Paul Frihauf

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We introduce a non-model based approach for locally stable convergence to Nash equilibria in static, noncooperative games with players. In classical game theory algorithms, each player employs the knowledge of the functional form of his payoff and the knowledge of the other players’ actions, whereas in the proposed algorithm, the players need to measure(More)
We consider a general, stable nonlinear dynamic system with N inputs and N outputs, where in the steady state, the output signals represent the non-quadratic payoff functions of a noncooperative game played by the values of the input signals. We introduce a non-model based approach for locally stable convergence to a steady-state Nash equilibrium. In(More)
We present a non-model based approach for asymptotic, locally exponentially stable attainment of the optimal open-loop control sequence for unknown, discrete-time linear systems with a scalar input, assumptions on the stability of the unknown system, but we do assume that the system is reachable. The proposed algorithm employs the multi-variable(More)
Precise knowledge of the volume and rate of early rapid left ventricular (LV) filling elucidates kinematic aspects of diastolic physiology. The Doppler E wave velocity-time integral (VTI) is conventionally used as the estimate of early, rapid-filling volume; however, this implicitly requires the assumption of a constant effective mitral valve area (EMVA).(More)
We introduce an approach for stable deployment of agents onto families of planar curves, namely, 1-D formations in 2-D space. The agents’ collective dynamics are modeled by the reaction–advection–diffusion class of partial differential equations (PDEs), which is a broader class than the standard heat equation and generates a rich geometric family of(More)
We introduce an approach for stable deployment of agents into planar curves (1-D formations in 2-D space) parameterized by the agent index. Stability is ensured by leader feedback, which is designed in a manner similar to boundary control of PDEs. By discretizing the model and the PDE controllers with respect to the continuous agent index, we obtain control(More)
With a single stochastic extremum seeking control signal, we steer multiple autonomous vehicles, modeled as nonholonomic unicycles, toward the maximum of an unknown, spatially distributed signal field. The vehicles, whose angular velocities are constant and distinct, travel at the same forward velocity, which is controlled by the stochastic extremum seeking(More)