Optimization by Record Dynamics

Large dynamical changes in thermalizing glassy systems are triggered by trajectories crossing record sized barriers, a behavior revealing the presence of a hierarchical structure in configuration space. The observation is here turned into a novel local search optimization algorithm dubbed Record Dynamics Optimization, or RDO. RDO uses the Metropolis rule to accept or reject candidate solutions depending on the value of a parameter akin to the temperature, and minimizes the cost function of the problem at hand through cycles where its `temperature' is raised and subsequently decreased in order to expediently generate record high (and low) values of the cost function. Below, RDO is introduced and then tested by searching the ground state of the Edwards-Anderson spin-glass model, in two and three spatial dimensions. A popular and highly efficient optimization algorithm, Parallel Tempering (PT) is applied to the same problem as a benchmark. RDO and PT turn out to produce solution of similar quality for similar numerical effort, but RDO is simpler to program and additionally yields geometrical information on the system's configuration space which is of interest in many applications. In particular, the effectiveness of RDO strongly indicates the presence of the above mentioned hierarchically organized configuration space, with metastable regions indexed by the cost (or energy) of the transition states connecting them.Comment: 14 pages, 12 figure

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Stable Torque Optimization for Redundant Robots Using a Short Preview

We consider the known phenomenon of torque oscillations and motion instabilities that occur in redundant robots during the execution of sufficiently long Cartesian trajectories when the joint torque is instantaneously minimized. In the framework of online local redundancy resolution methods, we propose basic variations of the minimum torque scheme to address this issue. Either the joint torque norm is minimized over two successive discrete-time samples using a short preview window, or we minimize the norm of the difference with respect to a desired momentum-damping joint torque, or the two schemes are combined together. The resulting local control methods are all formulated as well-posed linear quadratic problems, and their closed-form solutions also generate low joint velocities while addressing the primary torque optimization objectives. Stable and consistent behaviors are obtained along short or long Cartesian position trajectories, as illustrated with simulations on a 3R planar arm and with experiments on a 7R KUKA LWR robot

Optimization of force-limiting seismic devices connecting structural subsystems

This paper is focused on the optimum design of an original force-limiting floor anchorage system for the seismic protection of reinforced concrete (RC) dual wall-frame buildings. This protection strategy is based on the interposition of elasto-plastic links between two structural subsystems, namely the lateral force resisting system (LFRS) and the gravity load resisting system (GLRS). The most efficient configuration accounting for the optimal position and mechanical characteristics of the nonlinear devices is obtained numerically by means of a modified constrained differential evolution algorithm. A 12-storey prototype RC dual wall-frame building is considered to demonstrate the effectiveness of the seismic protection strategy

Environmental boundary tracking and estimation using multiple autonomous vehicles

In this paper, we develop a framework for environmental boundary tracking and estimation by considering the boundary as a hidden Markov model (HMM) with separated observations collected from multiple sensing vehicles. For each vehicle, a tracking algorithm is developed based on Page’s cumulative sum algorithm (CUSUM), a method for change-point detection, so that individual vehicles can autonomously track the boundary in a density field with measurement noise. Based on the data collected from sensing vehicles and prior knowledge of the dynamic model of boundary evolvement, we estimate the boundary by solving an optimization problem, in which prediction and current observation are considered in the cost function. Examples and simulation results are presented to verify the efficiency of this approach

Computational effectiveness of LMI design strategies for vibration control of large structures

Distributed control systems for vibration control of large structures involve a large number of actuation devices and sensors that work coordinately to produce the desired control actions. Design strategies based on linear matrix inequality (LMI) formulations allow obtaining controllers for these complex control problems, which are characterized by large dimensionality, high computational cost and severe information constraints. In this paper, we conduct a comparative study of the computational effectiveness of three different LMI-based controller design strategies: H-infinity, energy-to-peak and energy-to-componentwise-peak. The H-infinity approach is a well-known design methodology and has been widely used in the literature. The energy-to-peak approach is a particular case of generalized H2 design that is gaining a growing relevance in structural vibration control. Finally, the energy-to-componentwise-peak approach is a less common case of generalized H2 design that produces promising results among the three considered approaches. These controller design strategies are applied to synthesize active state-feedback controllers for the seismic protection of a five-story building and a twenty-story building both equipped with complete systems of interstory actuation devices. To evaluate the computational effectiveness of the proposed LMI design methodologies, the corresponding computation times are compared and a suitable set of numerical simulations is carried out to assess the performance of the obtained controllers. As positive results, two main facts can be highlighted: the computational effectiveness of the energy-to-peak control design strategy and the particularly well-balanced behavior exhibited by the energy-to-componentwise-peak controllers. On the negative side, it has to be mentioned the computational inefficiency of the considered LMI design methodologies to properly deal with very-large-scale control problems.Peer ReviewedPostprint (published version

Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers

PID control architectures are widely used in industrial applications. Despite their low number of open parameters, tuning multiple, coupled PID controllers can become tedious in practice. In this paper, we extend PILCO, a model-based policy search framework, to automatically tune multivariate PID controllers purely based on data observed on an otherwise unknown system. The system's state is extended appropriately to frame the PID policy as a static state feedback policy. This renders PID tuning possible as the solution of a finite horizon optimal control problem without further a priori knowledge. The framework is applied to the task of balancing an inverted pendulum on a seven degree-of-freedom robotic arm, thereby demonstrating its capabilities of fast and data-efficient policy learning, even on complex real world problems.Comment: Accepted final version to appear in 2017 IEEE International Conference on Robotics and Automation (ICRA

Entanglement dynamics in open two-qubit systems via diffusive quantum trajectories

We use quantum diffusive trajectories to prove that the time evolution of two-qubit entanglement under spontaneous emission can be fully characterized by optimal continuous monitoring. We analytically determine this optimal unraveling and derive a deterministic evolution equation for the system's concurrence. Furthermore, we propose an experiment to monitor the entanglement dynamics in bipartite two-level systems and to determine the disentanglement time from a single trajectory.Comment: 4 pages, 2 figures, changed title, abstract and fig. 2, corrected typo