The Uptake and Expected Impact of Electronic Stability Control (ESC) amongst the Australian Passenger Vehicle Fleet

Electronic Stability Program (ESP) is an in-vehicle active control system which acts in loss of control situations to stabilise a vehicle. Several studies have shown the road safety benefits of ESP in international contexts. However, little consideration has been given for factors which may inhibit the uptake and potential effectiveness of ESP amongst the Australian vehicle fleet. This study highlights some of these potential factors including the rate of uptake of ESP into the Australian new vehicle market, purchasing patterns, driver behaviour, culture and the media. Conclusions are drawn in terms of future research directions and good public policy to maximise the effects of ESP in Australia

Safe Control for Nonlinear Systems under Faults and Attacks via Control Barrier Functions

Safety is one of the most important properties of control systems. Sensor faults and attacks and actuator failures may cause errors in the sensor measurements and system dynamics, which leads to erroneous control inputs and hence safety violations. In this paper, we improve the robustness against sensor faults and actuator failures by proposing a class of Fault-Tolerant Control Barrier Functions (FT-CBFs) for nonlinear systems. Our approach maintains a set of state estimators according to fault patterns and incorporates CBF-based linear constraints for each state estimator. We then propose a framework for joint safety and stability by integrating FT-CBFs with Control Lyapunov Functions. With a similar philosophy of utilizing redundancy, we proposed High order CBF-based approach to ensure safety when actuator failures occur. We propose a sum-of-squares (SOS) based approach to verify the feasibility of FT-CBFs for both sensor faults and actuator failures. We evaluate our approach via two case studies, namely, a wheeled mobile robot (WMR) system in the presence of a sensor attack and a Boeing 747 lateral control system under actuator failures.Comment: 15 pages, 5 figures, submitted to IEEE Transactions on Automatic Contro

A New Method for the Evaluation of Vaccine Safety Based on Comprehensive Gene Expression Analysis

For the past 50 years, quality control and safety tests have been used to evaluate vaccine safety. However, conventional animal safety tests need to be improved in several aspects. For example, the number of test animals used needs to be reduced and the test period shortened. It is, therefore, necessary to develop a new vaccine evaluation system. In this review, we show that gene expression patterns are well correlated to biological responses in vaccinated rats. Our findings and methods using experimental biology and genome science provide an important means of assessment for vaccine toxicity

Quantifying impact on safety from cyber-attacks on cyber-physical systems

We propose a novel framework for modelling attack scenarios in cyber-physical control systems: we represent a cyber-physical system as a constrained switching system, where a single model embeds the dynamics of the physical process, the attack patterns, and the attack detection schemes. We show that this is compatible with established results in the analysis of hybrid automata, and, specifically, constrained switching systems. Moreover, we use the developed models to compute the impact of cyber attacks on the safety properties of the system. In particular, we characterise system safety as an asymptotic property, by calculating the maximal safe set. The resulting new impact metrics intuitively quantify the degradation of safety under attack. We showcase our results via illustrative examples.Comment: 8 pages, 5 figures, submitted for presentation to IFAC World Congress 2023, Yokohama, JAPA

Inventory control with seasonality of lead times

The practical challenges posed by the seasonality of lead times have largely been ignored within the inventory control literature. The length of the seasons, as well as the length of the lead times during a season, may demonstrate cyclical patterns over time. This study examines whether inventory control policies that anticipate seasonal lead-time patterns can reduce costs. We design a framework for characterizing different seasonal lead-time inventory problems. Subsequently, we examine the effect of deterministic and stochastic seasonal lead times within periodic review inventory control systems. We conduct a base case analysis of a deterministic system, enabling two established and alternating lead-time lengths that remain valid through known intervals. We identify essential building blocks for developing solutions to seasonal lead-time problems. Lastly, we perform numerical experiments to evaluate the cost benefits of implementing an inventory control policy that incorporates seasonal lead-time lengths. The findings of the study indicate the potential for cost improvements. By incorporating seasonality in length of seasons and length of lead times within the season into the control models, inventory controllers can make more informed decisions when ordering their raw materials. They need smaller buffers against lead-time variations due to the cyclical nature of seasonality. Reductions in costs in our experiments range on average between 18.9 and 26.4% (depending on safety time and the probability of the occurrence of stock out). Therefore, inventory control methods that incorporate seasonality instead of applying large safety stock or safety time buffers can lead to substantial cost reductions

Findings from the Philadelphia Detention Utilization and Planning Study

Across the country, juvenile detention systems have been experiencing tremendous pressures including population increases, facility crowding, litigation, and a wide range of forces not directly under its control. In turn, juvenile justice officials have come under increasing pressure to develop policies and procedures to effectively manage detention resources now and into the future. The National Council on Crime and Delinquency has a long standing reputation of helping jurisdictions use research-based evidence to effectively plan for bed space needs, alternative programs, and other issues. Currently, NCCD is working with approximately 43 communities to implement the juvenile justice planning process called the Comprehensive Strategy to Address Serious, Violent, and Chronic Juvenile Delinquency. What follows are the findings from an approach NCCD designed to help juvenile justice officials evaluate current detention utilization patterns, the projected needs for secure beds, and various program options. The overall goal of our work is to create a detention system that protects public safety and increases court hearing compliance while taking into account practical constraints and the welfare of the young people our systems handle

Pattern transition in spacecraft formation flying using bifurcating potential field

Many new and exciting space mission concepts have developed around spacecraft formation flying, allowing for autonomous distributed systems that can be robust, scalable and flexible. This paper considers the development of a new methodology for the control of multiple spacecraft. Based on the artificial potential function method, research in this area is extended by considering the new approach of using bifurcation theory as a means of controlling the transition between different formations. For real, safety or mission critical applications it is important to ensure that desired behaviours will occur. Through dynamical systems theory, this paper also aims to provide a step in replacing traditional algorithm validation with mathematical proof, supported through simulation. This is achieved by determining the non-linear stability properties of the system, thus proving the existence or not of desired behaviours. Practical considerations such as the issue of actuator saturation and communication limitations are addressed, with the development of a new bounded control law based on bifurcating potential fields providing the key contribution of this paper. To illustrate spacecraft formation flying using the new methodology formation patterns are considered in low-Earth-orbit utilising the Clohessy-Wiltshire relative linearised equations of motion. It is shown that a formation of spacecraft can be driven safely onto equally spaced projected circular orbits, autonomously reconfiguring between them, whilst satisfying constraints made regarding each spacecraft

Development of Robust Behaviour Recognition for an at-Home Biomonitoring Robot with Assistance of Subject Localization and Enhanced Visual Tracking

Our research is focused on the development of an at-home health care biomonitoringmobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this study, we tested the robustness of the robot system with several major environment changes, control parameter changes, and subject variation. First, an improved color tracker was analyzed to find out the limitations and constraints of the robot visual tracking considering the suitable illumination values and tracking distance intervals.Then, regarding subject safety and continuous robot based subject tracking, various control parameters were tested on different layouts in a room. Finally, the main objective of the system is to find out walking activities for different patterns for further analysis. Therefore, we proposed a fast, simple, and person specific new activity recognition model by making full use of localization information, which is robust to partial occlusion. The proposed activity recognition algorithm was tested on different walking patterns with different subjects, and the results showed high recognition accuracy

Reinforcement learning for condition-based control of gas turbine engines

A condition-based control framework is proposed for gas turbine engines using reinforcement learning and adaptive dynamic programming (RL-ADP). The system behaviour, specifically the fuel efficiency function and constraints, exhibit unknown degradation patterns which vary from engine to engine. Due to these variations, accurate system models to describe the true system states over the life of the engines are difficult to obtain. Consequently, model-based control techniques are unable to fully compensate for the effects of the variations on the system performance. The proposed RL-ADP control framework is based on Q-learning and uses measurements of desired performance quantities as reward signals to learn and adapt the system efficiency maps. This is achieved without knowledge of the system variation or degradation dynamics, thus providing a through life adaptation strategy that delivers improved system performance. In order to overcome the long standing difficulties associated with the application of adaptive techniques in a safety critical setting, a dual-control loop structure is proposed in the implementation of the RL-ADP scheme. The overall control framework maintains guarantees on the main thrust control loop whilst extracting improved performance as the engine degrades by tuning sets of variable geometry components in the RL-ADP control loop. Simulation results on representative engine data sets demonstrate the effectiveness of this approach as compared to an industry standard gain scheduling