Luis A. Montestruque

Brief On the model-based control of networked systems

In this paper the control of linear plants, where the sensor is connected to a linear controller/actuator via a network is addressed. Both, state and output feedback, are considered and results are derived for both continuous and discrete plants. A key idea is that knowledge of the plant dynamics is used to reduce the usage of the network. Necessary and sufficient conditions for stability are derived as simple eigenvalue tests of a well-structured test matrix, constructed in terms of the update time h, and the parameters of the plant and of its model. These tests are extended to include network delay as well.

Stability of model-based networked control systems with time-varying transmission times

In model-based networked control systems (MB-NCSs), an explicit model of the plant is used to produce an estimate of the plant state behavior between transmission times. In this paper, the stability of MB-NCSs is studied when the controller/actuator is updated with the sensor information at nonconstant time intervals. Networked control systems with transmission times that are varying either within a time interval or are driven by a stochastic process with identically independently distributed and Markov-chain driven transmission times are studied. Sufficient conditions for Lyapunov stability are derived. For stochastically modeled transmission times almost sure stability and mean-square sufficient conditions for stability are introduced.

Design of a wireless assisted pedestrian dead reckoning system - the NavMote experience

In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the NavMote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The network relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques.

Static and dynamic quantization in model-based networked control systems

In this paper the effects of quantization in an important class of networked control systems called model-based networked control systems (MB-NCS) are considered. The MB-NCS architecture uses an explicit model of the plant in the controller in order to reduce the network traffic, while attempting to prevent excessive performance degradation. Sufficient stability conditions for two types of static and a dynamic quantization schemes for MB-NCS are derived. An important feature is that the stability conditions are explicitly expressed in terms of the plant and controller dynamics, the error between the model and the plant parameters, the transmission or update times, the quantization parameters, and a robustness measure of the system to parameter uncertainty. This is important because it allows the design of the controller and network parameters to achieve the desired goals. Examples are used throughout to illustrate the main results.

Stochastic stability for model-based networked control systems

In this paper we study the stochastic stability properties of certain networked control system. Specifically we study the stability of the model-based network control system introduced in [L.A. Montestruque et. al., 41st IEEE Conference on Decision and Control, December 2002.] under time-varying communication. The model-based network control system uses knowledge of the plant to reduce the number of packet exchanges and thus the network traffic. Stability conditions for constant data packet exchange rates are analysed in [L.A. Montestruque et. al., 41st IEEE Conference on Decision and Control, December 2002, L.A. Montestruque et.al., ISIS Technical Report ISIS-2002-001, January 2001]. Here we concentrate on the stochastic stability of the networked system when the packet exchange times are time varying and have some known statistical properties. Conditions are derived for almost sure and mean square stability for independent, identically distributed update times. Mean square stability is also studied for Markov chain driven update times.

Networked Control Systems: A Model-Based Approach

In this article a class of networked control systems called Model-Based Networked Control Systems (MB-NCS) is considered. This control architecture uses an explicit model of the plant in order to reduce the network traffic while attempting to prevent excessive performance degradation. MB-NCS can successfully address several important control issues in an intuitive and transparent way. In this article the main results of this MB approach are described with examples. Specifically, conditions for the stability of state and output feedback continuous and discrete systems are derived under different scenarios that include delay compensation, constant and time varying update times, non-linear plants and quantization of the feedback signals. In addition, a performance measure for MB-NCS with noise inputs is introduced.

Study of the 77, 79, 81, 83Se level structure with the 76, 78, 80, 82Se(d,p) reaction

Abstract Differential cross-section and vector-analyzing-power data were obtained for an incident deuteron energy of 12.5 MeV for the 76,78,80,82 Se(d, p) 77,79,81,83 Se reactions. The results are compared with the predictions of the DWBA theory to determine l -values, spins, parities and spectroscopic factors of the resolved states. Magnetic spectrograph measurements were made to determine the excitation energies of the states studied as well as for a number of additional levels in each isotope. A precise determination of the 82 Se(d, p) 83 Se Q -value was also made, yielding a value of 3.5934±0.0030 MeV. Spin and parity assignments have been made to 16 states in 77 Se, 22 states in 79 Se, 17 states in 81 Se, and 15 states in 83 Se. In addition, tentative spin assignments were made to one state in 79 Se and to three states in 83 Se. A sum-rule analysis was carried out and comparisons made to previous work and to the predictions of the simple pairing theory. The results are in general agreement with the Coriolis-coupling model calculations of Heller and Friedman.

Model-based control of networked systems

1. Introduction.- PART I 2. Model-Based Control Systems: Stability.- 3. Model-Based Control Systems: Output Feedback and Delays.- 4. Model-Based Control Systems with Intermittent Feedback.- 5. Time-Varying and Stochastic Feedback Updates.- 6. Event-Triggered Feedback Updates.- 7. Model-Based Nonlinear Control Systems.- 8. Quantization Analysis and Design.- PART II 9. Optimal Control of Model-Based Event-Triggered Systems.- 10. Performance Analysis using Lifting Techniques.- 11. Reference Input Tracking.- 12. Adaptive Stabilization of Networked Control Systems.- 13. Multirate Model-Based Systems.- 14. Distributed Control Systems.- Appendix.- Index.- References.

18O(p, p)18O and 18O(p, p′)18O∗ for E = 6.1−16.6 MeV☆

Abstract Angular distributions of cross sections and analyzing powers have been measured for 18 O(p, p) 18 O and 18 O(p, p 1 ) 18 O ∗ (1.98 MeV) for proton energies between 6.1 and 16.6 MeV. The measurement were crarried out in 25 keV intervals between 6.1 and 8.0 MeV, and in 100 keV intervals between 8.0 and 16.6 MeV. Although the general appearance of the angular distributions changes quite smoothly with energy above about 8 MeV, structure is evident in the backangle excitation functions up to 14 MeV. A phase-shift analysis of the elastic scattering data yielded resonance parameters for 25 levels in 19 F in the excitation enrgy region 13.8−21.4 MeV. A large fraction of these levels have odd parity, and the energies of the 1 2 − and 3 2 − levels coincide closely with peaks seen in the 19 F photonuclear yield curves. A simple model involving proton single-particle states coupled to the 2 1 +; and 3 1 − levels of 18 O is able to account for some features of the observed structure. The energy-averaged elastic and inelastic scattering data for E p > 12 MeV agree reasonably well with the spherical optical model and the DWBA, respectively, as well as with coupled-channels calculations.

Model-Based Control Systems with Intermittent Feedback

Chapter 4 develops the idea of intermittent feedback in the context of MB-NCS. The general idea using the intermittent feedback approach is to operate a control system in open-loop mode for as long as possible in order to reduce the use of resources (communication resources when applied to NCS) and to change the mode of operation to closed-loop mode in order to recover some desired performance by applying a continuous feedback control action. The main difference is that the update strategy in previous chapters uses instantaneous feedback, that is, a single set of measurements is sent from the sensor node to the controller node once every h time units. In contrast, the intermittent approach establishes a closed-loop operation mode in which multiple measurements are transmitted starting at every h time units.