Conferences related to Approximation Based Control

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 59th IEEE Conference on Decision and Control (CDC)

The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.


2020 American Control Conference (ACC)

The ACC is the annual conference of the American Automatic Control Council (AACC, the U.S. national member organization of the International Federation for Automatic Control (IFAC)). The ACC is internationally recognized as a premier scientific and engineering conference dedicated to the advancement of control theory and practice. The ACC brings together an international community of researchers and practitioners to discuss the latest findings in automatic control. The 2020 ACC technical program will

  • 2019 American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2018 Annual American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2017 American Control Conference (ACC)

    Technical topics include biological systems, vehicle dynamics and control, adaptive control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2016 American Control Conference (ACC)

    Control systems theory and practice. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2015 American Control Conference (ACC)

    control theory, technology, and practice

  • 2014 American Control Conference - ACC 2014

    All areas of the theory and practice of automatic control, including but not limited to network control systems, model predictive control, systems analysis in biology and medicine, hybrid and switched systems, aerospace systems, power and energy systems and control of nano- and micro-systems.

  • 2013 American Control Conference (ACC)

    Control systems theory and practice. Conference themes on sustainability, societal challenges for control, smart healthcare systems. Conference topics include biological systems, vehicle dynamics and control, consensus control, cooperative control, control of communication networks, control of networked systems, control of distributed parameter systems, decentralized control, delay systems, discrete-event systems, fault detection, fault-tolerant systems, flexible structures, flight control, formation flying, fuzzy systems, hybrid systems, system identification, iterative learning control, model predictive control, linear parameter-varying systems, linear matrix inequalities, machine learning, manufacturing systems, robotics, multi-agent systems, neural networks, nonlinear control, observers, optimal control, optimization, path planning, navigation, robust control, sensor fusion, sliding mode control, stochastic systems, switched systems, uncertain systems, game theory.

  • 2012 American Control Conference - ACC 2012

    All areas of control engineering and science.

  • 2011 American Control Conference - ACC 2011

    ACC provides a forum for bringing industry and academia together to discuss the latest developments in the area of Automatic Control Systems, from new control theories, to the advances in sensors and actuator technologies, and to new applications areas for automation.

  • 2010 American Control Conference - ACC 2010

    Theory and practice of automatic control

  • 2009 American Control Conference - ACC 2009

    The 2009 ACC technical program will cover new developments related to theory, application, and education in control science and engineering. In addition to regular technical sessions the program will also feature interactive and tutorial sessions and preconference workshops.

  • 2008 American Control Conference - ACC 2008

  • 2007 American Control Conference - ACC 2007

  • 2006 American Control Conference - ACC 2006 (Silver Anniversary)

  • 2005 American Control Conference - ACC 2005

  • 2004 American Control Conference - ACC 2004

  • 2003 American Control Conference - ACC 2003

  • 2002 American Control Conference - ACC 2002

  • 2001 American Control Conference - ACC 2001

  • 2000 American Control Conference - ACC 2000

  • 1999 American Control Conference - ACC '99

  • 1998 American Control Conference - ACC '98

  • 1997 American Control Conference - ACC '97

  • 1996 13th Triennial World Congress of the International Federation of Automatic Control (IFAC)


2020 IEEE International Conference on Industrial Technology (ICIT)

ICIT focuses on industrial and manufacturing applications of electronics, controls, communications, instrumentation, and computational intelligence.


2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

The 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2020) will be held in Metro Toronto Convention Centre (MTCC), Toronto, Ontario, Canada. SMC 2020 is the flagship conference of the IEEE Systems, Man, and Cybernetics Society. It provides an international forum for researchers and practitioners to report most recent innovations and developments, summarize state-of-the-art, and exchange ideas and advances in all aspects of systems science and engineering, human machine systems, and cybernetics. Advances in these fields have increasing importance in the creation of intelligent environments involving technologies interacting with humans to provide an enriching experience and thereby improve quality of life. Papers related to the conference theme are solicited, including theories, methodologies, and emerging applications. Contributions to theory and practice, including but not limited to the following technical areas, are invited.


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Periodicals related to Approximation Based Control

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Circuits and Systems I: Regular Papers, IEEE Transactions on

Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


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Most published Xplore authors for Approximation Based Control

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Xplore Articles related to Approximation Based Control

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Locally Weighted Online Approximation-Based Control for Nonaffine Systems

IEEE Transactions on Neural Networks, 2007

This paper is concerned with tracking control problems for nonlinear systems that are not affine in the control signal and that contain unknown nonlinearities in the system dynamic equations. This paper develops a piecewise linear approximation to the unknown functions during the system operation. New control and parameter adaptation algorithms are designed and analyzed using Lyapunov-like methods. The objectives are ...


Approximation-based control of uncertain helicopter dynamics

IET Control Theory & Applications, 2009

In this study, the altitude and yaw angle tracking is considered for a scale model helicopter, mounted on an experimental platform, in the presence of model uncertainties, which may be caused by unmodelled dynamics, or aerodynamical disturbances from the environment. To deal with the uncertainties, approximation-based techniques using neural network (NN) are proposed. In particular, two different types of NN, ...


Self-Organizing Approximation-Based Control for Higher Order Systems

IEEE Transactions on Neural Networks, 2007

Adaptive approximation-based control typically uses approximators with a predefined set of basis functions. Recently, spatially dependent methods have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this paper, performance-dependent self-organizing approximators will be defined. The designer specifies a positive tracking error criteria. The self-organizing approximation-based controller then monitors the ...


A Locally Weighted Learning Method for Online Approximation Based Control

Proceedings of the 44th IEEE Conference on Decision and Control, 2005

This article is concerned with tracking control problems for nonlinear systems that are not affine in the control signal and that contain unknown nonlinearities in the system dynamic equations. This paper develops a piecewise linear approximation to the unknown functions during the system operation. New control and parameter adaptation algorithms are designed and analyzed using Lyapunov-like methods. The objectives are ...


Self-organizing approximation based control

2006 American Control Conference, 2006

Adaptive approximation based control typically uses approximators with a predefined set of basis functions. Recent methods, spatially dependent methods, have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this article, performance dependent self-organizing approximators will be defined. The designer specifies positive tracking error criteria. The self-organizing approximation based controller ...


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Educational Resources on Approximation Based Control

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IEEE-USA E-Books

  • Locally Weighted Online Approximation-Based Control for Nonaffine Systems

    This paper is concerned with tracking control problems for nonlinear systems that are not affine in the control signal and that contain unknown nonlinearities in the system dynamic equations. This paper develops a piecewise linear approximation to the unknown functions during the system operation. New control and parameter adaptation algorithms are designed and analyzed using Lyapunov-like methods. The objectives are to achieve semiglobal stability of the state, accurate tracking of bounded reference signals contained within a known domain , and at least boundedness of the function approximator parameter estimates. Numerical simulations are included to illustrate the effectiveness of the learning algorithm.

  • Approximation-based control of uncertain helicopter dynamics

    In this study, the altitude and yaw angle tracking is considered for a scale model helicopter, mounted on an experimental platform, in the presence of model uncertainties, which may be caused by unmodelled dynamics, or aerodynamical disturbances from the environment. To deal with the uncertainties, approximation-based techniques using neural network (NN) are proposed. In particular, two different types of NN, namely multilayer neural network and radial basis function neural network are adopted in control design and stability analysis. Based on Lyapunov synthesis, the proposed adaptive NN control ensures that both the altitude and the yaw angle track the given bounded reference signals to a small neighbourhood of zero, and guarantees semiglobal uniform ultimate boundedness of all the closed-loop signals at the same time. The effectiveness of the proposed control is illustrated through extensive simulations. Compared with the model-based control, approximation- based control yields better tracking performance in the presence of model uncertainties.

  • Self-Organizing Approximation-Based Control for Higher Order Systems

    Adaptive approximation-based control typically uses approximators with a predefined set of basis functions. Recently, spatially dependent methods have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this paper, performance-dependent self-organizing approximators will be defined. The designer specifies a positive tracking error criteria. The self-organizing approximation-based controller then monitors the tracking performance and adds basis elements only as needed to achieve the tracking specification. The method of this paper is applicable to general th-order input-state feedback linearizable systems. This paper includes a complete stability analysis and a detailed simulation example.

  • A Locally Weighted Learning Method for Online Approximation Based Control

    This article is concerned with tracking control problems for nonlinear systems that are not affine in the control signal and that contain unknown nonlinearities in the system dynamic equations. This paper develops a piecewise linear approximation to the unknown functions during the system operation. New control and parameter adaptation algorithms are designed and analyzed using Lyapunov-like methods. The objectives are to achieve global stability of the state, accurate tracking of bounded reference signals contained within a known domain D, and at least boundedness of the function approximation parameter estimates.

  • Self-organizing approximation based control

    Adaptive approximation based control typically uses approximators with a predefined set of basis functions. Recent methods, spatially dependent methods, have defined self-organizing approximators where new locally supported basis elements were incorporated when existing basis elements were insufficiently excited. In this article, performance dependent self-organizing approximators will be defined. The designer specifies positive tracking error criteria. The self-organizing approximation based controller then monitors the tracking performance and adds basis elements only as needed to achieve the tracking specification. The paper includes a complete stability analysis

  • Filter-Driven-Approximation-Based Control for a Class of Pure-Feedback Systems With Unknown Nonlinearities by State and Output Feedback

    This paper presents a new approximation-based control approach for uncertain nonlinear pure-feedback systems. The main idea of this paper is to estimate unknown continuous nonlinear functions through a linear combination of first- order filtered signals of state variables and a control input in the nonadaptive control framework, instead of using conventional adaptive neural or fuzzy function approximators. Based on the proposed filter-driven approximation technique, we first present a state-feedback control scheme for pure-feedback systems with unknown nonaffine nonlinearities and a dead-zone input. Then, a filter-driven-approximation-based output-feedback control scheme is proposed via a system transformation and an observer to estimate unmeasurable state variables. Based on the Lyapunov stability theorem, the control errors and the filter-driven approximation errors are considered to prove that the controlled closed-loop system is semi-globally uniformly ultimately bounded. Finally, simulation results are provided to show that the proposed filter-driven-approximation-based controller and the existing function-approximation-based adaptive controllers have similar control performance for nonlinear pure-feedback systems.

  • Decentralized adaptive approximation based control of a class of large-scale systems

    This paper considers the design of a decentralized adaptive approximation based control scheme for a class of interconnected nonlinear systems. Linearly parameterized neural networks are used to adaptively approximate the unknown dynamics of each subsystem and the unknown interconnections. The feedback control and adaptation laws are based only on local measurements of the state. A dead-zone modification is used to address the issues of stability and robustness in the presence of residual approximation errors. A simulation example is used to illustrate the proposed control design methodology.

  • Decentralized adaptive approximation based control with safety scheme outside the approximation region

    This paper presents a decentralized adaptive approximation based control scheme for a class of interconnected nonlinear systems. The feedback control law consists of two schemes, an adaptive approximation controller operating inside a chosen approximation region and a decentralized safety scheme for outside the approximation region. Within the approximation region, linearly parameterized neural networks with a dead-zone modification are used to adaptively approximate the unknown dynamics of each subsystem, as well as the unknown interconnections. Outside the approximation region, the decentralized safety control scheme is designed to steer back the trajectory by using an adaptive bounding approach. A rigorous stability analysis is presented and a simple simulation example is used to illustrate the decentralized adaptive control methodology.

  • Approximation-based control and avoidance of a mobile base with an onboard arm for MARS greenhouse operation

    MARS greenhouse needs mobile robots with on-board arms, that are capable of navigating autonomously in the greenhouse, performing tasks such as carrying plant trays, farming, harvesting, plucking fruits and vegetables and so on. An adaptive neural net (NN) is used for coordinated motion control of base and arm using Lyapunov's approach. A one-layer NN based controller is designed to estimate the unknown dynamics of the system after the incorporation of nonholonomic constraints. This approach provides an inner loop that accounts for possible motion of the arm, with changing loads, while the base is carrying out a task. The case of maintaining a desired course and speed or tracking a desired Cartesian trajectory as the arm moves to its desired orientation with a load is considered. Outer loops are designed not only to avoid both stationary and moving obstacles but also to navigate the mobile base with the onboard arm along the path. The net result is a base plus arm motion controller that is capable of achieving a coordinated motion of the base plus arm in the presence of uncertain dynamics, load and the environment.

  • Optimizing the scaling parameter for ρ/μ approximation based control synthesis

    We revisit the problem of designing a full-state feedback controller for a deterministic finite state machine, so as to maximize a performance parameter R while simultaneously ensuring that the closed loop system satisfies a given performance objective involving a positive scaling parameter τ. Under some additional assumptions, we show that the problem of choosing τ to optimize R in closed loop admits an analytical solution. We demonstrate the use of this approach via a numerical example, showing substantial computational savings over the existing sampling based method. We also provide an intuitive, graph- theoretic interpretation of our result.



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