4,052 research outputs found
Nonlinear stochastic controllers for semiactive and regenerative structural systems, with guaranteed quadratic performance margins
In many applications of vibration control, the circumstances of the application impose constraints on the energy available for the actuation of control forces. Semiactive dampers (i.e., viscous dampers with controllable coefficients) constitute the simplest example of such actuation in structural control applications. Regenerative Force Actuation (RFA) networks are an extension of semiactive devices, in which mechanical energy is first converted to electrical energy, which is then dissipated in a controllable resistive network. A fairly general class of semiactive and regenerative systems can be characterized by a differential equation which is bilinear (i.e., linear in state, linear in control input, but nonlinear in both). This paper presents a general approach to bilinear feedback control system design for semiactive and regenerative systems, which is analytically guaranteed to out-perform optimal linear viscous damping in stationary stochastic response, under the familiar Quadratic Gaussian performance measure. The design for full-state feedback and for the more practical case of noise-corrupted and incomplete measurements (i.e., output feedback) are separately discussed. Variants of the theory are shown to exist for other quadratic performance measures, including risk-sensitive and multi-objective frameworks. An illustrative application to civil engineering is presented
Stability and analytic expansions of local solutions of systems of quadratic BSDEs with applications to a price impact model
We obtain stability estimates and derive analytic expansions for local
solutions of multi-dimensional quadratic BSDEs. We apply these results to a
financial model where the prices of risky assets are quoted by a representative
dealer in such a way that it is optimal to meet an exogenous demand. We show
that the prices are stable under the demand process and derive their analytic
expansions for small risk aversion coefficients of the dealer.Comment: Final version, 28 page
Quantum control theory and applications: A survey
This paper presents a survey on quantum control theory and applications from
a control systems perspective. Some of the basic concepts and main developments
(including open-loop control and closed-loop control) in quantum control theory
are reviewed. In the area of open-loop quantum control, the paper surveys the
notion of controllability for quantum systems and presents several control
design strategies including optimal control, Lyapunov-based methodologies,
variable structure control and quantum incoherent control. In the area of
closed-loop quantum control, the paper reviews closed-loop learning control and
several important issues related to quantum feedback control including quantum
filtering, feedback stabilization, LQG control and robust quantum control.Comment: 38 pages, invited survey paper from a control systems perspective,
some references are added, published versio
Risk Aversion in Finite Markov Decision Processes Using Total Cost Criteria and Average Value at Risk
In this paper we present an algorithm to compute risk averse policies in
Markov Decision Processes (MDP) when the total cost criterion is used together
with the average value at risk (AVaR) metric. Risk averse policies are needed
when large deviations from the expected behavior may have detrimental effects,
and conventional MDP algorithms usually ignore this aspect. We provide
conditions for the structure of the underlying MDP ensuring that approximations
for the exact problem can be derived and solved efficiently. Our findings are
novel inasmuch as average value at risk has not previously been considered in
association with the total cost criterion. Our method is demonstrated in a
rapid deployment scenario, whereby a robot is tasked with the objective of
reaching a target location within a temporal deadline where increased speed is
associated with increased probability of failure. We demonstrate that the
proposed algorithm not only produces a risk averse policy reducing the
probability of exceeding the expected temporal deadline, but also provides the
statistical distribution of costs, thus offering a valuable analysis tool
A minimal HIV-AIDS infection model with general incidence rate and application to Morocco data
We study the global dynamics of a SICA infection model with general incidence
rate. The proposed model is calibrated with cumulative cases of infection by
HIV-AIDS in Morocco from 1986 to 2015. We first prove that our model is
biologically and mathematically well-posed. Stability analysis of different
steady states is performed and threshold parameters are identified where the
model exhibits clearance of infection or maintenance of a chronic infection.
Furthermore, we examine the robustness of the model to some parameter values by
examining the sensitivity of the basic reproduction number. Finally, using
numerical simulations with real data from Morocco, we show that the model
predicts well such reality.Comment: This is a preprint of a paper whose final and definite form is with
'Statistics Opt. Inform. Comput.', Vol. 7, No 2 (2019). See
[http://www.IAPress.org]. Submitted 16/Sept/2018; Revised 10 & 15/Dec/2018;
Accepted 15/Dec/201
Evolutionary Poisson Games for Controlling Large Population Behaviors
Emerging applications in engineering such as crowd-sourcing and
(mis)information propagation involve a large population of heterogeneous users
or agents in a complex network who strategically make dynamic decisions. In
this work, we establish an evolutionary Poisson game framework to capture the
random, dynamic and heterogeneous interactions of agents in a holistic fashion,
and design mechanisms to control their behaviors to achieve a system-wide
objective. We use the antivirus protection challenge in cyber security to
motivate the framework, where each user in the network can choose whether or
not to adopt the software. We introduce the notion of evolutionary Poisson
stable equilibrium for the game, and show its existence and uniqueness. Online
algorithms are developed using the techniques of stochastic approximation
coupled with the population dynamics, and they are shown to converge to the
optimal solution of the controller problem. Numerical examples are used to
illustrate and corroborate our results
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