23 research outputs found

    Stabilization of Linear Systems Across a Time-Varying AWGN Fading Channel

    Full text link
    This technical note investigates the minimum average transmit power required for mean-square stabilization of a discrete-time linear process across a time-varying additive white Gaussian noise (AWGN) fading channel that is presented between the sensor and the controller. We assume channel state information at both the transmitter and the receiver, and allow the transmit power to vary with the channel state to obtain the minimum required average transmit power via optimal power adaptation. We consider both the case of independent and identically distributed fading and fading subject to a Markov chain. Based on the proposed necessary and sufficient conditions for mean-square stabilization, we show that the minimum average transmit power to ensure stabilizability can be obtained by solving a geometric program

    Feedback Passivation of Linear Systems with Fixed-Structure Controllers

    Full text link
    This letter addresses the problem of designing an optimal output feedback controller with a specified controller structure for linear time-invariant (LTI) systems to maximize the passivity level for the closed-loop system, in both continuous-time (CT) and discrete-time (DT). Specifically, the set of controllers under consideration is linearly parameterized with constrained parameters. Both input feedforward passivity (IFP) and output feedback passivity (OFP) indices are used to capture the level of passivity. Given a set of stabilizing controllers, a necessary and sufficient condition is proposed for the existence of such fixed-structure output feedback controllers that can passivate the closed-loop system. Moreover, it is shown that the condition can be used to obtain the controller that maximizes the IFP or the OFP index by solving a convex optimization problem

    Plot of the percentage of messages in the event-triggered communication solver for various values of horizon <i>h</i> and decay <i>d</i>.

    No full text
    Note that a decay of 0 is used to represent 100% of the messages since an event of communication is triggered for this case at every iteration. We see that as the decay and horizon increases, the percentage of messages starts to decrease.</p

    Plot highlighting the number of messages sent by each of the 200 PEs to the left and right neighbors with the event-triggered communication algorithm considering horizon <i>h</i> = 750 and decay <i>d</i> = 0.8.

    No full text
    The number of iterations with the synchronous solver (Sync Iters in the plot legend) which is the same for all PEs is shown by the blue line for reference. In contrast, the number of iterations taken by each of the PEs in the event-triggered solver (Event Iters in the plot legend) is shown by the red asterisks. Further, the number of messages sent to the left neighbor and right neighbor (shown as Event Left Msgs and Event Right Msgs in the plot legend) by each of the PEs is shown respectively by the yellow star sign and the purple round sign. The number of messages for both the left and right neighbors for every PE are quite close. Hence the purple round signs overlap the corresponding yellow star signs for all the PEs. It is seen that the number of messages is considerably lesser than the number of iterations for any PE, thus illustrating the benefit of reduced messages in event-triggered communication.</p

    Evolution of the Manhattan or L-1 norm of the top boundary of 4 randomly chosen PEs.

    No full text
    Note that the x axis starts from 2000 to wait for the large scale oscillations to die out.</p

    Bubbles in a liquid illustrating multiphase flows in a periodic 3-D domain.

    No full text
    Only a small section of the domain is shown here.</p

    Corresponding thresholds in a semi-log plot for the boundaries shown in Fig 5.

    No full text
    It is seen that the thresholds overall decrease with iterations to reflect the decrease in slope of the norm of the boundaries in Fig 5.</p

    Parameters relevant to the simulation setup we consider in this paper.

    No full text
    Parameters relevant to the simulation setup we consider in this paper.</p

    Plot of time for simulation vs the decay <i>d</i> for various values of horizon <i>h</i>.

    No full text
    The decay and horizon are parameters that determine the event-triggered communication threshold.</p
    corecore