In this paper, we propose an index of the localizability for the distributed design of local controllers. We consider the case where a large-scale system is controlled by a retrofit controller. The retrofit controller is a local plug-in controller such that, rather than an entire system model, only a model of the subsystem of interest is required for the controller design. The localizability index is defined as the H_∞ norm of an error system defined based on the isolation of the subsystem of interest from the entire system. The proposed index measures how much the control performance of the subsystem of interest is invariant with respect to the variation of subsystems other than the subsystem of interest. Then, we show that the localizability index is estimated from only the model parameters of the subsystem of interest. Finally, a retrofit controller placement problem for power systems is analyzed by the localizability index.

In this study, the automatic tuning model error compensator (AT-MEC) is proposed. MEC is one of the two-degree-of-freedom control methods to suppress the influence of the model error. This compensator has simple and general structure, and is easy to be implemented. However, there is no systematic tuning method for compensation gain for multi-input multi-output nonlinear systems. In this paper, the fictitious reference iterative tuning (FRIT) method is applied as on-line adjustment method of MEC. FRIT calculates the optimal value for parameters of a controller based on the deviation from a response of a reference closed loop model. By combinating online tuning method to MEC, AT-MEC suppresses the influence of fluctuating model errors. In addition, to extend the applicability of the AT-MEC, the exact linearization technique is introduced to AT-MEC. As the result, the proposed method works for the fluctuating model error of a number of MIMO nonlinear systems. In this paper, the validity of this proposed method is verified by the experiment of an automatic driving vehicle.

In this paper, we study the problem of continuous-time state observation over lossy communication networks. We consider the situation in which the samplers for measuring the output of the plant are spatially distributed and their communication with the observer is scheduled according to a round-robin scheduling protocol. We allow the observer gains to dynamically change in synchronization with the scheduling of communications. In this context, we propose a linear matrix inequality (LMI) framework to design the observer gains that ensure the asymptotic stability of the error dynamics in continuous time. We illustrate the effectiveness of the proposed methods by several numerical simulations.

This paper deals with the machine learning based controller design for realizing nonlinear model predictive control (MPC) with low computational cost, and the application for a diesel engine air path system is shown. Since the solution of an optimal control problem considered in MPC depends on several variables at each time, the relationship between the variables and the solution, that is, the control law could be approximated by a neural network. In the case of high dimensional inputs, however, some efficient sampling methods for the approximation are needed to avoid a combinatorial explosion. To reduce the sampling dimension, we newly applied an efficient sampling method which is combined with a design of experiment. Using the method, the neural network structured optimal controller that operates the valves in the air path system was designed, and its tracking capability to the reference value was demonstrated in the simulation. The computational time of the controller was approximately 0.022ms at each cycle, indicating that fast computation of MPC was achieved.

In this paper, we address a relative position estimation problem for a line formation of multiple robots. The existing study assumed that the light of all distance sensors hits other robots, which does not always happen. Therefore, we propose a method for estimating the relative positions of the detected robots which is effective even if the light of distance sensors does not always hit the robots. We consider using information on the motion of the robots obtained by wireless communication. To fuse the information from the distance sensors and wireless sensors, the Extended Kalman Filter is employed. Finally, we verify the effectiveness of this method by a simulation.

Position control law for multiple unmanned aerial vehicles (UAVs) to monitor a moving target using virtual forces is proposed. A monitoring image of the target that acquired by a nearby UAV is sent to base through the UAVs network. Each UAV forms a communication network and calculates own moving amount with a virtual forces using only information in local area. Numerical simulations show that the proposed control law is valid.

Distributed coverage control is expected to be promising solution for stable operation of the large scale monitoring system, and one of the most commonly used methods is the Lloyd algorithm. However, when this algorithm is used, the result sometimes converges to a local optimal deployment. To avoid this problem, Voronoi based cut-in algorithm has been proposed and proved to derive the optimal deployment deterministically. From a practical point of view, however, the coverage system should be resilient under fuel exhaustion or sensor failure by minimizing the performance degradation and recovering its performance. Therefore, in this paper, we propose a resilient coverage control based on Voronoi based cut-in algorithm, so that remaining sound robots and newly added robots can maximize the total coverage performance. Through simulations and experiments, our proposed algorithm has shown better performance than the conventional coverage control.

This paper studies an algorithm to estimate an impulse disturbance force and its impact position using local measurement data of flexible space structures in free-free boundary condition. The measurement data are supposed to be obtained at any single sensing point. A time domain analysis scheme using modal decomposition technique along with a finite element model for the impulse response is considered for estimating external disturbance force magnitude and its applying position. The proposed technique is formulated for two representative space structures. Numerical simulation results demonstrate the validity of the new proposed method.

Proportional navigation (PN) is a well-known guidance law for a two-vehicle engagement. In the two-vehicle engagement, the miss distance (the closest distance between the two vehicles) is driven to be zero when the line-of-sight (LOS) rate becomes zero. The PN guidance law requires information of the LOS rate and the vehicle velocity, or, if it is available, the closing velocity (the relative approaching speed to the target). However, it is difficult to obtain accurate target information due to its velocity change and uncertain target maneuvers, and so on. For this reason, this paper proposes a guidance law (uncertainty and disturbance-compensated approach) based on the PN guidance law using only LOS rate information without using the closing velocity information. Comparison studies on the proposed guidance law against the optimal guidance law are conducted with some representative simulations.

In this paper, the model-based control system is applied to the premixed combustion in a diesel engine with 4-stage fuel injections. 2 inputs and 2 outputs (2I2O) feedforward (FF) controller is designed by our original discretized combustion model. In the FF controller, the model is linearized in the real-time operation and the inverse model is obtained from it. The control performance is evaluated by the 2I2O control test, where the interval of 1st and 2nd main heat release rate peak and the ratio between 1st and 2nd heat release rate peak are controlled by operating 2nd main injection timing and its quantity. The control system showed good performance in the reference following test.

This paper proposes method of identification of piecewise affine autoregressive exogenous systems using semi-supervised learning. The method enables a model designer to insert some class-labeled data to modeling scheme. The effectiveness of proposed method is ensured by numerical simulation.

This study estimates the phase state of water using an evaluation index, which is based on the absorbed light intensity ratio at two wavelengths. Experimental results showed that the evaluation index is a linear function of the ratio of ice. The results indicate that the index could be used to quantitatively evaluate the degree of water freezing. In this study, we apply the detection method to the two-dimensional distribution of the phase state of water using a near-infrared camera with new image-capturing optics, where two wavelength images of the same scene can be independently formed on one image element. For each pixel of the image, a value of the index is calculated from the output values of corresponding pixels of two wavelength images. Thus, a two-dimensional distribution of index values is obtained. The method is proven by experiment to be effective for two-dimensionally monitoring the water-freezing process with supercooling.