As a unit commitment in a power system operation, a generation schedule indicating optimal generations and on/off of generators is created so that the power needed by consumers can be generated and transmitted on transmission lines. In order to determine the huge number of on/off for each generator at each time, under numerous operational constraints for uncertainty generations and the maximum transmission capacity violations due to localized generations of renewable energy, a large-scale combinatorial optimization is required to solve for making a generation schedule. On the other hand, to solve this large-scale optimization problem by only a standard optimization method takes long computation time. Therefore, in order to shorten the computation time, by using machine learning, invalid constraints that do not affect a resultant schedule are eliminated to reduce the optimization scale, and the optimal on/off are estimated to restrict the optimization search domain. To evaluate the performance of the proposed method, it is applied to the IEEE-118 bus power system model. The results show that the proposed method enables up to 82% reduction in computation time while maintaining an optimization accuracy of 0.8%.

In high-speed and high-precision positioning control, a method has been proposed to calculate the feedforward input for each time step using a time polynomial through final state control. Furthermore, to avoid numerical issues, a method has also been proposed to divide the positioning time into multiple segments and to obtain the feedforward input using a piecewise polynomial. However, even with this method, numerical issues arise for a long-span motion by higher-order polynomials. In this study, we propose a method that uses a spline function to generate feedforward input. A spline function is a function that continuously connects polynomials in each interval, including derivatives. Since it is numerically more stable than the piecewise polynomial, it is possible to obtain appropriate feedforward inputs even when numerical issues occur even for the piecewise polynomial. The effectiveness of the proposed method is verified through simulations.

This paper studies control systems whose output signal suffers from signal loss due to communication/sensing failure. Part of the authors previously proposed a gain-switching state observer to address such signal loss, with focus on stability during the signal loss. In practice, however, it is natural to expect that such signal loss occurs less often than when the signal arrives successfully. In this sense, we need to design a control system giving a good performance if the signal arrives, while maintaining stability if it does not arrive. This paper proposes to guarantee stability during signal loss and achieve regional pole placement during signal reception, by solving a set of linear matrix inequalities (LMIs) simultaneously. The effectiveness of the proposed method is illustrated by numerical simulation of platooning.

The frequency of disasters has been increasing in recent years. A backup power supply method using an EV could be considered in preparation for power outages caused by disasters. We proposed a route planning algorithm with the estimated electricity demand of each family considered. The method proposed in this paper first estimates each household's future electricity demand using an extreme learning machine based on past household electricity consumption data. Afterward, we use the Dijkstra method to determine the driving route for one EV when delivering electric power to multiple households with the estimation results considered. Optimal routes for the EV are calculated for multiple patterns of electricity delivery situations using actual household electricity consumption data. It was confirmed that the proposed method can deliver electricity more efficiently in most cases compared with the conventional method which determine the driving route of the EV without estimating the electricity demand of each family.

This study proposes an arm-driven inverted pendulum (pendubot) using LEGO parts as a teaching material for “Control Engineering”. In its development, we use general-purpose motors/sensors and Arduino, which is supported by MATLAB/Simulink, instead of the disused LEGO motors/sensors and EV3 Intelligent Brick (microcontroller). Since the main unit is assembled using LEGO parts, no special machining techniques are required, and anyone can easily build it. Furthermore, this inverted pendulum can be treated as an arm in the vertical plane or a stable crane. As a result, several examples show that we can effectively learn PID control, modeling, and modern control.

This paper proposes non-conservative nonlinear stochastic model predictive control (SMPC) subject to time-invariant uncertainties in initial conditions and system parameters. SMPC imposes probabilistic constraints on the probability of satisfying constraints (chance constraints), which enable us to explicitly consider the trade-off between guaranteeing the constraint satisfaction and minimizing the cost function. This paper proposes a method to obtain non-conservative control inputs by deriving a probability inequality using higher-order moments for reformulating chance constraints. To estimate these moments, the proposed method adopts generalized polynomial chaos expansion. By applying the proposed method to a semibatch reactor system, we show that the generalized polynomial chaos expansion can estimate higher-order moments with fewer samples than the Monte Carlo method and that the proposed method performs better than SMPC using a probability inequality with only mean and variance.

Power-split HEV has two motors and an internal combustion engine. For the power-split HEV, minimization of fuel consumption through appropriate energy management has attracted a great deal of attention. In conventional energy management of HEVs, rule-based control, which utilizes an if-then rule created based on experimental data, is used. However, the control system design in this way may become complex due to the complicity of the HEV structure. In this paper, we propose an energy management strategy for HEVs using robust predictive control, which is one of the model predictive controls (MPC). We also verify that the proposed method can improve fuel efficiency compared with the existing rule-based control through numerical simulation.

This paper deals with a design problem of the high gain adaptive output feedback controller with a dynamic event-triggering mechanism. The designed controller is not necessary to add some compensations for the error between the current control signal and the last updated control signal. These benefits come from the controller being designed on the basis of high gain feedback.

This paper presents a new event-triggered control system to detect anomalies in communication links. With state feedback, the event-triggered control system is constructed for an augmented system consisting of the plant and an integrator with an auxiliary signal. If network disconnection occurs, then the output of the integrator exceeds a threshold due to the effect of the auxiliary signal. Monitoring this behavior can detect disconnection. Furthermore, the maximum detection time can be arbitrarily specified by the magnitude of the auxiliary signal.

In recent years, as the post-ISS and Lunar Gateway have been considered, automated docking technology by a cargo transfer vehicle has been attracting attention. This paper firstly introduces the final approach scenario and guidance, navigation, and control system design of the HTV-X, a cargo transporter to the ISS, for the demonstration mission of automatic docking technology. Then, Monte Carlo analysis clarifies the influence of the final approach and the free drift start points before docking on accuracy at initial contact conditions.

Control Lyapunov function-based practical stability assist control is classified in human assist control that is minimal under a certain constraint using control Lyapunov function. We show a relationship between the practical stability assist control and ρ stability; a global version of a practical stability with the name σ → ρ stability. We confirm the effectiveness of the proposed assist control by computer simulation.

This paper proposes an estimation method for input-time-delay system with a long time constant by COV (COVariance factorization) method with the shift operation. While it is shown that time delay estimation for a system with a short time constant is possible by checking the order of the estimated model according to the shift operation, it is difficult to apply for a system with a long time delay because the orders are hardly changed according the time delay. In this paper, it is shown that the unstable zeros of the estimated model are the key role to estimate the time delay by simulation, and that simultaneous estimation of the time delay and model are possible by checking zeros of the system.

This paper deals with the preview gust alleviation control system of an aircraft, which mitigates dangerous vertical acceleration due to wind gust using the forward wind velocity measured by the LIDAR (LIght Detection And Ranging). Although the vertical acceleration can be alleviated by preview control, the preview feedforward part tends to be sensitive to measurement errors. In order to maintain consistently high acceleration alleviation performance, the preview feedforward needs to be robust against errors. This paper presents a design method for preview feedforward gains that can both alleviate vertical acceleration and reduce the effect of measurement errors. The preview feedforward can be added to an existing feedback control system and is designed as a static gain to reduce the onboard computational load. Simulation results of a small jet aircraft are provided to show that the designed preview feedforward gain can both alleviate vertical acceleration and be robust against random and bias errors.

Neural ordinary differential equation (ODE) is a deep learning method that can represent continuous dynamics. Unlike general deep learning approaches, neural ODE comprises functions expressed by ordinary differential equations as layers, with similarities found between its learning algorithm and the solution of optimal control problems. The authors exploited this feature for an autonomous lunar landing trajectory control law and demonstrated effective control without relying on a reference trajectory in a previous study. The present study is conducted to further investigate the effectiveness of neural ODE for optimal control problems with higher degrees of freedom by applying it to a rendezvous trajectory control law in low Earth orbit, and evaluating the effects of deep learning parameter settings on the convergence and robustness of the algorithm.

Recently, with the increase in the number of small unmanned aircraft system (sUAS) flights, the risk of collisions between sUAS and manned aircraft such as helicopters flying at relatively low altitudes is also increasing. For mitigating the risk, we develop a collision avoidance method for multirotor sUAS, assuming the encounter with a manned helicopter in a multilateration (MLAT) environment, which is a surveillance system for manned aircraft. The MLAT system can also detect manned helicopter's position but has position error. The proposed collision avoidance method is formulated based on Markov decision process considering uncertainty from the position detection error in the MLAT environment. The optimal action for avoiding collision is derived by solving the formulated collision avoidance problem using dynamic programming. Numerical simulations are held for evaluating the impacts of uncertainty on the collision avoidance method. The results show that the proposed method can avoid the collision between sUAS and manned helicopter while considering uncertainty from the MLAT environment. Additionally, the proposed method is evaluated based on the collision risk ratio, and the evaluation results show the requirements for the MLAT performance for realizing safe collision avoidance.

The purpose of this study is to implement a trajectory-tracking control system with attitude compensation for a new 1-DOF-driven robot called Lizard-Inspired Single-Actuated Robot (LISA). Previous studies have proposed various morphologies for 1-DOF robots, which present certain challenges. LISA, a multi-legged robot capable of propulsion and turning within a single DOF, overcomes these challenges. In this study, we formulate the kinematics of LISA, considering turning angle, stride length, posture, and turning radius. A unique robot coordinate is defined to derive the kinematics, enabling a symmetric representation of crucial state quantities such as turning angles and link angles. Subsequently, we design the trajectory-tracking control system with attitude compensation, comprising feed forward control, PD control, and attitude compensation control. This control system exhibits the characteristic that, when LISA has a significant attitude error relative to the reference trajectory, the attitude compensator corrects LISA's orientation, while PD control is employed for smaller errors to control LISA's trajectory. This characteristic is achieved by tuning the output ratio of the PD control input to the attitude compensation input. The effectiveness of the designed control system is initially validated through numerical simulations, employing linear and circular trajectories for verification. We also demonstrate that the control system proposed in this paper has a broader stabilization region compared to the conventional LISA control system. Finally, we verify the effectiveness of the designed control system through implementation experiments, confirming its efficacy as a trajectory-following control system for LISA.

In this paper, an attitude detection system with capturing omni-directional image for ultra-small satellite on orbit is described. To detect the attitude angle for ultra-small satellite called as “Cubesat” on orbit, the omni-directional camera with strong distortion is adapted to the satellite to specify relative direction of some celestial bodies such as Sun and center of the Earth from the view of the satellite on orbit. Then it is revealed that a pixel grid of capturing image with strong distortion for the omni-directional camera is formulated as direction vector to analyze some fixed points from the satellite. The authors conduct the satellite attitude detection system on orbit that solar and center of the Earth relative direction from the satellite are determined by capturing image with omnidirectional camera even if the figure of the sun and the Earth is not appeared directly on the image. Then the solar and center of the Earth direction are introduced by the calculation of the Earth outline of capturing image, it is possible that an attitude angle of the satellite is calculated by relationship between the satellite position on orbit and these two fixed points consisted of the solar and center of the Earth direction. To confirm validity of the proposing attitude detection system, a numerical analyzing for the attitude detection by using a sample image with including Earth outline model is calculated. Moreover, some experiments on orbit are executed, capturing image from launched small satellite on orbit is analyzed and evaluated.

In the present paper, we consider stability of consensus of multi-agent systems with switching topologies and communication delays. Here, it is assumed that the communication delays are a series of time-dependent random variables. Due to these assumptions, the time evolution of the states of the agents is described by non-autonomous functional differential equations with distributed time delays. In a multi-agent system with switching topology and distributed time delays, we show that the switching manner determines the achievement of the consensus based on a Lyapunov functional. In order to demonstrate our theoretical result, we carry out numerical simulations.

In recent years, underwater drones have been actively developed and have been used in industrial applications such as marine debris collection, facility inspection at hydraulic power plants and sunken ship exploration. In many cases, underwater drones are remotely controlled by humans. Although in extreme environments such as caves or deep sea due to communication stability or cable length limit, remote control by humans is not available, and an autonomous control is required. However, the autonomous control of an underwater drones is difficult in an underwater environment with many problems. As typical problems in the underwater environment, there are unknown external disturbances such as waves and water currents, sensor noise caused by poor sensors and modeling errors that occur during system identification. Although Extended Model Predictive Control (EMPC) provides robust tracking control against unknown external disturbances and modeling errors, it is difficult to address sensor noise. On the other hand, well known Kalman filter can deal with sensor noise, however it is difficult to address modeling errors. In this research, we aim to develop a robust tracking control method for underwater drones with EMPC, Sliding Innovation Filter (SIF) and Bayesian Optimization (BO) against unknown external disturbances, sensor noise and modeling errors simultaneously. SIF is a robust sensor noise filter against modeling errors and has a simple control structure, requiring no assumptions. Also, BO is applied to efficiently and accurately estimate parameters that affect the estimation accuracy of SIF. The effectiveness of the proposed method is shown by numerical simulations using a model that assumes experimental device.

This paper considers equilibrium and stability analysis using the dynamics of a cap-and-trade system between firms considering corporate values from carbon dioxide (CO₂) reductions. It turns out that the total amount of CO₂ reduction increases by increasing the initial emission allowance of each agent, and the necessary conditions for achieving carbon neutrality are given by the relation between the sum of the initial CO₂ emissions and the parameters characterizing the reduction cost and the corporate values due to CO₂ emission reduction. In the simplest case with 2 agents, the dynamics can be expressed as a piecewise nonlinear system, and the analysis is facilitated by nonlinear coordinate transformation and dimensionality reduction. Finally, we derive a stability condition for a piecewise linearized system around the origin, and provide numerical examples to validate the stability condition.

This paper proposes a control method for swarm robots to avoid collisions via fluid dynamics. MPS (Moving Particle Semi-implicit) method, which is a type of particle method, is used to reflect fluid dynamics to swarm robots. To adapt the MPS method to swarm robots, we consider distributed calculation of the MPS method. Specifically, the simultaneous equations for pressure calculation in the MPS method are solved by DCG (Distributed Conjugate Gradient) method. Since the DCG method is a distributed iterative solution method, the trade-off between computational complexity and communication traffi is important. We cluster swarm robots to reduce the total communication traffic, taking into account the relationship between distributed calculation and communication traffic. Numerical experiments are conducted to demonstrate the effectiveness of fluid collision avoidance against various shaped obstacles.