Omni-directional vehicles have large degrees of freedom and can move flexibly, but this flexibility also increases the complexity of motion planning. To exploit the performance of the vehicle, multi-objective optimization is a powerful approach. However, minimizing a scalar evaluation function often leads to undesirable behavior due to local minima, and it is not easy to balance the priorities between tasks by adjusting the weight parameters. This study presents an approach to solve a multi-objective optimization problem by dividing it into hierarchical subproblems based on task priorities. The quality of the solution is improved by (1) narrowing down the search space reasonably and (2) combining gradient-based and sampling-based optimization methods. In the motion planning for navigating through obstacles, the proposed method obtained solutions that ensure reaching the goal more accurately compared to the conventional method (MPPI).

Human operation errors often lead to collisions involving mobile vehicles. Zeroing Control Barrier Functions (ZCBFs) are widely studied as a tool for guaranteeing safety. However, their definitions and conditions lack a unified framework. In this study, we propose a Relaxed ZCBF (R-ZCBF), which unifies and extends existing ZCBF theories by relaxing their conditions. This approach enables safer and more flexible control, expanding ZCBF applicability to a broader range of systems.

This study aims to examine the value of e-fuel (Electrofuels) in an energy system that includes e-fuel. In this paper, we particularly focus on verifying the contribution of e-fuel to decarbonization in the automotive sector. We conducted simulations for one year under 49 different conditions and proposed the optimal photovoltaic power generation capacity and e-fuel production patterns. The simulation results showed that there is a trade-off relationship between cost and CO₂ reduction in the most ideal condition and revealed the required money to reduce 1kg-CO₂. Additionally, we conducted simulations for the CO₂ emissions from automobiles over 20 years towards to the carbon neutrality under 6 scenarios, combining scenarios for the electrification of automobiles, an increase in photovoltaic power generation capacity, and the introduction of e-fuel. The results showed that by the introduction of e-fuel alongside the electrification of automobiles, a 14.9% reduction in CO₂ emissions from automobiles was achieved by 20 years. Over the 20-year period, a cumulative reduction of 4.2% in CO₂ emissions was revealed in this paper.

In this paper, we propose a design procedure for power system stabilizers (PSSs) in the context of data-adaptive retrofit control. The proposed procedure is modular in the sense that both design and implementation of PSSs can be performed using only a local subsystem model and local measurements. In particular, we consider the online identification of a dynamic feedback effect between the states of a generator of interest and the main grid to make the PSS adaptive to the variation of the grid characteristics depending on the power flow distributions. We show that the same retrofit controller developed in the literature for linear systems also works properly for nonlinear power systems where an operating point of interest varies depending on power flow distributions. In addition, we show an online identification algorithm of the network characteristics that exploits the physical structure of power systems. We demonstrate the effectiveness of the identification algorithm through a numerical simulation on the IEEE 68-bus test power system model.