arXiv subjects

Kenji Sugimoto

Publications and source records attributed to Kenji Sugimoto.

9 recordsLinked to original sources

arXiv2022

Static Output Feedback Synthesis of Time-Delay Linear Systems via Deep Unfolding

We propose a deep unfolding-based approach for stabilization of time-delay linear systems. Deep unfolding is an emerging framework for design and improvement of iterative algorithms and attracting significant attentions in signal processing. In this paper, we propose an algorithm to design a static output feedback gain for stabilizing time-delay linear systems via deep unfolding. Within the algorithm, the learning part is driven by NeuralODE developed in the community of machine learning, while the gain verification is performed with linear matrix inequalities developed in the systems and control theory. The effectiveness of the proposed algorithm is illustrated with numerical simulations.

math.OC↗

arXiv2021

Adaptive t-Momentum-based Optimization for Unknown Ratio of Outliers in Amateur Data in Imitation Learning

Behavioral cloning (BC) bears a high potential for safe and direct transfer of human skills to robots. However, demonstrations performed by human operators often contain noise or imperfect behaviors that can affect the efficiency of the imitator if left unchecked. In order to allow the imitators to effectively learn from imperfect demonstrations, we propose to employ the robust t-momentum optimization algorithm. This algorithm builds on the Student's t-distribution in order to deal with heavy-tailed data and reduce the effect of outlying observations. We extend the t-momentum algorithm to allow for an adaptive and automatic robustness and show empirically how the algorithm can be used to produce robust BC imitators against datasets with unknown heaviness. Indeed, the imitators trained with the t-momentum-based Adam optimizers displayed robustness to imperfect demonstrations on two different manipulation tasks with different robots and revealed the capability to take advantage of the additional data while reducing the adverse effect of non-optimal behaviors.

cs.LG↗

arXiv2021

Deep unfolding-based output feedback control design for linear systems with input saturation

In this paper, we propose a deep unfolding-based framework for the output feedback control of systems with input saturation. Although saturation commonly arises in several practical control systems, there is still a scarce of effective design methodologies that can directly deal with the severe non-linearity of the saturation operator. In this paper, we aim to design an anti-windup controller for enlarging the region of stability of the closed-loop system by learning from the numerical simulations of the closed-loop system. The data-driven framework we propose in this paper is based on a deep-learning technique called Neural Ordinary Differential Equations. Within our framework, we first obtain a candidate controller by using the deep-learning technique, which is then tested by the existing theoretical results already established in the literature, thereby avoiding the computational challenge in the conventional design methodologies as well as theoretically guaranteeing the performance of the system. Our numerical simulation shows that the proposed framework can significantly outperform a conventional design methodology based on linear matrix inequalities.

eess.SY↗

arXiv2020

Finite-time Control of Discrete-time Positive Linear Systems via Convex Optimization

In this paper, we study a class of finite-time control problems for discrete-time positive linear systems with time-varying state parameters. Although several interesting control problems appearing in population biology, economics, and network epidemiology can be described as the class of finite-time control problems, an efficient solution to the control problem has not been yet found in the literature. In this paper, we propose an optimization framework for solving the class of finite-time control problems via convex optimization. We illustrate the effectiveness of the proposed method by numerical simulation in the context of dynamical product development processes.

eess.SY↗

arXiv2020

Mixed $H_2/H_{\infty}$ Control Control of Delayed Markov Jump Linear Systems

This paper investigates state feedback control laws for Markov jump linear systems with state and mode-observation delays. An assumption in this study is that the delay of mode observation obeys an exponential distribution. Also, we raise an unknown time-varying state delay applied in the composition of the state feedback controller. A method of remodeling the closed-loop system as a standard Markov jump linear system with state delay is shown. Furthermore, on the basis of this remodeling, several Linear Matrix Inequalities (LMI) for designing feedback gains for stabilization and mixed $H_2/H_{\infty}$ control are proposed. Finally, we apply a numerical simulation for examining the effectiveness of the proposed mixed $H_2/H_{\infty}$ controller designing method.

eess.SY↗

arXiv2020

TAdam: A Robust Stochastic Gradient Optimizer

Machine learning algorithms aim to find patterns from observations, which may include some noise, especially in robotics domain. To perform well even with such noise, we expect them to be able to detect outliers and discard them when needed. We therefore propose a new stochastic gradient optimization method, whose robustness is directly built in the algorithm, using the robust student-t distribution as its core idea. Adam, the popular optimization method, is modified with our method and the resultant optimizer, so-called TAdam, is shown to effectively outperform Adam in terms of robustness against noise on diverse task, ranging from regression and classification to reinforcement learning problems. The implementation of our algorithm can be found at https://github.com/Mahoumaru/TAdam.git

cs.LG↗

arXiv2019

Stability Optimization of Positive Semi-Markov Jump Linear Systems via Convex Optimization

In this paper, we study the problem of optimizing the stability of positive semi-Markov jump linear systems. We specifically consider the problem of tuning the coefficients of the system matrices for maximizing the exponential decay rate of the system under a budget-constraint. By using a result from the matrix theory on the log-log convexity of the spectral radius of nonnegative matrices, we show that the stability optimization problem reduces to a convex optimization problem under certain regularity conditions on the system matrices and the cost function. We illustrate the validity and effectiveness of the proposed results by using an example from the population biology.

eess.SY↗

arXiv2019

Synergistic Effects in Networked Epidemic Spreading Dynamics

In this brief, we study epidemic spreading dynamics taking place in complex networks. We specifically investigate the effect of synergy, where multiple interactions between nodes result in a combined effect larger than the simple sum of their separate effects. Although synergistic effects play key roles in various biological and social phenomena, their analyses have been often performed by means of approximation techniques and for limited types of networks. In order to address this limitation, this paper proposes a rigorous approach to quantitatively understand the effect of synergy in the Susceptible-Infected-Susceptible model taking place in an arbitrary complex network. We derive an upper bound on the growth rate of the synergistic Susceptible-Infected-Susceptible model in terms of the eigenvalues of a matrix whose size grows quadratically with the number of the nodes in the network. We confirm the effectiveness of our result by numerical simulations on empirically observed human and animal social networks.

cs.SI↗

arXiv2018

Sampled-Data State Observation over Lossy Networks under Round-Robin Scheduling

In this paper, we study the problem of continuous-time state observation over lossy communication networks. We consider the situation in which the samplers for measuring the output of the plant are spatially distributed and their communication with the observer is scheduled according to a round-robin scheduling protocol. We allow the observer gains to dynamically change in synchronization with the scheduling of communications. In this context, we propose a linear matrix inequality (LMI) framework to design the observer gains that ensure the asymptotic stability of the error dynamics in continuous time. We illustrate the effectiveness of the proposed methods by several numerical simulations.

eess.SY↗