Published papers

This paper focuses on a bilateral control of teleoperation system with four-channel force-reflection (FR) algorithm and communication time delays. In this method, we propose a new system input of impedance control that relates to FR scheme of teleoperation. The goal of this paper is to improve the tracking performance and transparency, the control-based force-reflecting teleoperation uses a force feed forward channel in comparison with a conventional method to transfer the position, velocity and human force information from the master side to the slave side. Hence, we receive a four-channel architecture of the teleoperation system. Using variable damping values, the contact stability is achieved at the time when the slave robot contacts with the environment. To analyse stability of the system, one method based on Lyapunov technique is concerned, the input-to-state stability (ISS) small gain approach is used to show the overall force-reflecting teleoperation to be input-to-state stable. Several experimental results show the effectiveness of our proposed algorithm. © 2010 Inderscience Enterprises Ltd.

The formation control algorithm we propose for a multi-UAV uses LMI conditions. Starting with a linearized model of UAVs such as a quadrotor, we introduce a formation control algorithm based on a consensus algorithm, a leader-follower structure, graph theory and the Lyapunov stability theorem for a liner system. We propose the control algorithm using the Lyapunov theorem and LMI conditions for intermittent communication. We demonstrate the proposed control algorithm’s stability even when network links are disconnected. Numerical simulation and experimental validation show the proposed control’s effectiveness.

In this paper, we propose a fuel consumption optimization technique for a power-split HEV via gain-scheduled model predictive control. The control algorithms for the powertrain choose the power split between engine and electric motor in order to minimize the fuel consumption. Operating conditions of the HEV change during driving in real time by road conditions and driver demand. Therefore, it is necessary to have optimal control in real time and to consider the time-varying motor condition. We model the HEV and propose a gain-scheduled model predictive control. Using the proposed technique, we succeeded in optimizing fuel consumption for a power-split HEV. Finally, the proposed approach was validated by numerical simulations.

In this paper, the authors propose a cooperative formation control strategy with collision-avoidance capability for a multi-unmanned aerial vehicle (UAV) system using decentralized model predictive control (MPC) and consensus- based control. Consensus-based control algorithms are applied for formation flying in three-dimensional space. However, UAVs where these formation control algorithms are applied have not the ability to avoid collisions. Decentralized model predictive control (MPC) is applied to generate control inputs for formation flying with collision-avoidance capability. Using decentralized MPC, each UAV plans only its own action to track the trajectory specified by the formation control algorithm within the feasible regions satisfying collision-avoidance. The authors show how the optimization problems with coupled constraints such as collision-avoidance can be solved by each decoupled UAV in parallel with the other UAVs so that the decisions independently taken by each UAV can ensure consistency in coupled constraints of collision- avoidance. The computation time is also taken into account because it is a crucial factor to apply MPC to actual UAVs. Finally, the proposed approach is validated by some simulations.

© 2013 IEEE. In this paper, the Alternating Direction Method of Multipliers (ADMM) is investigated for distributed optimization problems in a networked multi-agent system. In particular, a new adaptive-gain ADMM algorithm is derived in a closed form and under the standard convex property in order to greatly speed up convergence of ADMM-based distributed optimization. Using Lyapunov direct approach, the proposed solution embeds control gains into weighted network matrix among the agents and uses those weights as adaptive penalty gains in the augmented Lagrangian. It is shown that the proposed closed loop gain adaptation scheme significantly improves the convergence time of underlying ADMM optimization. Convergence analysis is provided and simulation results are included to demonstrate the effectiveness of the proposed scheme.

© 2019 IEEE. In this paper, we propose a distributed power supply-demand management mechanism in a real-time market by utilizing various resources including demand reduction, balancing generators, and power flow changes. The proposed method aims to minimize the cost of balancing the supply with the demand. We simulate the results on the IEEJ EAST 30-machine system with four connected areas. The results show the feasibility of the proposed method and the establishment of a relationship between the demand response (DR) aggregator and the independent system operator (ISO).

This paper deals with a fault-tolerant sensor network configuration problem for a target navigation. A sensor network system consists of many sensor nodes and its network connections. Each sensor node can exchange information by wireless communication. A disadvantage of this system's property is that if there is an inaccurate information from a faulty sensor, this information has possibilities to be diffused to other sensors. Therefore, it is important to reduce effects of the inaccurate information by detecting a faulty sensor as possible as we can. And moreover, in feedback control system for navigation, we have to consider a lack of control inputs which happens depending on each sensor's intermittent observation. We propose two estimation methods for constructing a fault tolerant system. Specifically, we propose a fault-evaluation matrix for the fault detection, and we define a novel switching rule for shutting off inaccurate measurement data. Then we also propose a compensation algorithm for the problem of intermittent observation by using an estimated observation value.

This study deals with decentralized and hierarchical control for electric power networks by using overlapping information. The control objective is to minimize the cost function of the load frequency control problem. The control law consists of three steps. We apply the decentralized hierarchical control methodology to large-scale electrical power networks consisting of distributed power storage and generation sources such as batteries and wind power generators. Through several numerical simulation results of decentralized power networks, we verified their stability, compared the proposed and the previous decentralized methods, and showed the effectiveness of the proposed method.

This study addresses the problem of frequency control in a power system with the participation of demand response (DR). The pricing-based controller is applied to the power system so that DR participants and suppliers can contribute to the frequency regulation via electricity price. However, the high frequency of communication between frequency dynamics and pricing-based controllers is thought to be a problem. To address this problem, we derive event-triggered conditions that reduce the communication frequency while maintaining stability based on the passivity property of the system, and the asymptotical stability around the equilibrium point of the overall system is derived under the proposed event-triggered conditions. Furthermore, numerical simulation with simplified power network system is conducted so the effectiveness of the proposed system design and system stability is confirmed.

This research proposes a hierarchical control method using model predictive control and control barrier functions for CAVs (connected and automated vehicles). By using model predictive control for the upper layer and a control barrier function for the lower one, we aim to achieve control with reduced fuel consumption while ensuring safety even in environments where the behavior of the vehicle ahead is unpredictable. In this study, we aim to improve the feasibility of the lower layer by using Adaptive CBF. The upper layer takes preventive safety actions based on predictions of the preceding vehicle's behavior and ensures feasibility by using slack variables. The lower one using Adaptive CBF ensures the safety that is lacking. The effectiveness of the proposed method is demonstrated through simulations, comparing the results with those of previous studies.

This paper proposes a distributed control barrier functions (CBFs) approach to guarantee field-of-view overlap for cooperative visual localization in multi-agent systems. We formulate the problem on the Special Euclidean Group $\text{SE}(3)$ and introduce a probabilistic CBFs that evaluates the visibility of feature points to ensure that multiple agents maintain shared visual features. The proposed method incorporates a distributed optimization algorithm based on the primal-dual method of multipliers (PDMM), enabling each agent to compute control inputs locally while satisfying global safety constraints. Simulation results demonstrate the effectiveness of our approach in both centralized and distributed implementations, showing that agents can maintain shared field-of-view while following desired trajectories.

This paper investigates a dual-channel event-triggered prescribed performance model-free adaptive sliding mode control problem for a selective compliance assembly robot arm system with dual-channel packet loss. Firstly, a model-free adaptive sliding mode control method is designed to ensure robustness without relying on an accurate system model. Then, a dual-channel event-triggered mechanism is developed to reduce the waste of communication and computational resources. Additionally, a dual-channel packet loss compensation mechanism is designed to tackle data loss issues in both the forward and feedback channels. Finally, the theoretical analysis demonstrates the convergence of the control error, and simulation results validate the effectiveness of the proposed method.

This study proposes a Nonlinear Model Predictive Control (NMPC) framework for optimal power allocation in Fuel Cell Electric Vehicle (FCEV) to optimize both fuel economy and the long-term durability of the fuel cell (FC) system. The objective function is designed to reconcile competing requirements: minimizing hydrogen consumption and maintaining the battery's state of charge while simultaneously mitigating degradation of the power sources. To specifically suppress catalyst degradation caused by extreme low-load operation, a sigmoid-based cost function is integrated into the NMPC. Driving simulations using a real-vehicle-based model demonstrated a 47% reduction in low-load operation time and a 29% decrease in FC power fluctuations. Furthermore, in FC degradation verification conducted with the physicochemical simulator FC-DynaMo, the proposed strategy achieved a 6.9% suppression of FC degradation while ensuring superior fuel efficiency.