Stochastic Asymptotic Stabilizers for Deterministic Input-Affine Systems based on Stochastic Control Lyapunov Functions

In this paper, a stochastic asymptotic stabilization method is proposed for deterministic input-affine control systems, which are randomized by including Gaussian white noises in control inputs. The sufficient condition is derived for the diffucion coefficients so that there exist stochastic control Lyapunov functions for the systems. To illustrate the usefulness of the sufficient condition, the authors propose the stochastic continuous feedback law, which makes the origin of the Brockett integrator become globally asymptotically stable in probability.Comment: A preliminary version of this paper appeared in the Proceedings of the 48th Annual IEEE Conference on Decision and Control [14

Maternal estrogen controls retinoic acid metabolism and signaling in early vertebrate development

Fertilized eggs of lower vertebrates contain substantial amounts of steroidal hormones such as estrogen transferred from mother during oogenesis. However, molecular roles for maternal estrogen in the early embryonic development are largely unknown. Here we show that maternal estrogen and estrogen receptor-α modulate retinoic acid (RA) metabolism and RA-responsive gene expression in medaka embryos. Treatments with excess estradiol, an anti-estrogen (tamoxifen), overexpression or knockdown of estrogen receptor-α (ERα) resulted in misregulation of RA-related gene expression such as raldh2 (retinalaldehyde dehydrogenase), cyp26a1 (RA hydroxylase), fgf8 (fibroblast growth factor), rarα (RA receptor-α), and ahr1 (aryl hydrocarbon receptor). We propose that maternal estrogen/ERα plays a critical role in the feedback control of in vivo level of RA and that it also activates RA signaling for the development of hindbrain and vasculatures. This is the first report demonstrating that maternal estrogen supports successful embryonic development by controlling RA metabolism and signaling in early vertebrate embryos.Supported by grants from the Ministry of Education, Culture, Sports, Science and Technology of Japan

Block sparse design of distributed controllers for dynamical network systems

This study proposes a controller design method based on block sparse optimization for dynamical network systems. The objective of the controller is to stabilize dynamical network systems with a given convergence rate. The block sparse optimization minimizes the number of controlled nodes. This study is unique in that the structure of the controller is constrained by the network topology of the system. Additionally, the proposed design problem is separable in terms of the distributed optimization over networks. The proposed method is applicable to controller design for the pinning control of consensus systems and the optimal vaccine allocation for epidemic spreading processes

Uniformly Ultimate Boundedness Control with Decentralized Event-Triggering

Event-triggered control is a method that the control input is updated only when a certain condition is satisfied (i.e., an event occurs). In this paper, event-triggered control over a sensor network is studied based on the notion of uniformly ultimate boundedness. Since sensors are located in a distributed way, we consider multiple event-triggering conditions. In uniformly ultimate boundedness, it is guaranteed that if the state reaches a certain set containing the origin, the state stays within this set. Using this notion, the occurrence of events in the neighborhood of the origin is inhibited. First, the simultaneous design problem of a controller and event-triggering conditions is formulated. Next, this problem is reduced to an LMI (linear matrix inequality) optimization problem. Finally, the proposed method is demonstrated by a numerical example

Asymptotic stabilization with group-wise sparse input based on control Lyapunov function approach

This study proposes a novel stabilizing controller for nonlinear systems using group-wise sparse inputs. The input variables are divided into several groups. In the situations when the input constraints can be ignored, one input becomes active for each group at each moment. Our method improves energy efficiency, as sparse input vectors often reduce the standby power of inactive actuators. Large-scale systems, such as those consisting of multiple subsystems, often require the manipulation of multiple inputs simultaneously to be controlled. Our method can be applied to such systems due to the group-wise sparsity of the inputs. The proposed controller is based on the control Lyapunov function approach and includes Sontag's universal formula as a special case. The controllers designed in our method have best-effort property, which means even when a restriction for the decreasing rate of the Lyapunov function cannot be fulfilled, the controller minimizes the time derivative of the Lyapunov function within the input constraint. The effectiveness of the proposed method can be confirmed through simulations

Blockchain-Based Optimization of Distributed Energy Management Systems with Real-Time Demand Response

Design of distributed energy management systems composed of several agents such as factories and buildings is important for realizing smart cities. In addition, demand response for saving the power consumption is also important. In this paper, we propose a design method of distributed energy management systems with real-time demand response, in which both electrical energy and thermal energy are considered. Here, we use ADMM (Alternating Direction Method of Multipliers), which is well known as one of the powerful methods in distributed optimization. In the proposed method, demand response is performed in real-time, based on the difference between the planned demand and the actual value. Furthermore, utilizing a blockchain is also discussed. The effectiveness of the proposed method is presented by a numerical example. The importance of introducing a blockchain is pointed out by presenting the adverse effect of tampering the actual value

Distributed estimation based on weighted data aggregation over delayed sensor networks

In this paper, data aggregation laws and distributed observers over delayed sensor networks with any topology are proposed. In the proposed method, each node compensates communication delays of received data. For the delay compensation, each node predicts the future output based on state space models. To stabilize the aggregation data in any networks, the received data are multiplied by weight coefficients before the aggregation. The stability condition of the weighted aggregation laws is expressed by a weighted adjacency matrix. The aggregated value of the measurements at each node is expressed by a linear time-varying function of the current state. To estimate the state, we utilize the Kalman filters as the distributed observers. The effectiveness of the proposed method is confirmed by a numerical simulation. (c) 2020 Elsevier Ltd. All rights reserved

Virtual merge and split at intersection for vehicle platooning based on self-triggered pinning consensus control

In this paper, a new method of vehicle platooning at an intersection is proposed based on self-triggered pinning consensus control. Using the proposed method, collision avoidance is achieved with no vehicles stopping/backing. First, the outline of self-triggered pinning consensus control is explained. Next, the problem setting of vehicle platooning is given, and virtual merge and split of vehicle groups are proposed. Furthermore, performance analysis of self-triggered pinning consensus control for vehicle platooning at an intersection is conducted. Finally, a numerical simulation is presented to demonstrate the proposed method

LMI-Based Design of Output Feedback Controllers with Decentralized Event-Triggering

In this paper, event-triggered control over a sensor network is studied as one of the control methods of cyber-physical systems. Event-triggered control is a method that communications occur only when the measured value is widely changed. In the proposed method, by solving an LMI (Linear Matrix Inequality) feasibility problem, an event-triggered output feedback controller such that the closed-loop system is asymptotically stable is derived. First, the problem formulation is given. Next, the control problem is reduced to an LMI feasibility problem. Finally, the proposed method is demonstrated by a numerical example

Quantized Event-Triggered Control of Discrete-Time Linear Systems with Switching Triggering Conditions

Event-triggered control is a method that the control input is updated only when a certain triggering condition is satisfied. In networked control systems, quantization errors via A/D conversion should be considered. In this paper, a new method for quantized event-triggered control with switching triggering conditions is proposed. For a discrete-time linear system, we consider the problem of finding a state-feedback controller such that the closed-loop system is uniformly ultimately bounded in a certain ellipsoid. This problem is reduced to an LMI (Linear Matrix Inequality) optimization problem. The volume of the ellipsoid may be adjusted. The effectiveness of the proposed method is presented by a numerical example