Microgrid is one of the new power supply systems expected for expanding introduction of renewable energy and improving the power quality. However, generators in a microgrid have risk at step-out because a microgrid utilizes many changeable power resources. For a safe operation in a microgrid, we evaluate reachable sets for unsafe sets of gas-engine generators according to a microgrid model. Reachable sets have already been applied to a study on air traffic management and electric power system. The unsafe sets are defined to avoid large deviations of rotor speed. Setting plurality of unsafe set, system’s safe set can be obtained by calculating backward reachable sets for unsafe sets. This paper considers two compensation operations for load fluctuation. Based on the characteristic of reachable sets for unsafe sets, demand response strategy can be examined. Finally, numerical simulation shows that setting based on reachable sets can suppress the angular frequency variation of gas-engine generators in microgrid.
This paper studies approximation of probabilistic distributions behind discrete-time linear systems with stochastic dynamics for advances in control of the systems. We discuss two approximation methods leading to optimal discrete distributions whose errors from the probabilistic distribution behind the system are minimal in the sense of the L∞ and L1 norms of the differences between associated cumulative distribution functions. The effectiveness of those approximation methods is demonstrated with numerical examples, in which the methods are compared with another simple method of constructing discrete distributions based on random sampling.
Complex biological systems resemble a “black box,” as it is a priori unclear how interactions among individuals will affect the collective (group) behavioral performance. Network analysis is a suitable method to shed light on these black boxes by studying the collective behavior of highly integrated social organisms such as ants. Individual within the colony have their own personality and task allocation for sustaining the society. Individual-level data are also important for understanding network structure. To obtain individual-level data such as personality and task allocation, individual behavior was assessed using several well-established information processing methods. To detect individual personalities from the position data, trajectory patterns were used. To analyze trajectory patterns, behavioral motif detection was used. The behavioral motif is the minimum unit of the entire trajectory.
Kalman filter is one of the most famous state estimation methods. Many state estimations based on Kalman filter have been proposed for various conditions of systems and noise. These estimation methods need to know probability density functions of dynamical systems. However, the probability density functions and a prior information of noise in particular are rarely known in practice. On the other hand, in the field of machine learning, probability density estimations have been studied extensively, and conditional probability density estimations were proposed as extensions of the probability density estimations. In this paper, we propose a direct design method of probability density functions for dynamical systems from data by using conditional probability density estimations because the systems are represented as conditional probability density functions. In addition, we apply the method to particle filter, which is one of nonlinear Kalman filters, and propose a new state estimation method without a prior knowledge for the dynamical systems. Numerical simulations demonstrate the effectiveness of the proposed method.
We consider nonlinear model predictive control (NMPC) for an unmanned aerial vehicle (a hexacopter) with two of its six rotors failed. We consider three cases of failures: failure of two rotors in diagonal position, failure of two rotors separated by a functioning rotor, and failure of two adjacent rotors. Simulation results show that, by applying NMPC, the position and attitude of the hexacopter can be controlled from an initial state significantly deviated from an equilibrium state.
Feedback error learning (FEL) control attains accurate response to a target signal by tuning parameters in feedforward (FF) controller, provided that feedback (FB) control stabilizes the closed-loop in two-degree-of-freedom structure. It has been shown under a certain strictly positive real (SPR) condition that the output error converges to zero for any target signals. In this paper we propose a somewhat different parameter tuning law from a conventional one and give a proof for convergence. We also apply the proposed method to temporal sensing failure. It is known that in networked control the sensing signal may be lost from time to time due to congestion in communication channels. Such temporal sensing failure also happens due to occlusion of non-contact sensors. In this paper, we show that our FEL control scheme is effective for such sensing failure through numerical simulation.