This paper develops a disturbance rejection model predictive control (MPC) scheme for tracking nonholonomic vehicle with coupled input constraint and matched disturbances. Two disturbance observers ...(DOBs) are designed to estimate the unknown disturbances and the disturbances with known harmonic frequencies, respectively. By combining the DOB with MPC, a compound controller is presented. Recursive feasibility of the optimization problem is guaranteed by tightening the terminal region and the input constraint. We show that the system is input-to-state stable if no information about the disturbance is available and can reach an offset-free tracking performance if the harmonic frequencies of the disturbance are known. Finally, simulations and experiments are provided to show the efficiency of the proposed approaches.
Heteroatom doping, particularly with nonmetallic atoms such as N, P, and S, has proven to be an effective strategy for modulating the fluorescent properties of carbon dots (CDs). However, there are ...few reports on the regulation of the photoluminescence of CDs by transition-metal doping. In this work, nickel-doped CDs (Ni-CDs) were fabricated using the hydrothermal approach. Ni atoms were incorporated into the sp
domains of the CDs through Ni-N bonds, resulting in an increased degree of graphitization of the Ni-CDs. Additionally, Ni-atom doping served to shorten the electron transition and recombination lifetimes, and suppress the nonradiative recombination process, resulting in an absolute fluorescence quantum yield of 54.7% for the Ni-CDs. Meanwhile, the as-prepared Ni-CDs exhibited excellent biocompatibility and were utilized for fluorescent bioimaging of HeLa cells. Subsequently, the Ni-CDs were employed as fluorescent anticounterfeiting inks for the successful encryption of two-dimensional barcodes. Our work demonstrates a novel heteroatom doping strategy for the synthesis of highly fluorescence-emitting CDs.
This paper proposes a scheme of trajectory tracking control for the hovercraft. Since the model of the hovercraft is under-actuated, nonlinear, and strongly coupled, it is a great challenge for the ...controller design. To solve this problem, the control scheme is divided into two parts. Firstly, we employ differential flatness method to find a set of flat outputs and consider part of the nonlinear terms as uncertainties. Consequently, we convert the under-actuated system into a full-actuated one. Secondly, a reinforcement learning-based active disturbance rejection controller (RL-ADRC) is designed. In this method, an extended state observer (ESO) is designed to estimate the uncertainties of the system, and an actorcritic-based reinforcement learning (RL) algorithm is used to approximate the optimal control strategy. Based on the output of the ESO, the RL-ADRC compensates for the total uncertainties in real-time, and simultaneously, generates the optimal control strategy by RL algorithm. Simulation results show that, compared with the traditional ADRC method, RL-ADRC does not need to manually tune the controller parameters, and the control strategy is more robust.
This paper proposes a model predictive control (MPC) approach for ducted fan aerial robots using physics-informed machine learning (ML), where the task is to fully exploit the capabilities of the ...predictive control design with an accurate dynamic model by means of a hybrid modeling technique. For this purpose, an indigenously developed ducted fan miniature aerial vehicle with adequate flying capabilities is used. The physics-informed dynamical model is derived offline by considering the forces and moments acting on the platform. On the basis of the physics-informed model, a data-driven ML approach called adaptive sparse identification of nonlinear dynamics is utilized for model identification, estimation, and correction online. Thereafter, an MPC-based optimization problem is computed by updating the physics-informed states with the physics-informed ML model at each step, yielding an effective control performance. Closed-loop stability and recursive feasibility are ensured under sufficient conditions. Finally, a simulation study is conducted to concisely corroborate the efficacy of the presented framework.
Abstract
Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to ...predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong’s test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19.
The rapid increase of the scale and the complexity of the controlled plants bring new challenges such as computing power and storage for conventional control systems. Cloud computing is concerned as ...a powerful solution to handle com-plex large-scale control missions by using sufficient computing resources. However, the computing ability enables more com-plex devices and more data to be involved and most of the data have not been fully utilized. Meanwhile, it is even impossible to obtain an accurate model of each device in the complex control systems for the model-based control algorithms. Therefore, motivated by the above reasons, we propose a data-driven pre-dictive cloud control system. To achieve the proposed system, a practical data-driven predictive cloud control testbed is estab-lished and together a cloud-edge communication scheme is developed. Finally, the simulations and experiments demon-strate the effectiveness of the proposed system.
Background Labeled antibodies are excellent imaging agents in oncology to non-invasively visualize cancer-related antigens expression levels. However, tumor tracer uptake (TTU) of specific antibodies ...in-vivo may be inferior to non-specific IgG in some cases. Objectives To explore factors affecting labeled antibody visualization by PD-L1 specific and non-specific imaging of nude mouse tumors. Methods TTU was observed in RKO model on Cerenkov luminescence (CL) and near-infrared fluorescence (NIRF) imaging of radionuclide 131 I or NIRF dyes labeled Atezolizumab and IgG. A mixture of NIRF dyes labeled Atezolizumab and 131 I-labeled IgG was injected, and TTU was observed in the RKO and HCT8 model by NIRF/CL dual-modality in-situ imaging. TTU were observed by 131 I-labeled Atezolizumab and IgG in-vitro distribution. Results Labeled IgG concentrated more in tumors than Atezolizumab. NIRF/CL imaging in 24 to 168 h showed that TTU gradually decreased over time, which decreased more slowly on CL imaging compared to NIRF imaging. The distribution data in-vitro showed that TTU of 131 I-labeled IgG was higher than that of 131 I-labeled Atezolizumab at any time point. Conclusion Non-specific IgG may not be suitable as a control for Atezolizumab in comparing tumor PD-L1 expression in nude mice via labeled antibody optical imaging under certain circumstances.
Background
Programmed cell death 1 ligand 1(PD-L1) is overexpressed in many tumors. The radionuclide-labeled anti-PD-L1 monoclonal antibody can be used for imaging and therapy of PD-L1 overexpressing ...cancer. Here, we described
131
I-labeled Atezolizumab (
131
I-Atezolizumab, targeting PD-L1) as a therapeutic agent for colorectal cancer with PD-L1 overexpression.
Methods
131
I-Atezolizumab was prepared by the Iodogen method. The expression levels of PD-L1 in different human colorectal cells were determined by flow cytometry, western blot and cell binding assay. The immunoreactivity of
131
I-Atezolizumab to PD-L1 high-expressing cells was determined by immunoreactive fraction. The killing abilities of different concentrations of
131
I-Atezolizumab on cells with high and low expression of PD-L1 were detected by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) method. Cerenkov luminescence imaging (CLI) and radioimmunotherapy (RIT) of
131
I-Atezolizumab were performed on two human colorectal cancer models. The distribution and tumor targeting of
131
I-Atezolizumab were evaluated by imaging. Tumor volume and survival time were used as indicators to evaluate the anti-tumor effect of
131
I-Atezolizumab.
Results
The expression level of PD-L1 in vitro determined by the cell binding assay was related to the data of flow cytometry and western blot.
131
I-Atezolizumab can specifically bind to PD-L1 high-expressing cells in vitro to reflect the expression level of PD-L1. Immunoreactive fraction of PD-L1 high-expressing RKO cells with
131
I-Atezolizumab was 52.2%. The killing ability of
131
I-Atezolizumab on PD-L1 high-expressing cells was higher than that of low-expressing cells. CLI proved that the specific uptake level of tumors depends on the expression level of PD-L1. Effect of
131
I-Atezolizumab RIT showed an activity-dependent tumor suppressor effect on RKO tumor-bearing mice with high PD-L1 expression.
131
I-Atezolizumab (37 MBq) can improve the median survival time of mice (34 days), compared to untreated mice (27 days) (
P
= 0.027). Although a single activity(37 MBq) of
131
I-Atezolizumab also inhibited the tumors of HCT8 tumor-bearing mice with low PD-L1 expression (
P
< 0.05), it could not prolong the survival of mice(
P
= 0.29).
Conclusion
131
I-Atezolizumab can be used as a CLI agent for screening PD-L1 expression levels. It may be used as a radioimmunotherapy drug target for PD- L1 overexpressing tumors.
Considering that the inevitable disturbances and coupled constraints pose an ongoing challenge to distributed control algorithms, this paper proposes a distributed robust model predictive control ...(MPC) algorithm for a multi-agent system with additive external disturbances and obstacle and collision avoidance constraints. In particular, all the agents are allowed to solve optimization problems simultaneously at each time step to obtain their control inputs, and the obstacle and collision avoidance are accomplished in the context of full-dimensional controlled objects and obstacles. To achieve the collision avoidance between agents in the distributed framework, an assumed state trajectory is introduced for each agent which is transmitted to its neighbors to construct the polyhedral over-approximations of it. Then the polyhedral over-approximations of the agent and the obstacles are used to smoothly reformulate the original nonconvex obstacle and collision avoidance constraints. And a compatibility constraint is designed to restrict the deviation between the predicted and assumed trajectories. Moreover, recursive feasibility of each local MPC optimization problem with all these constraints derived and input-to-state stability of the closed-loop system can be ensured through a sufficient condition on controller parameters. Finally, simulations with four agents and two obstacles demonstrate the efficiency of the proposed algorithm.
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