論文

This paper proposes a game-theoretic approach to design a distributed real-time electricity pricing mechanism. Our approach is novel in that it guarantees individual rationality, meaning that it provides suppliers and consumers with a guaranteed incentive to participate in the real-time pricing market. Such an incentive is devised by offering a time-varying, situation-dependent subsidy that guarantees that a supplier/consumer profits by switching from a fixed-price contract to the real-time pricing contract. Although we assume that suppliers and consumers decide supply and demand quantities in a fully distributed manner to maximize their own profit, the proposed mechanism guarantees under moderate conditions that the market converges through an iterative process to a Nash equilibrium that maximizes social welfare. Furthermore, in order to guarantee safe operation of an electrical grid, our pricing mechanism increases stability of load frequency control, and at the same time, achieves supply-demand equilibrium by explicitly taking into account an equality constraint through dual decomposition method. We empirically demonstrate by simulations the individual rationality of the proposed mechanism as well as the convergence to supply-demand equilibrium.

© 2018 AACC. We consider the problem of secure state estimation in an adversarial environment with the presence of bounded noises. We assume the adversary has the knowledge of the healthy measurements and system parameters. To countervail the dangerous attacker, the problem is given as a min-max optimization, that is, the system operator seeks an estimator which minimizes the worst-case estimation error due to the manipulation by the attacker. On the proposed estimator, the estimation error is bounded at all times even if the system removing an arbitrary set of 2l sensors is not observable, where l is the number of the compromised sensors. To this end, taking the reach set of the system into account, we first show the feasible set of the state can be represented as a union of polytopes, and the optimal estimate is given as the Chebyshev center of the union. Then, for calculating the optimal state estimate, we provide a convex optimization problem that utilizes the vertices of the union. Additionally, the upper bound of the worst-case estimation error is derived theoretically, and we also show a rigorous analytical bound under a certain condition. The attacked sensor identification algorithm is further provided. A simple numerical example finally shows to illustrate the effectiveness of the proposed estimator.

© 2018 The purpose of this paper is to achieve a cut in peak electricity demand and lower electricity costs through the optimal management of a heating ventilation and air conditioning (HVAC) system. In addition, robust solar radiation prediction is required for optimal HVAC management. First, solar radiation prediction is applied using a clustering technique based on the k-means method, classifying all data on similar types of solar radiation. Second, an H∞ filter, which is a robust prediction method for outliers, is applied. Next, an optimal HVAC management system is considered for operating the air conditioning, such as the cooling and heating of each room. Model predictive control (MPC), which is used to predict and control the future changes in the temperature of the room and the electrical charge from solar radiation, is applied. Finally, we confirmed the validity of this research through numerical simulations.

© 1963-2012 IEEE. Recent control systems severe depend on information technology infrastructures, especially the Internet of things (IoT) devices, which create many opportunities for the interaction between the physical world and cyberspace. Due to the tight connection, however, cyber attacks have the potential to generate evil consequences for the physical entities, and therefore, securing control systems is a vital issue to be addressed for building smart societies. To this end, this paper especially deals with the state estimation problem in the presence of malicious sensor attacks. Unlike the existing work, in this paper, we consider the problem with a priori information of the state to be estimated. Specifically, we address three prior knowledge - the sparsity information, (\alpha, \bar{n}_0)-sparsity information, and side information, and in each scenario, we show that the state can be reconstructed even if more sensors are compromised. This implies that the prior information reinforces the system resilience against malicious sensor attacks. Then, an estimator under sensor attacks considering the information is developed and, under a certain condition, the estimator can be relaxed into a tractable convex optimization problem. Further, we extend this analysis to systems in the presence of measurement noises, and it is shown that the prior information reduces the state-estimation error caused by the noise. The numerical simulations in a diffusion process finally illustrate the reinforcement and error-reduction results with the information.

In this paper, we propose a control method which combine collision avoidance method and RISE (Robust Integral of the Sign of Error) for multiple Quad-rotor formation control methods, and confirm effectiveness of this method that is resistant to disturbance by lowering the risk of collision. First, we modeled quad-rotor as a linear system. Next, we described a method to suppress nonlinear disturbance using RISE which is a type of sliding mode control. In addition, we introduced collision avoidance method, and derived the conditions to achieve accurate formation by using Lyapunov stability theory. Finally, we implemented an algorithm in the simulation, and confirmed that the proposed method is functioning correctly.

This paper deals with a regional demand response method based on dynamic electricity pricing in a multi-period energy market. In the proposed method, first, the power demand and supply in each area are determined considering power flow using the retail and wholesale electricity prices in a day-ahead market. Next, to adjust for power deviations caused by errors in power generation, our proposed method solves the problem of optimal allocation between power demand reduction by consumers and increased power supply from balancing generators in a real-time market. This paper also shows a distributed algorithm for obtaining the optimal values of each market player in both the day-ahead and real-time markets, and finally, numerical simulation results demonstrate the effectiveness of the proposed method.

This paper deals with H-infinity Filter based SLAM which is also known as minimax filter to estimate robot and landmarks location whose able to stand for non-gaussian noise characteristics. Based on our findings, by selecting appropriate gamma and initial state covariance matrix in H-infinity Filter, the estimation results can show better performance in comparison to the Kalman Filter approach. From the analysis of convergence properties of H-infinity Filter, it is found that the filter is capable to provide a reliable estimation. Besides, from the simulation results, H-infinity Filter produces better outcome than the Kalman Filter in the nonlinear case estimation. These condition subsequently provides alternative estimation techniques with the capability to ensure and improve estimation in the robotic mapping problem especially in SLAM.

In this paper, we examine the H-infinity Filter-based SLAM especially about its convergence properties. In contrast to Kalman Filter approach that considers zero mean gaussian noise, H-infinity Filter is more robust and may provide sufficient solutions for SLAM in an environment with unknown statistical behavior. Due to this advantage, H-infinity Filter is proposed in this paper, to efficiently estimate the robot and landmarks location under worst case situations. H-infinity Filter requires the designer to appropriately choose the noise's covariance with respect to gamma to obtain a desired outcome. We show some of the conditions to be satisfy in order to achieve better estimation results than Kalman Filter. From the experimental results, H-infinity Filter performs better than Kalman Filter for a case of bigger robot initial uncertainties. Subsequently, this proved that H-infinity Filter can provide another available estimation method for especially in SLAM.

In this paper, hierarchical model predictive control is applied to mild HEVs 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 problems 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 such 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 account for 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, Cascaded MPC (Model Predictive Control) including Supervisory MPC, Heating MPC and Torque MPC 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 Heaitng MPC and Torque MPC, which are connected via the battery. Supervisory MPC treats the power and temperature systems comprehensively and calculates reference values for NOx emissions to be tracked in Heating MPC. In Heating MPC, 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 Torque MPC, the optimal torque distribution for the engine and motor is determined. Finally, the effectiveness of our proposed method is demonstrated by simulations.

The power generation from renewable energy sources, which is being promoted for mass adoption due to environmental issues, is affected by weather conditions, and therefore, it is necessary to predict the amount of power generation. In this study, we propose an algorithm for BEMS that takes into account the uncertainty of solar power generation using Tube-Based MPC, which is a type of robust MPC. We confirm through simulations that the algorithm is more robust against forecast errors by comparing it with a conventional method [3].

In this paper, we discuss the load frequency control problem for large-scale power network systems. The control is conducted to preserve the demand-supply power balance by controlling the output of generators in a power network. However, it becomes difficult to retain the balance because of the injection of a large amount of renewable energy whose output is uncertain. This paper proposes a new control application framework, especially focused on a large-scale connected power network system that contains a large-volume wind turbine generator, thermal turbine generator, and battery. More concretely, to satisfy the constraints of state and input control, we propose a method of load frequency control using moving horizon estimation and robust model predictive control.

The state estimation problem in the presence of malicious sensor attacks is commonly referred to as a secure state estimation problem. Central to addressing this problem is the concept of the sparse observability index, defined as the largest integer δ for which the system remains observable after the removal of any δ sensors. This index plays a critical role in quantifying the resilience of the system, as a higher δ enables unique state reconstruction despite the presence of more compromised sensors. In this study, for undirected multi-agent systems consisting of n agents, we analyze the relationship between the number of agents n and the sparse observability index δ for effective secure state estimation. In particular, we consider four typical graph structures: path, cycle, complete, and complete bipartite graphs. Our analysis reveals that δ does not increase monotonically with n, and that resilience is intricately tied to the underlying network structure. Notably, we demonstrate that the system exhibits enhanced resilience when the number of agents n is a prime number, although the specifics of this relationship vary depending on the graph topology.

This research investigates how to control multi-agent systems to maintain formation even when any agent, including the leader fails. We introduce cooperative control approach to solve the formation problem, ensuring that agents can maintain their relative geometric positions even in the event of a malfunction in any agent, including the leader. To achieve this, we developed an algorithm that uses the reliability of each agent, determined through fault detection using distributed observers, to limit information exchange with low-reliability agents. Additionally, we extend the adjacency matrix to allow any follower to assume the role of leader in the event of the leader's failure. This enables non-faulty agents to maintain the formation despite agent failures, including the leader. Finally, the proposed approach is validated through theoretical analysis and numerical simulations.