論文

Extended Kalman filter (EKF) is often employed in determining the position of mobile robot and landmarks in simultaneous localization and mapping (SLAM). Nonetheless, there are some disadvantages of using EKF, namely, the requirement of Gaussian distribution for the state and noises, as well as the fact that it requires the smallest possible initial state covariance. This has led researchers to find alternative ways to mitigate the aforementioned shortcomings. Therefore, this study is conducted to propose an alternative technique by implementing ∞ filter in SLAM instead of EKF. In implementing ∞ filter in SLAM, the parameters of the filter especially need to be properly defined to prevent finite escape time problem. Hence, this study proposes a sufficient condition for the estimation purposes. Two distinct cases of initial state covariance are analysed considering an indoor environment to ensure the best solution for SLAM problem exists along with considerations of process and measurement noises statistical behaviour. If the prescribed conditions are not satisfied, then the estimation would exhibit unbounded uncertainties and consequently results in erroneous inference about the robot and landmarks estimation. The simulation results have shown the reliability and consistency as suggested by the theoretical analysis and our previous findings.

© 2018 Technical Committee on Control Theory, Chinese Association of Automation. This paper addresses photovoltaics (PV) power prediction and energy storage problem which are known to be a key technology in energy management systems (EMS). Extending results of the point prediction of PV power, we first describe a prediction interval (PI) method using a copula, which can express the relation between a multivariable joint distribution and each marginal distribution. Then, resorting to the PI method, the energy storage optimization problem in a building is developed. A scenario robust (SR) optimization theorem, which calculates the robustness of the optimal solution, is applied to the proposed PI method, and hence we obtain an optimal energy storage solution taking the robustness of the solution into account. Additionally, we propose a method which combines a model predictive control (MPC) technique and SR to reduce the total electricity costs. The simulation results finally illustrate the cost reduction and robustness of the proposed method.

© 2019 JSME. In this paper, a distributed secure state estimation problem that is state estimation in distributed observer system in the presence of attacks is mainly discussed. We introduce the notion of graph-based distributed observer representation, virtual state, and a subgroup to derive a condition for the secure state estimation. By the graph-based distributed observer representation, we can consider the system's observability and communication pass connectivity as graph connectivity at once. With the notion of virtual state and the subgroup, we derive the state estimation condition which is the inequality relationship between the number of the allowable attacked observer and the graph connectivity as a theorem. Finally, a simple numerical example verifies the theorem.

© 2019 Institute of Control, Robotics and Systems - ICROS. In this paper, we aim to alleviate traffic congestion on each link online by traffic signal control for local road transport network to be controlled. We first propose a transportation model with time delay system considering the travel time of the vehicle and calculate the optimal signal phases with variable cycle length and offset using distributed model predictive control for that model to reduce congestion rate on local roads. We then explain algorithms for distributed control that each intersection solves the optimization problem in parallel sharing information only with adjacent intersection. Finally, the effectiveness of the proposed method is confirmed by numerical simulation.

A problem of attack detection and state estimation in continuous time distributed observer system under combination attack which is defined as in the presence of measurement attack and observer communication attack is considered in this paper. We adopt the notion of virtual state to design an algorithm for attack detection. The algorithm is distributedly used in each local observer and does not need nonlocal information, thus it can be run by only itself. Additionally, we derive the inequality relationship between system graph connectivity and the number of the allowable attacked observer to achieve the detection and estimation with the proposed algorithm as a necessary and sufficient condition. A simple numerical example, finally, illustrates the effectiveness of the proposed algorithm.

In this study, networks of interconnected heterogeneous micro-grids are studied. The transient dynamics is modelled as an averaging process whereby micro-grids are assimilated to dynamic agents in a network. An analysis of the convergence of the consensus dynamics is carried out under different assumptions on the damping and inertia parameters and the topology of the network. This study provides an insight into the relation between the network topology and the system's response. An analysis of the ways in which the heterogeneous inertial parameters affect the transient response of the network is also implemented. Additionally, the conditions that guarantee stability are identified when the system is under the influence of uncertain non-linear parameters. Finally, simulations are carried out based on a model calibrated on an existing network in the UK under parameter uncertainties.

In this paper, we address the new voltage control system of a single-phase grid-connected inverter. In this paper, we apply the standard H∞ control theory and specifically we formulate the voltage control problem as mixed sensitivity problem. By using this method, we can realize both the fastness and robustness of the voltage control under the condition that the uncertainty exists such as fluctuation of the load and the distribution lines. Finally, we confirm the effectiveness of the proposed method through numerical simulation.

This paper discusses a robust formation control for multi-Autonomous Underwater Vehicles (AUVs). The AUVs are disturbed by exogenous perturbation during the mission, thus, the Robust Integral Sign of Error (RISE) is adopted. For a formation control, a leader-follower structure based on consensus algorithm is adopted and the use of graph theorem named connected graph is useful to exchange the required information. An AUV called leader is determined to bring a group of information, while the others called followers, receive the information from a leader. Lyapunov analysis proves the stability as well as the error convergence of proposed controller, whilst some simulations are performed to compare between the proposed controller, RISE with consensus, and the existing robust controller, Sliding Mode Controller (SMC) which is combined with consensus algorithm. As a result, the proposed controller works better and produces smaller error.

This paper deals with a sensor scheduling algrithm considering estimation error variance and communication energy in sensor networked feedback systems. We propose a novel decentralized estimation algorithm with unknown inputs in each sensor node. Most existing works deal with the sensor network systems as sensing systems and it is difficult to apply them to the real physical feedback control systems Then some local estimates are merged and the merged estimates can be optimized in the proposed method and the estimation error covariance has a unique positive definite solution under some assumptions. Next, we propose a novel sensor scheduling algorithm in which each sensor node transmits information. A sensor node uses energy by communication between other sensor node or the plant. The proposed algorithm achieves a sub-optimal network topology with minimum energy and a desired variance. Experimental results show an effectiveness of the proposed method.

This paper deals with prediction algorithm applying for photovoltaic (PV) systems in smart grid. This prediction is aim to predict the amount of the next day of generation using the previous data and the weather forecast which get from Japan Meteorological Agency. The procedure of prediction consists of two steps, the data processing and the unknown parameters estimation. In the data processing, our proposed method considers the characteristics of PV generation using cluster ensemble. We propose the cluster ensemble based on k-means to choose the groups with a correlation with previous data. In the unknown parameters estimation, we provide the regression model for PV generation and the unknown parameters are estimated via H-infinity filtering. The effectiveness of the proposed prediction method is demonstrated through numerical simulations.

This paper deals with the simultaneous localization and mapping (SLAM) problem via the H-infinity filter with a known landmark. By adding the observation of a known landmark to those of unknown landmarks, the linearized SLAM model satisfies its observability, and its estimation accuracy is improved. To prove the improvement theoretically, this paper shows that the determinant of the estimated error covariance matrix with the observation of a known landmark becomes small compared with that of the conventional H-infinity filter. The convergence of the error covariance matrix is also proven in this paper. With simulations and experimental results, we confirm that the derived theorems for the convergence are correct and that we can accurately estimate the state of the robot and the environment.

This paper deals with the Simultaneous Localization and Mapping (SLAM) problem via the H-infinity filter with compensations for intermittent observations. In order to estimate positions of a robot and landmarks accurately under intermittent observations, we propose the method with a novel filter which detects and compensates intermittent observations by comparing the obtained data with their estimations. This paper also shows the convergence of the estimated error covariance matrices. With simulation and experimental results, we confirm that the state of the robot and the environmental conditions are estimated accurately via the proposed filter under intermittent observations and that the derived theorems of the convergence are correct.

This paper proposes a route optimization algorithm for a mixed package-passenger system that assigns the task of package delivery to taxicab drivers carrying passengers. The goal is to compensate for the shortage of manpower among package delivery drivers and to reduce CO2 emissions from delivery vehicles. In this paper, we propose a system that achieves fare reduction for passengers using taxicabs by incorporating stable passenger matching considering the cost sharing among passengers. In addition, we formulate it as a mixed integer linear programming problem to determine the allocation of delivery tasks to taxicab drivers and optimal route. Finally, the effectiveness of the proposed algorithm is shown through numerical simulations.

This research deals with the cooperative control of CAVs (Connected and Automated Vehicles) at signal-free intersection. CAVs communicate with each other and adjust their speeds to safely pass through the intersection without having to stop. This is expected to improve intersection throughput and reduce fuel consumption. In order to execute cooperative control, a two-stage control structure is considered. In the first stage, the merging time, defined for each CAV as the time at which the CAV reaches the intersection, is obtained by solving mixed integer linear programming (MILP). In the second stage, each CAV solves an optimal control problem to determine the control input that allows it to reach the intersection at the merging time obtained in the first stage. In this research, a nonlinear model that considers air resistance, rolling resistance, and slope resistance is used to take into account the real environment. We propose a method using a control barrier function to guarantee the safety of CAVs even with nonlinear models, and an input correction method using a disturbance observer to suppress modelling errors caused by complex dynamics. We also propose a method that consists of establishing a speed regulation zone to avoid the possibility of passing through an intersection at an unsafe speed to improve throughput. Finally, a comparison with previous studies shows the superiority of the proposed method in reducing fuel consumption.

This paper addresses the problem of secure state estimation in multi-agent systems consisting of n agents. For the problem, the sparse observability index is a well-known measure of the system resilience. This index is defined as the largest integer δ for which the system observability is preserved even if any δ sensors are eliminated. The larger δ is, the more the system state can be reconstructed even if the system is subjected to more sensor attacks. In this paper, we analyze the relationship between the number of agents n and the sparse observability index δ especially when the network structure is path and cycle graphs. Intuitively, it is assumed that, as the number of agents n increases, the system resilience, i.e., δ, increases accordingly. However, as this study provides, δ does not monotonically increase with an increase in n. In path graphs, δ depends on whether n+1 is prime or composite, and the system resilience is enhanced when n+1 is prime. In cycle graphs, δ obeys whether n is prime or composite, and the system is more resilient when n is prime.

Toward the realization of secure and resilient cyber-physical human systems (CPHS), this paper deals with the distributed secure state estimation problem considering security measure implementation for the systems. We present that the implementation enhances the system resilience, which means that the distributed state estimation can be achieved even if more networks are compromised. We also show that the implementation is coNP-hard, but, on the positive side, the implementation can be solved in polynomial time when the system satisfies a certain condition. This paper further provides a distributed secure state estimator exploiting the security implementation.

This study proposes a hierarchical control of CAVs (Connected and Automated Vehicles) in a mixed traffic scenario. Although there have been many studies using CAVs, there are still few studies on mixed traffic in which a HDV (Human Driven Vehicle) and CAVs coexist. Focusing on the merging control of mixed traffic scenario, many studies have fixed the merging point and merging order, which should be able to be determined freely. To address this problem, we propose a 3-layer hierarchical control method. The upper layer uses a coalitional game based on level-K reasoning. The middle layer uses model predictive control. Finally, the lower layer uses control barrier function. The upper layer decides when to merge in an area by considering the behavior of HDV. The middle layer generates trajectories within constraints. The lower layer uses elliptical constraints to follow trajectories under free merging order safely. Each layer operates at a different time step. Simulation verification of highway merging is performed to demonstrate the effectiveness of the proposed method.

This research proposes a safe trajectory planning method for AMR (Autonomous Mobile Robots) using game theory and Control Barrier Function (CBF). In game theory, trajectory planning is performed using ILQ Games, which is a nonlinear extension of dynamic games. However, ILQ Games have two difficult problems: safety and deadlock. Therefore, we define aggressiveness between AMRs and add CBF constraints using Augmented Lagrangian method based on aggressiveness. Vehicles with low aggressiveness are assigned CBF constraints, while those with high aggressiveness are assigned position constraints before changing to CBF to consider the advantages of both game theory and CBF. The effectiveness of the proposed method is verified through simulations in multiple scenarios and comparisons with existing conventional research results.

This paper proposes a secure state estimation method based on virtual distributed reduced-order observers. These observers are constructed by virtually excluding specific sensors or observers from a distributed reduced-order observer system. Unlike conventional approaches that assume undirected or strongly connected graphs, our method targets arbitrary directed graphs composed of multiple independent strongly connected components (iSCCs). By reducing the dimensions of each observer and constructing virtual states for each iSCC, the computational burden is significantly alleviated. We derive necessary and sufficient conditions for secure state estimation and attack detection based on reachability among iSCCs. Finally, we validate the proposed method through numerical simulations, demonstrating its effectiveness in accurately estimating states and detecting attacks even under adversarial conditions.

This paper addresses the problems of secure state estimation, attack detection, and safe control for linear systems subject to adversarial sensor attacks. In this work, we assume that the defender has side information on the initial state, which represents prior knowledge about the initial state and often reflects the physical characteristics of control systems. Leveraging this prior knowledge, we prove improved guarantees for secure state estimation and attack detection performance, even when a larger number of sensors are compromised, thereby strengthening the system's resilience against malicious sensor attacks. We also present a control input design method that leverages the side initial state information to ensure system safety in the presence of sensor attacks. Finally, numerical examples using a simple 2D vehicle model demonstrate the beneficial effect of initial state information on system resilience and safety against sensor attacks.