This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.

After an incipient fault mode has been detected a logical question to ask is: How long can the system continue to be operated before the incipient fault mode degrades to a failure condition? In many cases answering this question is complicated by the fact that further fault growth will depend on how the system is intended to be used in the future. The problem is then complicated even further when we consider that the future operation of a system may itself be conditioned on estimates of a system’s current health and on predictions of future fault evolution. This paper introduces a notationally convenient formulation of this problem as a Markov decision process. Prognostics-based fault management policies are then shown to be identified using standard Markov decision process optimization techniques. A case study example is analyzed, in which a discrete random walk is used to represent time-varying system loading demands. A comparison of fault management policies computed with and without future uncertainty is used to illustrate the limiting effects of model uncertainty on prognostics-informed fault management policies.

This paper addresses the problem of building trust in online predictions of a battery powered aircraft’s remaining available flying time. A set of ground tests is described that make use of a small unmanned aerial vehicle to verify the performance of remaining flying time predictions. The algorithm verification procedure described here uses a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected in flight. The fully integrated aircraft is repeatedly operated until the charge stored in powertrain batteries falls below a specified lower-limit. The time at which the lower- limit on battery charge is crossed is then used to measure the accuracy of remaining flying time predictions. Accuracy requirements are considered in this paper for an alarm that warns operators when remaining flying time is estimated to fall below a specified threshold.

Successful prediction and management of battery life using prognostic algorithms through ground and flight tests is important for performance evaluation of electrical systems. This paper details the design of test beds suitable for replicating loading profiles that would be encountered in deployed electrical systems. The test bed data will be used to develop and validate prognostic algorithms for predicting battery discharge time and battery failure time. Online battery prognostic algorithms will enable health management strategies. The platform used for algorithm demonstration is the EDGE 540T electric unmanned aerial vehicle (UAV). The fully designed test beds developed and detailed in this paper can be used to conduct battery life tests by controlling current and recording voltage and temperature to develop a model that makes a prediction of end-of-charge and end-of-life of the system based on rapid state of health (SOH) assessment.

Predicting whether or not vehicle batteries contain sufficient charge to support operations over the remainder of a given flight plan is critical for electric aircraft. This paper describes an approach for identifying upper and lower uncertainty bounds on predictions that aircraft batteries will continue to meet output power and voltage requirements over the remainder of a flight plan. Battery discharge prediction is considered here in terms of the following components; (i) online battery state of charge estimation; (ii) prediction of future battery power demand as a function of an aircraft flight plan; (iii) online estimation of additional parasitic battery loads; and finally, (iv) estimation of flight plan safety. Substantial uncertainty is considered to be an irremovable part of the battery discharge prediction problem. However, highconfidence estimates of flight plan safety or lack of safety are shown to be generated from even highly uncertain prognostic predictions.

Tracking the variation in battery dynamics as a function of health is presently attracting attention in academia and industry due to the increased usage of expensive batteries in dynamic systems such as aircraft and electric cars. The online adaptation of battery models to account for age-dependent changes in dynamics is necessary to maintain accurate estimates of the remaining system operations that can be supported under battery power. A novel method for the adaptation of parameters in an electrochemical model of a lithium- ion battery is presented here. An unscented Kalman filtering algorithm is shown to enable the production of internal battery state estimates and age-dependent electrochemical model parameter estimates using only battery current and voltage data collected over randomized discharge profiles. The use of only data collected over randomized discharge profiles distinguishes this work from other works that make use of reference discharge cycles to judge battery health. The experimental results presented here compare online model estimates produced by the proposed algorithm to offline model estimates obtained by periodically taking batteries offline to run reference discharge cycles.

Software-in-the-loop and hardware-in-the-loop testing of failure prognostics and decision making tools for aircraft systems will facilitate more comprehensive and cost-effective testing than what is practical to conduct with flight tests. A framework is described for the offline recreation of dynamic loads on simulated or physical aircraft powertrain components based on a real-time simulation of airframe dynamics running on a flight simulator, an inner-loop flight control policy executed by either an autopilot routine or a human pilot, and a supervisory fault management control policy. The offline testing framework is described for the example of battery charge depletion failure scenarios onboard a prototype electric unmanned aerial vehicle.

Validation of prognostic technologies through ground and flight tests is an important step in maturing these novel technologies and deploying them on real-world systems. To this end, a series of flight tests have been conducted using an un- manned electric vehicle during which the motor system batteries were monitored by a prognostic algorithm. The research presented here endeavors to produce and validate a technology for predicting the remaining time until end-of- discharge of the batteries on an electric aircraft as a function of an expected future flight and online estimates of the charge contained in the batteries. Flight data and flight experiment results are presented along with an assessment of model and algorithm performance.

A hybrid electric vehicle with a two-mode continuously variable transmission and four fixed gear operating modes is modeled to enable a comparative investigation of the charge sustaining energy management control problem. Control strategies are designed using three distinct approaches: (1) explicit operating logic is defined in terms of comparison threshold values that can be tuned, using heuristic reasoning and/or model-based calculations, to operate the engine efficiently; (2) a static optimization, applied at each time instant, is used to minimize some measure of equivalent fuel consumption for the corresponding sequence of time instants; (3) a dynamic optimization, applied just once over the appropriate time interval, is used to minimize the actual fuel consumption over a drive cycle. The first approach requires only limited modeling information, and thus has the benefit of quick and simple programming. The second approach is based on optimization so as to directly exploit available modeling information. The third approach will find the vehicle control for optimal fuel efficiency, but it does not lead to an implementable strategy since prior knowledge of the drive cycle is required.

This paper introduces a novel Markov process formulation of stochastic fault growth modeling, in order to facilitate the development and analysis of prognostics-based control adaptation. A metric representing the relative deviation between the nominal output of a system and the net output that is actually enacted by an implemented prognostics-based control routine, will be used to define the action space of the formulated Markov process. The state space of the Markov process will be defined in terms of an abstracted metric representing the relative health remaining in each of the system’s components. The proposed formulation of component fault dynamics will conveniently relate feasible system output performance modifications to predictions of future component health deterioration.

A generalized Markov chain representation of fault dynamics is presented for the case that available modeling of fault growth physics and future environmental stresses can be represented by two independent stochastic process models. A contrived but representatively challenging example will be presented and analyzed, in which uncertainty in the modeling of fault growth physics is represented by a uniformly distributed dice throwing process, and a discrete random walk is used to represent uncertain modeling of future exogenous loading demands to be placed on the system. A finite horizon dynamic programming algorithm is used to solve for an optimal control policy over a finite time window for the case that stochastic models representing physics of failure and future environmental stresses are known, and the states of both stochastic processes are observable by implemented control routines. The fundamental limitations of optimization performed in the presence of uncertain modeling information are examined by comparing the outcomes obtained from simulations of an optimizing control policy with the outcomes that would be achievable if all modeling uncertainties were removed from the system.

It is an inescapable truth that no matter how well a system is designed it will degrade, and if degrading parts are not repaired or replaced the system will fail. Avoiding the expense and safety risks associated with system failures is certainly a top priority in many systems; however, there is also a strong motivation not to be overly cautious in the design and maintenance of systems, due to the expense of maintenance and the undesirable sacrifices in performance and cost effectiveness incurred when systems are over designed for safety. This paper describes an analytical process that starts with the derivation of an expression to evaluate the desirability of future control outcomes, and eventually produces control routines that use uncertain prognostic information to optimize derived risk metrics. A case study on the design of fault-adaptive control for a skid-steered robot will illustrate some of the fundamental challenges of prognostics-based control design.

Most fault adaptive control research addresses the preservation of system stability or functionality in the presence of a specific failure (fault). This paper ex- amines the fault adaptive control problem for a generic class of incipient failure modes, which do not initially affect system stability, but will eventually cause a catas- trophic failure to occur. This risk of catastrophic failure due a component fault mode is some monotonically in- creasing function of the load on the component. Assum- ing that a probabilistic prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component loads to minimize risk of failure, while not overly de- grading nominal performance. A methodology is pro- posed for posing this problem as a finite horizon con- strained optimization, where constraints correspond to maximum risk of failure and maximum deviation from nominal performance. Development of the methodol- ogy to handle a general class of overactuated systems is given. Also, the fault adaptive control methodology is demonstrated on an application example of practical significance, an electro-mechanical actuator (EMA) con- sisting of three DC motors geared to the same output shaft. Similar actuator systems are commonly used in aerospace, transportation, and industrial processes to ac- tuate critical loads, such as aircraft control surfaces. The fault mode simulated in the system is a temperature de- pendent motor winding insulation degradation.

This paper introduces a control architecture incorporating prognostic information to extend the Remaining Useful Life (RUL) of a system while ensuring stability and performance requirements. A Model Predictive Control (MPC) framework is chosen to utilize the existing production controller by adjusting its reference points, thereby lowering its impact on the system. The MPC arrives at a control law by minimizing a quadratic cost function of control effort and tracking error while enforcing hard and soft constraints. Prognostic information is included in the cost function through the use of soft constraints whereas absolute boundary conditions (eg. input limits, tracking error) are incorporated as hard constraints. A relationship is provided between the terminal cost associated with the prognostic constraints and the quadratic costs. Asymptotic stability of the control architecture is demonstrated for the case of nominal system dynamics. The proposed fault-tolerant control design is applicable to a variety of application domains. An EMA is used to illustrate the efficacy of the proposed approach.

This paper examines the fault tolerant control problem for a generic class of incipient failure modes that grow in severity as a function of component loading. Assuming that a prognostic model is available to evaluate the risk of incipient fault modes growing into catastrophic failure conditions, then fundamentally the fault adaptive control problem is to adjust component loads to minimize risk of failure, while not overly degrading nominal performance. A methodology is proposed for posing this problem as a finite horizon constrained optimization, where constraints correspond to maximum risk of failure and maximum allowable deviation from nominal performance. Simulation results are given for an example system with an active redundant DC motor configuration. The fault mode simulated in this system is temperature dependent motor winding insulation degradation.

Actuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. In- cipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission. The ability to maintain a stable and credible operation, even in the presence of incipient failures, is of paramount importance to accomplish “must achieve” mission objectives. This paper introduces a novel methodology for the fault-tolerant design of critical subsystems, such as an Electro-Mechanical Actuator (EMA), that takes advantage of on-line, real-time estimates of the Remaining Useful Life (RUL) or Time-to-Failure (TTF) of a failing component and reconfigures the available control authority by trading off system performance with control activity. The primary goal is to complete critical mission objectives within a time window dictated by prognostic algorithms so that the fault mode is accommodated and an acceptable level of performance maintained for the duration of the mission. The proposed fault-tolerant control design is mathematically rigorous, generic and applicable to a variety of application domains. An EMA is used to illustrate the efficacy of the proposed approach.

We demonstrate a photonic crystal integrated microfluidic chip that is compatible with a 384-well microplate format for measuring kinetic reaction rate constants in high-throughput biomolecular interaction screening applications. The device enables low volume kinetic analysis of protein-protein interactions with low flow latency, and control of five analyte flow channels with a single control point. The structure is fabricated with a replica molding process that produces the submicron photonic crystal structure simultaneously with the micrometer-scale fluid channel structure. The device significantly reduces the kinetic assay time required compared with a conventional biosensor microplate in which reagents reach the active detection surface by diffusion. Using the photonic crystal sensor fluid network system, we demonstrate determination of the kinetic association/dissociation rate constants between immobilized ligands and analytes in the flow stream, using the heparin/lactoferrin system as an example.