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2nd Iberian Robotics Conference, ROBOT 2015

作者： Reis, Luís Paulo Moreira, António Paulo Lima, Pedro U. Montano, Luis Muñoz-Martinez, Victor University of Minho School of Engineering Information Systems Department Guimarães Portugal University of Porto INESC-TEC Porto Portugal University of Lisbon Instituto Superior Técnico Institute for Systems and Robotics Lisboa Portugal University of Zaragoza School of Engineering and Architecture Computer Systems Engineering Department Zaragoza Spain University of Malaga Superior Technical School of Industrial Engineers Automatic Control Systems Engineering Department Málaga Spain

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Simultaneous Extraction of the Moon and Stars at Night

Simultaneous Extraction of the Moon and Stars at Night

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2016 IEEE Chinese Guidance, Navigation and control Conference (IEEE CGNCC2016)

作者： Huajun Du Huosheng Hu Yunfeng Ai Beijing Aerospace Automatic Control Institute National Key Laboratory of Science and Technology on Aerospace Intelligence Control School of Computer and Electronics Engineering University of Essex School of Engineering Science University of Chinese Academy of Sciences

ISBN: (纸本)9781467383196

It is well known that star sensor is an accurate attitude sensor in various real applications,which however has not been fully used to observe stars and the moon *** paper investigates how the moon and stars can be observed by the star sensor,and how to process the images taken under different light conditions to extract information to determine attitude and location of the *** are conducted to show that the star sensor can be efficiently applied as an integrated celestial sensor,providing economical,reliable and autonomous benefits to the missions conducted on the earth,in the air,on other planets and so on.

关键词： star sensor the moon and stars image processing

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Sliding-mode formation control for cooperative nonholonomic robots with uncertainties

Sliding-mode formation control for cooperative nonholonomic ...

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World Congress on Intelligent control and Automation (WCICA)

作者： Lu Yu Jianhong Zhang Chengdong Li Dianwei Qian School of Control and Computer Engineering North China Electric Power University Beijing China Aerospace Automatic Control Institute Beijing CO China School of Information and Electrical Engineering Shandong Jianzhu University Jinan China

ISBN: (纸本)9781467384155

For the decentralized formation problem of multiple robots, this paper presents a kind of robust sliding mode controller based on nonlinear disturbance observer. According to the leader-follower-based formation mechanism, the dynamic model with external disturbances and system uncertainties is established. To perform a formation control and to guarantee system robustness, a novel formation algorithm combining sliding model control and nonlinear disturbance observer is presented. Sliding mode control (SMC) is a special nonlinear control strategy, which has invariance against the matched uncertainties, but the chattering produced by SMC limits its application to the practical system. By using nonlinear disturbance observer technology, the uncertainties cover both matched and mismatch uncertainties can be compensated. In addition, the SMC control law can be designed successfully so that chattering can be effectively alleviated. In the sense of Lyapunov, a sufficient condition is drawn to guarantee that the formation system can be asymptotically stabilized. Simulation results confirm the effectiveness of the proposed control scheme.

关键词： Uncertainty Robot kinematics Observers Sliding mode control Robustness

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Data and knowledge driven design of SIRMs connected fuzzy inference system with application to thermal comfort prediction

Data and knowledge driven design of SIRMs connected fuzzy in...

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World Congress on Intelligent control and Automation (WCICA)

作者： Li Wang Jianhong Zhang Chengdong Li Dianwei Qian Guiqing Zhang School of Information and Electrical Engineering Shandong Jianzhu University Jinan China Beijing Aerospace Automatic Control Institute Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9781467384155

This paper presents a data and knowledge driven design approach for the single input rule modules connected fuzzy inference system (SIRM-FIS) which can greatly reduce the number of fuzzy rules. The data and knowledge driven SIRM-FIS can be used to the modeling, identification or prediction problems that have monotonic input-output mappings. In this study, we firstly show how to encode the prior knowledge of monotonicity into the SIRM-FIS. Then, through combining the correlation analysis and the constrained least square algorithm, we present a data-driven parameter learning strategy to optimize the SIRM-FIS. At last, we apply the proposed approach to the thermal comfort prediction. Simulation and comparisons illustrate that the proposed method is efficient to the thermal comfort prediction and performs better than some other existing methods.

关键词： Input variables Fuzzy logic Predictive models Fuzzy sets Correlation Algorithm design and analysis Prediction algorithms

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Ground-tested control architecture for distributed aircraft fuel management

Ground-tested control architecture for distributed aircraft ...

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Conference on Digital Avionics Systems (DASC)

作者： Carlos C. Insaurralde Jose M. Giron-Sierra School of Science and Engineering Teesside University Middlesbrough United Kingdom Department of Computer Architecture & Automatic Control Complutense Universidad of Madrid Madrid Spain

ISBN: (纸本)9781509025244

The SmarFuel project aims to develop a new generation of AFMS (Aircraft Fuel Management System). It proposes a Distributed Avionics control Architecture (DACA) based on networked Smart Fuel Components (SFCs). This decentralized solution (opposite to conventionally-centralized approaches) reduces avionics wiring whilst the AFMS is fault tolerant. The DACA is realized by distributing the control system through microcontrollers embedded in SFCs and across a fieldbus. Thus, SFCs are able to implement advanced functions such as auto-calibration. The AFMS operation modes considered are three: pressure refueling, engine supply, and fuel transfer. The operation sequence of AFMS events is not fixed which makes the SFC decision-making process critical. The above DACA for AFMS was already realized and evaluated successfully in laboratory prototypes. This paper presents experimental results from verification and validation tests of the DACA realization carried out in a real-scaled helicopter rig. These ground experiments are based on the above fuel system operations. This paper also presents a verification tool for the SFC state machines. Conclusions and future research directions are also discussed.

关键词： Fuels Software Valves Cascading style sheets Aerospace electronics Helicopters Testing

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Generalized Norm Optimal Iterative Learning control with Intermediate Point and Sub-interval Tracking

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International Journal of Automation and computing 2015年第3期12卷 243-253页

作者： David H.Owens Chris T.Freeman Bing Chu Department of Automatic Control and Systems Engineering University of Sheffield School of Electronics and Computer Science University of Southampton Advanced Robotics Division Italian Institute of Technology

Norm optimal iterative learning control(NOILC) has recently been applied to iterative learning control(ILC) problems in which tracking is only required at a subset of isolated time points along the trial duration. This problem addresses the practical needs of many applications, including industrial automation, crane control, satellite positioning and motion control within a medical stroke rehabilitation context. This paper provides a substantial generalization of this framework by providing a solution to the problem of convergence at intermediate points with simultaneous tracking of subsets of outputs to reference trajectories on subintervals. This formulation enables the NOILC paradigm to tackle tasks which mix "point to point" movements with linear tracking requirements and hence substantially broadens the application domain to include automation tasks which include welding or cutting movements, or human motion control where the movement is restricted by the task to straight line and/or planar segments. A solution to the problem is presented in the framework of NOILC and inherits NOILC s well-defined convergence properties. Design guidelines and supporting experimental results are included.

关键词： Iterative learning control learning control optimization linear systems robotics.

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An effective PSO-TLBO algorithm for multi-objective optimization

An effective PSO-TLBO algorithm for multi-objective optimiza...

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Congress on Evolutionary Computation

作者： Tingli Cheng Minyou Chen Peter J. Fleming Zhile Yang Shaojun Gan School of Electrical Engineering Chongqing University Chongqing China Department of Automatic Control & System Engineering University of Sheffield Sheffield UK School of Electronics Electrical Engineering and Computer Science Queens University Belfast Belfast UK

In this study, we present an effective hybrid algorithm based on particle swarm optimization (PSO) and teaching-learning-based optimization (TLBO), called PSO-TLBO, to solve multi-objective optimization (MOO) problems. In PSO-TLBO, teacher phase and learner phase in TLBO are incorporated to a PSO to improve the exploration and exploitation ability. Another feature of PSO-TLBO is the improved circular crowed sorting (CCS) to truncate the external archive which stores the nondominated solutions found during the search. Moreover, the polynomial mutation is used only when the velocities of all solutions are very small. The performance of PSO-TLBO algorithm for MOO is tested on ZDT and DTLZ benchmarks functions and compared with some powerful MOO algorithms in terms of both convergence and spread performance. Also, the contribution of CCS is compared with crowded sorting in NSGA-II. Comparison results reveal the powerful ability of PSO-TLBO to obtain remarkable solutions in terms of inverted generational distance (IGD) metrics.

关键词： Optimization Sociology Statistics Sorting Particle swarm optimization Benchmark testing Convergence

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Identification of Stochastic Wiener Systems using Indirect Inference

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IFAC-PapersOnLine 2015年第28期48卷 620-625页

作者： Wahlberg, Bo Welsh, James Ljung, Lennart Department of Automatic Control and ACCESS School of Electrical Engineering KTH Royal Institute of Technology StockholmSE-100 44 Sweden School of Elect Engineering and Computer Science The University of Newcastle CallaghanNSW 2308 Australia Division of Automatic Control Linköping University LinköpingSE-581 83 Sweden

We study identification of stochastic Wiener dynamic systems using so-called indirect inference. The main idea is to first fit an auxiliary model to the observed data and then in a second step, often by simulation, fit a more structured model to the estimated auxiliary model. This two-step procedure can be used when the direct maximum-likelihood estimate is dificult or intractable to compute. One such example is the identification of stochastic Wiener systems, i.e., linear dynamic systems with process noise where the output is measured using a nonlinear sensor with additive measurement noise. It is in principle possible to evaluate the loglikelihood cost function using numerical integration, but the corresponding optimization problem can be quite intricate. This motivates studying consistent, but sub-optimal, identification methods for stochastic Wiener systems. We will consider indirect inference using the best linear approximation as an auxiliary *** show that the key to obtain a reliable estimate is to use uncertainty weighting when fitting the stochastic Wiener model to the auxiliary model estimate. The main technical contribution of this paper is the corresponding asymptotic variance analysis. A numerical evaluation is presented based on a first-order finite impulse response system with a cubic non-linearity, for which certain illustrative analytic properties are derived. © 2015

关键词： Stochastic models Cost functions Identification (control systems) Impulse response Linear control systems Linear systems Maximum likelihood estimation Nonlinear systems Stochastic systems Uncertainty analysis Best linear approximations Finite impulse response system Indirect inference Maximum likelihood estimate Numerical integrations Optimization problems Technical contribution Wiener systems

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Model Error Modeling and Stochastic Embedding

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IFAC-PapersOnLine 2015年第28期48卷 75-79页

作者： Ljung, Lennart Goodwin, Graham C. Agüero, Juan C. Chen, Tianshi Div. of Automatic Control Linköping University Sweden School of Electrical Engineering and Computer Science The University of Newcastle CallaghanNSW 2308 Australia

To estimate a model of useful complexity for control design, at the same time as having a good insight into its reliability is a central issue in system identification, in particular for identification for control. Basically one can think of a (simpler) design model and a (more complex and exible) error model. These concepts are discussed in terms of model error modeling and stochastic embedding in a framework that allows the error model to vary over time. It is then of interest to estimate a probabilistic description of this error model. This can be accomplished by estimating parameters that describe the pdf of the errors. In this contribution explicit expressions for the likelihood function for these parameters are derived by marginalization over the error model, in case this is a linear regression. © 2015

关键词： Stochastic models Errors Stochastic systems Design modeling Empirical Bayes Error quantification Estimating parameters Identification for control Likelihood functions Model errors Probabilistic descriptions

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A data-driven gradient algorithm for high-precision quantum control

arXiv

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arXiv 2017年

作者： Wu, Re-Bing Chu, Bing Owens, David H. Rabitz, Herschel Department of Automation Tsinghua University Beijing100084 China Center for Quantum Information Science and Technology BNRist Beijing100084 China School of Electronic and Computer Science University of Southampton SouthamptonSO17 1BJ United Kingdom Department of Automation Zhengzhou University Zhengzhou450001 China Department of Automatic Control and Systems Engineering University of Sheffield Mappin Street SheffieldS1 3JD United Kingdom Department of Chemistry Princeton University PrincetonNJ08544 United States

In the quest to achieve scalable quantum information processing technologies, gradient-based optimal control algorithms (e.g., GRAPE) are broadly used for implementing high-precision quantum gates, but their performance is often hindered by deterministic or random errors in the system model and the control electronics. In this paper, we show that GRAPE can be taught to be more effective by jointly learning from the design model and the experimental data obtained from process tomography. The resulting data-driven gradient optimization algorithm (d-GRAPE) can in principle correct all deterministic gate errors, with a mild efficiency loss. The d-GRAPE algorithm may become more powerful with broadband controls that involve a large number of control parameters, while other algorithms usually slow down due to the increased size of the search space. These advantages are demonstrated by simulating the implementation of a two-qubit CNOT gate. Copyright © 2017, The Authors. All rights reserved.

关键词： Random errors

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