The goal of this paper is to give an overview of some recent developments in the field of model predictive control. After a brief introduction to the basic concepts and available stability results, we in particular set our focus on the areas of distributed and economic model predictive control, where more general control objectives than setpoint stabilization are typically of interest.
In this paper, we propose using an ensemble Kalman filter (EnKF) and particle filters (PFs) to obtain superior state estimation accuracy for nonlinear continuous-discrete models. We discretize the Ito-type stochastic differential system model by means of the usual procedure and suppress the approximation error by using small discrete times. We then simply apply the EnKF and PFs originally developed for nonlinear discrete-time models to the discretized system models, yielding the non-Gaussian filtering algorithms. Since the nonlinear problems generally make the states non-Gaussian as time proceeds, these non-Gaussian filters are promising for improving estimation accuracy. Their filtering performance is evaluated using two benchmark simulation models and compared with the performance of existing Gaussian filters, such as extended and unscented Kalman filters, and with that estimated by using the recursive Cramér-Rao lower bounds.
This paper shows that the H∞ control is an effective tool for electric voltage regulation. We here consider robust AC voltage regulation of microgrids in an islanded mode. When a microgrid is disconnected from a utility grid, it automatically switches to an islanded mode to provide necessary power using a battery system until the grid is recovered. In the islanded mode, it is important to regulate the electric voltage generated in the grid to track a predetermined AC voltage reference. A difficulty is that there exits unmodeled dynamics in the grid that may cause large power fluctuations. To resolve this problem, we adapt the H∞ control with a sinusoidal internal model to robust AC voltage regulation in microgrids. Simulation and experimental results confirm that the proposed H∞ control can achieve robust tracking performance.
This paper considers the average consensus problem by the 2-hop communication with time-varying time-delays. Each agent updates its state by a discrete-time consensus dynamics with fixed and undirected communication networks to achieve the average consensus. We also consider an application of the proposed consensus dynamics to an inverse problem of diffusion phenomena in order to find a source of a diffusion process from time-series data measured by agents. The inverse problem of diffusion phenomena can be formulated as a linear least squares problem by the difference approximation. We show that agents can estimate the initial distribution of concentration by solving the least squares problem with the proposed consensus dynamics.
Resource allocation and scheduling under scarce resources and limited time are always critical and challenging tasks, not only because of the complex situation with diverse needs involved, but also of any unpredictable occurrence during the whole dynamic process. This work proposes an agent-based framework to integrate the resource allocation and scheduling under a set of limitations, which could respond to contingent changes as a dynamic system. We focus on the following research questions and formulate them as a constraint satisfaction problem: how many resources should be assigned and dispatched to which location, in which sequence and under what process scheduling with time, resource availability, and ability-matching limits. We first give the corresponding formal definition, and then combine real-coded genetic algorithm and dynamic scheduling of multi-functional resource assignment to tackle the above proposed research questions. In addition, we experiment the model with a small make-up case to suggest some preliminary scenarios. In future, this framework would be further applied to real life emergency situations with empirical data for training purposes and providing insight for relevant policy makers.
Investigations of small planetary bodies (SPBs) such as asteroids and comets have attracted increasing attention recently. In our previous research, a method to localize a space rover was proposed. This method can provide accurate localization even if the rover is located on a small planetary body (SPB). It uses a mother spacecraft as a source of radio-waves. The localization was formulated as an optimization problem to minimize a loss function defined according to estimation error, and gradient-based optimization was applied, however, this required the equation of motion of the mother spacecraft. The purpose of this paper is to introduce a method to localize a rover on an SPB that does not require motion information for the mother spacecraft. In this paper, we propose a solution based on Powell's conjugate direction method. The proposed method requires a large amount of computation. For computation reduction, a method to select the measurement data is proposed. Numerical simulations that assumed that a rover was located on an SPB were conducted along with indoor experiments to assess the proposed method of localization.
Model-free predictive control directly computes the control input from massive input/output datasets and does not use a mathematical model. In contrast, conventional model predictive control relies on mathematical models. Although the underlying principle of model-free predictive control utilizes linear regression vectors comprising input/output data, it can also be applied to control nonlinear systems. In this study, the linear regression vectors are extended to polynomial regression vectors, improving the control performance. Using numerical simulations, we demonstrate the effectiveness of this approach.
In this paper, we propose a novel risk-limiting real-time pricing mechanism for a regional electricity network of prosumers with distributed solar power generation. The target system is a regional electricity network that consists of many prosumers, each equipped with a battery and a renewable energy-based generator. In particular, we assume that each prosumer is in possession of a solar power generator, i.e., photovoltaic cells. In the proposed market mechanism, each prosumer sets an asking price and bids for the amount of electricity that he/she sells or buys, In addition, he/she sets an asking price and bids for a parameter to cope with the uncertainty induced by solar power generation in a day-ahead market, as well. It is a characteristic of regional electricity networks of prosumers that there is a strong correlation in the solar power generation of each house within a network. A market mechanism for a regional electricity network of prosumers with distributed solar power generation should consider such characteristics. The performance of the proposed mechanism is demonstrated numerically through a simulation experiment. It outperformed a conventional real-time pricing method that does not consider uncertainty, and a method that does not involve trading of the parameter for coping with uncertainty.
This paper considers Voronoi coverage control for two-dimensional space with a non-uniform density function. In general, the computation of a mass and a centroid of a Voronoi cell for a non-uniform density function requires spatial discretization since they cannot be represented by a closed form. However the spatial discretization approach may result in exhaustive computation. In this paper, we consider a transformation of a surface integral over a Voronoi cell into a line integral around its boundary by Green's theorem to alleviate such a computational issue. We show that the proposed method can be implemented only by coordinates of vertices of a Voronoi cell and parameters of a density function when the density function is represented by a sum of Gaussian functions.
This paper tackles the problem of decentralizing interval observers for robust controlling and monitoring a class of nonlinear systems. The observer-based output feedback system considered in this paper is very different from classical systems in that an observer-type system is constructed not only for controlling the state variables, but also for accurate component-wise estimation of the state variables for the entire time. Supposing that a given plant admits a classical decentralized Luenberger-type observer giving only asymptotic estimation when time is large enough in the absence of disturbances, this paper proposes an interval observer to which the classical observer can be upgraded. To achieve asymptotic convergence of the state and the estimated interval to zero in the presence of converging disturbances, the notion of integral input-to-state stability is utilized.