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Intelligent Control (ISIC), 2011 IEEE International Symposium on

Date 28-30 Sept. 2011

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Displaying Results 1 - 25 of 51
  • Welcome

    Page(s): 3 - 8
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  • Technical program overview

    Page(s): 9 - 11
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  • Plenary lectures

    Page(s): 12 - 13
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    Provides an abstract of the plenary presentations and a brief professional biography of the presenters. The complete presentation was not made available for publication as part of the conference proceedings. View full abstract»

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  • Freeway traffic control

    Page(s): 14
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  • 2011 IEEE MSC information

    Page(s): 15 - 16
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  • Workshops

    Page(s): 17 - 18
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  • Joint Society Invited Sessions

    Page(s): 19 - 20
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  • 2011 MSC program at a glance

    Page(s): 1 - 3
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  • Content list

    Page(s): 1 - 19
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  • Book of abstracts

    Page(s): 1 - 44
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    Summary form only given. Provides an abstract for each of the presentations in the conference proceedings. View full abstract»

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  • 2011 MSC author index

    Page(s): 1 - 7
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  • 2011 MSC keyword index

    Page(s): 1 - 4
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  • Performance information in risk-averse control of model-following systems

    Page(s): 593 - 600
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (178 KB) |  | HTML iconHTML  

    The paper presents an extension of the theory of risk-averse control of a linear-quadratic class of model-following control systems with incomplete state feedback. It is shown that performance information can improve control decisions with only available output measurements for system performance reliability but information structures can also be costly. Many of the results entail measures of the amount, value, and cost of performance information, and the design of model-following control strategy with risk aversion. It becomes clear that the topic of performance information in control is of central importance for future research and development of correct-by-design of high performance and reliable systems. View full abstract»

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  • Satellite formation flying with input saturation: An LMI approach

    Page(s): 810 - 815
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    In this paper, we consider a relative position control problem for a satellite formation flying system in a noncoplarnar and elliptical orbit. It is assumed that the angular rate and angular acceleration are not known, but they are bounded. The system dynamics is designed with the bounded uncertain parameters. In the presence of input saturation, we develop a state feedback controller that guarantees stability of the system. Linear matrix inequality (LMI) conditions are proposed to design the feedback controller. Finally, numerical simulation is presented to demonstrate the validity of the proposed controller. View full abstract»

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  • Distance-based control of cycle-free persistent formations

    Page(s): 816 - 821
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (361 KB) |  | HTML iconHTML  

    In this paper, we study distance-based control of cycle-free persistent formations of single-integrator modeled agents in the plane. First, we propose a sequential control law consisting of algebraic calculations and primitive motions for agent groups having cycle-free persistent formations. Second, we prove the local asymptotic stability of cycle-free persistent formations under the well-known gradient law, which can be interpreted as a simultaneous version of the proposed sequential law, based on input-to-state stability. Furthermore, we show that if the leader-agent of a cycle-free persistent formation moves sufficiently slowly, then the formation of the group remains in the neighborhood of the desired formation. View full abstract»

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  • An interpolation method of multiple terminal iterative learning control

    Page(s): 1528 - 1533
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (251 KB) |  | HTML iconHTML  

    In this paper, we present an iterative learning control (ILC) algorithm to track specified desired multiple terminal points at given time instants. A framework to update the desired trajectories from given points is developed based on the interpolation technique. The approach shows better rate of convergence of the errors. The simulation with a satellite antenna control model is demonstrated to show the effectiveness of our approach. View full abstract»

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  • Optimal network localization by particle swarm optimization

    Page(s): 620 - 625
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (371 KB) |  | HTML iconHTML  

    The practical and theoretical importance of network localization has determined a great focus from the scientific community. In recent years several schemes have been proposed to solve the localization problem under certain constraints. Here, we apply the particle swarm optimization (PSO) paradigm to the problem of constructing optimally localizable networks. Alternative solutions which yield non-optimal solutions are also discussed. Simulations are carried out to show the convergence properties of the proposed PSO algorithm. We conclude that optimal network localization can be achieved in an efficient way by using PSO. View full abstract»

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  • Probabilistic fault detection and handling algorithm for testing stability control systems with a drive-by-wire vehicle

    Page(s): 601 - 606
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (267 KB) |  | HTML iconHTML  

    This paper presents a probabilistic fault detection and handling algorithm (PFDH) for redundant and deterministic X-by-wire systems. The algorithm is specifically designed to guarantee safe operation of an experimental drive-by-wire vehicle used as test platform and development tool in research projects focusing on vehicle dynamics. The required flexibility of the overall system for use as a test bed influences significantly the redundancy structure of the onboard network. A “black box” approach to integrate newly developed user algorithms is combined with a hot-standby architecture controlled by PFDH. This way, functional redundancy for basic driving operations can be achieved despite unknown software components. PFDH is based on monitoring multiple criteria over time, including vehicle dynamics and relative error probabilities of hard- and software components provided by experts or statistical data. View full abstract»

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  • A framework for adaptive tuning of distributed model predictive controllers by Lagrange multipliers

    Page(s): 1516 - 1523
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (433 KB) |  | HTML iconHTML  

    In this work we show that some sort of altruism between controllers is require for a distributed approach to be globally optimal. This paper makes a contribution to the state-of-the-art by defining distributed MPC controllers as altruistic MPC agents and proposing an on-line tuning of the agent altruism (Lagrange multipliers). The tuning process will guarantee a minimal level of what we call satisfaction, for all MPC agents. The tuning adapts to the current conditions since it is performed in each control cycle. Further, a bargain scheme can be developed to deal with infeasibility. View full abstract»

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  • Formation control of mobile agent groups based on localization

    Page(s): 822 - 827
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (596 KB) |  | HTML iconHTML  

    In this paper, we study localization based formation control of mobile agent groups by using relative and partial measurements. Though formation control and localization are linked to each other, they have separately been addressed in the literature. As a novel approach, we propose a formation control strategy based on localization information for single- and double-integrator modeled agent groups. Subsequently, we show that the formation of a single-integrator modeled agent group globally exponentially converges to the desired formation with the estimated formation globally exponentially converging to the actual formation up to translation if and only if the interaction graph of the group contains a spanning tree. Furthermore, we provide a necessary and sufficient condition for the global exponential convergence of the formation of a double-integrator modeled agent group to the desired formation. Simulation results validate the effectiveness of the proposed strategy. View full abstract»

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  • Real-time PI controller tuning via unfalsified control

    Page(s): 1008 - 1013
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (455 KB) |  | HTML iconHTML  

    In this paper, we present a new unfalsified adaptive control algorithm. This algorithm leads to a real-time controller tuning method. The algorithm consists of two main elements: 1) Switching of controllers in a controller set by the e -hysteresis switching algorithm and 2) Optimization of the controller set via an evolutionary algorithm (EA). The real-time controller tuning is demonstrated for a nonminimum-phase continuous stirred tank reactor (CSTR) model. View full abstract»

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  • Norm-optimal control of time-varying discrete repetitive processes

    Page(s): 388 - 393
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (405 KB) |  | HTML iconHTML  

    This paper extends the norm-optimal control design methodology of iterative learning control to the case of time-varying discrete repetitive processes. As a first contribution, starting from a lifting-based formulation, we show a novel iteration-domain state-space description for discrete-repetitive processes. We then pose and solve the norm-optimal control problem for this class of systems. We present solutions for three cases: optimal regulation, two-degree-of-freedom optimal tracking, and optimal robust servomechanism-based tracking of iteration-invariant reference signals. View full abstract»

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  • Multi-agent coordination by iterative learning control: Centralized and decentralized strategies

    Page(s): 394 - 399
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (485 KB) |  | HTML iconHTML  

    Iterative learning control (ILC), an approach to achieve perfect trajectory tracking for uncertain dynamic systems that are periodic or repetitive, can be viewed as a kind of coordination or planning algorithm. This paper exploits this view to provide two coordination algorithms for distributed multi-agent systems. First we show how to achieve formation control for a class of nonholonomic mobile agents though an iterative update of each agent's angular velocity along the trajectory. The algorithm required to achieve this result uses local measurements, but a centralized computation of the control input. Second, we show a decentralized coordination strategy for a set of simple first-order integrator dynamic systems. In this case the control updates are computed locally by each agent using only local information, yet through the iterative update process the group achieves the desired formation. Numerical simulations illustrate the results. View full abstract»

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  • Non-zero sum games: Online learning solution of coupled Hamilton-Jacobi and coupled Riccati equations

    Page(s): 171 - 178
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    In this paper we present an online adaptive control algorithm based on policy iteration reinforcement learning techniques to solve the continuous-time (CT) multi player non zero sum (NZS) game with infinite horizon for linear and nonlinear systems. The adaptive algorithm learns online the solution of coupled Riccati equations and coupled Hamilton-Jacobi equations for linear and nonlinear systems respectively. This adaptive control method finds in real-time approximations of the optimal value and the NZS Nash-equilibrium, while also guaranteeing closed-loop stability. The optimal-adaptive algorithm is implemented as a separate actor/critic parametric network approximator structure for every player, and involves simultaneous continuous-time adaptation of the actor/critic networks. A persistence of excitation condition is shown to guarantee convergence of every critic to the actual optimal value function for that player. A detailed mathematical analysis is done for 2-player NZS games. Novel tuning algorithms are given for the actor/critic networks. The convergence to the ash equilibrium is proven and stability of the system is also guaranteed. Simulation examples show the effectiveness of the new algorithm. View full abstract»

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  • Robust adaptive finite-time parameter estimation and control of nonlinear systems

    Page(s): 1014 - 1019
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (318 KB) |  | HTML iconHTML  

    This paper exploits an alternative adaptive parameter estimation and control approach for nonlinear systems. An auxiliary filter is developed to derive a representation of the parameter estimation error, which is combined with an adaptive law to guarantee the exponential convergence of the control error as well as the estimation error. The proposed method is further improved via a sliding mode technique to achieve the finite-time (FT) error convergence. The traditional persistent excitation (PE) is simplified as an a priori verifiable sufficiently rich (SR) requirements on the demand signal. The robustness of the control schemes with bounded disturbances is also investigated. The developed methods are finally tested via simulations. View full abstract»

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