Institute for Systems Research

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Author: search.filters.author.Baras, John S.Subject: search.filters.subject.Sensor-Actuator Networks

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    Optimal Output Feedback Control Using Two Remote Sensors over Erasure Channels
    (2007) Gupta, Vijay; Martins, Nuno C.; Baras, John S.; Martins, Nuno C.; ISR
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    Efficient sampling for keeping track of an Ornstein-Uhlenbeck process
    (2006) Rabi, Maben; Moustakides, George; Baras, John S.; Baras, John S.; ISR; SEIL
    We consider estimation and tracking problems in sensor networks with constraints in the hierarchy of inference making, on the sharing of data and inter-sensor communications. We identify as a typical representative for such problems tracking of a process when the number and type of measurements in constrained. As the simplest representative of such problems, which still encompasses all the key issues involved we analyze efficient sampling schemes for tracking an Ornstein-Uhlenbeck process. We considered sampling based on time, based on amplitude (event-triggered sampling) and optimal sampling (optimal stopping). We obtained the solution in each case as a constrained optimization problem. We compare the performance of the various sampling schemes and show that the event-triggered sampling performs close to optimal. Implications and extensions are discussed.
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    Multiple Sampling for Estimation on a Finite Horizon
    (2006) Rabi, Maben; Baras, John S.; Moustakides, George; Baras, John S.; ISR; SEIL
    We discuss some multiple sampling problems that arise in real-time estimation problems with limits on the number of samples. The quality of estimation is measured by an aggregate squared error over a finite horizon. We compare the performances of the best detereministic, level-triggered and the optimal sampling schemes. We restrict the signal to be either a Wiener process or an Ornstein-Uhlenbeck process. For the Wiener process, we provide closed form expressions and series expansions. For the Ornstein Uhlenbeck process, we provide procedures for numerical computation. Our results show that level-triggered sampling is almost optimal when the signal is stable.

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    Sampling of Diffusion Processes for Real-time Estimation
    (2004) Rabi, Maben; Baras, John S.; Baras, John S.; ISR
    This paper addresses the causal sampling of observations of a diffusion process that results in a good quality continuous estimator based upon these samples. The optimal sampling scheme with a fixed number of samples is found by solving an optimal (multiple) stopping problem. This is solved explicitly in a special case. A class of threshold approximations is also described.
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    UAV Placement for Enhanced Connectivity in wireless Ad-hoc Networks
    (2004) Raissi-Dehkordi, Majid; Chandrashekar, Karthikeyan; Baras, John S.; ISR; CSHCN
    In this paper we address the problem of providing full connectivity in large (wide area) ad hoc networks by placing advantaged nodes like UAVs (as relay nodes) in appropriate places. We provide a formulation where we can treat the connectivity problem as a clustering problem with a summation-form distortion function. We then adapt the Deterministic Annealing clustering algorithm to our formulation and using that we nd the minimum number of UAVs required to provide connectivity and their locations. Furthermore, we describe enhancements that can be used to extend the basic connectivity problem to support notions of reliable connectivity that can lead to improved network performance. We establish the validity of our algorithm and compare its performance with optimal (exhaustive search) as well as non-opitmal (hard clustering) algorithms.We show that our algorithm is nearoptimal both for the basic connectivity problem as well as extended notions of connectivity.
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    Numerical Study on Joint Quantization and Control under Block-Coding
    (2004) Tan, Xiaobo; Xi, Wei; Baras, John S.; ISR; SEIL
    This paper addresses the joint quantization and control problem for hidden Markov chains with variable-length block-coding. The aim is to understand the impact of communication bandwidth and information delay (due to block-coding) on the control performance. A heuristic algorithm is developed to solve the dynamic programming (DP) equation through the introduction of a metric on the discrete observation space. Numerical results are presented demonstrating the attention division in block-coding and the tradeoffs between control performance and communication bandwidth.
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    Control of Autonomous Swarms Using Gibbs Sampling
    (2004) Baras, John S.; Tan, Xiaobo; ISR; SEIL
    A distributed control approach is proposed for self-organization of autonomous swarms. The swarm is modeled as a Markov random field (MRF) on a graph where the (mobile) nodes and their communication/sensing links constitute the vertices and the edges of the graph, respectively. The movement of nodes is governed by the Gibbs sampler. The Gibbs potentials, local in nature, are designed to reflect collective goals such as gathering, dispersion, and linear formation. The algorithm can be run completely in parallel, and hence it is robust and scalable. Simulation results are provided to illustrate the proposed method.
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    Adaptive Identification and Control of Hysteresis in Smart Material Actuators
    (2003) Tan, Xiaobo; Baras, John S.; ISR; CDCSS
    Hysteresis exhibited by smart materials hinders their wider applicability in actuators and sensors. In this paper methods are studied for recursive identification and adaptive inverse control of smart material actuators, where a Preisach operator with a piecewise uniform density function is used to model the hysteresis. Persistent excitation conditions for parameter convergence are discussed in terms of the input to the Preisach operator. Two classes of recursive identification schemes are explored, one based on the hysteresis output, the other based on the time difference of the output. Asymptotic tracking for the adaptive inverse control method is proved, and the condition for parameter convergence is given in terms of the reference trajectory. Practical implementation issues are also investigated. Simulation and experimental results based on a magnetostrictive actuator are used to illustrate the approach.
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    Control of Hysteresis in Smart Actuators with Application to Micro-Positioning
    (2003) Tan, Xiaobo; Baras, John S.; Krishnaprasad, Perinkulam S.; ISR; CDCSS
    Hysteresis in smart material actuators makes the effective use of these actuators quite challenging. The Preisach operator has been widely used to model smart material hysteresis. Motivated by positioning applications of smart actuators, this paper addresses the value inversion problem for a class of discretized Preisach operators, i.e., to find an optimal input trajectory given a desired output value. This problem is solved through optimal state transition of a finite state machine (FSM) that corresponds to the discretized Preisach operator. A state-space reduction scheme for the FSM is developed, which significantly saves the memory and the computation time. As an example, micro-positioning control of a magnetostrictive actuator is investigated. Experimental results are presented to demonstrate the effectiveness of the proposed approach.
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    Control of Hysteresis in Smart Actuators, Part II: A Robust Control Framework
    (2002) Tan, Xiaobo; Baras, John S.; ISR; CDCSS
    Hysteresis in smart actuators presents a challenge in control of these actuators. A fundamental idea to cope with hysteresis is inverse compensation. But due to the open loop nature of inverse compensation, its performance is susceptible to model uncertainties and to errors introduced by inverse schemes. In this paper we develop a robust control framework for smart actuators by combining inverse control with the $l_1$ robust control theory. We show that, for both the rate-independent hysteresis model and the rate-dependent one, the inversion error can be bounded in magnitude and the bound is quantifiable in terms of parameter uncertainties and the inversion scheme. Hence we can model the inversion error as an exogenous disturbance and attenuate its impact by robust control techniques. Through the example of controlling a magnetostrictive actuator, we present a systematic controller design method which guarantees robust stability and robust trajectory tracking while taking actuator saturation into account. Simulation and experimental results are provided.