Patent Description:
Modern automotive control generally makes use of a vehicle processing unit that controls a number of parameters to enhance engine performance. A variety of different control methodologies may be used, such as Model predictive control (MPC). MPC enables the user to formulate the control performance requirements (setpoint tracking quality, energy consumption and others) and technical/operational restrictions (physical constraints for valve positions and actuator values and others) into a form of objective function and accompanying constraints for the optimized variables. In automotive, these optimized variables usually correspond to manipulated variables, such as torques, normalized braking forces, valve openings etc. Using the internal model of the controlled system, the optimizer (optimization routine) finds the optimal values of the optimized variables over the specified prediction horizon such that the cost function is minimized, and the constraints are satisfied.

Predictive maintenance criterion may be used to generate system warnings to prevent failure. <FIG> shows a prior art approach. For a given component of a system, such as a turbocharger, one or more parameters (and links between such parameters) may be monitored <NUM> to determine whether and when failure is likely. When the measured variable satisfies faultless case conditions <NUM>, the process returns to monitoring <NUM> until a next iteration. Iterations may be performed at a desired/set frequency (such as every second, or longer or shorter time period). If the measured variable exceeds fault boundaries, a predictive maintenance approach will determine when the degradation of the underlying component has reached a point where failure is imminent, and proceeds from block <NUM> to block <NUM>, where the component at issues is identified, and a flag is set at <NUM> for that component. The flag may result in issuance of an alert to the user, for example, and may be set and recorded for use in maintenance operations. For example, the pressure into, and out of, a turbocharger compressor may be monitored in relation to the speed of the compressor; the amount of pressure increase at a given speed can be modeled to determine whether the operation of the compressor is degrading to a point where a failure is likely to occur in a foreseeable timeline. While predictive maintenance is useful to avoid system performance degradation as well as catastrophic failure, it does not extend the life of the components of the system between maintenance processes. What is needed then is an approach that will extend component life, a consideration referred to as "health optimization" herein.

Performance-focused control methods can under certain conditions accelerate the deterioration of health of system components. For example, a turbocharger that is operated to maintain a boost pressure setpoint may be optimized for precise reference tracking. When input conditions to the system change, the setpoint maintenance may call for significant and fast changes to the compressor wheel speed. However, large changes to compressor wheel speed are known to contribute to low cycle fatigue of the compressor wheel structure. Thus, performance optimization can compete with health optimization. New and alternative methods and systems adapted to balance these potentially competing health and performance goals are desired. Documents cited during prosecution include <CIT>.

Certain performance metrics of a system (whether automotive or otherwise) are considered likely to increase the rate of component degradation. For example, the turbocharger compressor may be operable within a set of compressor speeds and/or pressure in/out parameters, but within that operating range, some combinations of parameters can accelerate degradation. Operating the compressor at a high speed for a prolonged period, for example, can increase local heating and accelerate degradation. The present inventors have recognized that a problem to be solved is the need to adjust performance parameters to avoid such health degrading conditions, which may serve the useful purpose of extending component useful life.

Aspects and preferred embodiments of the invention are defined in the appended claims.

This overview is intended to provide an introduction to the subject matter of the present patent application. It is not intended to provide an exclusive or exhaustive explanation of the concepts.

<FIG> illustrates a general control architecture. A control apparatus is shown at <NUM> and includes a state observer <NUM>, which feeds a set of current state variables x(k) to the optimizer <NUM>. The optimizer calculates a solution for process parameters that can be applied to a set of actuators <NUM>, which in turn control operation of the physical plant <NUM>. The set of actuators may apply to control, for example and without limitation, fuel or other injectors, variable nozzle turbine position, throttle valve, engine brake, after-treatment (including exhaust), exhaust gas recirculation (EGR), turbocharger, an electric motor (in an electric turbocharger for example, which may be controlled via pulse width modulation (PWM)), the waste gate (WG) actuator position (stroke), position of the recirculation valve actuator, position of the variable compressor geometry actuator; and combinations thereof.

The physical plant <NUM> may be, for example and without limitation, an internal combustion engine, whether diesel or gasoline, or a subsystem thereof, such as a turbocharger, the system airpath as a whole, catalysts, etc. The present innovations may also be used more broadly in other systems outside of the engine or vehicle context, including, for example and without limitation, the management of clean room and/or more general ventilation systems, boilers, power plants, furnaces, and/or factory/manufacturing equipment and lines of such equipment, including for example an extruder or a fixture in a manufacturing line.

A plurality of sensors <NUM> are provided. Sensors <NUM> may include, for example, and without limitation, sensors detecting manifold absolute pressure (MAP), mass air flow (MAF), EGR flow, turbo speed, NOx, engine speed, fuel quantity, boost pressure, etc. Additional monitored parameters may include, for example, torque output of the electric motor of an electric turbocharger, waste gate (WG) normalized opening, recirculation valve (RCV) normalized opening, and/or a variable geometry compressor configuration. Such sensors may be configured to sample the underlying parameter being sensed and provide the result of such samples to the state observer <NUM>. The state observer <NUM> may record the underlying sensed parameters, as well as actuator positions, over time to provide history of the system operation.

The state observer <NUM> and optimizer <NUM> may be, for example, implemented in a microcontroller configured to operate on a set of stored instructions for performing a state observation and optimization routine. In another example, an application specific integrated circuit (ASIC) may provide state observer functions, which can include the capture or accumulation of data from the actuators <NUM> and/or sensors <NUM>, which in turn may be read periodically by a microcontroller configured with stored instruction sets for performing a control and/or optimization calculation using, for example, model predictive control (MPC) cost functions, linear quadratic regulator (LQR) control, proportional integral derivative (PID) control, or other control algorithms. The optimizer <NUM> may be integrated into, or provided separately from, an on-board diagnostics system (not shown) that can be used to record diagnostic variables and present them, as needed to the user or to store for later analysis, both of which may additionally be integrated, if desired, into the overall vehicle processing unit.

The output of the analysis is used to control the actuators <NUM> to operate the plant in a manner to minimize the distance of operating parameters from one or more target output values for the controllable outputs or physical plant operating characteristics. For example, the targets may be any of target turbocharger speed, target boost pressure, target pressure difference over the compressor, target air mass flow or a combination thereof. For example, with MPC functions, the distance to target or reference values for the one or more output values (or resulting operating characteristics) is minimized, thus optimizing performance. As an example, a traditional MPC cost function formation may be as shown in Equation <NUM>: <MAT> Where ud,k corresponds to the desired profile for the manipulated variable, uk stands for the manipulated variable, k denotes discrete time instance, and P stands for the prediction horizon of the predictive controller. In this example, yr,k represents output reference values, and yk represents predicted values provided according to mathematical modelling of the physical plant to be controlled during the relevant time horizon, while and W<NUM> and W<NUM> specify weighting terms. For simplicity the k terms may be omitted in subsequent equations herein. The traditional MPC cost function is minimized in operation in order to provide optimal control to the physical plant. Such a process may be performed by the optimizer <NUM>.

In another example, a PID controller can be used to account for each of proportional, integral, and derivative differences from a target operating point. The proportional difference may indicate current state, integral difference may identify a process shift over time, and derivative difference may indicate the direction of changes in operation. With PID control, a proportional difference is minimized while monitoring to ensure that the integral and derivative differences do not indicate changing performance which may, after further iterations, cause the proportional difference to increase. The control parameters output to the actuators <NUM> are, for a PID controller, adjusted to reduce or minimize the distance of actual performance from one or more targets on an iterative basis. The optimizer <NUM> may use PID control instead of MPC, for example. LQR control may be used instead, if desired, applying similar concepts.

<FIG> illustrates how health optimization may operate in coordination with traditional predictive maintenance. A set of health-related parameters can be monitored as indicated at <NUM>, and used to check for satisfaction of faultless case conditions <NUM>, with the process proceeding to component identification <NUM> and flag setting <NUM> as needed. In parallel, the monitored parameters (and others, as desired) are reused at <NUM> to optimize health of the underlying component. "Optimize health" in this context may mean controlling operations of the underlying component(s) to minimize health impacts. For example, a monitored pressure may be within safe bounds, but may be understood analytically as having both "safe" and "healthy" operating parameters, with the "healthy" operating parameter more limited than the "safe" operating parameter. For another example, each time certain actuators are used to move something (a valve, for example), this may count toward the rated life of the actuated thing; that is, a given valve may be rated for operation of <NUM>,<NUM> cycles, thus each cycle of operation may count toward the deterioration of health of that valve. Block <NUM> may be used to adjust the operations of the optimizer in one or more ways.

The monitored parameters from block <NUM> (and/or other information, such as history of use, user inputs, environmental information, etc.) may be used in the illustration of <FIG> as well. In particular, operations in the optimizer <NUM>, in some examples, are adjusted to further account for system health. In some examples, the optimizer calculations are modified to add factors related to system health. In other examples, target values used within optimizer calculations are shifted in response to monitored system health conditions. Some examples perform a two stage analysis by first determining an optimal performance solution, defining a maximum allowed deviation from the optimal performance solution, and then introducing factors related to system health into a second calculation of optimal performance/health, wherein the result minimizes impact to system health within the maximum allowed deviation. "System health" may refer here to the current state of components of a system, as well as the likelihood that certain settings can affect component health by accelerating aging of components or otherwise causing damage, whether near term or long term, to components.

In the turbocharger context, system and component health degradation may result from, for example and without limitation, fouling of components, aging, mechanical wear of the wastegate (WG), and cycle fatigue damage (high or low) to the compressor wheel, for example. Of these, WG mechanical wear and low cycle fatigue (LCF) may in particular be the focus of preventable health degradation efforts. In some examples, system health management is focused on WG and LCF issues. However, other system health factors and components may be accounted for as well.

Turning now to <FIG>, the control system shown in <FIG> is shown again, with a state observer <NUM>, optimizer <NUM>, actuators <NUM>, physical plant <NUM> and sensors <NUM> as before. A separate health monitor block is also included now at <NUM>, configured to receive information from the sensors <NUM>, physical plant <NUM>, and/or actuators <NUM>. The information from the physical plant <NUM> and/or actuators <NUM> may include, for example and without limitation, an indication of current state (for example, whether and to what extent a valve is open), though such information from the actuators may instead or in addition be obtained by monitoring control signals from the optimizer <NUM> to the physical plant <NUM>.

Block <NUM> is shown for illustrative purposes but may be understood as being integrated into the optimizer <NUM>. The operations of blocks <NUM>, <NUM> and <NUM> may be implemented in a microcontroller configured to operate on a set of stored instructions for performing a state observation, health management and optimization routine. In another example, an application specific integrated circuit (ASIC) may provide state observer <NUM> and/or health monitor block <NUM> functions, which can include the capture or accumulation of data from the actuators <NUM> and/or sensors <NUM>, which in turn may be read periodically (or in response to predefined events) by a microcontroller configured with stored instruction sets for performing an optimization calculation using, for example, MPC cost functions, LQR control, PID control, or other control approaches.

In operation, one approach to implementing system health optimization may be directed to replacing the use of one actuator with another to reduce wear on one or more actuators. For example, rather than relying on waste gate (WG) function to control the force applied to the turbine by exhaust gasses, to account for conditions in the compressor, eTurbo functions may be modified (as used herein, eTurbo refers to a system having an electric drive actuator that is operable to provide power to the turbocharger compressor when exhaust pressures are relatively low due to low engine speed (RPM)), or the recirculation valve (RCV) may be used to avoid making changes to WG operation for operation purposes. To achieve this, using WG as a non-limiting example, weighting factors related to the use of the WG function - specifically to changes in actuator position for the WG - may be included as health factors in the optimization calculation, or in a post-processing health optimization of a performance optimization calculation.

In an example, the health monitor block <NUM> may be used to change the optimization calculation in favor of using the RCV to manipulate boost pressure, rather than opening the WG, when aggressive driving (and thus aggressive changes to throttle valve position) is anticipated. For example, in typical use of the RCV, boundary limits to boost pressure are addressed by the RCV to prevent damage and/or to avoid surge. In contrast, when operating with the health monitor block <NUM>, the RCV may be used proactively to reduce WG actuator operation even when boost pressure conditions would not otherwise trigger RCV bypass. Thus when the input demands from the user indicate a change in throttle position that would limit input gas flow to the compressor, the RCV is triggered first, rather than the WG, to avoid surge. In an example, such usage would be tracked by the system (such as by the health monitor block <NUM>, the state observer <NUM>, or in the optimizer <NUM>, or by a microcontroller, microprocessor, or ASIC embodying one or more of these blocks), and the determination of which of the RCV or WG to use may change over time depending on which actuator receives more use. For example, if the RCV is rated for a number of cycles, N, and the WG is rated for a number of cycles, M, the control system may adjust factors related to the health impact of use of one or the other in view of how many cycles each has been subjected to since first use, as well as with consideration of the typical usage pattern of the system. For example, one or more factors of a cost minimizing function (such as MPC) may include actuator health factors related to each actuator, where the actuator health factors include coefficients that change by increasing as the number of cycles of use of such actuator (or other feature, such as a rechargeable battery), whether in linear, stepwise, or polynomial fashion.

In some examples, the configuration in <FIG> may be understood as including a low level controller (the optimizer <NUM>) and a supervisory controller (health monitor block <NUM>), in which the supervisory controller is configured to monitor ongoing operations and impacts to system and/or system component health. The supervisory controller may use a very long time horizon, such as by reference to a maintenance schedule timeframe, for example (months or years, depending on the implementation), for making determinations about next steps, while the low level controller is adapted for decision making using a shorter time horizon (such as a cycle or trip, typically on the order of minutes to hours, though possibly longer). Using the example above of replacing WG use with RCV use, the low level controller (the optimizer <NUM>) may perform an MPC function using one or more cost function inputs, for example coefficients and weighting factors. The supervisory controller can act to modify one or more cost function inputs, such as by changing a coefficient or weighting factor, to favor or disfavor certain operations such as actuation or change in position of the WG and/or RCV. In some examples, the supervisory controller may modify a cost function itself, such as by changing a cost function from a linear function to a quadratic or exponential function, for example. In another example, the low level controller may be a non-optimizing controller such as a PID controller instead, and the supervisory controller can operate on the low level controller activity by, again, modifying an input value such as a setpoint.

In another example, the system includes an eTurbo apparatus. Some examples of design for and use of an eTurbo are shown in <CIT> and <CIT>. In context, one use of eTurbo may be to avoid closing and opening valves (RCV and/or WG) when the engine is being used in a widely varying manner, as may happen as the user aggressively accelerates a vehicle from stop to highway speed, for example. The WG can be opened and the RCV remains closed, while the eTurbo tracks user demand to control compressor operation, providing air pressure to the engine while the throttle is open and ceasing to drive the compressor when the throttle is closed, for example; on reaching a selected gear, or a selected vehicle speed, the WG can be closed and ordinary operation of the turbocharger enabled. Having multiple actuators at one's disposal (not only the WG, but also the e-motor), their health degradation can be distributed more evenly preventing, for example, the WG from being repeatedly actuated, shortening WG actuator life.

For another example, again using eTurbo, the electric motor may be powered by a battery that can be charged to store the recuperated energy that would otherwise be lost when slowing down the eTurbo. From a power efficiency perspective, it may appear most efficient to recharge the battery whenever decreasing the eTurbo speed to maximize energy recovery. However, rechargeable batteries are generally rated for certain number of cycles, and short up- and down-charging cycles might accelerate battery degradation. To preserve battery health, it may be sensible, given particular driving/use conditions, to avoid charging when power draw and/or throttle position is highly variable, thus foregoing energy recovery in order to preserve battery longevity.

In another example, health degradation of the battery might depend on its internal temperature. One aim may be to limit charging or discharging of the battery when the battery temperature is not in a "healthy" range. While it may be possible to simply bar such use of the battery when outside of the healthy range, in some examples, the use of the battery outside the healthy temperature is incorporated into an optimization routine as a cost function having a zero value when battery internal temperature is in a healthy range, and a non-zero value otherwise.

In another example, an autonomous or self-driving vehicle may have separately actuated front- and rear-wheel brakes. With health-conscious controller taking not only the braking effect, safety and maneuverability but also the brakes degradation into account, the braking force might be distributed more evenly among the front and rear brakes. For example, in some situations (e.g. when approaching a stop sign), the controller might start braking slightly earlier using the rear brakes more, relieving the front brakes a little bit. In that case, earlier braking allows to accommodate for lower rear brakes efficiency. In that case, safety is maintained, but degradation of the front brakes is lower. Again, to implement the feature, the use of front and/or rear braking may each be added to the cost calculation when optimizing performance and health solutions.

For implementation purposes, the weighting of health indicators may be modified, or the health indicators themselves may be modified, for select vehicle conditions, such as when a vehicle is stopped or moving at low speed. Thus one or more coefficients or weighting factors may operate as a function of ambient or system conditions, such as humidity, temperature, air pressure, O2, airspeed, etc..

The following examples show how these changes can be implemented in several embodiments. In a first approach, an MPC method is used for optimizing performance after taking into account health indicators, the impact of which is incorporated as shown here in Equation <NUM>: <MAT> Where JMPC,T represents the traditional MPC criterion for the physical plant from Equation <NUM>, excluding health indicators, HIk represents a health indicator which decreases as health degrades, and w<NUM> and w<NUM> are weighting factors determining relative performance and health indicator weights. As an example, HIk may represent WG health. When no change to the WG actuator is called for within the time horizon of the optimized solution, HIk may be static. If there is a change to the WG actuator position called for in the optimized solution, JMPC,T, the change to HIk that results from the change in WG actuator position can be reflected by reducing HIk allowing the approach in Equation <NUM> to factor in the health cost of this particular action. Using the WG example, HIk may represent the remaining fraction of the number of actuation cycles for which the WG is rated. Thus, as HIk drops, the equation as shown increases the argument, adding cost to account for health impact.

HIk may be a composite of health indicators for a plurality of component health factors, or may be a stand-alone indicator for a single component. In some examples, more than one HIk factor may be included for each of several health impacts, such as by having a first term for WG, a second term for EGR valve, and a third term for a change in compressor speed, for example, each with a separate weighting factor. In other examples, Equation <NUM> may be modified to account for multiple health factors by nesting a summation inside the HIk term to account for all considered health impacts. Individual health impacts within HIk term may be further weighted, as by having a separate weighting for compressor impacts relative to EGR impacts, since replacing an EGR is potentially cheaper and simpler than replacing the compressor.

An alternative formulation is shown in Equation <NUM>: <MAT> Where ΔHIk = HIk-<NUM> - HIk is an indication of the health indicator degradation that results from steps that occur in the time horizon of the calculation. Using WG as the example, the delta from time instance k-<NUM> to time instance k may reflect a change in the remaining fraction of actuation cycles for which the WG is rated. The other terms are as in Equation <NUM>. Equation <NUM> thus represents a health state maximizing MPC approach, while Equation <NUM> represents a health degradation minimizing MPC approach.

A controlled health degradation approach may instead use a slack variable to manage health indicator degradation. Equation <NUM> is illustrative: <MAT> Here, the MPC criterion (excluding health factors) can be calculated alongside a slack variable, and the weighted sum minimized. The slack variable can be determined with Equation <NUM>: <MAT> Where ΔHIk is a maximum allowed health degradation factor, which can be determined using historical data or may be defined as a percentage decrease from nominal. The slack variable may be bounded by zero at the low end.

In another example, a two stage approach is used to limit performance degradation while also accounting for health degradation. In this example, a performance optimizing solution is determined in the first stage, and adjustments are made in a second stage to reduce health degradation with limited performance degradation. The nominal performance solution may be, for example and without intending limitation to MPC, using Equation <NUM>. Here, any of formulas <NUM>-<NUM> may be operated after a nominal performance solution is achieved, while applying Equation <NUM> to limit performance degradation: <MAT> Where ΔJ defines a maximum performance degradation that the health degradation optimization is allowed to impart. Thus, the updated solution of JHMPC is limited by a maximum degradation to the optimized solution.

<FIG> is illustrative of the two-stage approach. The x-axis is time and the y-axis is modeled or predicted performance at a given time/iteration in <FIG>. At each of several iterations of the control process, user inputs and physical plant state information are used to determine optimized solutions using, for example, MPC, PID, LQR, or other control processes. The optimized solutions are shown at points <NUM>, <NUM>, <NUM>, indicating the optimized performance P(t) for each iteration. Points <NUM>, <NUM>, <NUM> represent the output of the first stage of the method. Relative to each solution <NUM>, <NUM>, <NUM>, the second stage of the method determines a maximum degradation of performance, indicated at <NUM>, <NUM>, <NUM>. The second stage of the operation then determines a performance and health optimized solution, shown at <NUM>, <NUM>, <NUM>, which is bounded between the performance optimized solution <NUM>, <NUM>, <NUM> and the maximum degradation for each optimized solution <NUM>, <NUM>, <NUM>.

The two stage approach illustrated by <FIG> may be performed either as an overall cost calculation relating to general performance of the physical plant, or individual elements of the general performance may be analyzed separately in separate cost function analyses. For example, the cost function may be calculated using each of several performance metrics (targets may be any of consumed fuel/energy source, target turbocharger speed, target boost pressure, target pressure difference over the compressor, or target air mass flow, for example) summed together in one cost function, or two or more such targets can be analyzed separately. In one example, the first stage provides an optimized solution across all performance targets, and the second stage is performed individually for each performance target. In still another example each of the first and second stages are performed individually for each performance target.

In another example, a bilevel control is implemented using a low level controller that implements a performance target control method on an ongoing basis to determine optimized (performance only or health and performance) solutions for the physical plant. Also operating in the bilevel control is a supervisory controller that monitors for health impacts and is allowed to modify elements of the low level controller analysis.

For example, the supervisory controller can manipulate parameters of a low level controller using a longer time horizon. Assuming a low level controller using MPC, the supervisory controller can manipulate tuning parameters or weights within the cost function applied by the MPC controller to achieve a long term performance/health goal. Assuming a low level controller using PID control, the supervisory controller may be configured to modify setpoints or other control parameters or weights used by the PID control to reduce health impacts or otherwise optimize system health.

For example, the supervisory controller may command the low level controller to use a first set of control parameters or control values for a first period of time, and to determine if the health impact of using the first set of control values is acceptable, such as by comparison to a threshold health impact (or health cost). If the first set of control values yields health impacts that are out of range, as analyzed by the supervisory controller, the supervisory controller instructs the low level controller to apply a second set of control values to preferably reduce health impact. After instructing use of the second set of control values, the supervisory controller may continue to monitor operation to determine whether the change to the second set of control values achieves lower health impact. The supervisory controller may also determine how much performance deterioration is experienced by the switch from the first set to the second set of control values to ensure deviation of performance is limited.

In another example, which is more exploratory in nature, the supervisory controller may apply a plurality of sets of control values over time and observe performance and health impacts of the different parameter sets to select a "best" set of parameters in view of the performance and health impacts. The sets of parameters may be pre-scripted according to an overarching model of health and performance, if desired. For example, the supervisory controller may instruct the low level controller to use a first set of control values for a first period of time with the supervisory controller monitoring health impacts to associate a first health impact with the first set of control values. Next, the supervisory controller may instruct the low level controller to use a second set of control values for a second period of time, with the supervisory controller monitoring health impact to associate a second health impact with the second set of control values. The supervisory controller can then select whichever of the first or second sets of control values (or third or fourth, as the method or system may apply more than two control value sets) causes lesser health impact, and instructs the low level controller to use the selected set of control values.

In an example, rather than manipulating the optimization calculations by changing the cost function with additional terms, the terms within the optimization calculation may be changed. Thus and for example, using Equation <NUM>, there are a number of monitored variables each having maximum and/or minimum values, stored as a mapping function as shown in <FIG>. An original set of these maximum and minimum values can be stored as shown at <NUM>. During utilization of the system, it may be determined that a change in one of more variables to reduce health impacts is advisable if, for example, the cumulative effect of use is deemed to be approaching a limit, or if in the context of use, certain conditions create a need to reduce performance optimization to avoid health impacts. So, for example, in <FIG>, in the automotive context, if the turbo speed maximum is used in a pattern that creates a potential health degradation, the system can automatically change the turbo speed maximum value, creating a new mapping as shown at <NUM>, where ΔTSH is added (noting the value may be negative) to the maximum turbo speed to adjust this boundary and reduce health impacts. Such a modification can take place after the system has been in use.

<FIG> shows another example of remapping control variables by a supervisory controller. Here, the target values for optimized operation may be stored in a mapping at <NUM>. As information about the user's tendencies is learned over time, and the impact of those tendencies is analyzed, one or more adjustments to the target variables can be made, regenerating the map as indicated at <NUM>.

In some examples, a time horizon of system health considerations may be longer than the time horizon of process controls, if desired. For example, optimization of control settings for performance purposes may be directed to a relatively short time frame, such as the duration of a trip (in the automotive context), which may be determined using historical usage patterns of a vehicle, while health setting time horizons are more likely to look to longer term periods, such as months or years before certain maintenance activities are required. The system health considerations may, in some examples, adjust to account for cumulative effects of previous use. Such considerations can be used in the examples of <FIG> to adjust the boundaries and targets within the optimization calculation, with or without additionally adding terms to the optimization calculations (Equations <NUM>-<NUM>) or using a two stage approach (<FIG>).

Bilevel control and two stage control may be used together. The low level controller and supervisory controller architecture is introduced above in <FIG>, with the optimizer <NUM> serving as an example of a low level controller, and the health monitoring block <NUM> as a supervisory controller. In some examples, the low level controller performs two stage control using for example the approach outlined relative to Equation <NUM>, and the supervisory controller is configured to modify any of the factors used in the low level controller, including factors related to performance optimization as well as factors related to health optimization, and/or the maximum performance degradation, as needed.

In an example, the supervisory controller may observe whether the user inputs change in response to performance degradation and operates in response. For example, in the automotive context, if a driver becomes more aggressive in the use of accelerator and/or braking under conditions where the health optimization is reducing performance optimization, the maximum performance degradation factor may be reduced, to determine whether the driver becomes less aggressive. "Aggressive" in this context may be determined by observing the quantity and severity of changes to accelerator and/or brake actuations by the user. If the driver is observed to be more aggressive when performance degradation is at its maximum, it may be determined that the driver is dissatisfied with performance under such conditions, and the maximum degradation of performance used in Equation <NUM> may be reduced. The converse may also be the base; if the driver usage of accelerator and/or braking inputs is not observed to change when health factors cause performance degradation, the supervisory controller may allow for more performance degradation until the driver is observed to react.

In some examples, an off-line analysis may be used to drive the changes shown in <FIG>. For example, some systems may incorporate extremum-based control (ESC). With extremum seeking control frameworks, the aim is to perturb a steady state operating system by the assumption that performance of the system is stable across the range of inputs. The analysis may be performed off-line, and may be used to aid in determining, for example, weighting factors to use in the above description. By varying inputs, ESC can identify extreme operating points. In some examples, similar to Equations <NUM> and <NUM>, above, the ESC control scheme can be modified to incorporate health indicators to maximize health state (as in Equation <NUM>, above), or to minimize health degradation (as in Equation <NUM>, above). In a further example, ESC can be modified to incorporate a slack variable (as shown in Equation <NUM>, above).

It can be mathematically shown that each of these solutions can be used to improve operations, while in some contexts the two-stage approach may be better at achieving optimized performance and decreased health impact. Taking, for example, a simple system having one output Y, one actuator A, and an uncontrolled disturbance D, that interacts with the system according to { Y = A - D }. Assuming no constraints on the actuator, A, a nominal MPC approach would use the cost criterion of Equation <NUM>: <MAT> Where R stands for the output reference (target) value. One may further assume that a health degradation factor for the actuator may be expressed as: <MAT> Using the above example of integrating the health cost into the optimization function, Equation <NUM> is representative of the resulting MPC function: <MAT> Where JHC represents the health conscious MPC cost function.

Using the same inputs, a two stage approach may be defined instead where the nominal cost function optimized in the first stage yields a (health indifferent) actuator value A*, giving Equation <NUM> as the nominal optimized criterion: <MAT> Then, the second stage can be defined as a minimization of the health degradation factor for the actuator expressed in Equation <NUM>, with respect to performance degradation constraints given by Equation <NUM>. The second stage analysis can be defined then using Equation <NUM>: <MAT> In which ΔJ is the user defined, positive allowable performance degradation. To then analyze how these two approaches may be compared, it should be understood first that the minimum value for these cost functions can be identified when the derivative of either is zero. The minimum point for the formulation in Equation <NUM> may be determined as follows in Derivation <NUM>: <MAT> <MAT> <MAT> Where A*<NUM> would denote the actuator value that minimizes the health conscious MPC cost function JHC.

Starting with the non-health conscious optimization (stage <NUM> of the two-stage procedure), Derivation <NUM> is helpful in the basic understanding: <MAT> <MAT> <MAT> Here, A*<NUM>,I denotes the actuator value minimizing JMPC. The optimal cost function value for the first stage of the two-stage approach is then calculated using Equation <NUM>: <MAT> Substituting the result of Derivation <NUM>, into Equation <NUM>, if no actuator constraints are assumed, and only reference tracking error is minimized, the effect of a disturbance can be eliminated, yielding a zero cost function value, as shown in Equation <NUM>: <MAT> Then adding a constraint by including the ΔJ term, Derivation <NUM> provides a simplified result: <MAT> Here, A is then bounded between these two values, for the second stage of the two stage approach. The value A*<NUM>,II being the minimum of the cost function for the second stage of the two-stage approach and the outcome of the whole two-stage procedure can be summarized within these three formulae in Formula Set <NUM>:.

To use the above for analysis of the two approaches, one may assume, without losing generality, that R = <NUM>. Several examples of a disturbance, D, can then be analyzed to compare the results using a health optimized MPC function versus the two stage controller approach. For purposes of the analysis, ΔJ is set here as <NUM>.

In a first example, the disturbance D = <NUM>. <FIG> shows how this disturbance affects operation under each of the two control methods. Applying the above formulas and equations, the health conscious controller analysis at <NUM> provides the same result as the two stage controller analysis at <NUM>, both yielding a health degradation factor of <NUM>.

In a second example, the disturbance D = -<NUM>. <FIG> shows how this disturbance affects operation under each of the two control methods. Applying the above formulas and equations, the health conscious controller analysis at <NUM> provides a different outcome than the two-stage controller shown at <NUM>. Here, the health conscious controller minimizes the cost function to <NUM> and yields a health degradation of <NUM>, while the two stage approach minimizes the performance cost function to <NUM> and a health degradation of <NUM>. This means that performance would be affected more in the two stage approach, though within the user-defined limitation on performance degradation, while health degradation would be driven to zero.

In a third example, the disturbance D = -<NUM>. <FIG> shows how this disturbance affects operation under each of the two control methods. Applying the above formulas and equations, the health conscious controller analysis at <NUM> provides a different outcome than the two stage controller shown at <NUM>. Here, the health conscious controller minimizes the cost function to <NUM> and yields a health degradation of <NUM>, while the two stage approach minimizes the performance cost criterion to <NUM> and provides a health degradation of <NUM>. This means that performance degradation is more limited by the two stage approach, due to the user-defined limitation on performance degradation, though the health degradation cost is higher than when using a single stage controller.

As shown in the examples in <FIG>, a two stage approach may be desirable to balance health and performance impacts. Such behavior may be desired in applications where a user wants to control and limit performance degradation caused by health state optimization. For example, applications having safety constraints not otherwise considered in the cost function, or applications with significant potential economic effects.

The drawings show, by way of illustration, specific embodiments. " In this document, the terms "a" or "an" are used, as is common in patent documents, to include one or more than one, independent of any other instances or usages of "at least one" or "one or more. " Moreover, in the following claims, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects.

The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments can be used, such as by one of ordinary skill in the art upon reviewing the above description. Some examples can include elements in addition to those shown or described. The present inventors also contemplate examples using any combination or permutation of those elements shown or described (or one or more aspects thereof), either with respect to a particular example (or one or more aspects thereof), or with respect to other examples (or one or more aspects thereof) shown or described herein.

Claim 1:
A configurable controller for controlling a physical plant (<NUM>) having associated therewith a plurality of actuators (<NUM>) for controlling operation of the physical plant and a plurality of sensors (<NUM>, <NUM>) for observing a plurality of characteristics of the physical plant operations, the configurable controller comprising:
a state observer (<NUM>) configured to capture a current state of the physical plant by communication with the plurality of sensors; and
an optimizer (<NUM>) configured to optimize behavior of the physical plant using at least the actuators;
wherein the optimizer is operable using a model predictive control cost function including a first term representing a traditional model predictive control cost function based on the current state of the physical plant, multiplied by a first weighting factor, and
wherein the model predictive control cost function further comprises at least one second term representing a health indicator, the at least one second term being one of:
a) a second term indicating degradation of one or more components of the physical plant that results from steps that occur in time horizon of the optimization calculation, multiplied by a second weighting factor, wherein the first term and second term are summed by the optimizer; or
b) a second term which decreases as health of one or more components of the physical plant degrades, multiplied by a second weighting factor, wherein the second term is subtracted from the first term by the optimizer; or
c) a second term representing a slack variable calculated as difference between health impacts within the time horizon and a maximum allowed health degradation factor, multiplied by a second weighting factor, wherein the first and second terms are summed by the optimizer; and
wherein the optimizer operates to direct use of parameters in the actuators to minimize the sum of the first and second terms in the event the second term is either (a) or (c) or the difference of the first and second terms in the event the second term is (b).