Jongeun Choi

Distributed learning and cooperative control for multi-agent systems

This paper presents an algorithm and analysis of distributed learning and cooperative control for a multi-agent system so that a global goal of the overall system can be achieved by locally acting agents. We consider a resource-constrained multi-agent system, in which each agent has limited capabilities in terms of sensing, computation, and communication. The proposed algorithm is executed by each agent independently to estimate an unknown field of interest from noisy measurements and to coordinate multiple agents in a distributed manner to discover peaks of the unknown field. Each mobile agent maintains its own local estimate of the field and updates the estimate using collective measurements from itself and nearby agents. Each agent then moves towards peaks of the field using the gradient of its estimated field while avoiding collision and maintaining communication connectivity. The proposed algorithm is based on a recursive spatial estimation of an unknown field. We show that the closed-loop dynamics of the proposed multi-agent system can be transformed into a form of a stochastic approximation algorithm and prove its convergence using Ljungs ordinary differential equation (ODE) approach. We also present extensive simulation results supporting our theoretical results.

Mobile Sensor Network Navigation Using Gaussian Processes With Truncated Observations

In this paper, we consider mobile sensor networks that use spatiotemporal Gaussian processes to predict a wide range of spatiotemporal physical phenomena. Nonparametric Gaussian process regression that is based on truncated observations is proposed for mobile sensor networks with limited memory and computational power. We first provide a theoretical foundation of Gaussian process regression with truncated observations. In particular, we demonstrate that prediction using all observations can be well approximated by prediction using truncated observations under certain conditions. Inspired by the analysis, we then propose a centralized navigation strategy for mobile sensor networks to move in order to reduce prediction error variances at points of interest. For the case in which each agent has a limited communication range, we propose a distributed navigation strategy. Particularly, we demonstrate that mobile sensing agents with the distributed navigation strategy produce an emergent, swarming-like, collective behavior for communication connectivity and are coordinated to improve the quality of the collective prediction capability.

On parameter estimation for biaxial mechanical behavior of arteries

This article considers the parameter estimation of multi-fiber family models for biaxial mechanical behavior of passive arteries in the presence of the measurement errors. First, the uncertainty propagation due to the errors in variables has been carefully characterized using the constitutive model. Then, the parameter estimation of the artery model has been formulated into nonlinear least squares optimization with an appropriately chosen weight from the uncertainty model. The proposed technique is evaluated using multiple sets of synthesized data with fictitious measurement noises. The results of the estimation are compared with those of the conventional nonlinear least squares optimization without a proper weight factor. The proposed method significantly improves the quality of parameter estimation as the amplitude of the errors in variables becomes larger. We also investigate model selection criteria to decide the optimal number of fiber families in the multi-fiber family model with respect to the experimental data balancing between variance and bias errors.

Adaptive Sampling for Learning Gaussian Processes Using Mobile Sensor Networks

This paper presents a novel class of self-organizing sensing agents that adaptively learn an anisotropic, spatio-temporal Gaussian process using noisy measurements and move in order to improve the quality of the estimated covariance function. This approach is based on a class of anisotropic covariance functions of Gaussian processes introduced to model a broad range of spatio-temporal physical phenomena. The covariance function is assumed to be unknown a priori. Hence, it is estimated by the maximum a posteriori probability (MAP) estimator. The prediction of the field of interest is then obtained based on the MAP estimate of the covariance function. An optimal sampling strategy is proposed to minimize the information-theoretic cost function of the Fisher Information Matrix. Simulation results demonstrate the effectiveness and the adaptability of the proposed scheme.

Design and fabrication of a novel bimorph microoptomechanical sensor

We have designed a so-called flip-over bimaterial (FOB) beam to increase the sensitivity of micromechanical structures for sensing temperature and surface stress changes. The FOB beam has a configuration such that a material layer coats the top and bottom of the second material at different regions along the beam length. By multiple interconnections of FOB beams, the deflection or sensitivity can be amplified, and the out-of-plane motion of a sensing structure can be achieved. The FOB beam has 53% higher thermomechanical sensitivity than a conventional one. Using the FOB beam design, we have developed a microoptomechanical sensor having a symmetric structure such that beam deflection is converted into a linear displacement of a reflecting surface, which is used for optical interferometry. The designed sensor has been fabricated by surface micromachining techniques using a transparent quartz substrate for optical measurement. Within a sensor area of 100 /spl mu/m/spl times/100 /spl mu/m, the thermomechanical sensitivity S/sub T/=180 nm/K was experimentally obtained.

Swarm intelligence for achieving the global maximum using spatio-temporal Gaussian processes

This paper presents a novel class of self-organizing multi-agent systems that form a swarm and learn a spatio- temporal process through noisy measurements from neighbors for various global goals. The physical spatio-temporal process of interest is modeled by a spatio-temporal Gaussian process. Each agent maintains its own posterior predictive statistics of the Gaussian process based on measurements from neighbors. A set of biologically inspired navigation strategies are identified from the posterior predictive statistics. A unified way to prescribe a global goal for the group of agents is presented. A reference trajectory state that guides agents to achieve the maximum of the objective function is proposed. A switching protocol is proposed for achieving the global maximum of a spatio- temporal Gaussian process over the surveillance region. The usefulness of the proposed multi-agent system with respect to various global goals is demonstrated by several numerical examples.

Linear Parameter-Varying Control for Engineering Applications

Introduction.- Linear Parameter-Varying Modeling and Control Synthesis Methods.- Weight Selection and Tuning.- Gain-Scheduling Control of Port-Fuel-Injection Processes.- Mixed H2/H-infinity Observer-Based LPV Control of a Hydraulic Engine Cam Phasing Actuator.

Design and control of a thermal stabilizing system for a MEMS optomechanical uncooled infrared imaging camera

In this paper, the design and control of a thermal stabilizing system for an optomechanical uncooled infrared (IR) imaging camera is presented, which uses an array of MEMS bimaterial cantilever beams to sense an IR image source. A one-dimensional lumped parameter model of the thermal stabilization system was derived and experimentally validated. A model-based discrete time linear quadratic gaussian regulator (LQGR) control scheme, with a stochastic ambient noise model, was implemented. The control system incorporates a reference model, which generates desired reference temperature trajectory, and integral action to respectively diminish overshoots and achieve zero steady state error in closed loop. Simulation results show that the designed LQGR is able to enhance ambient temperature low frequency disturbance attenuation by more than 50 dB. The control system is able to regulate the focal-plane array (FPA) temperature with a standard deviation of about 100mK, in spite of the fact that the temperature measurement noise has a standard deviation of 1 mK. Noise analysis results for the present stage of the optomechanical IR imaging system are summarized. The noise equivalent temperature difference (NETD) of the current stage of the IR camera system can achieve about 200 mK. # 2003 Elsevier Science B.V. All rights reserved.

Gaussian Process Regression for Sensor Networks Under Localization Uncertainty

In this paper, we formulate Gaussian process regression with observations under the localization uncertainty due to the resource-constrained sensor networks. In our formulation, effects of observations, measurement noise, localization uncertainty, and prior distributions are all correctly incorporated in the posterior predictive statistics. The analytically intractable posterior predictive statistics are proposed to be approximated by two techniques, viz., Monte Carlo sampling and Laplaces method. Such approximation techniques have been carefully tailored to our problems and their approximation error and complexity are analyzed. Simulation study demonstrates that the proposed approaches perform much better than approaches without considering the localization uncertainty properly. Finally, we have applied the proposed approaches on the experimentally collected real data from a dye concentration field over a section of a river and a temperature field of an outdoor swimming pool to provide proof of concept tests and evaluate the proposed schemes in real situations. In both simulation and experimental results, the proposed methods outperform the quick-and-dirty solutions often used in practice.

Environmental Monitoring Using Autonomous Aquatic Robots: Sampling Algorithms and Experiments

This brief presents a practical solution to the problem of monitoring an environmental process in a large region by a small number of robotic sensors. Optimal sampling strategies are developed, taking into account the quality of the estimated environmental field and the lifetime of the sensors. We also present experimental results for monitoring a temperature field of an outdoor swimming pool sampled by an autonomous aquatic surface robot. Simulation and experimental results are provided to validate the proposed scheme.