Linear Model-Predictive Control of Cooperative Multi-Vehicle Systems for Time-Dependent Sampling Applications

Employing autonomous multi-vehicle systems to perform tasks like surveillance or environment exploration has many advantages, e.g. they reduce the risk of life-threatening missions that might otherwise be carried out by human-piloted vehicles. For such systems, one of the key problems is the optimal control of cooperative mobility. In this thesis, an approach concerning this problem is developed and investigated, which can be employed to time-dependent sampling applications. The optimal control problem of cooperative multi-vehicle systems investigated in this thesis cov- ers two specific aspects: task allocation by priority and obstacle avoidance. The system model is built upon discrete logical rules and continuous vehicle dynamics. Hence, the problem constraints together with the objective function form a hybrid optimal control problem. Approximating this problem by Mixed-Integer Linear Program (MILP) reduces the complexity of the problem and improves the computational efficiency. Solutions of the hybrid problem are obtained by a model-predictive control (MPC) approach, since its feedback mechanism allows the multi-vehicle system to react to a changing environment. An existing decentralized MILP-based MPC approach KRvS11 is extended to cover extra prob- lem constraints with respect to the time-dependent task allocation and obstacle avoidance. The optimal control problem is modeled employing the mixed-logical-dynamical (MLD) framework. In this thesis, the validity of the developed method will be proved, its applicability to real-time (small scale) systems will also be shown to be convincing. The presented investigations set up a possible basis for further research and development of optimal control of cooperative multi-vehicle systems

Design, Validation and Comparison of Path Following Controllers for Autonomous Vehicles

As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimization problem is solved by CVXGEN. The nonlinear active disturbance rejection control (ADRC) method is applied to the second lateral controller that is easy to be implemented and robust to parametric uncertainties and disturbances, and the pure pursuit algorithm serves as a benchmark. Simulation results in different scenarios demonstrate the effectiveness of the proposed control schemes, and a comparison is made to highlight the advantages and drawbacks. It can be concluded that the LPV-MPC has some trouble to handle uncertainties while the nonlinear ADRC performs slight worse tracking but has strong robustness. With the parallel development of the control theory and computing power, robust MPC may be the future direction

A Bibliometric Visualization Analysis on Vaccine Development of Coronavirus Disease 2019 (COVID-19)

Coronavirus disease 2019 (COVID-19), beginning in December 2019, has spread worldwide, leading to the death of millions. Owing to the absence of definitive treatment, vaccination against COVID-19 emerged as an effective strategy against the spread of the pandemic. Acceptance of the COVID-19 vaccine has advanced considerably, and vaccine-related research has significantly increased over the past three years. This study aimed to evaluate the content and external characteristics of COVID-19 vaccine-related literature for tracking research trends related to the global COVID-19 vaccine with the means of bibliometrics and visualization maps. A total of 18,285 records in 3499 journals were retrieved in the Web of Science Core Collection database and included in the final analysis. China was the first to focus on COVID-19 vaccine research, while European and American countries started late but developed rapidly. The USA and the UK are the top contributors to COVID-19 vaccine development, with the largest number of publications. The University of Washington and Harvard Medical School were the leading institutions, while Krammer, F. from Icahn School of Medicine at Mount Sinai was the author most active and influential to the topic. The New England Journal of Medicine had the highest number of citations and the highest TLS, and was the most cited and influential journal in the field of COVID-19 vaccine research. COVID-19 vaccine research topics and hotspots focused on populations’ attitudes towards vaccination, immunity-related information analysis of spike proteins, the effectiveness and side effects of the COVID-19 vaccine, and the public management of epidemic transmission. The findings of this study provide the global status, research hotspots and potential trends in the field of COVID-19 vaccine research, which will assist researchers in mastering the knowledge structure, and evaluating and guiding future developmental directions of COVID-19 vaccin

Coupling Thermodynamics Modeling and Analysis of a Free Piston Linear Generator Range-extender for HEVs

This paper proposes a concept of free piston linear generator (FPLG) range-extender for the application of hybrid electric vehicles (HEVs). The coupling thermodynamics model of the FPLG range-extender is developed mathematically. Its stable operation characteristics are analyzed accordingly. In addition, the effects of the variations of the key operational parameters on the effective output power and overall efficiency are investigated. The simulation results verify that the designed FPLG range-extender can provide 15kW effective charging power with the overall efficiency of 38.2%, and the peak power can even reach as high as 47.5kW. It is a promising alternative range-extender of the HEVs that can dramatically enhance the battery life

Multi Objective Optimization of Permanent Magnet Synchronous Motor Based on Taguchi Method and PSO Algorithm

To solve the optimization issues of interior permanent magnet synchronous motors (IPMSMs) and ensure a large output torque while minimizing torque ripple and core loss, the multi-objective optimization strategy should be employed. In this study, we took an 8-pole, 48-slot IPMSM as a specimen. First, the width and thickness of the permanent magnet (PM) and the rotor bridge structures were pre-selected as optimization parameters, while torque ripple and core loss were taken as optimization targets. Then, the Taguchi method to perform orthogonal experiments was employed to select the multi-parameter combinations that make the experimental results stable and with little fluctuation. To ensure the optimal results, the function equations were obtained by multivariate nonlinear fitting, while the parameters were optimized by particle swarm optimization (PSO). Finally, the optimal results were verified by the Finite Element Method (FEM). The results show that our proposed hybrid method can provide an optimal design strategy with better performance such as smaller torque ripple and core loss while maintaining a larger output torque

Study on Breakdown Voltage and Minimum Ignition Energy of Sparking System for Free-Piston Linear Generator

This paper is aimed at studying the breakdown voltage and minimum ignition energy of sparking system for the free-piston linear generator (FPLG). Combing the ignition characteristics and demands specifically for the FPLG system, the breakdown voltage and conduction duration of the primary coil are determined theoretically with the Townsend and Paschen Laws. A computational fluid dynamics (CFD) mode is developed to investigate the in-cylinder pressure and temperature at various igniting moments. The theoretical minimum ignition energy is determined with the laminar combustion theory, which contributes to the design and optimization of the sparking system. The effects of the ignition points and initial in-cylinder temperature on the breakdown voltage and minimum ignition energy are analyzed. The results show that advanced ignition contributes to decreasing the breakdown voltage and the conduction duration of the primary coil, meanwhile has no negative influence on the minimum ignition energy. However, rising initial in-cylinder temperature lowers the breakdown voltage, shortens the conduction duration of the primary coil and reduces the minimum ignition energy

The Action Representation Elicited by Different Types of Drug-Related Cues in Heroin-Abstinent Individuals

Drug related cue-induced reactivity plays a significant role in maintaining drug use and relapse in addicted individuals. The activation of Dorsolateral striatum-Sensorimotor system (DLS-SM) has been suggested as an important route through which drug cues may induce automatic drug using behavior. The current study used fMRI to investigate the reactivity of heroin abstinent individuals to different types of cues, to clarify the characteristics of the cues that induce the activation of the sensorimotor area. Forty heroin-dependent abstinent individuals and 29 healthy subjects were recruited to perform the heroin cue-reactivity task during fMRI. The participants’ subjective craving and physical signs were evaluated before and after scanning. Whole-brain analysis showed that compared to drug use tool and drug cues, cues related to drug use action were more likely to activate posterior central gyrus, para-hippocampus, supra marginal gyrus, superior parietal lobule (SPL) and inferior parietal lobule (IPL). These areas are involved in motor preparation and output, indicating that the sensorimotor area is also an important neural basis of craving and automatic drug using behavior, and may mediate craving and drug seeking behavior. Our findings thus suggest that cues related to drug using action may induce automatic drug seeking behavior more than cues related only to the drug itself

U12, a UDCA derivative, acts as an anti-hepatoma drug lead and inhibits the mTOR/S6K1 and cyclin/CDK complex pathways.

U12, one of 20 derivatives synthesized from ursodeoxycholic acid (UDCA), has been found to have anticancer effects in liver cancer cell lines (SMMC-7721 and HepG2) and to protect normal liver cells from deoxycholic acid (DCA) damage (QSG-7701). Its anticancer mechanism was investigated using computer-aided network pharmacology and comparative proteomics. Results showed that its anti-malignancy activities were activated by mTOR/S6K1, cyclinD1/CDK2/4 and caspase-dependent apoptotic signaling pathways in hepatocellular carcinoma cells (HCC). The action of U12 may be similar to that of rapamycin. Animal testing confirmed that U12 exerted better anti-tumor activity than UDCA and had less severe side effects than fluorouracil (5-Fu). These observations indicate that U12 differs from UDCA and other derivatives and may be a suitable lead for the development of compounds useful in the treatment of HCC