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Originated from the art of paper cutting and folding, kirigami and origami have shown promising applications in a broad range of scientific and engineering fields. Developments of kirigami-inspired inverse design methods that map target three-dimensional (3D) geometries into two-dimensional (2D) patterns of cuts and creases are desired to serve as guidelines for practical applications. In this paper, using programed kirigami tessellations, we propose two design methods to approximate the geometries of developable surfaces and nonzero Gauss curvature surfaces with rotational symmetry. In the first method, a periodic array of kirigami pattern with spatially varying geometric parameters is obtained, allowing formation of developable surfaces of desired curvature distribution and thickness, through controlled shrinkage and bending deformations. In the second method, another type of kirigami tessellations, in combination with Miura origami, is proposed to approximate nondevelopable surfaces with rotational symmetry. Both methods are validated by experiments of folding patterned thin copper films into desired 3D structures. The mechanical behaviors of the kirigami designs are investigated using analytical modeling and finite element simulations. The proposed methods extend the design space of mechanical metamaterials and are expected to be useful for kirigami-inspired applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel real-time kinematic simulation algorithm for planar N-bar linkage mechanisms, both single- and multi-degrees-of-freedom, comprising revolute and/or prismatic joints and actuators. A key feature of this algorithm is a reinterpretation technique that transforms prismatic elements into a combination of revolute joint and links. This gives rise to a unified system of geometric constraints and a general-purpose solver which adapts to the complexity of the mechanism. The solver requires only two types of methods--fast dyadic decomposition and relatively slower optimization-based--to simulate all types of planar mechanisms. From an implementation point of view, this algorithm simplifies programming without requiring handling of different types of mechanisms. This versatile algorithm can handle serial, parallel, and hybrid planar mechanisms with varying degrees-of-freedom and joint types. Additionally, this paper presents an estimation of simulation time and structural complexity, shedding light on computational demands. Demonstrative examples showcase the practicality of this method. [ABSTRACT FROM AUTHOR]

This paper presents a new method to efficiently update workpiece and determine cutter-workpiece engagement (CWE) in multi-axis milling simulation based on a uniform voxel modeling space. At each cutter location, a novel algorithm named direct voxel tracing is developed and used to generate a functional cutter surface voxel model to reliably establish the internal space of the milling cutter. The cutter internal space is represented by its voxel boundary with small memory usage. Through the Boolean subtraction between two successive voxel boundaries of the cutter internal space, a minimal voxel deactivation region is attained within which all active workpiece voxels are deactivated (removed) to update the workpiece model. To determine the associated CWE map, a 3D circle voxelization algorithm is employed. By slicing the cutter surface by a sequence of planes perpendicular to and along the cutter axis, CWE can be determined as the sliced 3D circles are voxelized. Quantitative comparisons of the proposed method against existing voxel modeling and vector modeling-based methods have been made. The results have demonstrated much improved computational efficiency of the proposed method in simulating the complex multi-axis milling operations. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical formulation and implicit numerical algorithm for solving the integral of a three-dimensional momentum balance based on the inelastic evolution of microstructural vectors for thin plates in Eulerian formulation. A recent theoretical discussion (Lee and Rubin, 2020, "Modeling Anisotropic Inelastic Effects in Sheet Metal Forming Using Microstructural Vectors--Part I: Theory," Int. J. Plast., 134, p. 102783. 10.1016/j.ijplas.2020.102783) showed that Eulerian constitutive equation based on microstructural vectors for thin plates has the advantage of capturing the anisotropic behavior of the material axis with insensitivity to the randomness of the reference configuration. However, all the discussions were theoretically conducted only at a local material point in homogeneous deformation conditions, which do not require consideration of the momentum balance with flexible velocity gradients in a three-dimensional volume. For usability, numerical algorithms are needed to solve evolution of the microstructural vectors in the three-dimensional space. This paper presents the first numerical algorithm to solve the inelastic evolution of microstructural vectors in the Eulerian formulation. A generalized material coordinated system is matched to the microstructural vectors in a three-dimensional space by considering the Eulerian constitutive equations insensitive to the superposed rigid body motions (SRBM). Numerical algorithms were then introduced to implicitly solve the nonlinear momentum balance, evolution of the microstructural vectors, and tangent modulus. The formula and numerical algorithms were validated by predicting the tension tests when the principal loading angle varied from the reference axis. The results show that the proposed numerical algorithm can describe the evolution of the microstructure based on the Eulerian formulation. [ABSTRACT FROM AUTHOR]

In the field of gas turbine (GT) monitoring and diagnostics, GT trip is of great concern for manufactures and users. In fact, due to the number of issues that may cause a trip, its occurrence is not infrequent, and its prediction is a quite unexplored field of research. This is demonstrated by the fact that, despite its relevance, a comprehensive study on the reliability of predicting GT trip has not been proposed yet. To fill this gap, this paper investigates the fusion of five data-driven base models by means of voting and stacking, in order to improve prediction accuracy and robustness. The five benchmark supervised machine learning and deep learning classifiers are k-nearest neighbors, support vector machine (SVM), Naïve Bayes (NB), decision trees (DTs), and long short-term memory (LSTM) neural networks. While voting just averages the predictions of base models, without providing additional pieces of information, stacking is a technique used to aggregate heterogeneous models by training an additional machine learning model (namely, stacked ensemble model) on the predictions of the base models. The analyses carried out in this paper employ filed observations of both safe operation and trip events, derived from a large fleet of industrial Siemens GTs in operation. The results demonstrate that the stacked model provides higher accuracy than base models and also outperforms voting by proving more effective, especially when the reliability of the prediction of base models is poor. [ABSTRACT FROM AUTHOR]

For discrete time nonlinear networked control systems, a novel self-triggered adaptive model predictive control (MPC) strategy is developed. Different from the existing self-triggered MPC methods that determine the triggering instants based on the difference between the optimal and real states at one single instant, the proposed approach updates the MPC system according to the differential form of the state error of two consecutive sampling moments to effectively reduce the computation and communication burden while maintaining the ideal control performance. In addition, this paper introduces a new adaptive prediction horizon mechanism to the self-triggered MPC, so that the amplitude of prediction horizon contraction is sufficiently large to further reduce the computational burden of the MPC method. Finally, the recursive feasibility and robust stability of this proposed strategy are proved strictly by theoretical analysis, and the simulation comparison results are shown to verify the proposed framework. [ABSTRACT FROM AUTHOR]

Can we provide evidence-based guidance to instructors to improve the delivery of the course based on students' reflection on doing? Over three years at the University of Oklahoma, Norman, USA, we have collected about 18,000 Take-aways from almost 400 students who participated in an undergraduate design, build, and test course. In this paper, we illustrate the efficacy of using the Latent Dirichlet Algorithm to respond to the question posed above. We describe a method to analyze the Take-aways using a Latent Dirichlet Allocation (LDA) algorithm to extract topics from the Take-away data and then relate the extracted topics to instructors' expectations using text similarity. The advantage of the LDA algorithm is anchored in that it provides a means for summarizing large amount of take-away data into several key topics so that instructors can eliminate the labor-intensive evaluation of it. By connecting and comparing what students learned (embodied in Take-aways) and what instructors expected the students to learn (embodied in stated Principles of Engineering Design), we provide evidence-based guidance to instructors on how to improve the delivery of AME4163: Principles of Engineering Design. Our objective in this paper is to introduce a method for quantifying text data to facilitate an instructor to modify the content and delivery of the next version of the course. The proposed method can be extended to other courses patterned after AME4163 to generate similar data sets covering student learning and instructor expectations, and the LDA algorithm can be used for dealing with the large amount of textual data embodied in students' Take-aways. [ABSTRACT FROM AUTHOR]

The fixed-point stopping of high-speed trains in stations is generally accomplished through manual operation in China. This situation often leads to a failure to stop at fixed-point signs and causes a fluctuation in the longitudinal acceleration due to the lack of experience of the drivers. To achieve precise, stable, and automatic stopping, a distributed multiparticle precise stopping control model based on the distributed model predictive control (MPC) algorithm is developed in this paper. A two-level hierarchical control structure for the subcontroller of each vehicle is adopted to bring itself to a controlled stop. In the upper control of subcontroller, the MPC algorithm is designed in turn based on the multiparticle mechanism model of the train. In the lower control of subcontroller, the target input from the upper control is converted and distributed. The controlled object, a comprehensive numerical computing model including the spatial dynamic model of the train and its electropneumatic blending braking model, is established and controlled by the corresponding subcontroller and employed to verify the performance of the controller. The influence of the model control parameters on the stopping performance is discussed, and the optimal combination of control parameters is selected. The proposed control model using the optimization parameters is tested and verified through the comprehensive numerical computing model. The results indicate that the stopping error is 0.0075 m, which is much less than the accuracy requirements for fixed-point stopping. The computing time of each subcontroller in real-time is stable at 0.09 s. The coupler impact force between two adjacent vehicles can also be effectively inhibited and eliminated. Its control performance outperforms a proportional-integral-derivative (PID) algorithm. The proposed precise stopping model and comprehensive numerical computing model provide references for the application and algorithm optimization of automatic train operation technology in high-speed trains. [ABSTRACT FROM AUTHOR]

This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies. [ABSTRACT FROM AUTHOR]

Thermoacoustic instabilities have plagued the operation of gas turbine engines for years and significant research is being conducted in detecting and understanding them. In this paper, an output only identification technique is employed for a noise induced dynamical system representing combustion instability behavior. This approach is called the output only observer Kalman filter identification (O³KID) and its first step solves for least squares from a set of algebraic equations constructed from just the measured output. The least squares solution gives the Markov parameters (impulse response) and the output residuals. The subsequent step takes the Markov parameters or the residuals to solve for the system matrices using any deterministic subspace identification method. In using this direct noniterative two-step algorithm, it is possible to estimate the eigenmodes and damping coefficients from output measured data. To validate the algorithm, a system of independent harmonic oscillators, excited by random noise is used to generate surrogate data representing pressure oscillations in a combustor prior to an instability. The error in estimating the eigen frequencies and damping are <1%. This fast direct approach could be used to provide an early warning indicator in industrial gas turbines by tracking the rate of damping of dominant eigenmodes. Additionally, saving the state space parameters periodically can serve as a data-lean option to track changes of the dynamics and across a gas turbine fleet. [ABSTRACT FROM AUTHOR]

Electric vehicle production is subjected to high manufacturing cost and environmental impact. Disassembling and remanufacturing the lithium-ion power packs can highly promote electric vehicle market penetration by procuring and regrouping reusable modules as stationary energy storage devices and cut life-cycle cost and environmental impact. Disassembly efficiency is crucial for battery remanufacturing companies in reverse supply chains. However, disassembly planning suffers from high computational complexity and inferior solutions. This paper developed a multi-objective mathematical model and presented a novel hybrid genetic-firework algorithm based on the precedence graph for obtaining solutions to disassemble the electric vehicle power pack into module levels in an efficient manner. The objectives for the model include not only smoothness of working stations, cycle time, and economic returns, but also consider operation safety and energy consumption. The proposed hybrid algorithm explored the performance of the novel solution searching mechanism of combining the firework and genetic algorithms. The proposed approach is compared with the commonly used multi-objective evolutionary algorithms in the literature, showing its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

The standard techniques used to detect misalignment in rotor systems are loopy orbits, multiple harmonics with predominant 2X component, and high axial vibration. This paper develops a new approach for the identification of misalignment in coupled rotor systems modeled using two-node Timoshenko beam finite elements. The coupling connecting the turbine and generator rotor systems is modeled by a stiffness matrix, which has both static and additive components. While the magnitude of static stiffness component is fixed during operation, the time-varying additive stiffness component displays a multiharmonic behavior and exists only in the presence of misalignment. To numerically simulate the multiharmonic nature coupling force/moment as observed in experiments, a pulse wave is used as the steering function in the mathematical model of the additive coupling stiffness (ACS). The representative turbogenerator (TG) system has two rotor systems, each having two disks and supported on two flexible bearings--connected by coupling. An active magnetic bearing (AMB) is used as an auxiliary bearing on each rotor for the purposes of vibration suppression and fault identification. The formulation of mathematical model is followed by the development of an identification algorithm based on the model developed, which is an inverse problem. Least-squares linear regression technique is used to identify the unbalances, bearing dynamic parameters, AMB constants, and importantly the coupling static and additive stiffness coefficients. The sensitivity of the identification algorithm to signal noise and bias errors in modeling parameters has been tested. The novelty of paper is the representation and identification of misalignment using the ACS matrix coefficients, which are direct indicators of both type and severity of the misalignment. [ABSTRACT FROM AUTHOR]

This paper presents a multilevel model-based hierarchical estimation framework for complex thermal management systems of electrified vehicles. System dynamics are represented by physics-based lumped parameter models derived from a graph-based modeling approach. The complexity of the hierarchical models is reduced by applying an aggregation-based model-order reduction technique that preserves the physical correspondence between a reduced-order model and the physical system. This paper also presents a case study in which a hierarchical observer is designed to estimate the dynamics of a candidate system. The hierarchical observer is connected to a previously developed hierarchical controller for closed-loop control, and the closed-loop performance is demonstrated through simulation and real-time experimental results. A comparison between the proposed hierarchical observer and a centralized observer shows the tradeoff between the estimation accuracy and the computational complexity of the two approaches. [ABSTRACT FROM AUTHOR]

In this paper, we report on our investigation of human-AI collaboration for mind-mapping. We specifically focus on problem exploration in pre-conceptualization stages of early design. Our approach leverages the notion of query expansion--the process of refining a given search query for improving information retrieval. Assuming a mind-map as a network of nodes, we reformulate its construction process as a sequential interaction workflow wherein a human user and an intelligent agent take turns to add one node to the network at a time. Our contribution is the design, implementation, and evaluation of algorithm that powers the intelligent agent (IA). This paper is an extension of our prior work (Chen et al., 2019, "Mini-Map: Mixed-Initiative Mind-Mapping Via Contextual Query Expansion," AIAA Scitech 2020 Forum, p. 2347) wherein we developed this algorithm, dubbed Mini-Map, and implemented a web-based workflow enabled by ConceptNet (a large graph-based representation of "commonsense" knowledge). In this paper, we extend our prior work through a comprehensive comparison between human-AI collaboration and human-human collaboration for mind-mapping. We specifically extend our prior work by: (a) expanding on our previous quantitative analysis using established metrics and semantic studies, (b) presenting a new detailed video protocol analysis of the mind-mapping process, and (c) providing design implications for digital mind-mapping tools. [ABSTRACT FROM AUTHOR]

Energy consumption in commercial buildings is significantly affected by the performance of heating, ventilation, and air-conditioning (HVAC) systems, which are traditionally operated using centralized controllers. HVAC control requires adjusting multiple setpoints such as chilled water temperatures and supply air temperature (SAT). Supervisory control framework in a distributed setting enables optimal HVAC operation and provides scalable solutions for optimizing energy across several scales from homes to regional areas. This paper proposes a distributed optimization framework for achieving energy efficiency in large-scale building energy systems. It is highly desirable to have building management systems that are scalable, robust, flexible, and are low cost. For addressing the scalability and flexibility, a modular problem formulation is established that decouples the distributed optimization level from local thermal zone modeling level. We leverage a recently developed generalized gossip algorithm for robust distributed optimization. The supervisory controller aims at minimizing the energy input considering occupant comfort. For validating the proposed scheme, a numerical case study based on a physical testbed in the Iowa Energy Center is presented. We show that the distributed optimization methodology outperforms the typical baseline strategy, which is a rule-based controller to set a constant supply air temperature. This paper also incorporates a software architecture based on the VOLTTRON platform, developed by the Pacific Northwest National Laboratory (PNNL), for practical implementation of the proposed framework via the BACnet system. The experimental results show that the supervisory control framework proposed in this paper can save energy by approximately 11%. [ABSTRACT FROM AUTHOR]

Estimating central aortic blood pressure (BP) is important for cardiovascular (CV) health and risk prediction purposes. CV system is a multichannel dynamical system that yields multiple BPs at various body sites in response to central aortic BP. This paper concerns the development and analysis of an observer-based approach to deconvolution of unknown input in a class of coprime multichannel systems applicable to noninvasive estimation of central aortic BP. A multichannel system yields multiple outputs in response to a common input. Hence, the relationship between any pair of two outputs constitutes a hypothetical input-output system with unknown input embedded as a state. The central idea underlying our approach is to derive the unknown input by designing an observer for the hypothetical input-output system. In this paper, we developed an unknown input observer (UIO) for input deconvolution in coprime multichannel systems. We provided a universal design algorithm as well as meaningful physical insights and inherent performance limitations associated with the algorithm. The validity and potential of our approach were illustrated using a case study of estimating central aortic BP waveform from two noninvasively acquired peripheral arterial pulse waveforms. The UIO could reduce the root-mean-squared error (RMSE) associated with the central aortic BP by up to 27.5% and 28.8% against conventional inverse filtering (IF) and peripheral arterial pulse scaling techniques. [ABSTRACT FROM AUTHOR]

This paper proposes an automatic time-energy efficient robust control (ATERC) deployment approach for selecting either a near-optimal closed-loop control law or a robust control law based on the requirement of the system. The near-optimal closed-loop control law is designed by applying the population-based sine-cosine algorithm (SCA) to the considered interceptor problem. While the robust control law is formulated by using an artificial time delayed control (TDC) approach. In presence of external disturbances, the ATERC methodology deploys the TDC-based robust guidance law to the interceptor, while in the absence of such uncertainties the SCA-based near-optimal guidance law is applied in order to improve the time-energy minimization. This guidance approach also incorporates input saturation which expands its applicability. Using Lyapunov stability analysis, this work establishes an uniformly ultimately bounded (UUB) stability for the discussed system on application of the proposed control approach. Extensive simulation studies involving nonmaneuvering targets and targets performing bank-to-bank maneuver, affirms the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Riccati equation-based control approaches such as linear-quadratic regulator (LQR) and time-varying LQR (TVLQR) are among the most common methods for stabilizing linear and nonlinear systems, especially in the context of optimal control. However, model inaccuracies may degrade the performance of closed-loop systems under such controllers. To mitigate this issue, this paper extends and encompasses Riccati-equation based controllers through the development of a robust stabilizing control methodology for uncertain nonlinear systems with modeling errors. We begin by linearizing the nonlinear system around a nominal trajectory to obtain a time-varying linear system with uncertainty in the system matrix. We propose a modified version of the continuous differential Riccati equation (MCDRE), whose solution is updated based upon the estimates of model uncertainty. An optimal least squares (OLS) algorithm is presented to identify this uncertainty and inform the MCDRE to update the control gains. The unification of MCDRE and OLS yields a robust time-varying Riccati-based (RTVR) controller that stabilizes uncertain nonlinear systems without the knowledge of the structure of the system's uncertainty a priori. The convergence of the system states is formally proven using a Lyapunov argument. Simulations and comparisons to the baseline backward-in-time Riccati-based controller on two real-world examples verify the benefits of our proposed control method. [ABSTRACT FROM AUTHOR]

Vertical jump height is widely used for assessing motor development, functional ability, and motor capacity. Traditional methods for estimating vertical jump height rely on force plates or optical marker-based motion capture systems limiting assessment to people with access to specialized laboratories. Current wearable designs need to be attached to the skin or strapped to an appendage which can potentially be uncomfortable and inconvenient to use. This paper presents a novel algorithm for estimating vertical jump height based on foot-worn inertial sensors. Twenty healthy subjects performed countermovement jumping trials and maximum jump height was determined via inertial sensors located above the toe and under the heel and was compared with the gold standard maximum jump height estimation via optical marker-based motion capture. Average vertical jump height estimation errors from inertial sensing at the toe and heel were -2.2±2.1 cm and -0.4±3.8 cm, respectively. Vertical jump height estimation with the presented algorithm via inertial sensing showed excellent reliability at the toe (ICC(2,1)=0.98) and heel (ICC(2,1)=0.97). There was no significant bias in the inertial sensing at the toe, but proportional bias (b=1.22) and fixed bias (a=-10.23cm) were detected in inertial sensing at the heel. These results indicate that the presented algorithm could be applied to foot-worn inertial sensors to estimate maximum jump height enabling assessment outside of traditional laboratory settings, and to avoid bias errors, the toe may be a more suitable location for inertial sensor placement than the heel.

In this paper, a multibody model was developed in the framework of biotribology of lower limb artificial joints. The presented algorithm performs the inverse dynamics of musculoskeletal systems with the aim to achieve a tool for the calculation of the joint reaction forces. The revolute joint, the cam joint, the spherical joint and the free joint were considered in the analyzed lower limb system by introducing a novel analytical formulation of the rheonomic constraint equations based on the quaternions theory. Within the kinematical analysis, the curved muscle paths were modeled by simulating their geodesic wrapping over bony surfaces while the muscle actuations were formulated through the Hill muscle model. The developed theoretical model was developed in matlab environment allowing to follow the classical musculoskeletal analysis pipeline: kinematical analysis, inverse dynamics, and static optimization, applied to the lower limb during the gait kinematics. The validation of the results was obtained by comparing the calculated hip joint reactions with the ones obtained in vivo by Bergmann and calculated by Opensim software, showing a satisfactory agreement. The proposed model and algorithm represent a fully open and controllable synovial joint tribological configuration generator tool, useful to be coupled with numerical lubrication/contact models in the framework of the in silico artificial joints tribological optimization. [ABSTRACT FROM AUTHOR]

In this paper, we propose a method based on a two-stage algorithm to simultaneously identify the coefficients and fractional differentiation orders of fractional order systems (FOSs) with commensurate order. The proposed method adopts the fractional integral operational matrix of block pulse functions (BPFs) to convert the FOS to a linear parameter regression equation. Then, a two-stage algorithm is developed to identify the coefficients and orders. First, with the orders fixed, the coefficients are identified using the instrumental variable-based recursive least square algorithm. Then, with the identified coefficients fixed, the orders are estimated using the Gauss-Newton iterative algorithm. The above process iterates until the stop criterion is met. Two identification examples are given to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In structural health monitoring (SHM) applications carried out by mobile robots, the precise locating of the SHM robot is essential for accurate detection and quantification of defects. The traditional dead reckoning (DR) approach can only provide local position in the horizon, which is not enough for SHM applications in three dimensions in large buildings. In this paper, a new robot positioning algorithm for active building structural defect detection and localization is proposed. The two-stage robot positioning scheme is designed through the self-misalignment calibration and the positioning during SHM task stages, fusing the absolute and relative measurements. In order to overcome the drawback of the DR algorithm, in the full analysis of existing localization mode that can be applied to mobile robots, this paper adopted the inertial navigation system (INS) approach to measure the absolute motion information of a moving robot. On this basis, through the transformation between the absolute positioning coordinates and the local positioning coordinates of buildings, the mobile robot's optimal trajectory on building surface was designed for self-calibration of coordinate misalignments. The proposed method could effectively achieve the robot local positioning in building coordinate frame by fusing the external relative assistant measurements with absolute measurement. By using the designed strategies, the coordinate misalignment can also be self-calibrated effectively, improving local positioning accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a method for real-time identification of sensor statistics especially aimed for low-cost automotive-grade sensors. Based on recent developments in adaptive particle filtering (PF) and under the assumption of Gaussian distributed noise, our method identifies the slowly time-varying sensor offsets and variances jointly with the vehicle state, and it extends to banked roads. While the method is primarily focused on learning the noise characteristics of the sensors, it also produces an estimate of the vehicle state. This can then be used in driver-assistance systems, either as a direct input to the control system or indirectly to aid other sensor-fusion methods. The paper contains verification against several simulation and experimental data sets. The results indicate that our method is capable of bias-free estimation of both the bias and the variance of each sensor, that the estimation results are consistent over different data sets, and that the computational load is feasible for implementation on computationally limited embedded hardware typical of automotive applications. [ABSTRACT FROM AUTHOR]

In this paper, an output feedback sliding mode position controller/estimator scheme is proposed to control an single input single output (SISO) system subject to bounded nonlinearities and parametric uncertainties. Various works have been published addressing the theoretical effectiveness of the third-order sliding mode control (3-SMC) in terms of chattering alleviation and controller robustness. However, the application of 3-SMC with a feedback estimator to a flight actuators has not been treated explicitly. This is due to the fact that the accurate full state estimation is required since SMCs performance can be severely degraded by measurement or estimation noise. Aerodynamic control surface actuators in air vehicles mostly employ linear position controllers to achieve guidance and stability. The main focus of the paper is to experimentally demonstrate the stability and positioning performance of a third-order SMC applied to a class of system with high relative degree and bounded parametric uncertainties. The performance of the closed-loop system is also compared with a lower level SMC and classical controller to show the effectiveness of the algorithm. Realization of the proposed algorithm from an application perspective is the main target of this paper and it demonstrates that a shorter settling time and higher control action attenuation can be achieved with the proposed strategy. [ABSTRACT FROM AUTHOR]

High-definition metrology (HDM) has gained significant attention for surface quality inspection since it can reveal spatial surface variations in detail. Due to its cost and durability, such HDM measurements are occasionally implemented. The limitation creates a new research opportunity to improve surface variation characterization by fusing the insights gained from limited HDM data with widely available low-resolution surface data during quality inspections. A useful insight from state-of-the-art research using HDM is the revealed relationship and positive correlation between surface height and certain measurable covariates, such as material removal rate (MRR). Such a relationship was assumed spatially constant and integrated with surface measurements to improve surface quality modeling. However, this method encounters challenges when the covariates have nonstationary relationships with the surface height over different surface areas, i.e., the covariate-surface height relationship is spatially varying. Additionally, the nonstationary relationship can only be captured by HDM, adding to the challenge of surface modeling when most training data are measured at low resolution. This paper proposes a transfer learning (TL) framework to deal with these challenges by which the common information from a spatial model of an HDM-measured surface is transferred to a new surface where only low-resolution data are available. Under this framework, the paper develops and compares three surface models to characterize the nonstationary relationship including two varying coefficient-based spatial models and an inference rule-based spatial model. Real-world case studies were conducted to demonstrate the proposed methods for improving surface modeling. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of "virtual sensors" capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations. [ABSTRACT FROM AUTHOR]

The paper initially reports about the properties of an expression of dual generalized inverse matrix currently available in the literature. It is demonstrated that such a matrix does not fulfill all the Penrose conditions. Hence, novel and computationally efficient algorithms/formulas for the computation of the Moore-Penrose dual generalized inverse (MPDGI) are herein proposed. The paper also contains a new algorithm for the singular value decomposition (SVD) of a dual matrix. The availability of these formulas allows the simultaneous solution of overdetermined systems of dual linear equations without requiring the traditional separation in primal and dual parts. This should prove useful for the solution of many kinematic problems. The algorithms/formulas herein deduced have been also tested on the kinematic synthesis of the constant transmission ratio RCCC spatial linkage. [ABSTRACT FROM AUTHOR]

The concept of hidden genes was recently introduced in genetic algorithms (GAs) to handle systems architecture optimization problems, where the number of design variables is variable. Selecting the hidden genes in a chromosome determines the architecture of the solution. This paper presents two categories of mechanisms for selecting (assigning) the hidden genes in the chromosomes of GAs. These mechanisms dictate how the chromosome evolves in the presence of hidden genes. In the proposed mechanisms, a tag is assigned for each gene; this tag determines whether the gene is hidden or not. In the first category of mechanisms, the tags evolve using stochastic operations. Eight different variations in this category are proposed and compared through numerical testing. The second category introduces logical operations for tags evolution. Both categories are tested on the problem of interplanetary trajectory optimization for a space mission to Jupiter, as well as on mathematical optimization problems. Several numerical experiments were designed and conducted to optimize the selection of the hidden genes algorithm parameters. The numerical results presented in this paper demonstrate that the proposed concept of tags and the assignment mechanisms enable the hidden genes genetic algorithms (HGGA) to find better solutions. [ABSTRACT FROM AUTHOR]

In this paper, a new approach in estimating the lateral tire forces and hitch-forces of a vehicle-trailer system is introduced. It is shown that the proposed hitch-force estimation is independent of trailer mass and geometry, by utilizing the vehicle velocity, acceleration, torque engine, wheel's speed, and steering angle measurements. The designed lateral tire forces and hitch-force estimations' algorithm can be used for any ball type trailer without any priori information on the trailer parameters. A vehicle-trailer dynamic model is proposed to design an observer for the estimation of the hitch-forces and lateral tire forces. Simulations' studies in carsim along with experimental tests are used to validate the presented method. The results confirm the accuracy of the developed observer, and the experimental tests' results show that there is a good agreement between the estimated and actual lateral tire forces as well as the hitch-forces. [ABSTRACT FROM AUTHOR]

Biofidelity response corridors developed from post-mortem human subjects are commonly used in the design and validation of anthropomorphic test devices and computational human body models (HBMs). Typically, corridors are derived from a diverse pool of biomechanical data and later normalized to a target body habitus. The objective of this study was to use morphed computational HBMs to compare the ability of various scaling techniques to scale response data from a reference to a target anthropometry. HBMs are ideally suited for this type of study since they uphold the assumptions of equal density and modulus that are implicit in scaling method development. In total, six scaling procedures were evaluated, four from the literature (equal-stress equal-velocity, ESEV, and three variations of impulse momentum) and two which are introduced in the paper (ESEV using a ratio of effective masses, ESEV-EffMass, and a kinetic energy approach). In total, 24 simulations were performed, representing both pendulum and full body impacts for three representative HBMs. These simulations were quantitatively compared using the International Organization for Standardization (ISO) ISO-TS18571 standard. Based on these results, ESEV-EffMass achieved the highest overall similarity score (indicating that it is most proficient at scaling a reference response to a target). Additionally, ESEV was found to perform poorly for two degree-of-freedom (DOF) systems. However, the results also indicated that no single technique was clearly the most appropriate for all scenarios.

In this paper, online policy iteration reinforcement learning (RL) algorithm is proposed for motion control of four wheeled omni-directional robots. The algorithm solves the linear quadratic tracking (LQT) problem in an online manner using real-time measurement data of the robot. This property enables the tracking controller to compensate the alterations of dynamics of the robot's model and environment. The online policy iteration based tracking method is employed as low level controller. On the other side, a proportional derivative (PD) scheme is performed as supervisory planning system (high level controller). In this study, the followed paths of online and offline policy iteration algorithms are compared in a rectangular trajectory in the presence of slippage drawback and motor heat. Simulation and implementation results of the methods demonstrate the effectiveness of the online algorithm compared to offline one in reducing the command trajectory tracking error and robot's path deviations. Besides, the proposed online controller shows a considerable ability in learning appropriate control policy on different types of surfaces. The novelty of this paper is proposition of a simple-structure learning based adaptive optimal scheme that tracks the desired path, optimizes the energy consumption, and solves the uncertainty problem in omni-directional wheeled robots. [ABSTRACT FROM AUTHOR]

In many system-engineering problems, such as surveillance, environmental monitoring, and cooperative task performance, it is critical to allocate limited resources within a restricted area optimally. Static coverage problem (SCP) is an important class of the resource allocation problem. SCP focuses on covering an area of interest so that the activities in that area can be detected with high probabilities. In many practical settings, primarily due to financial constraints, a system designer has to allocate resources in multiple stages. In each stage, the system designer can assign a fixed number of resources, i.e., agents. In the multistage formulation, agent locations for the next stage are dependent on previous-stage agent locations. Such multistage static coverage problems are nontrivial to solve. In this paper, we propose an efficient sequential sampling algorithm to solve the multistage static coverage problem (MSCP) in the presence of resource intensity allocation maps (RIAMs) distribution functions that abstract the event that we want to detect/monitor in a given area. The agent's location in the successive stage is determined by formulating it as an optimization problem. Three different objective functions have been developed and proposed in this paper: (1) L2 difference, (2) sequential minimum energy design (SMED), and (3) the weighted L2 and SMED. Pattern search (PS), an efficient heuristic algorithm has been used as optimization algorithm to arrive at the solutions for the formulated optimization problems. The developed approach has been tested on two- and higher dimensional functions. The results analyzing real-life applications of windmill placement inside a wind farm in multiple stages are also presented. [ABSTRACT FROM AUTHOR]

The paper discusses thin part inspection using three-dimensional (3D) non rigid registration. The main objective is to match measurement point data to its nominal representation, so as to identify form defects. Since form defects have the same size order as the thickness of the part, establishing such matching is a challenging task. The originality of the method developed in this paper is using a deformable iterative closet point algorithm (ICP), and integrating modal approach to express form defects. The method described improves the matching through iteration of the ICP and establishes a definition of the error. The results of the application show that the present method is efficient. [ABSTRACT FROM AUTHOR]

This paper presents two bio-inspired algorithms for coalition formation of multiple modular robot systems. An effective and efficient coalition formation system can help modular robot system take full advantage of reconfigurability of modular robots. In this paper, the multirobot coalition formation problem is illustrated and a mathematical model for the problem is described. Two bio-inspired algorithms, ant-colony algorithm (ACA) and genetic algorithm (GA), are introduced for solving the mathematical model. With the two algorithms, it is able to form a large number of robots into many different groups for a variety of applications, such as parallel performance of multiple tasks by multiple teams of robots. The paper compares the efficiency and effectiveness of two algorithms for solving the presented problem with case study. The results for the comparison study are analyzed and discussed. Also, the implementation details of the simulation and experiment using ACA are presented in the paper. [ABSTRACT FROM AUTHOR]

The application of explicit dynamics to simulate quasi-static events often becomes impractical in terms of computational cost. Different solutions have been investigated in the literature to decrease the simulation time and a family of interesting, increasingly adopted approaches are the ones based on the proper orthogonal decomposition (POD) as a model reduction technique. In this study, the algorithmic framework for the integration of the equation of motions through POD is proposed for discrete linear and nonlinear systems: a low dimensional approximation of the full order system is generated by the so-called proper orthogonal modes (POMs), computed with snapshots from the full order simulation. Aiming to a predictive tool, the POMs are updated in itinere alternating the integration in the complete system, for the snapshots collection, with the integration in the reduced system. The paper discusses details of the transition between the two systems and issues related to the application of essential and natural boundary conditions (BCs). Results show that, for one-dimensional (1D) cases, just few modes are capable of excellent approximation of the solution, even in the case of stress-strain softening behavior, allowing to conveniently increase the critical time-step of the simulation without significant loss in accuracy. For more general three-dimensional (3D) situations, the paper discusses the application of the developed algorithm to a discrete model called lattice discrete particle model (LDPM) formulated to simulate quasi-brittle materials characterized by a softening response. Efficiency and accuracy of the reduced order LDPM response are discussed with reference to both tensile and compressive loading conditions. [ABSTRACT FROM AUTHOR]

Image feature-based localization and mapping applications useful in field robotics are considered in this paper. Exploiting the continuity of image features and building upon the tracking algorithms that use point correspondences to provide an instantaneous localization solution, an extended Kalman filtering (EKF) approach is formulated for estimation of the rigid body motion of the camera coordinates with respect to the world coordinate system. Recent results by the authors in quantifying uncertainties associated with the feature tracking methods form the basis for deriving scene-dependent measurement error statistics that drive the optimal estimation approach. It is shown that the use of certain relative motion models between a static scene and the moving target can be recast as a recursive least squares problem and admits an efficient solution to the relative motion estimation problem that is amenable to real-time implementations on board mobile computing platforms with computational constraints. The utility of the estimation approaches developed in the paper is demonstrated using stereoscopic terrain mapping experiments carried out using mobile robots. The map uncertainties estimated by the filter are utilized to establish the registration of the local maps into the global coordinate system. [ABSTRACT FROM AUTHOR]

This paper is concerned with the filtering problem in continuous time. Three algorithmic solution approaches for this problem are reviewed: (i) the classical Kalman-Bucy filter, which provides an exact solution for the linear Gaussian problem; (ii) the ensemble Kalman-Bucy filter (EnKBF), which is an approximate filter and represents an extension of the Kalman-Bucy filter to nonlinear problems; and (iii) the feedback particle filter (FPF), which represents an extension of the EnKBF and furthermore provides for a consistent solution in the general nonlinear, non-Gaussian case. The common feature of the three algorithms is the gain times error formula to implement the update step (to account for conditioning due to the observations) in the filter. In contrast to the commonly used sequential Monte Carlo methods, the EnKBF and FPF avoid the resampling of the particles in the importance sampling update step. Moreover, the feedback control structure provides for error correction potentially leading to smaller simulation variance and improved stability properties. The paper also discusses the issue of nonuniqueness of the filter update formula and formulates a novel approximation algorithm based on ideas from optimal transport and coupling of measures. Performance of this and other algorithms is illustrated for a numerical example. [ABSTRACT FROM AUTHOR]

In this paper, we present multiple methods to detect fasteners (bolts, screws and nuts) from tessellated mechanical assembly models. There is a need to detect these geometries in tessellated formats because of features that are lost during the conversions from other geometry representations to tessellation. Two geometry-based algorithms, projected thread detector (PTD) and helix detector (HD) and four machine learning classifiers, voted perceptron (VP), Naïve Bayes (NB), linear discriminant analysis and Gaussian process (GP), are implemented to detect fasteners. These six methods are compared and contrasted to arrive at an understanding of how to best perform this detection in practice on large assemblies. Furthermore, the degree of certainty of the automatic detection is also developed and examined so that a user may be queried when the automatic detection leads to a low certainty in the classification. This certainty measure is developed with three probabilistic classifier approaches and one fuzzy logic-based method. Finally, once the fasteners are detected, the authors show how the thread angle, the number of threads, the length and major and root diameters can be determined. All of the mentioned methods are implemented and compared in this paper. A proposed combination of methods leads to an accurate and robust approach of performing fastener detection. [ABSTRACT FROM AUTHOR]

In recent years, the usage of additive manufacturing (AM) provides new capabilities for component repair, which includes low heat input, small heat-affected zone, and freeform near-net-shape fabrication. Because the geometry of each worn component is unique, the automated repair process is a challenging and important task. The focus of this paper is to investigate and develop a general best-fit and shape-adaption algorithm for automating alignment and defect reconstruction for component repair. The basic principle of using features for rigid-body best-fitting is analyzed and a multifeature-fitting method is proposed to best fit the 3D mesh model of a worn component and its nominal component. The multifeature-fitting algorithm in this paper couples the least-squares method and a density-based outlier detection method. These two methods run alternately to approach the best-fit result gradually and eliminate the disturbance caused from the defect geometry. The shape-adaption algorithm is used to do cross section comparison and defect reconstruction based on the best-fitted 3D model. A "point-line-surface" fracture surface detection method is proposed to construct fracture surface and the fracture surface boundary is dilated to trim the nominal 3D model to obtain defect geometry. Illustrative examples with typical components and different kinds of defects are used to demonstrate the flexibility and capability of using multifeature-fitting and shape-adaption algorithm developed in this paper. [ABSTRACT FROM AUTHOR]

When selecting ideas or trying to find inspiration, designers often must sift through hundreds or thousands of ideas. This paper provides an algorithm to rank design ideas such that the ranked list simultaneously maximizes the quality and diversity of recommended designs. To do so, we first define and compare two diversity measures using determinantal point processes (DPP) and additive submodular functions. We show that DPPs are more suitable for items expressed as text and that a greedy algorithm diversifies rankings with both theoretical guarantees and empirical performance on what is otherwise an NP-Hard problem. To produce such rankings, this paper contributes a novel way to extend quality and diversity metrics from sets to permutations of ranked lists. These rank metrics open up the use of multi-objective optimization to describe trade-offs between diversity and quality in ranked lists. We use such trade-off fronts to help designers select rankings using indifference curves. However, we also show that rankings on trade-off front share a number of top-ranked items; this means reviewing items (for a given depth like the top ten) from across the entire diversity-to-quality front incurs only a marginal increase in the number of designs considered. While the proposed techniques are general purpose enough to be used across domains, we demonstrate concrete performance on selecting items in an online design community (OpenIDEO), where our approach reduces the time required to review diverse, high-quality ideas from around 25 h to 90 min. This makes evaluation of crowd-generated ideas tractable for a single designer. Our code is publicly accessible for further research. [ABSTRACT FROM AUTHOR]

Fixture layout can affect deformation and dimensional variation of sheet metal assemblies. Conventionally, the assembly dimensions are simulated with a quantity of finite element (FE) analyses, and fixture layout optimization needs significant user intervention and unaffordable iterations of finite element analyses. This paper therefore proposes a fully automated and efficient method of fixture layout optimization based on the combination of 3dcs simulation (for dimensional analyses) and global optimization algorithms. In this paper, two global algorithms are proposed to optimize fixture locator points, which are social radiation algorithm (SRA) and GAOT, a genetic algorithm (GA) in optimization toolbox in matlab. The flowchart of fixture design includes the following steps: (1) The locating points, the key elements of a fixture layout, are selected from a much smaller candidate pool thanks to our proposed manufacturing constraints based filtering methods and thus the computational efficiency is greatly improved. (2) The two global optimization algorithms are edited to be used to optimize fixture schemes based on matlab. (3) Since matlab macrocommands of 3dcs have been developed to calculate assembly dimensions, the optimization process is fully automated. A case study of inner hood is applied to demonstrate the proposed method. The results show that the GAOT algorithm is more suitable than SRA for generating the optimal fixture layout with excellent efficiency for engineering applications. [ABSTRACT FROM AUTHOR]

This paper concerns the design and rigorous in silico evaluation of a closed-loop hemorrhage resuscitation algorithm with blood pressure (BP) as controlled variable. A lumped-parameter control design model relating volume resuscitation input to blood volume (BV) and BP responses was developed and experimentally validated. Then, three alternative adaptive control algorithms were developed using the control design model: (i) model reference adaptive control (MRAC) with BP feedback, (ii) composite adaptive control (CAC) with BP feedback, and (iii) CAC with BV and BP feedback. To the best of our knowledge, this is the first work to demonstrate model-based control design for hemorrhage resuscitation with readily available BP as feedback. The efficacy of these closed-loop control algorithms was comparatively evaluated as well as compared with an empiric expert knowledge-based algorithm based on 100 realistic virtual patients created using a well-established physiological model of cardiovascular (CV) hemodynamics. The in silico evaluation results suggested that the adaptive control algorithms outperformed the knowledge-based algorithm in terms of both accuracy and robustness in BP set point tracking: the average median performance error (MDPE) and median absolute performance error (MDAPE) were significantly smaller by >99% and >91%, and as well, their interindividual variability was significantly smaller by >88% and >94%. Pending in vivo evaluation, model-based control design may advance the medical autonomy in closed-loop hemorrhage resuscitation. [ABSTRACT FROM AUTHOR]

Automated valet parking is a part of autonomous vehicles. Path tracking is a vital capability of autonomous vehicles. In the scenario of automatic valet parking, the existing control algorithm will produce a high tracking error and a high computational burden. This paper proposes a path-tracking algorithm based on model predictive control to adapt to low-speed driving. By using the model predictive control method and vehicle kinematics model, a path tracking controller is designed. Combining the dual algorithm to further optimize the solver, a new quadratic programming (QP) knows what it knows (QPKWIK) solver is proposed. The simulation results show that the solution time of the QPKWIK solver is 25% less than that of the QP solver, and the tracking error is reduced by up to 41% compared with the QP solver. In the parking lot, the tracking performance is tested under four common scenarios, and the experimental results show that this controller has better tracking performance. [ABSTRACT FROM AUTHOR]

Micro-milling is widely used in various crucial fields with the ability of machining micro- and meso-scaled functional structures on various materials efficiently. However, the micro-milling force model is not comprehensively developed yet when tool feature sizes continually decrease to under 200 µm in a low-stiffness system. This paper proposes an analytical force model considering the influence of tool radius, size effect, tool runout, tool deflection, and the actual trochoidal trajectories and the interaction of historical tool teeth trajectories (IHTTT). Different micro-milling status are recognized by analyzing the cutting process of different tool teeth. Conditions of single-tooth cutting status are determined by a proposed numerical algorithm, and entry angle and exit angle are analyzed under various cutting conditions for the low-stiffness system. Three micro-milling status, including single-tooth cutting status, are distinguished based on the instantaneous undeformed chip thickness resulting in three types of material removal mechanisms in predicting micro-milling force components. Discontinuous change rates of undeformed chip thickness are found in the low-stiffness micro-milling system. The proposed micro-milling force model is then verified through experiments of micro slot milling Elgiloy alloy with a 150-µm-diametrical two-teeth micro-end mill. The experimental results show a root-mean-square error (RSME) of 0.092 N in the predicted resultant force, accounting for approximately 5.12% of the measured force, by which the proposed theoretical model is verified to be of good prediction accuracy. [ABSTRACT FROM AUTHOR]

In this paper, a new computational algorithm for the numerical solution of the adjoint equations for the nonlinear optimal control problem is introduced. To this end, the main features of the optimal control theory are briefly reviewed and effectively employed to derive the adjoint equations for the active control of a mechanical system forced by external excitations. A general nonlinear formulation of the cost functional is assumed, and a feedforward (open-loop) control scheme is considered in the analytical structure of the control architecture. By doing so, the adjoint equations resulting from the optimal control theory enter into the formulation of a nonlinear differential-algebraic two-point boundary value problem, which mathematically describes the solution of the motion control problem under consideration. For the numerical solution of the problem at hand, an adjoint-based control optimization computational procedure is developed in this work to effectively and efficiently compute a nonlinear optimal control policy. A numerical example is provided in the paper to show the principal analytical aspects of the adjoint method. In particular, the feasibility and the effectiveness of the proposed adjoint-based numerical procedure are demonstrated for the reduction of the mechanical vibrations of a nonlinear two degrees-of-freedom dynamical system. [ABSTRACT FROM AUTHOR]

This paper deals with rotating effects simulation of steady flows in turbomachinery. To take into account the rotating nature of the flow, the frozen rotor approach is one of the widely used approaches. This technique, known in a more general context as a multiple rotating frame (MRF), consists on building axisymmetric interfaces around the rotating parts and solves for the flow in different frames (static and rotating). This paper aimed to revisit this technique and propose a new algorithm referred to it by a virtual multiple rotating frame (VMRF). The goal is to replace the geometrical interfaces (part of the computer-aided design (CAD)) that separate the rotating parts replaced by the virtual ones created at the solver level by a simple user input of few point locations and/or parameters of basic shapes. The new algorithm renders the MRF method easy to implement, especially for edge-based numerical schemes, and very simple to use. Moreover, it allows avoiding any remeshing (required by the MRF approach) when one needs to change the interface position, shape, or simply remove or add a new one, which frequently happened in practice. Consequently, the new algorithm sensibly reduces the overall computations cost of a simulation. This work is an extension of a first version published in an ASME conference, and the main new contributions are the detailed description of the new algorithm in the context of cell-vertex finite volume method and the validation of the method for viscous flows and the three-dimensional (3D) case which is of significant importance to the method to be attractive for real and industrial applications. [ABSTRACT FROM AUTHOR]