Instrumentation and Measurement, 71, pp.1-11.[5] Hallemans, N., Widanage, W.D., Zhu, X., Moharana, S., Rashid, M., Hubin, A. and Lataire, J., 2022. Operando electrochemical impedance spectroscopy and its application to commercial Li-ion batteries. Journal of Power Sources, 547, p.232005.[6] Zhu, X., Hallemans, N., Wouters, B., Claessens, R., Lataire, J. and Hubin, A., 2022. Operando odd random phase electrochemical impedance spectroscopy as a promising tool for monitoring lithium-ion batteries during fast charging. Journal of Power Sources, 544, p.231852.

Time-Varying Trajectory Tracking Controller Design for Cable-Suspended Planar Parallel Robots

Time-Varying Trajectory Tracking Controller Design for Cable-Suspended Planar Parallel Robots

Tracking Control of Unicycle Mobile Robots With Event-Triggered and Self-Triggered Feedback

This paper studies the tracking control problem for unicycle mobile robots with event-triggered and self-triggered feedback. Based on a modified dynamic feedback linearization technique and a refined stability analysis, we first propose a new class of saturated sampled-data controllers for unicycle mobile robots of which the state is subject to sampling (measurement) errors. It is guaranteed that the generated control inputs are restricted to within desired ranges, and the unicycle trajectory practically converges towards a time-varying trajectory as long as the sampling errors satisfy a saturated sector bound condition with a nonnegative offset. The proposed robust controller gives rise to a novel event-triggered sampling mechanism with a saturated threshold signal, which adjusts the sampling errors online to steer the tracking error to a desired neighborhood of the origin and guarantees a positive lower bound for the intersampling intervals. As a special case, by designing appropriately the controller and the event-triggered sampling mechanism, asymptotic tracking is achievable. New self-triggered and periodic sampling mechanisms are also developed as natural extensions of the ev

A Punishment Mechanism-Combined Recurrent Neural Network to Solve Motion-Planning Problem of Redundant Robot Manipulators.

In order to make redundant robot manipulators (RRMs) track the complex time-varying trajectory, the motion-planning problem of RRMs can be converted into a constrained time-varying quadratic programming (TVQP) problem. By using a new punishment mechanism-combined recurrent neural network (PMRNN) proposed in this article with reference to the varying-gain neural-dynamic design (VG-NDD) formula, the TVQP problem-based motion-planning scheme can be solved and the optimal angles and velocities of joints of RRMs can also be obtained in the working space. Then, the convergence performance of the PMRNN model in solving the TVQP problem is analyzed theoretically in detail. This novel method has been substantiated to have a faster calculation speed and better accuracy than the traditional method. In addition, the PMRNN model has also been successfully applied to an actual RRM to complete an end-effector trajectory tracking task.

Discrete Cosserat Static Model-Based Control of Soft Manipulator

In this letter, the continuous Cosserat static model is re-formulated by employing the piecewise linear strain (PLS) approach, which can obtain the analytic formula of the model to facilitate the controller design. The robust closed-loop control architecture based on the PLSCosserat static model for the soft arm around its workspace is then established. To validate the performance of the proposed model-based control scheme, the point-to-point and time-varying trajectory tracking experiments under external disturbances have been conducted on the soft manipulator actuated by cables.

A study of lateral trajectory tracking control under fused slip rate and road random inputs

To improve the maneuvering stability of uncrewed armored vehicles during emergency steering and obstacle avoidance under high-speed driving conditions in off-road undulating terrain, in this paper, we establish a 7DOF dynamics model based on the time-varying trajectory curvature for the lateral and vertical motion of the spring-loaded mass and the vertical jump of the unsprung mass. Also, we consider two slip motion states with tires in the linear zone and tire slip nonlinear zone based on the Dugoff model and use trip and non-trip rollover under random road input as rollover warning constraint, and conduct flat road DLC test, 20° side slope test, JLTV twisted road respectively by Carsim-Simulink jointly according to NATO double shift line test standard. The improved LTV-MPC algorithm solves the optimal tracking control sequences of 2DOF and 7DOF kinetic models, and the trajectory tracking is completed, compared, and analyzed.

Experimental evaluation of event-triggered sliding mode control for trajectory tracking of a quadcopter

This paper presents an experimental assessment of event-triggered sliding mode control (ET-SMC) on a Quadcopter for trajectory tracking. Sufficient triggering conditions using Lyapunov analysis and a lower bound of inter-event time to avoid Zeno behaviour are derived to ensure robust stability. Real-time flights are performed for tracking constant and time-varying trajectories. Results reveal that while using the proposed controller, there is a significant reduction in the number of updates implying a substantial saving in the computations.