Robust Adaptive Learning-based Path Tracking Control of Autonomous Vehicles under Uncertain Driving Environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the λ-norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods

The role of nanotechnology-based approaches for clinical infectious diseases and public health

Given the high incidence of infection and the growing resistance of bacterial and viral infections to the traditional antiseptic, the need for novel antiseptics is critical. Therefore, novel approaches are urgently required to reduce the activity of bacterial and viral infections. Nanotechnology is increasingly being exploited for medical purposes and is of significant interest in eliminating or limiting the activity of various pathogens. Due to the increased surface-to-volume ratio of a given mass of particles, the antimicrobial properties of some naturally occurring antibacterial materials, such as zinc and silver, increase as particle size decreases into the nanometer regime. However, the physical structure of a nanoparticle and the way it interacts with and penetrates the bacteria also appear to provide unique bactericidal mechanisms. To measure the efficacy of nanoparticles (diameter 100 nm) as antimicrobial agents, it is necessary to comprehend the range of approaches for evaluating the viability of bacteria; each of them has its advantages and disadvantages. The nanotechnology-based disinfectants and sensors for SARS-CoV-2 provide a roadmap for creating more effective sensors and disinfectants for detecting and preventing coronaviruses and other infections. Moreover, there is an increasing role of nanotechnology-based approaches in various infections, including wound healing and related infection, nosocomial infections, and various bacterial infections. To meet the demand for patient care, nanotechnology-based disinfectants need to be further advanced with optimum approaches. Herein, we review the current burden of infectious diseases with a focus on SARS-CoV-2 and bacterial infection that significantly burdens developed healthcare systems and small healthcare communities. We then highlight how nanotechnology could aid in improving existing treatment modalities and diagnosis of those infectious agents. Finally, we conclude the current development and future perspective of nanotechnology for combating infectious diseases. The overall goal is to update healthcare providers on the existing role and future of nanotechnology in tackling those common infectious diseases