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Software of GAMMA-3 system for synthesis and mathematical modelling of UAV control systems 4

Software of GAMMA-3 system for synthesis and mathematical mo...

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4th international conference on Information Technology and Nanotechnology, ITNT 2018

作者： Stepanov, M.F. Stepanov, A.M. Salikhova, A.R. Yuri Gagarin State Technical University of Saratov Politechnicheskaya str. 77 Saratov410054 Russia Institute of Precision Mechanics Control of Russian Academy of Sciences Rabochaya str. 24 Saratov410028 Russia

control system design for complex technical plants includes execution of computationally complicated procedure sets. Many of those demand repeated iterative execution. Iterations are caused by uncertainty of a control task, incompleteness of the information on control plant and conditions of its functioning. Another important reason is related to insufficiently perfect methods of the automatic control theory, because of its ongoing active development. Therefore, the task of developing the computer-aided control system design is as relevant as before. The publication is devoted to the statement of an approach to expansion of GAMMA-3 multipurpose system opportunities. An important feature of GAMMA-3 system includes opportunities for solving the tasks via both traditionally procedural and non-procedural statements. At the first stage, the proposed approach provides the expansion of classes of procedural task statements as the most formalized process. At the following stage, formalization and generalization of saved procedural knowledge in new expanded model of the subject domain takes place. The domain in question allows for non-procedural task statement. Solving non-procedural declared tasks requires using intellectual components of GAMMA-3 system. In this paper, we illustrate the first stage of the proposed approach on the example of creating GAMMA-3 software package for solving the problems on the synthesis of a control system by modeling UAV movement on defined trajectory. One of the main tasks conducted by UAV is surveying remote areas, where data acquisition by habitual ways is complicated or there is a danger to lives and health of the people. © 2018 Institute of Physics Publishing. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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complex issues in regulator designing

Complex issues in regulator designing

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2018 international Russian Automation conference, RusAutoCon 2018

作者： Zhmud, Vadim A. Hardt, Wolfram Dimitrov, Lubomir V. Roth, Hubert Department of Automation Novosibirsk State Technical University Novosibirsk Russia Department of Computer Science Chemnitz University of Technology Chemnitz Germany Technical University of Sofia Sofia Bulgaria Department of Automatic Control Engineering University of Siegen Siegen Germany

ISBN: (纸本)9781538649381

The problem of control object with the use of feedback loop is extremely relevant, because only this method provides for the accurate control. It happened that the theory of automatic control paid much attention to the methods of regulators design based on mathematical transformations, using a mathematical model of the object. It describes the transformation of an input signal into an output signal inside the object. The methods were imperfect, so one of the following simplification methods was the most frequently used: the mathematical model of the object was simplified, or the calculations were carried out with approximation, for example, the oscillations in the system were studied by the amplitude and phase of the first harmonic only. Therefore, the result of using such regulators in practice did not exactly correspond to the theory provisions. To get mathematical and experimental basis of system developer tool, a solution of the elementary sub-problems was required. Among them there are tasks of the control of an object of the first and second order. Today the situation has changed. There is a means of sufficiently accurate mathematical modeling of the behavior of the system consisting of the loop with an object and a regulator. There are software and mathematical means for regulator calculating by the method of numerical optimization. Now there is sufficient software for calculating the regulators of the object, having a rather complex mathematical model. However, this did not change the situation. A lot of articles are still published, in which it is suggested performing additional fine tuning to achieve the required parameters after calculating the regulator and its technical implementation. Quite a big number of articles is still being published;they consider the methods of the empirical tuning of regulators based on the parameters of the transient process with a given fixed gain coefficient. This situation demonstrates insufficient using of the important th

关键词： Mathematical transformations

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Differential Flatness-Based Robust control of Self-balanced Robots 5

Differential Flatness-Based Robust Control of Self-balanced ...

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5th international-Federation-of-Automatic-control (IFAC) conference on Engine and Powertrain control, Simulation and modeling (E-COSM)

作者： Liang, Dingkun Sun, Ning Wu, Yiming Fang, Yongchun Nankai Univ IRAIS Tianjin Key Lab Intelligent Robot tjKLIR Tianjin 300350 Peoples R China

Underactuated self-balanced robots (SBRs) can be used to implement various tasks. However, with complicated system dynamics and existing external disturbances, the control issue of SBRs is still open and challenging. In order to deal with such problems, by constructing flat outputs, this paper proposes an active disturbance rejection control (ADRC) method for SBRs both on the horizontal plane and the inclined plane, which achieves control objectives of tracking reference signals and can reject perturbations online. Additionally, the integral algebraic estimation and the generalized proportional integral (GPI) methods are applied. Finally, to verify the effectiveness of the proposed controller, numerical simulation results are provided. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Self-balanced robot (SBR) differential flatness active disturbance rejection control (ADRC) underactuated systems

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Reinforcement Learning for Cyber-Physical systems

Reinforcement Learning for Cyber-Physical Systems

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Industrial Internet (ICII)

作者： Xing Liu Hansong Xu Weixian Liao Wei Yu Towson University Towson MD USA

ISBN: (数字)9781728129778

ISBN: (纸本)9781728129785

Cyber-Physical systems (CPS), including smart industrial manufacturing, smart transportation, and smart grids, among others, are envisioned to convert traditionally isolated automated critical systems into modern interconnected intelligent systems via interconnected human, system, and physical assets, as well as providing significant economic and societal benefits. The characteristics of CPS include complexity, dynamic variability, and heterogeneity, arising from interactions between cyber and physical subsystems. These characteristics introduce critical challenges in addition to existing and vital safety and reliability requirements from traditional critical systems. To overcome these challenges, Artificial Intelligence (AI) and Machine Learning (ML) schemes, which have proven effective in numerous fields (robotics, automation, prediction, etc.), can be leveraged as solutions for CPS. In particular, reinforcement learning can make precise decisions automatically to maximize cumulative reward via systematic trial and error in an unknown environment. Yet, challenges still remain for integrating complex reinforcement learning systems with dynamic and diverse CPS domains. In this paper, we conduct a thorough investigation of existing research on reinforcement learning for CPS, and propose a framework for future research. In addition, we carry out two case studies on reinforcement learning in transportation CPS and industrial CPS to validate the effectiveness of reinforcement learning in targeted applications. Using realistic simulation platforms, we validate the effectiveness of reinforcement learning for decision making in routing for transportation CPS and production control for industrial CPS. Finally, we outline some future research challenges that remain.

关键词： Learning (artificial intelligence) Computational modeling Robots Transportation Routing Machine learning Process control

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Comparative study of simulation-assisted approaches for distributed model predictive control in building energy systems 31

Comparative study of simulation-assisted approaches for dist...

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31st international conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy systems, ECOS 2018

作者： Baranski, Marc Meyer, Lina Fütterer, Johannes Müller, Dirk RWTH Aachen University E.ON Energy Research Center Institute for Energy Efficient Buildings and Indoor Climate Aachen Germany

ISBN: (纸本)9789729959646

Model predictive control (MPC), although considered a high-potential control approach, usually requires considerable effort for model-creation and parametrization. Moreover, many previous studies use linear models that over-simplify the complex phenomena in building energy systems (BES). By contrast, simulation models can be too computationally intensive for control applications. Distributed model predictive control (DMPC) is a promising approach that avoids the construction of a complex model of the total system and thus facilitates modelling and supports the use of exact simulation models. DMPC divides the optimization problem into sub-problems, with the advantage that pre-fabricated simulation models from standard libraries and models provided by component manufacturers can be used. Moreover, each optimization problem, considered for its own, becomes smaller and, in whole, can be solved faster compared to an integrated system model. The distributed optimizations must be coordinated to achieve near-global-optimum performance. This coordination requires a suitable scheme, which, in many publications, is based on iterative data exchange between the subsystems. In previous works, the authors developed a non-iterative algorithm based on the exchange of lookup tables. In this paper, they compare and benchmark the previously developed approach against a second iterative approach to show advantages and limitations of both algorithms. To perform a comparative case study, the authors implement the two approaches in a flexible object-oriented framework that allows selection of various optimization cost functions such as exergetic and monetary costs. The authors apply both approaches to a real-life air-handling unit and its Modelica simulation model while using Modelica models and non-linear solvers for the DMPC algorithms. They make simplifying assumptions to provide mathematical justification regarding the optimality of the approach. Judging from the indicators for control

关键词： Electronic data interchange

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A unifying framework for manipulation problems: Extended abstract 17

A unifying framework for manipulation problems: Extended abs...

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17th international conference on Autonomous Agents and Multiagent systems, AAMAS 2018

作者： Knop, Duan Koutecky, Martin Mnich, Matthias Dept. of Informatics Univ. of Bergen Charles University Prague Czech Republic Israel Institute of Technology Israel Universität Bonn Maastricht University Netherlands

ISBN: (纸本)9781510868083

Manipulation models for electoral systems are a core research theme in social choice theory;they include bribery (unweighted, weighted. swap, shift,...), control (by adding or deleting voters or candidates), lobbying in referenda and others. We develop a unifying framework for manipulation models with few types of people, one of the most commonly studied scenarios. A critical insight of our framework is to separate the descriptive complexity of the voting rule 1? from the number of types of peop le. This allows us to finally settle the computational complexity of R-SwAI' BRIBERY, one of the most fundamental manipulation problems. In particular, we prove that 1?-SWAP BRIBERY is fixed- parameter tractable when 1? is Dodgson's rule and Young's rule, when parameterized by the number of candidates. This way, we res olve a long-standing open question from 2007 which was explicitly asked by Faliszewski et al. (JAW 40, 2011]. Our algorithms reveal that the true hardness of bribery problems often stems from the complexity of the voting rules. On one hand, we give a fixed-parameter algorithm parameterized by number of types of people for complex voting rules. Thus, we reveal that T?-SwAP BRIBERY with Dodgson's rule is much harder than with Condorcet's rule, which can be expressed by a conjunction of linear inequalities, while Dodson's rule requires quantifier alternation and a bounded number of disjunctions of linear systems. On the other hand, we give an algorithm for quantifier-free voting rules which is parameterized only by the number of conjunctions of the voting rule and runs in time polynomial in the number of types of people. This way, our framework explains why Siurr BRIBERY is polynomial-time solvable for the plurality voting rule, making explicit that the rule is simple in that it can be expressed with a single linear inequality, and that the number of voter types is polynomial. © 2018 international Foundation for Autonomous Agents and Multiagent systems.

关键词： Linear systems

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Air pollution and population morbidity forecasting with artificial neural networks

Air pollution and population morbidity forecasting with arti...

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international conference and Early Career Scientists School on Environmental Observations, modeling and Information systems, ENVIROMIS 2018

作者： Gornov, A Yu Zarodnyuk, T.S. Efimova, N.V. Institute for System Dynamics and Control Theory Siberian Branch of Russian Academy of Sciences Lermontov str. 134 Post Box 64033 Irkutsk Russia East-Siberian Institute of Medical and Ecological Research 12a District 3 Angarsk665827 Russia

Incidence prediction models for urban population have not yielded consistent or highly accurate results. The complex nature of the interrelationship between "environmental factors and incidence" has many nonlinear associations with outcomes. We explore artificial neural networks (ANNs) to predict the complex interactions between the risk factors of incidence among the urban population. ANN modeling using a standard feed-forward, back-propagation neural network with three layers (i.e., an input layer, a hidden layer, and an output layer) is used to predict the incidences of diseases of children and adults. A receiver-operating characteristic (ROC) analysis is used to assess the model accuracy. We develop a mathematical model taking into account factors of natural, anthropogenic, and social environments. The model effectiveness is proved by computing experiments for the Bratsk industrial centre (Irkutsk region, Russia). Optimal air pollution levels are offered to achieve a background morbidity level among different age groups of the population. The prediction of incidence is most accurate when using the ANN model with several univariate influences on the outcome. An incorporation of some computerized learning systems might improve decision making and outcome prediction. © Published under licence by IOP Publishing Ltd.

关键词： Forecasting

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sEMG-Based Torque Estimation Using Time-Delay ANN for control of an Upper-Limb Rehabilitation Robot

sEMG-Based Torque Estimation Using Time-Delay ANN for Contro...

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IEEE international conference on Cyborg and Bionic systems (CBS)

作者： Wang, Chen Peng, Liang Hou, Zeng-Guang Luo, Lincong Chen, Sheng Wang, Weiqun Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Beijing 100190 Peoples R China

ISBN: (纸本)9781538673546

Robotic-assisted rehabilitation of the upper limb following neurological injury can achieve best possible functional recovery when patients are engaged in the therapy. However, implementation of active training is still difficult as it's challenging to detect human motion intention online and impose corresponding robot control. This paper introduces a novel upper-limb rehabilitation robot, and proposes a sEMG-driven (sEMG: surface Electromyography) torque estimation model based on artificial neural networks (ANN). The robot has three DOFs, of which the first two DOFs adopt a planar parallel structure, and the wrist module has an exoskeleton form. In this study, we design an impedance controller and an admittance controller for the first two DOFs and the wrist module, respectively. Specifically, for the first two DOFs, the assistance/resistance force at the end-effector was controlled according to its motions and desired interaction impedance;for the wrist module, an sEMG armband was used to collect 8 channels of sEMG signals from the forearm muscles, and a time-delay ANN model was developed to estimate the wrist pronation/supination torque, based on which the wrist rotation was controlled according to the human motion intention. To overcome the overfitting problem, besides the experimental samples of wrist rotation, both resting and co-contraction samples were collected for training. Finally, combining with the design of a virtual reality game and force fields, the proposed methods were implemented and tested experimentally on the upper-limb rehabilitation robot.

关键词： Robots Wrist Muscles Torque Training Dynamics Solid modeling

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Systematic Co-Optimization From Chip Design, Process Technology To systems For GPU AI Chip

Systematic Co-Optimization From Chip Design, Process Technol...

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international Symposium on VLSI Technology, systems and Application (VLSI-TSA)

作者： Hu, John R. Chen, James Liew, Boon-khim Wang, Yanfeng Shen, Lianxi Cong, Lin Nvidia Corp Santa Clara CA 95051 USA

ISBN: (纸本)9781538648254

In this paper we present a systematic approach to identify, predict and optimize design process interaction (DPI);and to co-optimize holistically from technology to chip/system. This has enabled the best performance, power and yield for GPU/SOC for high performance computing (HPC), artificial intelligence (AI) and autonomous vehicles applications. GPU HPC improved 100x in past 10 years, more than Moore's Law. AI deep learning performance has improved even faster, by similar to 100x in 5 years, enabled by co-optimizations in architecture, circuit, and process technology. The era of defects per trillion (DPPT) level of perfection has also arrived. DPPT criteria for defect and variability outlier control is required to meet yield and reliability for today's complex GPU with 100s of billions of layout pieces. Intelligent test chip designs are employed to identify issues and margins, to help predict large chip and high-volume issues with smart sample learning. TCAD defect/reliability margin modeling, design for manufacturing (DFM), design for reliability (DFR), are also used to optimize design and process, to maximize PPAYRT (power, performance, area, yield, reliability, time to market).

关键词： Layout Graphics processing units Optimization Artificial intelligence Logic gates Integrated circuit reliability

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Model based Diesel Injection Profile Optimization to Reduce Transient Soot Peaks 5

Model based Diesel Injection Profile Optimization to Reduce ...

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5th international-Federation-of-Automatic-control (IFAC) conference on Engine and Powertrain control, Simulation and modeling (E-COSM)

作者： Grossbichler, Martin Polterauer, Philipp Zhang, Zhen Waschl, Harald Johannes Kepler Univ Linz Inst Design & Control Mechatron Syst Linz Austria AVL Commercial Driveline & Tractor Engn GmbH Steyr Austria

To meet current legislation limits, modern Diesel engines achieve low raw emission levels and utilize complex aftertreatment systems. Still, during fast transients undesired emission peaks may occur for both soot and NOx. These are caused by deviations in the in-cylinder conditions between the quasi steady engine calibration and the transient operation, e.g. during tip-ins. In this work a case study is performed to analyze the potential reduction of transient soot emissions during a specified engine maneuver. An additional target is to investigate potential benefits of a novel in-situ soot sensor based on the Laser Induced Incandescence (LII) principle which offers a high temporal resolution. Measurement data from a Diesel engine is used to develop time varying setpoint deviation models which will be utilized in a numerical optimization problem to determine an optimized injection profile. The modeling and optimization is carried out in parallel for both sensors and results are compared against each other. In the experimental validation the optimized injection profiles were capable of reducing undesired overshoots during transients with minimal impact on the torque response. Furthermore, the novel sensor allows to gain additional insights in the relation between input parameters and soot response, which is reflected by the obtained models and control action. (C) 2018, IFAC (international Federation of Automatic control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Combustion control Compression Ignition Engine control

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