N. Eva Wu

Detection, estimation, and accommodation of loss of control effectiveness

In this paper, an adaptive Kalman filtering algorithm is developed for use to estimate the reduction of control effectiveness in a closed-loop setting. Control effectiveness factors are used to quantify faults entering control systems through actuators. A set of covariance-dependent forgetting factors is introduced into the filtering algorithm. As a result, the change in the control effectiveness is accentuated to help achieve a more accurate estimate more rapidly. A weighted sum-squared bias estimate is defined for the change detection. The state estimate is fed back to achieve the steady-state regulation, while the control effectiveness estimate is used for the on-line tuning of the control law. A stability analysis is performed for the adaptive regulator. Copyright

Adaptive Linear Parameter Varying Control Synthesis for Actuator Failure

A robust linear parameter varying (LPV) control synthesis is carried out for a Highly Maneuverable Aircraft Technology (HiMAT) vehicle subject to loss of control effectiveness. The scheduling parameter is selected to be a function of the estimates of the control effectiveness factors. The estimates are provided online by a two-stage adaptive Kalman filter estimator. The inherent conservatism of the LPV design is reduced through the use of a scaling factor on the uncertainty block that represents the estimation errors of the effectiveness factors. Simulations of the controlled system with the online estimator show that a superior fault tolerance can be achieved.

Spatial sparsity based emitter localization

In classical TDOA/FDOA emitter location methods, pairs of sensors share the received data to compute the CAF and extract the ML estimates of TDOA/FDOA. The TDOA/FDOA estimates are then transmitted to a common site where they are used to estimate the emitter location. However, the two-stage method is not necessarily optimal because in the first stage of these methods, the TDOA and FDOA are estimated by ignoring the fact that all measurements should be consistent with a single emitter location. In this paper, we derive a one-stage localization method based on spatial sparsity of the grid plane. In this method, we directly estimate the location of the emitter without going through the intermediate stage of TDOA/FDOA estimation. The Monte Carlo simulation results show that the proposed method has better performance compared to two-stage classic method and also to another available one-stage method named Direct Position Determination (DPD). We will show that the proposed method is also a very effective and beneficial solution to deal with multipath scenarios.

Reliability and supervisory control

Abstract This paper is intended to provide a brief tutorial on using Markov models for reliability analysis of fault tolerant control systems. Fault coverage is viewed as a means to effecting supervisory control that maximizes the overall system reliability dictated by the Markov failure process.

Fault Diagnosis for A Ship Propulsion Benchmark: Part I

Abstract This paper describes a fault diagnosis performed on the benchmark problem of a ship propulsion system (Izadi-Zamanabadi and Blanke, 1997). The model used for the ship propulsion system is nonlinear, for which two types of additive sensor faults, an additive incipient fault, and a multiplicative parametric fault are simulated. The estimation of the fault severity is accomplished by using an adaptive two-stage extended Kalman filter. A set of statistical detection variables is formed from the residuals of the bias and measurement estimates of the filter. These variables are then used in a threshold based hypothesis test to ratify the occurrence of a fault, in a linear regression analysis to single out the incipient fault, and through a binary logic filter to identify the fault type. The simulation results showed that our diagnostic scheme fulfilled some of the benchmark requirements reasonably well in the face of some prescribed perturbations in the model and disturbances of external signals.

Finding optimal trajectory points for TDOA/FDOA geo-location sensors

In emitter geo-location estimation systems, it is well known that the geometry between sensors and the emitter can seriously impact the accuracy of the location estimate. Here we consider a case where a set of sensors is tasked to perform a sequence of location estimates on an emitter as the sensors progress throughout their trajectories. The goal is to select the trajectories so as to optimally improve the location estimate at each step in the sequence. To build the optimal trajectories, the aircraft, at their current locations, need to know their optimal next states at the time of next estimation, under the constraint of a reachable set due to limited reachable velocity or thrust. In this paper, we propose a one-step method to tackle the optimal next state(ONS) problem using the Particle Swarm Optimization(PSO) by solving the optimal amount of applied thrust along the flying trajectories. Simulation results show that the proposed method dramatically improves the estimation accuracy along the flying trajectories, compared to the random walk and constant velocity scheme. We also show that the estimation accuracy performance is also insensitive to the problem dimensionality.

Linear Parameter Varying Control Synthesis for Actuator Failure, Based on Estimated Parameter

The design of a linear parameter varying (LPV) controller for an aircraft at actuator failure cases is presented. The controller synthesis for actuator failure cases is formulated into linear matrix inequality (LMI) optimizations based on an estimated failure parameter with pre-defined estimation error bounds. The inherent conservatism of an LPV control synthesis methodology is reduced using a scaling factor on the uncertainty block which represents estimated parameter uncertainties. The fault parameter is estimated using the two-stage Kalman filter. The simulation results of the designed LPV controller for a HiMXT (Highly Maneuverable Aircraft Technology) vehicle with the on-line estimator show that the desired performance and robustness objectives are achieved for actuator failure cases.

Probing the NASA generic transport aircraft in real-time for health monitoring

This brief paper describes the development of a pulse compression probing algorithm for real-time monitoring of nonlinear dynamic systems. The probing outputs of a system being monitored are estimates of the sequences of Markov parameters at the current operating point. By imposing signal to noise ratios at both the controlled outputs and the probing outputs, the probing inputs are designed to be non-intrusive and with the intention that the resulting probing outputs are unaffected by other applied signals to the system. The probing algorithm is implemented on a Virtex-5 field-programmable gate-array evaluation platform to expedite real-time processing, and is applied to monitoring the health of the NASA Generic Transport Aircraft Model (GTM). Samples of probing output residuals are evaluated using the Hotellings T2-test for change detection when significant noise is present in the system. This brief paper focuses its discussion on the method of rapid collection of independent samples to reduce detection delay.

Data-Availability-Constrained Placement of PMUs and Communication Links in a Power System

This paper presents a solution to placing a minimum number of phasor measurement units (PMUs) and communication links in a power system so that the steady-state availability of synchrophasor data at each bus meets a prescribed level. For this purpose, a Markov model suitable for the evaluation of synchrophasor availability is built. The model contains a set of binary decision variables representing actions or inactions to maintain PMUs or to utilize communication links. The decision variables are solved to minimize an expected cost for maintaining a PMU network and utilizing data links for synchrophasor inference, subject to a set of synchrophasor availability requirements at the buses. The new aspects of development in this paper concern first the selection of decision variables so that their solutions immediately imply whether a PMU should be placed at a bus and whether a communication link should be established between two buses and, second, the solution to PMU network placement in a large-scale electric network. In addition, permanent and random intermittent PMU outages are modeled. A five-bus power system is used to demonstrate the problem formulation, and placement results are presented. A method to treat a large power system as interconnected smaller power systems with common boundary buses is proposed to address the curse of dimensionality encountered. Each smaller power system formulates an independent optimal placement problem with constraints on matching boundary placement. The steps involved are illustrated through a 14-bus system. The complexity and optimality of a divided problem are compared to that of the original problem.

AN OPERATIONAL APPROACH TO BUDGET-CONSTRAINED RELIABILITY ALLOCATION

Abstract In this paper the problem of maximal increase of system reliability is formulated as a resource allocation problem under a budget constraint. Dynamic programing is used for the optimal solution. Time to system failure is dictated by a Markov process. The system is composed of several subsystems. Each subsystem has several possible configurations that exhibit different levels of fault tolerance and incur different incremental costs at different times. Configuration dependence among subsystems is allowed. An example resembling a fault tolerant industrial process is presented, for which the proposed algorithm is used to obtain a set of maximally reliable solutions corresponding to a set of budget constraints.