The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Based on an exact kinematics model, this paper considers two strategies aimed at diagnosing the health of a 3-axis rate gyro. In the first strategy, noisy attitude measurements are used to estimate angular velocity; comparing these estimates to the actual rate-gyro measurements provides the means for assessing the health of the rate gyro. In the second strategy, noisy attitude and angular velocity...
In data-driven modelling in dynamic networks, it is commonly assumed that all measured node variables in the network are noise-disturbed and that the network (vector) noise process is full rank. However when the scale of the network increases, this full rank assumption may not be considered as realistic, as noises on different node signals can be strongly correlated. In this paper it is analyzed how...
In this paper, we propose a non-iterative direct data-driven control approach, such that the controller is directly identified from input/output data without plant identification step. First we formulate the problem of designing a controller in order to match the behavior of an assigned reference model in terms of an equivalent set-membership errors-in-variables problem and we define the feasible...
A two-sided (simultaneous) optimization technique is presented for the two-player linear quadratic Gaussian (LQG) multistage game. This direct solution method allows for further interpretation of results as compared to previously used formal solution methods. For example, this direct solution method naturally decomposes the problem into separate deterministic and stochastic terms. This decomposition...
Vision-based robotic applications with aggressive maneuvers suffer from the low sensing speed of standard cameras that sample frames at constant time intervals. On the other hand, although neuromorphic vision sensors are promising candidates to provide the needed high-frequency sensing, a new class of algorithms needs to be synthesized that can deal with the uncommon output from each pixel of these...
A kernel-based nonparametric approach to identification of linear systems in the presence of bounded noise affecting both input and output measurements is proposed in this paper. The problem to be solved is firstly formulated in terms of robust optimization. The solution to such a problem is then obtained by proving that the originally formulated robust optimization problem is equivalent to a standard...
In the present work we consider the problem of subspace-based system identification of batch processes subject to multi-rate and missing data. To this end, we develop a state-space system identification approach for batch processes capable of handling multi-rate and missing data by utilizing the incremental singular value decomposition technique. Simulation case studies involving application to the...
This paper provides a novel formulation relating underwater range measurements to body-fixed position when several acoustic transceivers are mounted on the vehicle and only one transponder is placed in the vehicle's surroundings. This formulation is used in a novel three-stage filter for aided inertial navigation that has both global convergence and near-optimal performance w.r.t. variance of the...
This paper presents the implementation of an adaptive fading multiplicative extended Kalman filter (AFMEKF), applied to the problem of attitude estimation in the context of quadrotors. The extended Kalman filter is adapted for use with quaternions and made adaptive to account for inaccurate measurement information. Simulations have been conducted in order to validate the filter performance.
Many applications require reliable, high precision navigation (sub-meter accuracy) while using low-cost inertial and global navigation satellite systems (GNSS). Success requires optimal state estimate while mitigating measurement outliers. Common implementations use an Extended Kalman Filter (EKF) combined with the Receiver Autonomous Integrity Monitoring (RAIM) on a single epoch. However, if the...
In this paper a sequential Monte Carlo approach is used to track targets using multiple agents, where lack of measurements by individual agents aide in the estimation procedure. The proposed agents are equipped with cameras or sensors, where the fields of view or dynamic ranges are limited in the measurement model. In current approaches, measurements that are not available due to saturation, occlusion,...
In this paper a novel partition-based state prediction method is proposed for interconnected stochastic systems using sensor networks. Each sensor locally computes a prediction of the state of the monitored subsystem based on the knowledge of the local model and the communication with neighboring nodes of the sensor network. The prediction is performed in a distributed way, not requiring a centralized...
We analyze performance of a class of time-delay first-order consensus networks from a graph topological perspective and present methods to improve it. The performance is measured by the network's square of ℋ2-norm and it is shown that it is a convex function of Laplacian eigenvalues and the coupling weights of the underlying graph of the network. First, we propose a tight convex, but simple, approximation...
We discuss the notion of systemic risk in noisy consensus systems with delay as a measure of robustness and resilience. We propose a risk measure to characterize systemic events of the dynamic network, based on quantile functions theory. We provide explicit calculations or estimates for our consensus dynamic both in transient and steady-state dynamics. Our analysis highlights the dependence of risk...
In this paper, the problem of designing Robust Gain-Scheduling observers for continuous-time Linear Parameter-Varying systems via parameter-dependent Lyapunov function is addressed. The scheduling parameters are assumed to be imprecisely measured, i.e., corrupted with additive noise. Multi-simplex modeling approach is utilized to model the time-varying parameters and associated uncertainties. Sufficient...
This paper presents a new method to design Robust Switching State-Feedback Gain-Scheduling (RSSFGS) controllers for Linear Parameter Varying (LPV) systems with uncertain scheduling parameters. The domain of scheduling parameters are divided into several overlapped subregions to undergo hysteresis switching among a family of simultaneously designed LPV controllers over the corresponding subregion with...
It is shown how differential invariance can be used to extract an underlying signal from its noisy measurement towards constructing a non-asymptotic state estimator for linear systems. While the model of the system is assumed known, the noise can have arbitrary characteristics. The differential invariance is rendered by the Cayley-Hamilton theorem and the system is represented in terms of a output...
Distributed coverage control problem of a circle by a network of mobile agents subject to bounded measurement errors is investigated. The mobile agents are homogeneous in terms of their maximum velocities. Our goal is to study the effect of measurement errors on a coverage cost function, which is defined as the largest time required for the agent network to arrive at any point on the circle. Coverage...
We consider distributed filtering of a scalar linear stochastic process under communication corrupted by Gaussian noise. We investigate how communication noise degrades the performance of an existing distributed algorithm and develop a novel algorithm that mitigates these problems. We rigorously investigate the properties of the new distributed estimator and discuss optimal tuning of (fixed) gains...
We develop an algorithm that can detect the identity of false data-injection attackers in distributed optimization loops for estimating oscillation modes in power system models when the measurements used for the estimation are noisy. The fundamental set-up for the optimization is based on a distributed version of total least-squares (TLS) executed via Alternating Direction Multiplier Method (ADMM)...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.