186,603 research outputs found
Sensor Attacks and Resilient Defense on HVAC Systems for Energy Market Signal Tracking
The power flexibility from smart buildings makes them suitable candidates for
providing grid services. The building automation system (BAS) that employs
model predictive control (MPC) for grid services relies heavily on sensor data
gathered from IoT-based HVAC systems through communication networks. However,
cyber-attacks that tamper sensor values can compromise the accuracy and
flexibility of HVAC system power adjustment. Existing studies on
grid-interactive buildings mainly focus on the efficiency and flexibility of
buildings' participation in grid operations, while the security aspect is
lacking. In this paper, we investigate the effects of cyber-attacks on HVAC
systems in grid-interactive buildings, specifically their power-tracking
performance. We design a stochastic optimization-based stealthy sensor attack
and a corresponding defense strategy using a resilient control framework. The
attack and its defense are tested in a physical model of a test building with a
single-chiller HVAC system. Simulation results demonstrate that minor
falsifications caused by a stealthy sensor attack can significantly alter the
power profile, leading to large power tracking errors. However, the resilient
control framework can reduce the power tracking error by over 70% under such
attacks without filtering out compromised data
Ultraprecise Controller for Piezoelectric Actuators Based on Deep Learning and Model Predictive Control
Piezoelectric actuators (PEA) are high-precision devices used in applications requiring micrometric displacements. However, PEAs present non-linearity phenomena that introduce drawbacks at high precision applications. One of these phenomena is hysteresis, which considerably reduces their performance. The introduction of appropriate control strategies may improve the accuracy of the PEAs. This paper presents a high precision control scheme to be used at PEAs based on the model-based predictive control (MPC) scheme. In this work, the model used to feed the MPC controller has been achieved by means of artificial neural networks (ANN). This approach simplifies the obtaining of the model, since the achievement of a precise mathematical model that reproduces the dynamics of the PEA is a complex task. The presented approach has been embedded over the dSPACE control platform and has been tested over a commercial PEA, supplied by Thorlabs, conducting experiments to demonstrate improvements of the MPC. In addition, the results of the MPC controller have been compared with a proportional-integral-derivative (PID) controller. The experimental results show that the MPC control strategy achieves higher accuracy at high precision PEA applications such as tracking periodic reference signals and sudden reference change
Variable neural networks for adaptive control of nonlinear systems
This paper is concerned with the adaptive control of continuous-time nonlinear dynamical systems using neural networks. A novel neural network architecture, referred to as a variable neural network, is proposed and shown to be useful in approximating the unknown nonlinearities of dynamical systems. In the variable neural networks, the number of basis functions can be either increased or decreased with time, according to specified design strategies, so that the network will not overfit or underfit the data set. Based on the Gaussian radial basis function (GRBF) variable neural network, an adaptive control scheme is presented. The location of the centers and the determination of the widths of the GRBFs in the variable neural network are analyzed to make a compromise between orthogonality and smoothness. The weight-adaptive laws developed using the Lyapunov synthesis approach guarantee the stability of the overall control scheme, even in the presence of modeling error(s). The tracking errors converge to the required accuracy through the adaptive control algorithm derived by combining the variable neural network and Lyapunov synthesis techniques. The operation of an adaptive control scheme using the variable neural network is demonstrated using two simulated example
Bibliographic Review on Distributed Kalman Filtering
In recent years, a compelling need has arisen to understand the effects of distributed information structures on estimation and filtering. In this paper, a bibliographical review on distributed Kalman filtering (DKF) is provided.\ud
The paper contains a classification of different approaches and methods involved to DKF. The applications of DKF are also discussed and explained separately. A comparison of different approaches is briefly carried out. Focuses on the contemporary research are also addressed with emphasis on the practical applications of the techniques. An exhaustive list of publications, linked directly or indirectly to DKF in the open literature, is compiled to provide an overall picture of different developing aspects of this area
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