Masaaki Nagahara

Masaaki Nagahara
Kitakyushu University · Institute of Environmental Science and Technology

Ph.D

About

198
Publications
18,400
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1,621
Citations
Citations since 2016
100 Research Items
1108 Citations
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
2016201720182019202020212022050100150
Introduction
Dr. Masaaki Nagahara is currently a full professor at Institute of Environmental Science and Technology, University of Kitakyushu, Japan. He is also a visiting professor at Indian Institute of Technology (IIT) Bombay, India, from 2017. His research interests include optimal control, cyber-physical systems, artificial intelligence, signal processing, and machine learning. He received Transition to Practice Award from IEEE Control Systems Society in 2012. He is a Senior Member of IEEE.
Education
April 2000 - March 2003
Kyoto University
Field of study
  • Informatics

Publications

Publications (198)
Article
In this paper, we review the basics of compressed sensing and introduce its application to optimal control, called the maximum hands-off control. First, we present the mathematical formulation of compressed sensing and show a heuristic approach to the problem using the ℓ¹ norm with efficient numerical algorithms. Then, we introduce the maximum hand...
Article
This paper investigates closed-loop stability of linear discrete-time plants subject to bounded disturbances when controlled according to packetized predictive control (PPC) policies. In the considered feedback loop, the controller is connected to the actuator via a digital communication channel imposing bounded dropouts. Two PPC strategies are tak...
Article
Full-text available
In this short paper, we study platooning control of drones using only the information from a camera attached to each drone. For this, we adopt real-time objection detection based on a deep learning model called YOLO (you only look once). The YOLO object detector continuously estimates the relative position of the drone in front, by which each drone...
Article
Full-text available
In this paper, we propose an efficient numerical computation method of reduced-order controller design for linear time-invariant systems. The design problem is described by linear matrix inequalities (LMIs) with a rank constraint on a structured matrix, due to which the problem is non-convex. Instead of the heuristic method that approximates the ma...
Article
In the field of systems and control, many cooperative control problems of multi-agent systems have been actively studied in the past two decades. This article aims to organize extensive existing work on different cooperative control problems into three categories, based on three different types of graph Laplacian matrices involved. A Laplacian matr...
Chapter
We consider the problem of discretization of analog filters and propose a novel method based on sampled-data H∞ control theory with sparse representation. For optimal discretization, we adopt minimization of the H∞ norm of the error system between a (delayed) target analog filter and a digital system consisting of an ideal sampler, the zero-order h...
Article
This work discusses a data‐driven approach to controller parameter tuning based on Bayesian optimization. In particular, we propose to design the prior mean function based on a model of the plant. By encoding the information on the model, the optimization needs a much fewer iterations than standard approaches. The effectiveness of the proposed meth...
Article
The paper proposes a novel time-optimal control that is also sparse in both time and space domain to take account of resource constraints in networked control systems. The control problem is described as minimization of a weighted sum of the terminal time and the L0 norm of multi-input control for a linear time-invariant dynamical system, with stat...
Article
We investigate the implementation of a sparsity promoting hands-off control scheme using the alternating direction method of multiplies (ADMM). In order to minimize the numerical control effort along with the actuation one, only a single ADMM iteration per time step is considered. We analyze the resulting closed-loop dynamics for linear systems wit...
Article
Full-text available
This letter treats a novel maximum hands-off control problem in which two types of sparsity are considered. Our optimization problem is mathematically formulated as an L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> optimal control problem with an ℓ <sup xmlns:mml="http://www.w3.org/1998/Math...
Article
Full-text available
In this survey article, we give a comprehensive review of sparse control for continuous‐time systems, called maximum hands‐off control. The maximum hands‐off control is the optimal control, for which we introduce fundamental properties such as necessary conditions, existence, and equivalence to the optimal control. We also show an efficient numeric...
Preprint
Full-text available
This paper investigates closed-loop stability of a linear discrete-time plant subject to bounded disturbances when controlled according to packetized predictive control (PPC) policies. In the considered feedback loop, the controller is connected to the actuator via a digital communication channel imposing bounded dropouts. Two PPC strategies are ta...
Article
In recent years, Thermally Activated Building System (TABS) has been introduced in Japan as an air conditioning system that achieves both comfort and energy saving. TABS in which the building frame (mainly the concrete slab) is used as a component for radiating and storing heat have been installed mainly in Europe since the late 1990s. In recent ye...
Article
In this paper, we consider the problem of constructing constrained smoothing splines, which are of great importance in data science. The novelty of this work is to formulate the problem as an optimal control problem, and we mathematically analyze the optimal smoothing spline with intermediate constraints using first-order optimality condition from...
Article
Full-text available
In this paper, we propose a novel method to find matrices that satisfy sparsity and LMI (linear matrix inequality) constraints at the same time. This problem appears in sparse control design such as sparse representation of the state feedback gain, sparse graph representation with fastest mixing, and sparse FIR (finite impulse response) filter desi...
Article
In this short paper, we propose a new direction of cross-cutting research for prediction and control of spreading COVID-19 viruses over a human social network. Such a network consists of human agents whose behaviors are highly uncertain and biased. To predict and control such an uncertain network, we need to employ various researches such as contro...
Article
This paper constructs a model of wheelchair dynamics in a data-driven manner. In particular, we focus on the forward movement of the wheelchair, for which we adopt a Linear-Parameter-Varying Finite-Impulse-Response (LPV-FIR) model. To avoid the over fitting behavior, we employ the Bayesian estimation method. We also show the identified model is eff...
Preprint
Full-text available
This article treats optimal sparse control problems with multiple constraints defined at intermediate points of the time domain. For such problems with intermediate constraints, we first establish a new Pontryagin maximum principle that provides first order necessary conditions for optimality in such problems. Then we announce and employ a new nume...
Article
Full-text available
Majority determination is one of the fundamental problems in multi-agent systems. It aims to cooperatively and distributedly determine the majority opinion of agents in a network, where the agents initially vote "in favor" or "opposed" for a proposal. An interesting aspect of this issue is to clarify the lowest resolution of communication required...
Book
Full-text available
The method of sparsity has been attracting a lot of attention in the fields related not only to signal processing, machine learning, and statistics, but also systems and control. The method is known as compressed sensing, compressive sampling, sparse representation, or sparse modeling. More recently, the sparsity method has been applied to systems...
Article
This paper constructs a mathematical model of wheelchair dynamics in a data-driven manner. In particular, we focus on the forward-backward movement of the wheelchair, for which we adopt a linear-parameter-varying finite-impulse-response model. To avoid overfitting behavior, we employ the Bayesian estimation method. We show by experimental results t...
Preprint
Full-text available
This paper presents analyses for the maximum hands-off control using the geometric methods developed for the theory of turnpike in optimal control. First, a sufficient condition is proved for the existence of the maximum hands-off control for linear time-invariant systems with arbitrarily fixed initial and terminal points using the relation with $L...
Article
Full-text available
Digital sounds and images are used everywhere today, and they are all generated originally by analogue signals. On the other hand, in digital signal processing, the storage or transmission of digital data, such as music, videos or image files, necessitates converting such analogue signals into digital signals via sampling. When these data are sampl...
Article
We study wireless networked control systems (WNCSs), where controllers (CLs), controlled objects (COs), and other devices are connected through wireless networks. In WNCSs, COs can become unstable due to bursty packet losses and random delays on wireless networks. To reduce these network-induced effects, we utilize the packetized predictive control...
Article
Model predictive control together with its offline computation is utilized for sparse control. Since a conventional type of sparse control is in an open-loop style, it is meaningful to realize it in a closed-loop style using the model predictive control scheme. Noting that the input of sparse control basically takes only three values, one needs to...
Article
In this paper, we propose sparse representation of FIR (Finite Impulse Response) feedback filters in delta-sigma modulators. The filter has a sparse structure, that is, only a few coefficients are non-zero, that stabilizes the feedback modulator, and minimizes the maximum magnitude of the noise transfer function at low frequencies. The optimization...
Article
In this paper, we propose optimal control that is both sparse and continuous, unlike previously proposed alternatives to maximum hands-off control. The maximum hands-off control is the L0-optimal (or sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin wi...
Article
Recent developments in computation and sensing technology have enabled us to access a variety of data sources. In signal processing and machine learning, sparse modeling has attracted much attention as a means of processing such high-dimensional data by harnessing the sparsity of a data structure. On the other hand, the importance of distributed sp...
Article
In this paper, we propose a design method of self-interference cancelers for in-band full-duplex wireless relaying taking account of baseband signal subspaces. We model the relaying system with self-interference as a sampled-data feedback control system. Then we formulate the design problem as a sampled-data H∞ control problem with a generalized sa...
Article
Wireless networked control systems (WNCSs) are control systems whose components are connected through wireless networks. In WNCSs, a controlled object (CO) could become unstable due to bursty packet losses in addition to random packet losses and round-trip delays on wireless networks. In this paper, to reduce these network-induced effects, we propo...
Article
This paper proposes a novel distributed proximal minimization algorithm for constrained optimization problems over fixed strongly connected networks. At each iteration, each agent updates its own state by evaluating a proximal operator of its objective function under a constraint set and compensating the unbalancing due to unidirectional communicat...
Article
In this article, we investigate theoretical properties of the time-optimal hands-off control for linear time-invariant systems. The purpose of the control is to maximize the time duration on which the control value is exactly zero (maximum hands-off control) and also to minimize the response time to achieve a given state transition (time-optimal co...
Article
In this paper, we propose a distributed control algorithm for consensus of multi-agent systems with first- and second-order dynamics via sampled-data state observation. Our consensus scheme is based on the maximum hands-off control. It is a control that maximizes the time duration on which the control is exactly zero among the feasible controls. Su...
Article
This paper develops approaches to the hands-off control problem subject to performance constraints for discrete-time linear systems. The approaches minimize the l1-norm of the control input to acquire the hands-off property, while satisfying the performance constraints that are given in terms of the quadratic cost of states and inputs with respect...
Article
In this paper, we design a no-overloading error feedback quantizer based on a ΔΣ modulator, composed of an error feedback filter and a static quantizer. To guarantee no-overloading in the quantizer, we impose an l∞ norm constraint on the feedback signal in the quantizer. Then, for a prescribed l∞ norm constraint on the error at the system output in...
Preprint
In this paper, we consider hands-off control via minimization of the CLOT (Combined LOne and Two) norm. The maximum hands-off control is the L 0-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In general,...
Article
Full-text available
In this paper, we propose a new design method of discrete-valued model predictive control for continuous-time linear time-invariant systems based on sum-of-absolute-values (SOAV) optimization. The finite-horizon discrete-valued control design is formulated as an SOAV optimal control, which is an expansion of L¹ optimal control. It is known that und...
Conference Paper
Hands-off control (or sparse control) is a control that has a small support length that achieves a given control objective. In other words, hands-off control takes zero on a relatively long time duration. Recently, this idea is extended to distributed control for consensus, called distributed hands-off control. In this control, each agent uses hand...
Conference Paper
Full-text available
This paper studies the disturbance rejection problem for sampled-data control systems, where disturbance signal occurs below and above the Nyquist frequency simultaneously. Two discrete-time controllers are designed via H ∞ optimal control in two steps; at first a controller is designed to reject the low-frequency components, and then we construct...
Article
Full-text available
Quantization is a fundamental process in digital signal processing. ΔΣ modulators are often utilized for quantization, which can be easily implemented with static uniform quantizers and error feedback filters. In this paper, we analyze the mean squared quantization error of the quantizer with error feedback including the ΔΣ modulators. First, we st...
Data
In this paper, we consider hands-off control via minimization of the CLOT (Combined L-One and Two) norm. The maximum hands-off control is the L0-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In general,...
Article
In this paper, we consider hands-off control via minimization of the CLOT (Combined $L$-One and Two) norm. The maximum hands-off control is the $L^0$-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In gen...
Article
Full-text available
In a networked control system, quantization is inevitable to transmit control and measurement signals. While uniform quantizers are often used in practical systems, the overloading, which is due to the limitation on the number of bits in the quantizer, may significantly degrade the control performance. In this paper, we design an overloading-free f...
Article
Full-text available
This paper shows that the H∞ control is an effective tool for electric voltage regulation. We here consider robust AC voltage regulation of microgrids in an islanded mode. When a microgrid is disconnected from a utility grid, it automatically switches to an islanded mode to provide necessary power using a battery system until the grid is recovered....
Article
Full-text available
Recently, a novel method for signal reconstruction, called compressed sensing or sparse modeling, has been attracting considerable attention in signal processing and machine learning. This method has been extended to optimal control, which is called sparse optimal control or dynamical sparse modeling. This control can stop actuators for a time dura...
Conference Paper
In this paper, we propose a design procedure to obtain infinite impulse response (IIR) error feedback filters for ΔΣ modulators. It is known that the design of optimal finite impulse response (FIR) error feedback filters for ΔΣ modulators can be cast into a convex optimization problem, while the design of IIR error feedback filters cannot. To desig...
Article
Full-text available
In this paper, we propose a new design method of discrete-valued control for continuous-time linear time-invariant systems based on sum-of-absolute-values (SOAV) optimization. We first formulate the discrete-valued control design as a finitehorizon SOAV optimal control, which is an extended version of L1 optimal control.We then give simple conditio...
Article
Full-text available
In this paper, we consider hands-off control via minimization of the CLOT (Combined L-One and Two) norm. The maximum hands-off control is the L0-optimal (or the sparsest) control among all feasible controls that are bounded by a specified value and transfer the state from a given initial state to the origin within a fixed time duration. In general,...