Robust Dynamic Average Consensus for a Network of Agents with Time-varying Reference Signals

This paper presents continuous dynamic average consensus (DAC) algorithms for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose consensus algorithms that are robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithms are edge-based protocols with smooth functions in their internal structure to avoid the chattering effect. Furthermore, each agent is only capable of performing local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the agents' estimate of their average to asymptotically converge to the average of the time-varying reference signals of the agents. We provide simulation results to validate the proposed consensus algorithms and to perform a performance comparison of the proposed algorithms to existing algorithms in the literature

Fabrication of Novel In-Situ Remediation Tools for Unconventional Oil Contamination

The aftermath of unconventional oil (UO) accidents highlights the lack of preparedness of governments to deal with UO emergencies. Because bioremediation is considered slow process, physicochemical treatment processes are necessary in removing contaminants to constrain the spread of oil. In preliminary phase of study, bed systems for adsorption of oil compounds packed with modified dolomite were applied as pre-treatment for bioremediation systems. The high affinity of oil molecules to the active sites due to hydrophobic nature of dolomite surface, as well as low solubility of oil in water, resulted in rapid process of oil adsorption on external surface of modified dolomite. UO contaminated site contain high concentration of polyaromatic hydrocarbons (PAHs). Thus, the final phase of study focused on finding enzyme mixture for biodegradation of PAHs contaminated sites for water and soil treatment. In this regard, screening of indigenous bacteria, identification of involved enzymes, and biodegradation tests were carried out. Several combinations of the pre-selected strains were used to create most prompting consortium for enzyme production. To mimic in situ application of enzyme mixture, bioremediation of pyrene contaminated soil was carried out in soil column tests. The average values of pyrene removal after 6 weeks indicated that the enzyme cocktail can be an appropriate concentration for soil enzymatic bioremediation in the soil column system. A bioinspired device was fabricated as a sustainable remedial method. Our results showed that after 200 seconds of circulating the enzyme solution 100% of anthracene in 1.5 L of 4.6 mg/L was removed from the beaker side. In addition to the circulation of PAH degrading enzymes in hollow fiber lumens, aliphatic degrading enzymes confined in multilayer nanofibrous membrane systems play an important role in the removal of oily compounds. Based on our studies, modified polyimide aerogels were suitable to support enzyme immobilization. The degradation tests clearly showed that immobilized enzymes had biodegradation ability for model substrate in contaminated water. Our results confirmed that immobilization of cocktail enzyme mixture enhanced their storage stability, more than 45% of its residual activity at 15 ± 1 ºC for 16 days. This study could set the guideline for the enzymatic bioremediation of aromatic pollutants especially polycyclic aromatic hydrocarbons in highly contaminated soil and water body

Leaderless Swarm Formation Control: From Global Specifications to Local Control Laws

This paper introduces a distributed leaderless swarm formation control framework to address the problem of collectively driving a swarm of robots to track a time-varying formation. The swarm's formation is captured by the trajectory of an abstract shape that circumscribes the convex hull of robots' positions and is independent of the number of robots and their ordering in the swarm. For each robot in the swarm, given global specifications in terms of the trajectory of the abstract shape parameters, the proposed framework synthesizes a control law that steers the swarm to track the desired formation using the information available at the robot's local neighbors. For this purpose, we generate a suitable local reference trajectory that the robot controller tracks by solving the input-output linearization problem. Here, we select the swarm output to be the parameters of the abstract shape. For this purpose, we design a dynamic average consensus estimator to estimate the abstract shape parameters. The abstract shape parameters are used as the swarm state feedback to generate a suitable robot trajectory. We demonstrate the effectiveness and robustness of the proposed control framework by providing the simulation of coordinated collective navigation of a group of car-like robots in the presence of robots and communication link failures

Designing an Organizational Performance Model Based on the Digital Status of the Organization During the Covid-19 outbreak in the Ministry of Industry, Mine and Trade

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Nutrition and osteoporosis prevention and treatment

Introduction: Osteoporosis falls among the major general health issues, specifically in the elderly, and is a widespread disease these days. According to various studies, good nutrition plays a significant role in osteoporosis prevention and treatment. The aim of this study was to conduct an extensive literature review on the effects of different nutrients to understand how macronutrients, micronutrients, and non-nutritive substances affect bone health. Methodology: To find relevant studies, the main keyword "osteoporosis" was searched in combination with "zinc," "vitamin K," "phosphorus," "vitamin D," "calcium," "lipid," "protein," and "phytoestrogens" in PubMed (MEDLINE), Web of Science, SID, and Iran Medex databases. Findings: The most important element for bone health is calcium, which has a direct link to the bone mass density (BMD). In the case of calcium deficiency, high phosphorus content can damage bone tissue. The acceptable ratio of phosphorus to calcium is 0.5-1.5:1. Vitamin D is another important nutrient for bones; serum levels of vitamin D less than 20 ng/ml reduce bone density and increase the risk of fracture. High protein intake results in calcium excretion and loss of bone mass. In addition, calcium deficiency increases the risk of osteoporosis, specifically in the elderly. According to the literature, there is an inverse correlation between saturated fats and BMD. Vitamin K and magnesium deficiencies are correlated with BMD reduction and increased risk of osteoporosis. Copper and zinc are used as co-factors in the formation of collagen and elastin, and in mineralization of bone. As a result, deficiency of these elements may disrupt the process of incorporating minerals into the bone matrix. Conclusion: Good nutrition may play a significant role in osteoporosis prevention and treatment. Indeed, a healthy diet containing calcium (1,200 mg/day); vitamin D (600 IU); and certain amounts of protein, magnesium, and vitamin K can contribute greatly to bone health

Heterogeneity-Aware Graph Partitioning for Distributed Deployment of Multiagent Systems

In this work, we examine the distributed coverage control problem for deploying a team of heterogeneous robots with nonlinear dynamics in a partially known environment modeled as a weighted mixed graph. By defining an optimal tracking control problem, using a discounted cost function and state-dependent Riccati equation (SDRE) approach, a new partitioning algorithm is proposed to capture the heterogeneity in robots dynamics. The considered partitioning cost, which is a state-dependent proximity metric, penalizes both the tracking error and the control input energy that occurs during the movement of a robot, on a straight line, to an arbitrary node of the graph in a predefined finite time. We show that the size of the subgraph associated with each robot depends on its resources and capabilities in comparison to its neighbors. Also, a distributed deployment strategy is proposed to optimally distribute robots aiming at persistently monitoring specified regions of interest. Finally, a series of simulations and experimental studies is carried out to demonstrate the viability and efficacy of the proposed methodology in deploying heterogeneous multiagent systems

On design of nonlinear event-triggered suboptimal tracking controller

In this paper, using the state-dependent Riccati equation (SDRE) technique, a suboptimal control law is designed to solve the tracking problem for networked nonlinear discrete-time systems. Based on the proposed method, an event-triggered technique is also developed to reduce the information exchange between the controller and the actuator in a networked control system. It is shown that the obtained closed-loop system is asymptotically stable under mild conditions. The proposed method is applied to a nonlinear benchmark (Vander Pol's oscillator system) and the simulation results demonstrate the effectiveness of the proposed approach for solving the tracking problem of a nonlinear system in a networked control framework. Keywords: Nonlinear system, Suboptimal tracking controller, State-dependent Riccati equation (SDRE), Networked control system, Event-triggered. 1 2017 IEEE.Scopu

Robust Controller and Fault Diagnoser Design for Linear Systems with Event-based Communication

In order to improve the effectiveness and safety of control systems, the problem of integrated fault diagnosis and control (IFDC) design has attracted significant attention in the recent years, both in the research and in the application domains. The integrated design unifies the control and diagnosis units into a single unit which leads to less complexity as compared to the case of separate designs. Nowadays, the IFDC modules are implemented on digital platforms. However, in almost all of these implementations, the IFDC task is executed periodically with a constant sampling period, which is called “time-triggering” sampling. However, the time-triggering sampling scheme produces many useless messages if the current sampled signal has not significantly changed in contrast to the previous sampled signal, which leads to a conservative usage of the communication bandwidth. This is especially disadvantageous in applications where the measured outputs and/or the actuator signals have to be transmitted over a shared (and possibly wireless) communication network, where the bandwidth of the network (and power consumption of the wireless radios) should be constrained. To mitigate the unnecessary waste of computation and communication resources in conventional time-triggered IFDC design, the problem of event-triggered integrated fault diagnosis and control (E-IFDC) for discrete-time linear systems is considered in this paper. A single E-IFDC module based on a dynamic filter is proposed which produces two signals, namely the residual and the control signals. The parameters of the E-IFDC module should be designed such that the effects of disturbances on the residual signals are minimized (for accomplishing the fault detection objective) subject to the constraint that the mapping matrix function from the faults to the residuals is equal to a pre- assigned diagonal mapping matrix (for accomplishing the fault isolation objective), while the effects of disturbances and faults on the specified control output are minimized (for accomplishing the fault-tolerant control objective). Two event-triggered conditions are proposed and designed to reduce the transmissions from the sensor to the E- IFDC module and from the E-IFDC module to the actuator. These event-triggered conditions determine whether the newly measured data or control output, respectively, should be transmitted or not. Indeed, the sensor measurement (controller output) is sent to the E-IFDC module (actuator) only when the difference between the latest transmitted sensor (controller) value and the current sensor measurement (controller output) is sufficiently large as compared to the current sensor (controller) value. This property reduces the burden on the network communication and saves the communication bandwidth in the network. Consequently, it is possible to significantly reduce the usage of communication resources for diagnosis and control tasks as compared to a conventional time-triggered IFDC approach. A multi-objective formulation of the problem is presented based on the H∞ and H- performance indices. The sufficient conditions for solvability of the problem are obtained in terms of linear matrix inequality (LMI) feasibility conditions. Indeed, the filter parameters and the event-triggered conditions are simultaneously obtained using strict LMI conditions. The main advantage of the proposed LMI formulation is that it is convex, and is therefore solved effectively using interior-point methods. Application of our methodology to a linearized model of the Subzero III ROV is presented to illustrate the effectiveness and capabilities of our proposed methodology. Remotely operated vehicles (ROVs) are underwater robotic platforms that have become increasingly important tools in a wide range of applications including offshore oil operations, fisheries research, dam inspection, salvage operations, military applications, among others. Since transmission resources are limited under water, using an event-triggered scheme for communication is more efficient. Therefore, the results of this paper are applied for designing an event-triggered IFDC module for the Subzero III ROV.qscienc

Sensor fault detection and isolation of an autonomous underwater vehicle using partial kernel PCA

In this paper, partial kernel principal component analysis (PKPCA) is studied for sensor fault detection and isolation (FDI) of an autonomous underwater vehicle (AUV). Principal component analysis (PCA) is an effective health monitoring tool which can achieve acceptable results only for linear processes. In the case of nonlinear systems such as autonomous underwater vehicles, kernel PCA approach can be used which leads to more accurate health monitoring and fault diagnosis. In order to achieve fault isolation, partial KPCA is proposed where a set of residual signals is generated based on the parity relation concept. The simulation studies demonstrate that using the proposed methodology, the occurrence of sensor faults in the nonlinear six degrees of freedom (DOF) model of an AUV can be effectively detected and isolated

Robust Dynamic Average Consensus for a Network of Agents with Time-varying Reference Signals

This paper presents a continuous dynamic average consensus (DAC) algorithm for a group of agents to estimate the average of their time-varying reference signals cooperatively. We propose a consensus algorithm that is robust to agents joining and leaving the network, at the same time, avoid the chattering phenomena and guarantee zero steady-state consensus error. Our algorithm is an edge-based protocol with smooth functions in its internal structure to avoid the chattering effect. Furthermore, each agent can only perform local computations and can only communicate with its local neighbors. For a balanced and strongly connected underlying communication graph, we provide the convergence analysis to determine the consensus design parameters that guarantee the estimate of the average to asymptotically converge to the average of the time-varying reference signals. We provide simulation results to validate the proposed consensus algorithm and perform a performance comparison of the proposed algorithm to existing algorithms in the literature