This paper extends the theory of positive systems to sampled-data systems. When considering positivity of LTI systems, where to take the initial time is unimportant. However, since sampled-data systems are periodically time-varying from the perspective of continuous-time, introducing an adequate definition for their positivity should be relevant to considering which time to take as the initial time. This paper begins by studying positivity of sampled-data systems first by considering the case where a sampling instant is regarded as the initial time, and secondly by further considering the case where an arbitrary intersample instant is regarded as the initial time. These arguments are combined to derive the necessary and sufficient conditions for a unified notion of (i.e., initial-time-independent) positivity of sampled-data systems. This paper then applies this concept to the analysis of the L_q/L_∞ Hankel norm, known as a measure for evaluating the effects of disturbances in dynamical systems. It is shown that positive sampled-data systems lead to concise expressions for the (quasi) L_q/L_∞ Hankel norms not only for q = ∞ but also for q = 1,2, where the latter are hard to handle for general sampled-data systems. Some relevant properties for these norms are also shown. The theoretical results derived in this paper are confirmed through numerical examples.

Recently, robot swarm systems have been developed and cooperative control of human-robotic network based on passivity of a human operator has been proposed. However, it is difficult to guarantee control performance of human-robotic network because it depends on the property of human. In this paper, we propose a data-driven method to search for the graph structure of the vehicle swarm improving control performance in cooperative control of human-vehicle network, assuming that humans are unknown linear systems. We use the idea of FRIT and define a cost function which means the error between the output of the ideal transfer function and the vehicle swarm. Then, we propose a method to calculate the cost function with an one-shot input output data. In this way, we can search for the graph structure of the vehicle swarm that is easy for humans to control. Finally, we demonstrate the effectiveness of the proposed method through numerical simulations.

In this paper, hierarchical model predictive control is applied to mild HEVs (Hybrid Electric Vehicles) in order to improve fuel economy and reduce NOx emissions. There is a difference in the timescale between the temperature control (high-level) and the torque control (low-level), which are connected via the battery. In high-level of the hierarchical control, the optimal amount of heating from the battery to the catalyst in the exhaust gas after-treatment system is determined. The amount of heating must be determined in a way that the catalyst can optimally purify NOx emitted from the engine. At the low-level of the hierarchical control, the optimal torque distribution for the engine and motor is determined. The controller at both levels then applies model predictive control to consider the future behavior of the HEV's components. Fuel consumption, battery SoC and NOx emissions from the engine are taken into account. Finally, the effectiveness of our proposed method is demonstrated by simulations.

In this paper, we propose a stabilizing controller design method to use the lower bounds of the stability margins estimated by a data-driven method in a situation where a plant is unknown. To design a stabilizing controller, we derive a theorem which gives a class of a stabilizing controller. By using this theorem, we can design a controller to stabilize the closed-loop system without the mathematical model of a plant. In the numerical example, we estimate the lower bounds of the stability margins and design a stabilizing controller with only this.

This paper presents a distributed flocking control methodology for a group of multirotor UAVs (drones) under input disturbances. The proposed method follows Reynolds's three flocking rules: cohesion; alignment; and separation. Each drone determines its velocity control input by solving a fully distributed optimization problem with the constraints for these three rules. Here, the conflict between cohesion and separation is handled by adding a relaxation term to the constraint for cohesion. Moreover, the effect of input disturbances, evaluated in preliminary experiments, is incorporated into the constraint for separation. The effectiveness of the proposed flocking control method is demonstrated via simulation and hardware experiments.

With the goal of exploring the origin of the Earth, lunar exploration has been attracting much attention. In order to achieve more flexible missions, landing with less position error is required. In addition, space missions always call for severe minimum fuel consumption. In this research, the model predictive control (MPC) is implemented to the guidance and control system for the vertical landing phase of a lunar lander. Moreover, for the purpose of reducing the computational load of MPC, successive convexification (SCvx) is also applied. The results of simulations show high feasibility in terms of trackability and robustness for landing.

State Estimation and control problems fot the Gray-Scott model are considerd. The model is descirbed as nonlinear partial equations with real-valued two-dimensional spatio-temporal variables, which are related to a chemical reaction. Control problem for the Gray-Scott model is formulated. An observer which estimates the state of the Gray-Scott model is proposed. A necessary condition of the observer gain is derived from analysis of the linearlized models around the equilibrium points. A result of pattern formation control is presented.

The goal of this paper is to design a feedforward input which achieves a fast convergence to the reference under input constraint. In particular, this paper focuses on data-driven design which does not require a model of the system. We employ the data-driven prediction technique which has been developed for two-degrees-of-freedom systems. With this technique, the problem reduces to a quadratic programming whose optimization variable is impulse response of a feedforward controller. We also propose to employ regularization when the observation is contaminated to avoid overfitting. The effectiveness of the proposed method is demonstrated through a numerical example and a practical experiment.

Biped robots have a body structure similar to that of humans, and therefore have a high affinity for environments and tasks set for humans. Therefore, they are expected to be utilized in particularly hazardous disaster sites. In the process of accomplishing a walking task at a disaster site, contact between the body and the outside world may be unavoidable, and there are cases where conventional online trajectory generation of center of mass without prior consideration of contact between the body and the outside world cannot handle such a situation. In this paper, we extend the conventional method to the control considering contact with the wall surfaces by using MCMPC, a sample-based method of model predctive control, and confirm that it shows better performance than the control without considering contact with the wall surfaces. Furthermore, we confirmed that the proposed method can be executed in real time under the conditions we set.

In this paper, we propose a new smart parking system that determines the optimal parking allocation in the long run. In this system, the demand situation is acquired earlier than the respective parking start time by motivating drivers to apply for early parking, and the system performs long-term optimization instead of optimization only for the current time. We propose three optimization methods focusing on the time axis. The first one ignores the time axis constraint, the second one has the time axis constraint but lacks driver motivation, and the third one is the proposed method that solves those problems. Finally, numerical simulations will be used to confirm the effectiveness of the proposed method.

In this paper, we propose tuning methods of free parameters for control systems with stabilizing compensators. Here, we study a generalized internal model control system (GIMC). This control systems are generally designed based on a nominal models. However, it is difficult to redesign them in case of a nominal model have modeling errors between the original one. Therefore, we consider tuning the free parameters in this control systems using Virtual Internal Model Tuning (VIMT) which is one of the data-driven control. At the end of this paper, we verify the usefulness of the proposed method using numerical examples.

In this study, we deal with bilateral automatic negotiation and examine the fundamental properties of the optimal offer sequence under a new model. The space of offers and the value of utilities of both players are continuous and we consider a simplified situation in which both utility functions of the two players are affine. Then, we theoretically show that “stubborn negotiation strategy,” which is to stubbornly make a selfish proposal at the beginning of a negotiation process, and to make a compromise proposal toward the end of the negotiation, is reasonable in terms of optimality with respect to their utilities. We also quantify the stubbornness and discuss the relationship between the stubbornness and the parameters of the utility function.