Number Of Sensor Measurements Research Articles

Array optimization of sparse regularization equivalent source acoustic holography algorithm

In order to improve the accuracy of the sparse regularization equivalent source acoustic holography algorithm, based on the analysis of the holographic algorithm theory, an optimized array arrangement is proposed. The sensing matrix constructed by the array parameters directly affects the accuracy of the acoustic imaging algorithm. By analyzing the influence of the sensing matrix on the imaging algorithm, the Restricted Isometry Constant (RIC) is chosen to evaluate the sensing matrix. Using genetic algorithm (GA), the RIC is taken as the fitness value, and the optimal pseudo-random array is selected and compared with the conventional array arrangement for acoustic imaging. Experiments show that the optimized pseudo-random array has better imaging effect under the same number of sensor measurements, and provides an optimization method for the design of acoustic array.

Design of a Hybrid Controller for Pressure Swing Adsorption Processes

The aim of this paper is to synthesize a hybrid controller for pressure swing adsorption (PSA) processes. Since the process is described by a set of partial differential algebraic equations, first a local reduced-order model (LROM) for the process is developed and is formalized as a hybrid system. A hybrid controller is designed for purity control of the process in the presence of external disturbances by determining the maximal safe set of the LROM. A hybrid backward reachability analysis is performed for this purpose. Considering a realistic scenario for PSA processes where the states are not available and the number of measurement sensors is very limited, the desired states are estimated by using a hybrid observer. The controller is designed and applied to a two-bed, six-step PSA process whose dynamical behavior is simulated by a full-order principle-based model of the process. An excellent performance of the controller is obtained.

Subdiffusive Source Sensing by a Regional Detection Method.

Motivated by the fact that the danger may increase if the source of pollution problem remains unknown, in this paper, we study the source sensing problem for subdiffusion processes governed by time fractional diffusion systems based on a limited number of sensor measurements. For this, we first give some preliminary notions such as source, detection and regional spy sensors, etc. Secondly, we investigate the characterizations of regional strategic sensors and regional spy sensors. A regional detection approach on how to solve the source sensing problem of the considered system is then presented by using the Hilbert uniqueness method (HUM). This is to identify the unknown source only in a subregion of the whole domain, which is easier to be implemented and could save a lot of energy resources. Numerical examples are finally included to test our results.

Design of APOD-based switching dynamic observers and output feedback control for a class of nonlinear distributed parameter systems

The output feedback control problem for a class of nonlinear distributed parameter systems with limited number of continuous measurement sensors that describes a wide range of physico-chemical systems is investigated using adaptive proper orthogonal decomposition (APOD) method. Specifically, APOD is used to initiate and recursively revise locally valid reduced order models (ROMs) that approximate the dominant dynamic behavior of such physico-chemical systems. The controller is designed based on ROMs by combining a robust state controller with an APOD-based nonlinear Luenberger-type switching dynamic observer of the system states to reduce measurement sensor requirements. The important static observer requirements on the number of measurement sensors (that they must be supernumerary to the ROM dimension) and their location are circumvented by synthesizing dynamic observers. Three different approaches are introduced to recursively compute the dynamic observer gains at the ROM revisions. The stability of the closed-loop system is proven via Lyapunov and hybrid system stability arguments without invoking the separation principle between control and observation. The proposed method is successfully used to regulate a physico-chemical system that can be described in the form of the Kuramoto–Sivashinsky equation when the process exhibits significant nonlinear behavior.

A Near-Optimal Sensor Scheduling Strategy for an on–off Controller With an Expensive Sensor

This paper describes an efficient method for scheduling an energy-consuming sensor sparingly in combination with an on-off controller, specifically for a finite horizon control problem in which only end states are critical. In certain low-power applications, such as autonomous microrobotics, on-off controllers can be very efficient in operating piezoelectric actuators (and other capacitive actuation schemes) compared to traditional analog and pulsewidth modulation controllers. However, with existing sensing circuitry, sensing at the same frequency as control can be prohibitively expensive, because energy consumption in the sensing circuitry may be comparable or even much higher than energy consumption for actuation. Instead, a method is presented for best scheduling a limited number of sensor measurements and updates to control inputs during a finite horizon on-off control problem, in response to Gaussian disturbances and measurement noise. To simplify the problem, a lower bound for the expected value of a quadratic error function of the end states is found, which permits rapid evaluation of candidate sensor times. When actuator energy consumption is incorporated in the optimization, this produces a numerically efficient near-optimal strategy for determining best measurement times and updates to the control input sequence.

Shooter localization in urban terrain

Detecting and accurately locating snipers has been an elusive goal of the armed forces and law enforcement agencies for a long time. Most successful sniper-detecting systems are based on acoustic measurements. We develop an acoustic system that works well even in complex urban environments. Funded through the Network Embedded Systems Technology program of the US Defense Advanced Research Projects Agency's Information Exploitation Office, the PinPtr system uses a wireless network of many low-cost sensors to determine both a shooter's location and the bullet's trajectory by measuring both the muzzle blast and the shock wave. The PinPtr sensor-fusion algorithm, which runs on a base station, performs a search on a hyper-surface defined by a consistency function. This function provides the number of sensor measurements that are consistent with hypothetical shooter positions and shot times. The algorithm automatically classifies measurements and eliminates those that result from multipath effects or are otherwise erroneous. A fast search algorithm finds the global maximum of the surface, which corresponds to the shooter position.

A generalized approach to modal filtering for active noise control. II. Acoustic sensing

Many techniques for controlling the noise radiated by large structures require a large number of inputs to the controller to produce global attenuation. Unfortunately, processing the large number of inputs required is often beyond the capabilities of current controllers. In attempting to overcome this problem, many researchers have adopted various modal-filtering-type techniques. Such techniques involve resolving a small number of important global quantities (traditionally structural modes) from a large number of sensor measurements. However, current approaches require detailed structural information at the design stage. Determining this for complex, real-world structures may be very difficult, preventing many techniques from going beyond the laboratory. The technique presented here outlines a new sensing system strategy, where the radiated sound field is decomposed using multipole radiation patterns, thereby alleviating the need for detailed structural information. Simulation and experimental results are presented.

Integrated optimal actuator/sensor placement and robust control of uncertain transport-reaction processes

This paper focuses on transport-reaction processes with unknown time-varying parameters and disturbances described by quasi-linear parabolic PDE systems, and addresses the problem of computing optimal actuator/sensor locations for robust nonlinear controllers. Initially, Galerkin's method is employed to derive finite-dimensional approximations of the PDE system which are used for the synthesis of robust nonlinear state feedback controllers via geometric and Lyapunov techniques and the computation of optimal actuator locations. The controllers enforce boundedness and uncertainty attenuation in the closed-loop system. The optimal actuator location problem is subsequently formulated as the one of minimizing a meaningful cost functional that includes penalty on the response of the closed-loop system and the control action. Owing to the boundedness of the state, the cost is defined over a finite-time interval (the final time is defined as the time needed for the process state to become smaller than the desired uncertainty attenuation limit), while the optimization is performed over a broad set of initial conditions and time-varying disturbance profiles. Subsequently, under the assumption that the number of measurement sensors is equal to the number of slow modes, we employ a standard procedure for obtaining estimates for the states of the approximate finite-dimensional model from the measurements. The optimal location of the measurement sensors is computed by minimizing a cost function of the estimation error in the closed-loop infinite-dimensional system. We show that the use of these estimates in the robust state feedback controller leads to a robust output feedback controller, which guarantees boundedness of the state and uncertainty attenuation in the infinite-dimensional closed-loop system, provided that the separation between the slow and the fast eigenvalues is sufficiently large. We also establish that the solution to the optimal actuator/sensor problem, which is obtained on the basis of the closed-loop finite-dimensional system, is near-optimal in the sense that it approaches the optimal solution for the infinite-dimensional system as the separation between the slow and fast eigenvalues increases. The theoretical results are successfully applied to a typical diffusion-reaction process with nonlinearites and uncertainty to design a robust nonlinear output feedback controller and compute the optimal actuator/sensor locations for robust stabilization of an unstable steady state.

Integrating nonlinear output feedback control and optimal actuator/sensor placement for transport-reaction processes

This paper proposes a general and practical methodology for the integration of nonlinear output feedback control with optimal placement of control actuators and measurement sensors for transport-reaction processes described by a broad class of quasi-linear parabolic partial differential equations (PDEs) for which the eigenspectrum of the spatial differential operator can be partitioned into a finite-dimensional slow one and an infinite-dimensional stable fast complement. Initially, Galerkin's method is employed to derive finite-dimensional approximations of the PDE system which are used for the synthesis of stabilizing nonlinear state feedback controllers via geometric techniques. The optimal actuator location problem is subsequently formulated as the one of minimizing a meaningful cost functional that includes penalty on the response of the closed-loop system and the control action and is solved by using standard unconstrained optimization techniques. Then, under the assumption that the number of measurement sensors is equal to the number of slow modes, we employ a procedure proposed in Christofides and Baker (1999) for obtaining estimates for the states of the approximate finite-dimensional model from the measurements. The estimates are combined with the state feedback controllers to derive output feedback controllers. The optimal location of the measurement sensors is computed by minimizing a cost function of the estimation error in the closed-loop infinite-dimensional system. It is rigorously established that the proposed output feedback controllers enforce stability in the closed-loop infinite-dimensional system and that the solution to the optimal actuator/sensor problem, which is obtained on the basis of the closed-loop finite-dimensional system, is near-optimal in the sense that it approaches the optimal solution for the infinite-dimensional system as the separation of the slow and fast eigenmodes increases. The proposed methodology is successfully applied to a representative diffusion–reaction process and a nonisothermal tubular reactor with recycle to derive nonlinear output feedback controllers and compute optimal actuator/sensor locations for stabilization of unstable steady states.

Mixed modality recursive least squares and radialbasisfunction-based scheme for transit time estimation inmultiphase flow

Estimation of transit time in multiphase flow from a number of sensor measurements has major importance in the oil and chemical industries. A new mixed modality scheme is proposed which employs recursive least squares adaptive filtering together with a radial basis function network to estimate, in real-time, multiphase flow transit time. The technique also provides a measure of the turbulence within the flow and an output which indicates the confidence in the estimates. The performance of the network is demonstrated on multiphase flow measurement datasets.

Comparison of the Adjoint and Influence Coefficient Methods for Solving the Inverse Hyperthermia Problem

An adjoint formulation is derived and used to determine the elements in the Jacobian matrix associated with the inverse problem of estimating the blood perfusion and temperature fields during hyperthermia cancer treatments. This method and a previously developed influence coefficient method for obtaining that matrix are comparatively evaluated by solving a set of numerically simulated inverse hyperthermia problems. The adjoint method has the advantage of requiring fewer solutions of the bioheat transfer equation to estimate the Jacobian than does the influence coefficient method when the number of measurement sensors is significantly smaller than the number of unknown parameters. Thus, it could be a preferable method to use in hyperthermia applications where the number of sensors is strictly limited by patient considerations. However, the adjoint method requires that CPU time intensive convolutions be numerically evaluated. Comparisons of the performance of the adjoint formulation and the influence coefficient method show that, first, there is a critical ratio of the number of measurement sensors to the number of unknown parameters at which the CPU time per iteration required to calculate the Jacobian matrix is the same for both methods. The adjoint method is faster than the influence coefficient method only when the value of the ratio is less than that critical value. For the hyperthermia problems investigated in the present study, this only occurs for cases with a very small number of measurement sensors. This presents a potential problem for clinical applications because the fewer measurement sensors used, the less information that can be gathered to correctly solve the inverse problem.(ABSTRACT TRUNCATED AT 250 WORDS)

Parameter estimation of one- and two-dimensional heat conduction processes using a minicomputer

To estimate unknown parameters in one- and two-dimensional heat conduction processes, a gradient-type algorithm was implemented on a minicomputer. This combination yielded successful results in the experiment for the estimation of constant parameters contained in the system equation (diffusion equation) or in the boundary equation. The results also showed the suitability of, and limitations of, present small computers for high-order (lumped parameter) problems. The relationship between the number of measurement sensors and the convergence of the estimation algorithm was also experimentally studied.