arXiv subjects

Yasuaki Wasa

Publications and source records attributed to Yasuaki Wasa.

7 recordsLinked to original sources

arXiv2026

Partial Observation Amplifies Model Mismatch in MAP Estimation via Information-Curvature Margins

This paper theoretically analyzes how system model mismatch displaces finite-horizon maximum a posteriori (MAP) initial-state estimates in controlled dynamical systems under partial observation. From pathwise sensitivity analysis, the initial-state nominal-oracle displacement called MAP shift is decomposed into a model-side mismatch injection and an estimator-side curvature resistance to identify a sensor-dependent information-curvature margin as the amplification bottleneck. The margin is governed by the weakest posterior-curvature direction, so that sensor configurations that maximize aggregate information can still be fragile to mismatches. We connect the margin to nominal Gauss-Newton curvature and to the Bayesian Fisher information matrix, distinguishing instance-wise mismatch robustness from design-time inferability. The margin admits a computable nominal proxy in nonlinear systems, becomes explicit in the linear time-invariant case, and is validated through two numerical examples.

eess.SY↗

arXiv2025

Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networks

This article proposes a data-driven PID controller design based on the principle of adaptive gain optimization, leveraging Physics-Informed Neural Networks (PINNs) generated for predictive modeling purposes. The proposed control design method utilizes gradients of the PID gain optimization, achieved through the automatic differentiation of PINNs, to apply model predictive control using a cost function based on tracking error and control inputs. By optimizing PINNs-based PID gains, the method achieves adaptive gain tuning that ensures stability while accounting for system nonlinearities. The proposed method features a systematic framework for integrating PINNs-based models of dynamical control systems into closed-loop control systems, enabling direct application to PID control design. A series of numerical experiments is conducted to demonstrate the effectiveness of the proposed method from the control perspectives based on both time and frequency domains.

eess.SY↗

arXiv2024

Net-Zero Energy House-oriented Linear Programming for the Sizing Problem of Photovoltaic Panels and Batteries

The global drive towards carbon neutrality has led to a significant increase in the number of power plants based on renewable energy sources (RES). Concurrently, numerous households are adopting RES to generate their own energy, aiming to decrease both electricity costs and carbon footprints. To support these users, many papers have been devoted to developing optimal investment strategies for residential energy systems. However, there is still a significant gap as these studies often neglect important aspects like carbon neutrality. For this reason, in this paper, we explore the concept of net-zero energy houses (ZEHs) -- houses designed to have an annual net energy consumption around zero -- by presenting a constrained optimization problem to find the optimal number of photovoltaic panels and the optimal size of the battery system for home integration. Solving this constrained optimization problem is difficult due to its nonconvex constraints. Nevertheless, by applying a series of transformations, we reveal that it is possible to find an equivalent linear programming (LP) problem which is computationally tractable. The attainment of ZEH can be tackled by introducing a single constraint in the optimization problem. Additionally, we propose a sharing economy approach to the investment problem, offering a strategy that could potentially reduce investment costs and facilitate the attainment of ZEH more efficiently. Finally, we apply the proposed frameworks to a neighborhood in Japan as a case study, demonstrating the potential for long-term ZEH attainment. The results show that, under the right incentive, users can achieve ZEH, reduce their electricity costs and have a minimal impact on the main grid.

math.OC↗

arXiv2023

Stochastic Optimal Investment Strategy for Net-Zero Energy Houses

In this research, we investigate Net-Zero Energy Houses (ZEH), which harness regionally produced electricity from photovoltaic(PV) panels and fuel cells, integrating them into a local power system in pursuit of achieving carbon neutrality. This paper examines the impact of electricity sharing among users who are working towards attaining ZEH status through the integration of PV panels and battery storage devices. We propose two potential scenarios: the first assumes that all users individually invest in storage devices, hence minimizing their costs on a local level without energy sharing; the second envisions cost minimization through the collective use of a shared storage device, managed by a central manager. These two scenarios are formulated as a stochastic convex optimization and a cooperative game, respectively. To tackle the stochastic challenges posed by multiple random variables, we apply the Monte Carlo sample average approximation (SAA) to the problems. To demonstrate the practical applicability of these models, we implement the proposed scenarios in the Jono neighborhood in Kitakyushu, Japan.

econ.GN↗

arXiv2021

Passivity-based Analysis and Design for Population Dynamics with Conformity Biases

This paper addresses mechanisms for boundedly rational decision makers in discrete choice problem. First, we introduce two mathematical models of population dynamics with conformity biases. We next analyze the models in terms of delta-passivity, and show that the conformity biases work to break passivity of decision makers. Based on the passivity perspective, we propose mechanisms so as to induce decision makers to a desired population state. Furthermore, we analyze a convergence property of designed mechanisms, and present parameter conditions to guarantee stable inducements.

eess.SY↗

arXiv2018

Contract Theory Approach to Incentivizing Market and Control Design

We discuss an incentivizing market and model-based approach to design the energy management and control systems which realize high-quality ancillary services in dynamic power grids. Under the electricity liberalization, such incentivizing market should secure a high speed market-clearing by using the market players' private information well. Inspired by contract theory in microeconomics field, we propose a novel design method of such incentivizing market based on the integration of the economic models and the dynamic grid model. The conventional contract problems are analyzed for static systems or dynamical systems with control inputs directly operated by the principal. The analysis is, however, in discord with the incentivizing market. The main challenge of our approach is to reformulate the contract problems adapted to the market from the system and control perspective. We first establish the fundamental formulas for optimal design and clarify the basic properties of the designed market. We also discuss possibilities, limitation and some challenges in the direction of our approach and general market-based approaches.

eess.SY↗

arXiv2013

Cooperative Environmental Monitoring for PTZ Visual Sensor Networks: A Payoff-based Learning Approach

This paper investigates cooperative environmental monitoring for Pan-Tilt-Zoom (PTZ) visual sensor networks. We first present a novel formulation of the optimal environmental monitoring problem, whose objective function is intertwined with the uncertain state of the environment. In addition, due to the large volume of vision data, it is desired for each sensor to execute processing through local computation and communication. To address the issues, we present a distributed solution to the problem based on game theoretic cooperative control and payoff-based learning. At the first stage, a utility function is designed so that the resulting game constitutes a potential game with potential function equal to the group objective function, where the designed utility is shown to be computable through local image processing and communication. Then, we present a payoff-based learning algorithm so that the sensors are led to the global objective function maximizers without using any prior information on the environmental state. Finally, we run experiments to demonstrate the effectiveness of the present approach.

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