Zobrazeno 1 - 10
of 157
pro vyhledávání: '"Kishida, Masako"'
Autor:
Kishida, Masako, Ono, Shunsuke
This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a known range. By incorporating t
Externí odkaz:
http://arxiv.org/abs/2404.08176
Autor:
Kishida, Masako
This paper proposes control approaches for discrete-time linear systems subject to stochastic disturbances. It employs Kalman filter to estimate the mean and covariance of the state propagation, and the worst-case conditional value-at-risk (CVaR) to
Externí odkaz:
http://arxiv.org/abs/2312.15638
Autor:
Kishida, Masako
This paper proposes a risk-aware control approach to enforce safety for discrete-time nonlinear systems subject to stochastic uncertainties. We derive some useful results on the worst-case Conditional Value-at-Risk (CVaR) and define a discrete-time r
Externí odkaz:
http://arxiv.org/abs/2308.14265
Autor:
Hashimoto, Wataru, Hashimoto, Kazumune, Wachi, Akifumi, Shen, Xun, Kishida, Masako, Takai, Shigemasa
In this paper, we consider a way to safely navigate the robots in unknown environments using measurement data from sensory devices. The control barrier function (CBF) is one of the promising approaches to encode safety requirements of the system and
Externí odkaz:
http://arxiv.org/abs/2308.05306
Autor:
Kishida, Masako
This paper addresses the co-design problem of control inputs and execution decisions for event- and self-triggered controls subject to constraints given by the control Lyapunov function and control barrier function. The proposed approach computes the
Externí odkaz:
http://arxiv.org/abs/2302.12435
In this paper, we propose a control synthesis method for signal temporal logic (STL) specifications with neural networks (NNs). Most of the previous works consider training a controller for only a given STL specification. These approaches, however, r
Externí odkaz:
http://arxiv.org/abs/2212.05200
Autor:
Kishida, Masako
This paper introduces the notions of stability, ultimate boundedness, and positive invariance for stochastic systems in the view of risk. More specifically, those notions are defined in terms of the worst-case Conditional Value-at-Risk (CVaR), which
Externí odkaz:
http://arxiv.org/abs/2204.07329
Autor:
Cetinkaya, Ahmet, Kishida, Masako
In this paper, we investigate constrained control of continuous-time linear stochastic systems. We show that for certain system parameter settings, constrained control policies can never achieve stabilization. Specifically, we explore a class of cont
Externí odkaz:
http://arxiv.org/abs/2107.09882
Autor:
Kishida, Masako, Ogura, Masaki
This paper proposes a computational technique based on "deep unfolding" to solving the finite-time maximum hands-off control problem for discrete-time nonlinear stochastic systems. In particular, we seek a sparse control input sequence that stabilize
Externí odkaz:
http://arxiv.org/abs/2104.01755
Autor:
Weyns, Danny, Schmerl, Bradley, Kishida, Masako, Leva, Alberto, Litoiu, Marin, Ozay, Necmiye, Paterson, Colin, Tei, Kenji
Two established approaches to engineer adaptive systems are architecture-based adaptation that uses a Monitor-Analysis-Planning-Executing (MAPE) loop that reasons over architectural models (aka Knowledge) to make adaptation decisions, and control-bas
Externí odkaz:
http://arxiv.org/abs/2103.10847