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A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function
A novel autotuning procedure is presented through application to an industrial in-line pH control system. The procedure has three advantages over classical relay auto-tuners: experiment duration is very short (no need for limit-cycle convergence); all data is used for identification (instead of only peaks and switch instances); a parameter uncertainty model is identified and utilized for robust co
Today’s multimedia and DSP applications impose requirements on performance and power consumption that only custom processor architectures with SIMD capabilities can satisfy. However, the specific features of such architectures, including vector operations and high-bandwidth complex memory organization, make them notoriously complicated and time consuming to program. In this paper we present an aut
Temporal logics have proven effective for correct-by-construction synthesis of controllers for a wide range of robotic applications. Receding horizon frameworks mitigate the computational intractability of reactive synthesis for temporal logic, but have thus far been limited by pursuing a single sequence of short horizon problems to the goal. We propose a receding horizon algorithm for reactive sy
Correct-by-construction synthesis of high-level reactive control relies on the use of formal methods to generate controllers with provable guarantees on their behavior. While this approach has been successfully applied to a wide range of systems and environments, it scales poorly. A receding horizon framework mitigates this computational blowup, by decomposing the global control problem into sever
We study the use of the System Optimum (SO) Dynamic Traffic Assignment (DTA) problem to design optimal traffic flow controls for freeway networks as modeled by the Cell Transmission Model, using variable speed limit, ramp metering, and routing. We consider two optimal control problems: the DTA problem, where turning ratios are part of the control inputs, and the Freeway Network Control (FNC), wher
Distributed averaging is one of the simplest and most widely studied network dynamics. Its applications range from cooperative inference in sensor networks, to robot formation, to opinion dynamics. A number of fundamental results and examples scattered through the literature are gathered here and some original approaches and generalizations are presented, emphasizing the deep interplay between the
We study System Optimum Dynamic Traffic Assignment (SO-DTA) for realistic traffic dynamics controlled by variable speed limits, ramp metering, and routing controls. We consider continuous-time cell-based Dynamic Network Loading models that include as special cases the Cell Transmission Model (CTM) with FIFO rule at the diverge junctions as well as non-FIFO diverge rules. While a straightforward co
Many notions of network centrality can be formulated in terms of invariant probability vectors of suitably defined stochastic matrices encoding the network structure. Analogously, invariant probability vectors of stochastic matrices allow one to characterize the asymptotic behavior of many linear network dynamics, e.g., arising in opinion dynamics in social networks as well as in distributed avera
Several strategic interactions over social networks display both negative and positive externalities at the same time. E.g., participation to a social media website with limited resources is more appealing the more of your friends participate, while a large total number of participants may slow down the website (because of congestion effects) thus making it less appealing. Similarly, while there a
Many linear dynamics over networks can be related by duality to the evolution of a Markov chain with state space coinciding with the node set of the network. Examples include opinion dynamics over social networks as well as distributed averaging algorithms for estimation or control. When the transition probability matrix P associated to the Markov chain is irreducible, a key quantity is its invari
This paper designs distributed dynamic traffic signal control policies for urban traffic networks. Vehicles at the end of an approach to an intersection queue up in separate lanes corresponding to different possible turn maneuvers at the upcoming intersection, according to fixed turn ratios. The departure rate of vehicles from the queue is governed by traffic signal control at the intersections. W
We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, disturbance process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but a
We consider the problem of localizing a novel image in a large 3D model, given that the gravitational vector is known. In principle, this is just an instance of camera pose estimation, but the scale of the problem introduces some interesting challenges. Most importantly, it makes the correspondence problem very difficult so there will often be a significant number of outliers to handle. To tackle
In computer vision, many problems can be formulated as finding a low rank approximation of a given matrix. Ideally, if all elements of the measurement matrix are available, this is easily solved in the L2-norm using factorization. However, in practice this is rarely the case. Lately, this problem has been addressed using different approaches, one is to replace the rank term by the convex nuclear no
Many modern software applications have performance requirements, like mobile and embedded systems that must keep up with sensor data, or web services that must return results to users within an acceptable latency bound. For such applications, the goal is not to run as fast as possible, but to meet their performance requirements with minimal resource usage, the key resource in most systems being en
We provide a framework for 6 DOF pose estimation in seam-tracking applications using particle filtering. The particle filter algorithm developed incorporates measurements from both a 2 DOF laser seam tracker and the robot forward kinematics under an assumed external force. Special attention is paid to modeling of disturbances in the respective measurements, and methods are developed to assist the
This paper presents a simple and effective way of solving the robust subspace estimation problem where the corruptions are column-wise. The method we present can handle a large class of robust loss functions and is simple to implement. It is based on Iteratively Reweighted Least Squares (IRLS) and works in an iterative manner by solving a weighted least-squares rank-constrained problem in every it
Cloud computing technology provides the means to share physical resources among multiple users and data center tenants by exposing them as virtual resources. There is a strong industrial drive to use similar technology and concepts to provide timing sensitive services. One such is virtual networking services, so called services chains, which consist of several interconnected virtual network funct
This paper combines the Metropolis-Hastings Improved Particle Smoother (MHIPS) with marginalized models. It demonstrates the effectiveness of the combination by looking at two examples; a degenerate model of a double integrator and a fifth order mixed linear/nonlinear Gaussian (MLNLG) model. For the MLNLG model two different methods are compared with the non-marginalized case; the first marginaliz
