Closed-loop Approach Research Articles

The Closed-Loop Control of the Half-Bridge-Based MMC Drive with Variable DC-Link Voltage

The modular multilevel converter (MMC) based on half-bridge modules is a power converter topology suitable for high-power medium-voltage variable-speed drives. However, the voltage of its flying capacitors is negatively affected when low frequencies appear at the AC port. This paper analyzes the influence of using a variable DC port voltage in a machine-side MMC by implementing a closed-loop approach, ensuring a constant voltage fluctuation in the capacitors of the MMC during the whole operating range. The effectiveness of the proposed control scheme is demonstrated through simulation studies and experimental validation tests conducted using a 7.5 kW experimental prototype composed of an induction machine fed by an MMC with 18 half-bridge cells.

A closed loop recycling strategy for sustainable recovery of group 11 metals (Cu, Au, and Ag) from waste PCBs: An amalgamation of low-temperature NH4Cl roasting, HCl leaching and cementation

Circular economy is not a choice but a necessity for every sector with growing demand and limited resources. E-waste, being considered as a strategic resource because of its enriched metallic content the implementation of effective utilization is necessary, but not at the cost of the environment. Therefore, to maximize resource recovery and minimize environmental impact, a closed-loop approach was proposed for the recovery of group 11 metals from waste printed circuit boards (WPCBs). Herein, a combination of low-temperature NH4Cl roasting, HCl leaching, and cementation methods was employed for the separation of metals. The NH4Cl roasting assisted in altering metals to metal chlorides, whereas HCl solvates produced metal chlorides. Ultimately, cementation methods were employed for individual separation of metals from leached solution. The obtained results revealed that >98% Cu, 86% Ag, and 76% Au were extracted under optimum conditions (Temperature of 260 °C, roasting time of 2.5 h, NH4Cl dose of 2 g/g, and 2 M HCl). Further, a three-step cementation technique was employed for the separation of Au, Ag, and Cu using Ag, Cu, and Fe as sacrificial metals, respectively. Finally, all the reagents used in the process were recovered in the form of NH4Cl and Fe(OH)2 making a closed-loop process.

Integrating Continuous and Batch Processes with Shared Resources in Closed-Loop Scheduling: A Case Study on Tuna Cannery.

Scheduling tasks in production facilities are usually hybrid optimization problems of a large combinatorial nature. They involve solving, in near-real time, the integration of the operation of several batch units of continuous dynamics with the discrete manufacture of items in processing lines. Moreover, one has to deal with uncertainty (process delays, unexpected stops) and the management of shared resources (energy, water, etc.) including decisions made by plant operators: still, some tasks in the scheduling layers are done manually. Manufacturing Execution Systems (MESs) are intended to support plant personnel at this level. However, there is still much work to do in terms of performing automatic scheduling, computed in real time, that guides managers to achieve an optimal operation of such complex cyber-physical systems. This work proposes a closed-loop approach to handle the uncertainty arising when facing the online scheduling of supply lines and parallel batch units. These units often share some resources, so effects due to concurrent resource consumption on the system dynamics are explicitly considered in the presented formulation. The proposed decision support system is tested onsite in a tuna cannery, to handle short-term online scheduling of sterilization processes that deal with limited steam, carts, and operators as shared resources.

ID: 213578 Sensing Capability of BurstDR as a Sub-Paresthesia Therapeutic Waveform

The BurstDRTM waveform (burst) for spinal cord stimulation to relieve chronic pain demonstrated superior to classical tonic stimulation efficacy. Burst stimulation allows effective stimulation at sub-paresthesia level, improving patient’s experience. Regarding sensing and closed-loop approach, burst waveform raises a question if a spinal cord stimulation evoked compound action potential (ECAP) can be recorded and analyzed using this complicated waveform.

Optogenetic stimulation of the superior colliculus suppresses genetic absence seizures.

While anti-seizure medications are effective for many patients, nearly one-third of individuals have seizures that are refractory to pharmacotherapy. Prior studies using evoked preclinical seizure models have shown that pharmacological activation or excitatory optogenetic stimulation of the deep and intermediate layers of the superior colliculus (DLSC) display multi-potent anti-seizure effects. Here we monitored and modulated DLSC activity to suppress spontaneous seizures in the WAG/Rij genetic model of absence epilepsy. Female and male WAG/Rij adult rats were employed as study subjects. For electrophysiology studies, we recorded single unit activity from microwire arrays placed within the DLSC. For optogenetic experiments, animals were injected with virus coding for channelrhodopsin-2 or a control vector, and we compared the efficacy of continuous neuromodulation to that of closed-loop neuromodulation paradigms. For each, we compared three stimulation frequencies on a within-subject basis (5, 20, 100 Hz). For closed-loop stimulation, we detected seizures in real time based on the EEG power within the characteristic frequency band of spike-and-wave discharges (SWDs). We quantified the number and duration of each SWD during each 2 h-observation period. Following completion of the experiment, virus expression and fibre-optic placement was confirmed. We found that single-unit activity within the DLSC decreased seconds prior to SWD onset and increased during and after seizures. Nearly 40% of neurons displayed suppression of firing in response to the start of SWDs. Continuous optogenetic stimulation of the DLSC (at each of the three frequencies) resulted in a significant reduction of SWDs in males and was without effect in females. In contrast, closed-loop neuromodulation was effective in both females and males at all three frequencies. These data demonstrate that activity within the DLSC is suppressed prior to SWD onset, increases at SWD onset, and that excitatory optogenetic stimulation of the DLSC exerts anti-seizure effects against absence seizures. The striking difference between open- and closed-loop neuromodulation approaches underscores the importance of the stimulation paradigm in determining therapeutic effects.

Development and Validation of Machine Learning and Electronic Medical Records-Based Characterization of Stiff Person Syndrome (P5-8.001)

<h3>Objective:</h3> To develop and validate an objective framework in identifying medical history items that best characterize the diagnosis of Stiff Person Syndrome (SPS). <h3>Background:</h3> SPS is a rare neurologic disorder with characteristic muscle spasms and painful rigidity. While electronic medical records may contain items that can help refine its diagnostic criteria, the rarity of SPS leads to statistical underpowering in conventional tests. A machine learning approach that leverages underutilized electronic medical records may reveal clinical features that are strongly associated with the diagnosis of SPS. <h3>Design/Methods:</h3> This retrospective cohort study assessed 23 patients carrying SPS diagnoses and 25 controls who were all anti-GAD positive and were treated at Dartmouth Hitchcock Medical Center. After binarizing the 319 clinical features, we employed an iterative machine-learning-based feature selection approach (Contribution Selection Algorithm) to identify variables that best discriminate between patients with SPS vs. anti-GAD positive controls. Each iteration generated SHapley Additive exPlanation (SHAP) values for each binarized feature. Classifier accuracy, AUC, and Matthews Correlation Coefficients (MCC) for the Support Vector Machine models were calculated using repeated stratified 4-fold cross-validation. <h3>Results:</h3> The feature selection algorithm identified depression, pain in joint, hypothyroidism, and GERD as the top 4 predictors of SPS, as the SVM model that employed these features achieved the best predictive performance with a binary classification accuracy of 0.775 (95%CI, 0.759–0.790), an AUC of 0.808 (95%CI, 0.791–0.825), and a MCC of 0.565 (95%CI, 0.534–0.597). <h3>Conclusions:</h3> Our machine learning-based architecture identified features that are strongly associated with SPS. By circumventing the limitations of statistical underpowering and focusing on identifying associations through induction, this approach may act as a powerful tool for generating new hypotheses for rare disorders. This framework complements hypothesis-driven investigations, together forming a closed loop approach to aid diagnostic challenges in neurology. <b>Disclosure:</b> Mr. Park has nothing to disclose. Dr. Song has nothing to disclose.

Regulation of arousal and performance of a healthy non-human primate using closed-loop central thalamic deep brain stimulation.

Application of closed-loop approaches in systems neuroscience and brain-computer interfaces holds great promise for revolutionizing our understanding of the brain and for developing novel neuromodulation strategies to restore lost function. The anterior forebrain mesocircuit (AFM) of the mammalian brain is hypothesized to underlie arousal regulation of the cortex and striatum, and support cognitive functions during wakefulness. Dysfunction of arousal regulation is hypothesized to contribute to cognitive dysfunctions in various neurological disorders, and most prominently in patients following traumatic brain injury (TBI). Several clinical studies have explored the use of daily central thalamic deep brain stimulation (CT-DBS) within the AFM to restore consciousness and executive attention in TBI patients. In this study, we explored the use of closed-loop CT-DBS in order to episodically regulate arousal of the AFM of a healthy non-human primate (NHP) with the goal of restoring behavioral performance. We used pupillometry and near real-time analysis of ECoG signals to episodically initiate closed-loop CT-DBS and here we report on our ability to enhance arousal and restore the animal's performance. The initial computer based approach was then experimentally validated using a customized clinical-grade DBS device, the DyNeuMo-X, a bi-directional research platform used for rapidly testing closed-loop DBS. The successful implementation of the DyNeuMo-X in a healthy NHP supports ongoing clinical trials employing the internal DyNeuMo system (NCT05437393, NCT05197816) and our goal of developing and accelerating the deployment of novel neuromodulation approaches to treat cognitive dysfunction in patients with structural brain injuries and other etiologies.

A closed-loop approach for enhanced biodiesel recovery from rapeseed biodiesel-based byproducts through integrated glycerol recycling by black soldier fly larvae

The present study evaluated an innovative zero-waste approach for efficient recycling of byproducts from rapeseed-based biodiesel industry including lipid-free rapeseed cake (RSC) and waste glycerol by black soldier fly larvae (BSFL). RSC showed high organic matter and protein contents of 92.7 dw% and 32.1 dw%, respectively. Therefore, it was utilized as the main feed source for BSFL rearing, while waste glycerol was supplemented at different concentrations (0–10%, w/w). Application of 5.0% waste glycerol significantly enhanced larval biomass gain rate and areal productivity by 13.0% and 17.2%, respectively, over the control. Interestingly, lipid content also increased by 25.9% over the control. The study suggested a synergistic action in the mixture, where RSC is digested to achieve the growth and development, while waste glycerol can be integrated directly into fatty acid biosynthesis pathway to accumulate lipids. In addition to being a carbon source, morphological examination and FTIR analysis showed that waste glycerol has the potential to penetrate RSC biomass fibers easing its decomposition. Overall, the highest recorded fatty acid methyl esters (FAMEs) productivity of 98.0 mg m−2 was recorded in BSFL treated with 5.0% waste glycerol, which represented 57.7% higher than the control. Comparing to rapeseed biodiesel, saturated fatty acids proportion in the BSFL-based FAMEs at all studied treatments was much higher, leading to 11.9–12.9% higher cetane number. In conclusion, findings of this study provide new insights to BSFL as a promising mini-livestock for biodiesel production through innovative recirculation route of biodiesel byproducts towards waste reduction and sustainable development.

Closed-loop agriculture systems meta-research using text mining

The growing global population and climate change threaten the availability of many critical resources, and have been directly impacting the food and agriculture sector. Therefore, new cultivation technologies must be rapidly developed and implemented to secure the world's future food needs. Closed-loop greenhouse agriculture systems provide an opportunity to decrease resource reliance and increase crop yield. Greenhouses provide versatility in what can be grown and the resources required to function. Greenhouses can become highly efficient and resilient through the application of a closed-loop systems approach that prioritizes repurposing, reusing, and recirculating resources. Here, we employ a text mining approach to research the available research (meta-research) and publications within the area of closed-loop systems in greenhouses. This meta-research provides a clearer definition of the term “closed-loop system” within the context of greenhouses, as the term was previously vaguely defined. Using this meta-research approach, we identify six major existing research topic areas in closed-loop agriculture systems, which include: models and controls; food waste; nutrient systems; growing media; heating; and energy. Furthermore, we identify four areas that require further urgent work, which include the establishment of better connection between academic research to industry applications; clearer criteria surrounding growing media selection; critical operational requirements of a closed-loop system; and the functionality and synergy between the many modules that comprise a closed-loop greenhouse systems.

Investigation of the Mechanical Properties of Sandwich Composite Panels Made with Recyclates and Flax Fiber/Bio-Based Epoxy Processed by Liquid Composite Molding

Despite significant advancements in bio-based natural-fiber-reinforced composites, the recyclability/reprocessing of thermoset composites remains a persistent challenge that needs to be addressed. In the present study, an effort is made to provide a justification for the recyclability/reprocessing assessment of sandwich composite panels made with ‘recyclate’ (i.e., recycled flax/bio-based epoxy composite) cores and (flax/bio-based epoxy) skins produced by liquid composite molding. Resin transfer molding and vacuum-assisted resin infusion processes were used to investigate the influence of production processes on mechanical properties. Two different recyclate sizes—4 mm and 10 mm—were used to fabricate sandwich composite panels to study the effect of size on the mechanical properties of the panels. This study aims to compare the qualities of sandwich panels to those of virgin composite panels in terms of their physical (density) and mechanical properties (tensile and flexural). Additionally, the recyclate packing was verified by employing digital microscopy. The results illustrated that the sandwich panels made with the 4 mm recyclates exhibited better mechanical properties compared to those made with the 10 mm recyclates. In comparison with virgin composite panels, the sandwich composite panels made of flax fiber and (flax/epoxy) recyclate exhibited significantly higher flexural moduli, which was attributed to their moments of inertia. This article emphasizes recycling/reprocessing and demonstrates an effective closed-loop approach. Thus, by preserving the structural integrity of recyclates, sandwich panels could be advantageous for semi-structural applications.

A Closed-Loop Output Error Approach for Physics-Informed Trajectory Inference Using Online Data.

While autonomous systems can be used for a variety of beneficial applications, they can also be used for malicious intentions and it is mandatory to disrupt them before they act. So, an accurate trajectory inference algorithm is required for monitoring purposes that allows to take appropriate countermeasures. This article presents a closed-loop output error approach for trajectory inference of a class of linear systems. The approach combines the main advantages of state estimation and parameter identification algorithms in a complementary fashion using online data and an estimated model, which is constructed by the state and parameter estimates, that inform about the physics of the system to infer the followed noise-free trajectory. Exact model matching and estimation error cases are analyzed. A composite update rule based on a least-squares rule is also proposed to improve robustness and parameter and state convergence. The stability and convergence of the proposed approaches are assessed via the Lyapunov stability theory under the fulfilment of a persistent excitation condition. Simulation studies are carried out to validate the proposed approaches.

A new full closed-loop brain-machine interface approach based on neural activity: A study based on modeling and experimental studies

BackgroundThe bidirectional brain-machine interfaces algorithms are machines that decode neural response in order to control the external device and encode position of artificial limb to proper electrical stimulation, so that the interface between brain and machine closes. Most BMI researchers typically consider four basic elements: recording technology to extract brain activity, decoding algorithm to translate brain activity to the predicted movement of the external device, external device (prosthetic limb such as a robotic arm), and encoding interface to convert the motion of the external machine to set of the electrical stimulation of the brain. New methodIn this paper, we develop a novel approach for bidirectional brain-machine interface (BMI). First, we propose a neural network model for sensory cortex (S1) connected to the neural network model of motor cortex (M1) considering the topographic mapping between S1 and M1. We use 4-box model in S1 and 4-box in M1 so that each box contains 500 neurons. Individual boxes include inhibitory and excitatory neurons and synapses. Next, we develop a new BMI algorithm based on neural activity. The main concept of this BMI algorithm is to close the loop between brain and mechaical external device. ResultsThe sensory interface as encoding algorithm convert the location of the external device (artificial limb) into the electrical stimulation which excite the S1 model. The motor interface as decoding algorithm convert neural recordings from the M1 model into a force which causes the movement of the external device. We present the simulation results for the on line BMI which means that there is a real time information exchange between 9 boxes and 4 boxes of S1-M1 network model and the external device. Also, off line information exchange between brain of five anesthetized rats and externnal device was performed. The proposed BMI algorithm has succeeded in controlling the movement of the mechanical arm towards the target area on simulation and experimental data, so that the BMI algorithm shows acceptable WTPE and the average number of iterations of the algorithm in reaching artificial limb to the target region.Comparison with existing methods and Conclusions: In order to confirm the simulation results the 9-box model of S1-M1 network was developed and the valid “spike train” algorithm, which has good results on real data, is used to compare the performance accuracy of the proposed BMI algorithm versus “spike train” algorithm on simulation and off line experimental data of anesthetized rats. Quantitative and qualitative results confirm the proper performance of the proposed algorithm compared to algorithm “spike train” on simulations and experimental data.

Event-based feature tracking in a visual inertial odometry framework.

Introduction: Event cameras report pixel-wise brightness changes at high temporal resolutions, allowing for high speed tracking of features in visual inertial odometry (VIO) estimation, but require a paradigm shift, as common practices from the past decades using conventional cameras, such as feature detection and tracking, do not translate directly. One method for feature detection and tracking is the Eventbased Kanade-Lucas-Tomasi tracker (EKLT), an hybrid approach that combines frames with events to provide a high speed tracking of features. Despite the high temporal resolution of the events, the local nature of the registration of features imposes conservative limits to the camera motion speed. Methods: Our proposed approach expands on EKLT by relying on the concurrent use of the event-based feature tracker with a visual inertial odometry system performing pose estimation, leveraging frames, events and Inertial Measurement Unit (IMU) information to improve tracking. The problem of temporally combining high-rate IMU information with asynchronous event cameras is solved by means of an asynchronous probabilistic filter, in particular an Unscented Kalman Filter (UKF). The proposed method of feature tracking based on EKLT takes into account the state estimation of the pose estimator running in parallel and provides this information to the feature tracker, resulting in a synergy that can improve not only the feature tracking, but also the pose estimation. This approach can be seen as a feedback, where the state estimation of the filter is fed back into the tracker, which then produces visual information for the filter, creating a "closed loop". Results: The method is tested on rotational motions only, and comparisons between a conventional (not event-based) approach and the proposed approach are made, using synthetic and real datasets. Results support that the use of events for the task improve performance. Discussion: To the best of our knowledge, this is the first work proposing the fusion of visual with inertial information using events cameras by means of an UKF, as well as the use of EKLT in the context of pose estimation. Furthermore, our closed loop approach proved to be an improvement over the base EKLT, resulting in better feature tracking and pose estimation. The inertial information, despite prone to drifting over time, allows keeping track of the features that would otherwise be lost. Then, feature tracking synergically helps estimating and minimizing the drift.

Criticality in probabilistic models of spreading dynamics in brain networks: Epileptic seizures.

The spread of seizures across brain networks is the main impairing factor, often leading to loss-of-consciousness, in people with epilepsy. Despite advances in recording and modeling brain activity, uncovering the nature of seizure spreading dynamics remains an important challenge to understanding and treating pharmacologically resistant epilepsy. To address this challenge, we introduce a new probabilistic model that captures the spreading dynamics in patient-specific complex networks. Network connectivity and interaction time delays between brain areas were estimated from white-matter tractography. The model's computational tractability allows it to play an important complementary role to more detailed models of seizure dynamics. We illustrate model fitting and predictive performance in the context of patient-specific Epileptor networks. We derive the phase diagram of spread size (order parameter) as a function of brain excitability and global connectivity strength, for different patient-specific networks. Phase diagrams allow the prediction of whether a seizure will spread depending on excitability and connectivity strength. In addition, model simulations predict the temporal order of seizure spread across network nodes. Furthermore, we show that the order parameter can exhibit both discontinuous and continuous (critical) phase transitions as neural excitability and connectivity strength are varied. Existence of a critical point, where response functions and fluctuations in spread size show power-law divergence with respect to control parameters, is supported by mean-field approximations and finite-size scaling analyses. Notably, the critical point separates two distinct regimes of spreading dynamics characterized by unimodal and bimodal spread-size distributions. Our study sheds new light on the nature of phase transitions and fluctuations in seizure spreading dynamics. We expect it to play an important role in the development of closed-loop stimulation approaches for preventing seizure spread in pharmacologically resistant epilepsy. Our findings may also be of interest to related models of spreading dynamics in epidemiology, biology, finance, and statistical physics.

Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites

• A closed-loop control approach is successfully developed for online detection of defects and adjustment of process parameters by deep learning, which is never achievable in any conventional methods of composite fabrication. • Deep learning is implemented to detect the CFRP defects for AM in real-time with high accuracy and low latency. The proposed method is able to identify different types of CFRP defects (i.e., misalignment and abrasion). • The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. Real-time defect detection and closed-loop adjustment of additive manufacturing (AM) are essential to ensure the quality of as-fabricated products, especially for carbon fiber reinforced polymer (CFRP) composites via AM. Machine learning is typically limited to the application of online monitoring of AM systems due to a lack of accurate and accessible databases. In this work, a system is developed for real-time identification of defective regions, and closed-loop adjustment of process parameters for robot-based CFRP AM is validated. The main novelty is the development of a deep learning model for defect detection, classification, and evaluation in real-time with high accuracy. The proposed method is able to identify two types of CFRP defects (i.e., misalignment and abrasion). The combined deep learning with geometric analysis of the level of misalignment is applied to quantify the severity of individual defects. A deep learning approach is successfully developed for the online detection of defects, and the defects are effectively controlled by closed-loop adjustment of process parameters, which is never achievable in any conventional methods of composite fabrication.

Blowing pressure stabilization method for the artificial excitation of reed instruments (L).

To investigate the acoustics of reed instruments without the need for a human player, blowing machines are needed, which can generate air pressures up to 8 kPa and flow rates up to 40 liters per minute. Due to reed flexibility and the changing pressure gradient across the reed, the relationship between flow and pressure is highly non-linear. Since the output pressure of ventilators is highly dependent on flow, non-linear pressure regulation is a difficult task that requires a closed-loop approach. Since reed vibration starts suddenly when blowing pressure is gradually increased, an abrupt change in airflow through the instrument is present, resulting in a change in pressure in the artificial mouth. To avoid that, a method is presented to achieve a fast response to abrupt flow changes, which is tested in an existing blowing machine. The enhanced blowing machine exhibits a settling time below 200 ms, which allows for the generation of blowing pressures with linear responses.

Infralimbic Projections to the Nucleus Accumbens Shell and Amygdala Regulate the Encoding of Cocaine Extinction Learning.

Prior evidence indicates that the infralimbic cortex (IL) mediates the ongoing inhibition of cocaine seeking following self-administration and extinction training in rats, specifically through projections to the nucleus accumbens shell (NAshell). Our own data indicate that IL activity immediately following an unreinforced lever press is critical for encoding the extinction contingencies in such procedures. Whether extinction encoding requires activity in the IL exclusively or also activity in its outputs, such as those to the NAshell and amygdala, is unknown. To address this issue, we used a closed-loop optogenetic approach in female and male Sprague Dawley rats to silence IL-NAshell or IL-amygdala activity following an unreinforced lever press during extinction training. Optical illumination (20 s) was given either immediately after a lever press or following a 20 s delay. IL-NAshell inhibition immediately following an unreinforced lever press increased lever pressing during extinction training and impaired retention of extinction learning, as assessed during subsequent extinction sessions without optical inhibition. Likewise, IL-amygdala inhibition given in the same manner impaired extinction retention during sessions without inhibition. Control experiments indicate that critical encoding of extinction learning does not require activity in these pathways beyond the initial 20 s post-lever press period, as delayed IL-NAshell and IL-amygdala inhibition had no effect on extinction learning. These results suggest that a larger network extending from the IL to the NAshell and amygdala is involved in encoding extinction contingencies following cocaine self-administration.SIGNIFICANCE STATEMENT Infralimbic cortex (IL) activity following an unreinforced lever press during extinction learning encodes the extinction of cocaine-seeking behavior. However, the larger circuitry controlling such encoding has not been investigated. Using closed-loop optogenetic pathway targeting, we found that inhibition of IL projections to the nucleus accumbens shell and to the amygdala impaired the extinction of cocaine seeking. Importantly, these effects were only observed when activity was disrupted during the first 20 s post-lever press and not when given following a 20 s delay. These findings suggest that successful cocaine extinction encoding requires activity across a larger circuit beyond simply inputs to the IL.

Design and control analysis of a deployable clustered hyperbolic paraboloid cable net

This paper presents an analytical and experimental design and deployment control analysis of a hyperbolic paraboloid cable net based on clustering actuation strategies. First, the dynamics and statics for clustered tensegrity structures (CTS) are given. Then, we propose the topology design of the deployable hyperbolic paraboloid cable net. The deployability of the cable net is achieved by using clustered cables. It is shown that the clustered cables significantly reduce the number of actuators required for control. The deployment trajectory and actuation prestress in the cables are designed to ensure the tensions are feasible during the deployment process. Then, we compare the deployment analysis's open-loop and closed-loop control strategies. Finally, a lab-scale model is constructed to validate the actuation laws. We test the static performance and deployment process of the experimental model. Results show that the closed-loop control approach is more stable and smoother than the open-loop one in the deployment process. The approaches developed in this paper can also be used for various deployable tensegrity structures.

Verification and validation of automated driving systems utilizing probabilistic FMEA and simulation approaches

One of the main challenges for deploying highly automated driving is the need for a new strategy for verification and validation ensuring a safety and reliability in all traffic scenarios and conditions. In this context, the two projects of the German PEGASUS family VV Methoden and Set Level are focusing on this challenge addressing advanced methods for verification and validation as well as a holistic simulation environment for development and testing of automated driving functions. Within this paper, the overall concept of defining system and test requirements based on a probabilistic FMEA will be presented and discussed in view of applicability and limitations. The discussion will be complemented by outlining the closed-loop simulation approach and demonstration its potential by means of a first XiL test rig for a camera system applying a virtual traffic scenario.

IMPLEMENTATION OF CIRCULAR ECONOMY APPROACHES IN THE TERRITORIAL COMMUNITIES OF THE REGION

Purpose. This article aims to investigate the implementation of circular economy practices based on existing empirical studies. Methodology of research. General scientific and special methods were used in the research process, in particular: induction and deduction – at the stage of collecting, systematizing and processing the necessary information; analysis and comparison - in the process of identifying the reflection of waste management problems in the strategic documents of the selected communities and the characteristics of the implementation of the principles of the closed cycle economy; abstract and logical – to form theoretical conclusions. The choice of communities fell on the sampled sample because they represent city, village, and settlement species and evenly geographically located territory of the Mykolaiv region. Findings. It has been found that in the existing strategic documents of the analysed communities solving the problem of waste management has been identified among the priority tasks. None of the analysed strategies specifies successful practices that implement the principles of a closed cycle economy. Two city communities have legal landfills for the accumulation of solid waste but do not use technologies of sorting, processing, and energy production, instead, there is a threat of exhaustion of the repository resource. Four rural communities only declared the need to allocate a specially equipped waste landfill for each settlement and began educational work among the citizens regarding the sorting of garbage, the danger of its burning, and awareness of its value as a resource for reuse or processing. Among the particularly alarming conditions, the low level of activity of community residents in solving existing problems is found, so the involvement of citizens is to be increased first of all. Originality. The substantiation of the expediency of introducing closed-loop economy approaches for territorial communities has been further developed. Practical value. Proposals, which were formulated on the base on the results of the study, can be used by representatives of local governments to involve community residents in the implementation of closed-loop economy practices for the socio-economic development of communities. Key words: closed-loop economy, resources, waste, linear model of economy.