Adaptive Cruise Control for Intelligent City Bus Based on Vehicle Mass and Road Slope Estimation

Adaptive cruise control (ACC), as a driver assistant system for vehicles, not only relieves the burden of drivers, but also improves driving safety. This paper takes the intelligent pure electric city bus as the research platform, presenting a novel ACC control strategy that could comprehensively address issues of tracking capability, driving safety, energy saving, and driving comfort during vehicle following. A hierarchical control architecture is utilized in this paper. The lower controller is based on the nonlinear vehicle dynamics model and adjusts vehicle acceleration with consideration to the changes of bus mass and road slope by extended Kalman filter (EKF). The upper controller adapts Model Predictive Control (MPC) theory to solve the multi-objective optimal problem in ACC process. Cost functions are developed to balance the tracking distance, driving safety, energy consumption, and driving comfort. The simulations and Hardware-in-the-Loop (HIL) test are implemented; results show that the proposed control strategy ensured the driving safety and tracking ability of the bus, and reduced the vehicle’s maximum impact to 5 m/s3 and the State of Charge (SoC) consumption by 10%. Vehicle comfort and energy economy are improved obviously.

Permutation Entropy as a Universal Disorder Criterion: How Disorders at Different Scale Levels Are Manifestations of the Same Underlying Principle

What do bacteria, cells, organs, people, and social communities have in common? At first sight, perhaps not much. They involve totally different agents and scale levels of observation. On second thought, however, perhaps they share everything. A growing body of literature suggests that living systems at different scale levels of observation follow the same architectural principles and process information in similar ways. Moreover, such systems appear to respond in similar ways to rising levels of stress, especially when stress levels approach near-lethal levels. To explain such communalities, we argue that all organisms (including humans) can be modeled as hierarchical Bayesian controls systems that are governed by the same biophysical principles. Such systems show generic changes when taxed beyond their ability to correct for environmental disturbances. Without exception, stressed organisms show rising levels of ‘disorder’ (randomness, unpredictability) in internal message passing and overt behavior. We argue that such changes can be explained by a collapse of allostatic (high-level integrative) control, which normally synchronizes activity of the various components of a living system to produce order. The selective overload and cascading failure of highly connected (hub) nodes flattens hierarchical control, producing maladaptive behavior. Thus, we present a theory according to which organic concepts such as stress, a loss of control, disorder, disease, and death can be operationalized in biophysical terms that apply to all scale levels of organization. Given the presumed universality of this mechanism, ‘losing control’ appears to involve the same process anywhere, whether involving bacteria succumbing to an antibiotic agent, people suffering from physical or mental disorders, or social systems slipping into warfare. On a practical note, measures of disorder may serve as early warning signs of system failure even when catastrophic failure is still some distance away.

Reversible microscale assembly of nanoparticles driven by the phase transition of a thermotropic liquid crystal

The arrangement of nanoscale building blocks into patterns with microscale periodicity is challenging to achieve via self-assembly processes. Here, we report on the phase transition-driven collective assembly of gold nanoparticles in a thermotropic liquid crystal. A temperature-induced transition from the isotropic to the nematic phase leads to the assembly of individual nanometre-sized particles into arrays of micrometre-sized aggregates, whose size and characteristic spacing can be tuned by varying the cooling rate. This fully reversible process offers hierarchical control over structural order on the molecular, nanoscopic, and microscopic level and is an interesting model system for the programmable patterning of nanocomposites with access to micrometre-sized periodicities.

A Distributed Hierarchical Control Framework for Economic Dispatch and Frequency Regulation of Autonomous AC Microgrids

Motivated by the single point of failure and other drawbacks of the conventional centralized hierarchical control strategy, in this paper, a fully distributed hierarchical control framework is formulated for autonomous AC microgrids. The proposed control strategy operates with a distinct three-layer structure, where: a conventional droop control is adopted at the primary layer; a distributed leaderless consensus-based control is adopted at the secondary layer for active power and, hence, frequency regulation of distributed generating units (DGUs); and the tertiary layer is also based on the distributed leaderless consensus-based control for the optimal power dispatch. Under the proposed strategy, the three constituent control layers work in a coordinated manner. Not only is the load dispatched economically with a negligible power mismatch, but also the frequencies of all the DGUs are regulated to the reference value. However, the frequency regulation is achieved without requiring any central leader agent that has been reported in the contemporary distributed control articles. As compared to the conventional centralized hierarchical control, the proposed strategy only needs local inter-agent interaction with a sparse communication network; thus, it is fully distributed. The formulated strategy is tested under load perturbations, on an autonomous AC microgrid testbed comprising both low-inertia-type (inverter-interfaced) and high-inertia (rotating)-type DGUs with heterogeneous dynamics, and found to successfully meet its targets. Furthermore, it can offer the plug-and-play operation for the DGUs. Theoretical analysis and substantial simulation results, performed in the MATLAB/Simulink environment, are provided to validate the feasibility of the proposed control framework.

Governance and Performance of Agri-Food Exporting SMEs

Moroccan SME's are involved in local development and are currently the subject of several studies. Furthermore, Moroccan SMEs are crucial to the country's economic growth since they produce value, create employment, and play an important role in local development. The goal is to identify the factors that influence its export performance. This performance can be influenced by the governance system, which is defined as a set of mechanisms that have the effect of limiting managers' powers and influencing their decisions. In this light, the purpose of this paper is to demonstrate, using a sample of food SME's in Agadir city, that a good governance system improves export performance and accelerates the process of SME internationalization. Based on the findings of this study, we can infer that SMEs with effective internal governance mechanisms, such as an internal control and consultation body and strong hierarchical control, may improve their export performance and, as a result, their internationalization potential.

A model of response efficiency in the hierarchical control system based on the assessment of readiness of fire departments

Introduction. The readiness of all levels of subsystems that comprise the Unified State System for Emergency Prevention and Liquidation (USSEPL) is one of the most important characteristics that determine its effectiveness. To support decision-making at the upper levels of the management hierarchy, it is important to have a set of models that adequately represent the dependence between key response efficiency indicators and particular indicators of lower levels of the system (fire and rescue departments). In most cases, a regulatory approach to the construction of such models, by virtue of which analysts set their structure and parameters, turns out to be unproductive due to their non-adaptive nature in the context of dynamically changing external conditions and technological capabilities of modern devices. The use of an approach based on solving inverse problems that close the feedback loop and provide for an adaptive adjustment of parameters and the structure of models, ensures the current adequacy of models amid changing conditions.The relevance of the study lies in the development of a technology for constructing polynomial models that allow to assess the USSEPL response effectiveness based on estimated indicators of readiness of subsystems at lower levels obtained using expert evaluation techniques (testing) by means of internal control.Goals and objectives. The aim of the work is to build and test the technology for developing analytical polynomial models that allow to adequately assess performance indicators of the USSEPL response depending on the readiness indicators of lower-level subsystems (fire and rescue departments). In compliance with this goal, the tasks of choosing the type of model and methods of obtaining the necessary initial data are also set.Methods. The study uses methods of analysis of hierarchically organized systems, mathematical statistics, simulation modelling, and methods of expert evaluation. The research is backed by materials from domestic and foreign publications.Results and discussion. The proposed method of constructing an efficiency model of the USSEPL operation, relying on the readiness of subsystems, serves as the basis for constructing models that can take into account other indicators of subsystems.Conclusions. The solution to the problem of constructing a polynomial model, that features dependence between the USSEPL response efficiency and lower-level readiness indicators, serves as the basis for other similar models that will support decision making systems.