In most industrial additive manufacturing (AM) applications a set of AM machines (AM-Fleet) are used in parallel. An AM-Fleet often consists of machines from various vendors and may include different AM processes. AM ...
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ISBN:
(纸本)9781538635933
In most industrial additive manufacturing (AM) applications a set of AM machines (AM-Fleet) are used in parallel. An AM-Fleet often consists of machines from various vendors and may include different AM processes. AM processes often suffer from poor repeatability within a single build, between builds on the same machine, and from machine to machine. AM's lack of robustness is often attributed to insufficient in-process monitoring and feedback control, as well as unknown modeling dynamics, and a lack of process standards. To effectively monitor and control AM-Fleets, system-level approaches must be devised. In this work, a centralized approach is proposed for the system-level control and management of AM-Fleets. Integrating such an approach has advantages in terms of system-level intelligent decision making for AM-Fleets. Key problems that needs to be solved and the challenges for a centralized approach are discussed in this work. The architecture of the proposed framework is presented with discussions on the individual components. A discrete event model for the system-level monitoring and control of AM machines is also proposed to support the presented framework. The use of discrete event models creates an abstract representation of the AM machine that enables the supervision of the physical system. An illustrative example that demonstrates a system-level run-to-run anomaly detection case is discussed. The proposed framework will provide an analytical foundation for systematic anomaly detection, scheduling, and decision making in AM-Fleets.
Vehicles with car-like kinematics are ubiquitous, therefore an ability to algorithmize (i.e., how to plan and effectively execute) complex maneuvers in the presence of obstacles is vital to mobile robotics and intelli...
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ISBN:
(纸本)9781538680940
Vehicles with car-like kinematics are ubiquitous, therefore an ability to algorithmize (i.e., how to plan and effectively execute) complex maneuvers in the presence of obstacles is vital to mobile robotics and intelligent vehicles. Traditionally, this problem is solved using the well known motion planning algorithms, which generate the open-loop control signals neglecting the effects of measurement noises, modeling uncertainties and imperfect robot actuation. While such effects can be compensated to some extent by online replanning, the application of feedback control algorithms to motion execution is unavoidable if robustness of the system is desired. Consequently, the recent works focus on integration of both motion planning and control algorithms to obtain motion plans robust to uncertainty of the initial conditions. In accordance with this trend, we propose a modified VFO (Vector Field Orientation) control law, which is designed to satisfy the state and input constraints resulting from the presence of obstacles in the environment, respect the steering angle limits in conjunction with steering dynamics of the car-like robot, and preserve continuity of the control input signals. Thanks to analytic characterization of admissible funnels (i.e. positively invariant subsets of the configuration space) developed from an analysis of the VFO control law, we guarantee satisfaction of all the mentioned constraints in the continuous domains of time and configuration space of the robot without sacrificing computational efficiency of the planning process. A specific funnel is planned with a highly parallelized deterministic sampling-based algorithm achieving quasi-real-time performance.
The biological processes for biofuels generation are highly nonlinear systems submitted to external disturbances and parameter uncertainties which require estimation, control and optimization strategies to maintain st...
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The biological processes for biofuels generation are highly nonlinear systems submitted to external disturbances and parameter uncertainties which require estimation, control and optimization strategies to maintain stability and optimal production. In this work, a nonlinear neural network for unknown nonlinear systems in the presence of external disturbances and parameter uncertainties is proposed. The objective is to estimate unmeasurable complex variables in a two continuous stages anaerobic digestion process for hydrogen and methane production. Simulation results are presented, where it is demonstrated that the neural model is efficient to calculate complex dynamics of the process in presence of disturbances. As future work, control and optimization algorithms based on the neural model can be developed to biofuel production optimization. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
The paper demonstrates a new approach to the nonlinear control of a tracked robotic platform. As a control object, a tracked mobile robot chosen because, among other ground vehicles, it is the most maneuverable and de...
The paper demonstrates a new approach to the nonlinear control of a tracked robotic platform. As a control object, a tracked mobile robot chosen because, among other ground vehicles, it is the most maneuverable and designed to work in conditions of limited space. To take into account the nonlinear characteristics of the control object, the article deals with the analysis of a mathematical model of a mobile robotic platform. Also, provides an overview of modern methods and approaches to the control of tracked robotic platform. The problems of controlling the mobile robot are highlighted, in particular, the application of methods and approaches based on the application of system linearization methods, which makes a robotic system with these control laws limited to certain local control algorithms. Therefore, the paper presents an explanation of the use of new non-linear approaches to the mobile robots control, in particular, synergetic control theory. The main method, within the framework of this theory, is the method of analytical design of aggregated regulators (ADAR), which allows to synthesize control laws for complex nonlinear systems of large dimensionality without using linearization procedures or other simplifications, so this method is used to synthesize the synergetic control law of a mobile robot. The resulting control laws takes into account the nonlinear properties of the mobile robotic platform model; therefore, this control strategy ensures the asymptotic stability of the close-loop system and the precise execution of the specified invariants. To verify the resulting system, computer simulation is used. The simulation results confirm that in the synthesized closed-loop mobile robot control system, movement to a given point of the working plane with a given angle of orientation of the platform is provided.
One of the main elements of automation of industrial enterprises is the use of robotic systems consisting of mechanical manipulators and controlsystems. In recent years, the market of service robotics has been active...
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Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point ...
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ISBN:
(纸本)9781538649756
Models of biological systems often have many unknown parameters that must be determined in order for model behavior to match experimental observations. Commonly-used methods for parameter estimation that return point estimates of the best-fit parameters are insufficient when models are high dimensional and under-constrained. As a result, Bayesian methods, which treat model parameters as random variables and attempt to estimate their probability distributions given data, have become popular in systems biology. Bayesian parameter estimation often relies on Markov Chain Monte Carlo (MCMC) methods to sample model parameter distributions, but the slow convergence of MCMC sampling can be a major bottleneck. One approach to improving performance is parallel tempering (PT), a physics-based method that uses swapping between multiple Markov chains run in parallel at different temperatures to accelerate sampling. The temperature of a Markov chain determines the probability of accepting an unfavorable move, so swapping with higher temperatures chains enables the sampling chain to escape from local minima. In this work we compared the MCMC performance of PT and the commonly-used Metropolis-Hastings (MH) algorithm on six biological models of varying complexity. We found that for simpler models PT accelerated convergence and sampling, and that for more complex models, PT often converged in cases MH became trapped in non-optimal local minima. We also developed a freely-available MATLAB package for Bayesian parameter estimation called PTEMPEST (http://***/RuleWorld/ptempest), which is closely integrated with the popular BioNetGen software for rule-based modeling of biological systems.
In the reversible cold rolling mill, it is critical to control the gauge or thickness of the cold-rolled steel strip for end-user requirements. To obtain a high precision in the output thickness of the strip, the auto...
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The authors analyses the problems of using artificial intelligence to manage complex techno-organizational systems on the basis of the convergence of the telematic, computing and information services in order to manag...
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In this paper, we present the results on describing and modeling dynamical properties of collective systems. In particular, we consider the problems of activation and deactivation of collectives, represented by networ...
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The interconnection of dynamically decoupled subsystems, each exhibiting a certain dissipativity property considered in the context of economic MPC, is investigated. Interconnection of the subsystems is by means of th...
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The interconnection of dynamically decoupled subsystems, each exhibiting a certain dissipativity property considered in the context of economic MPC, is investigated. Interconnection of the subsystems is by means of their cost functions being separable in a purely local economic and a coupling cost term. For certain classes of quadratic interconnection costs we provide conditions on the interconnection structure under which the overall system exhibits the same dissipativity property. Moreover, we apply a non-iterative distributed MPC scheme to the interconnected system which yields asymptotic stability of the overall optimal steady state by exploiting the structural properties of the system interconnection. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.
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