On-line adaptive PID control of air charging system

An internal combustion engine includes an air charging system. A method to control the air charging system includes providing a desired operating target command for the air charging system, and monitoring operating parameters of the air charging system. An error between the desired operating target command for the air charging system and the corresponding one of said operating parameters of the air charging system is determined, and scheduled PID gains are determined based on the error utilizing a PID controller. An adaptive algorithm is applied to modify the scheduled PID gains, and a system control command for the air charging system is determined based upon the modified scheduled PID gains. The air charging system is controlled based upon the system control command for the air charging system.

TECHNICAL FIELD

This disclosure is related to control of internal combustion engines.

BACKGROUND

Engine control includes control of parameters in the operation of an engine based upon a desired engine output, including an engine speed and an engine load, and resulting operation, for example, including engine emissions. Parameters controlled by engine control methods include air flow, fuel flow, and intake and exhaust valve settings.

Boost air can be provided to an engine to provide an increased flow of air to the engine relative to a naturally aspirated intake system to increase the output of the engine. A turbocharger utilizes pressure in an exhaust system of the engine to drive a compressor providing boost air to the engine. Exemplary turbochargers can include variable geometry turbochargers (VGT), enabling modulation of boost air provided for given conditions in the exhaust system. A supercharger utilizes mechanical power from the engine, for example as provided by an accessory belt, to drive a compressor providing boost air to the engine. Engine control methods control boost air in order to control the resulting combustion within the engine and the resulting output of the engine.

Exhaust gas recirculation (EGR) is another engine control parameter. An exhaust gas flow within the exhaust system of an engine is depleted of oxygen and is essentially an inert gas. When introduced to or retained within a combustion chamber in combination with a combustion charge of fuel and air, the exhaust gas moderates the combustion, reducing an output and an adiabatic flame temperature. EGR can also be controlled in combination with other parameters in advanced combustion strategies, for example, including homogeneous charge compression ignition (HCCI) combustion. EGR can also be controlled to change properties of the resulting exhaust gas flow. Engine control methods control EGR in order to control the resulting combustion within the engine and the resulting output of the engine.

Air handling systems for an engine manage the flow of intake air and EGR into the engine. Air handling systems must be equipped to meet charge air composition targets (e.g. an EGR fraction target) to achieve emissions targets, and meet total air available targets (e.g. the charge flow mass flow) to achieve desired power and torque targets. The actuators that most strongly affect EGR flow generally affect charge flow, and the actuators that most strongly affect charge flow generally affect EGR flow. Therefore, an engine with a modern air handling system presents a multiple input multiple output (MIMO) system with coupled input-output response loops.

MIMO systems, where the inputs are coupled, i.e. the input-output response loops affect each other, present well known challenges in the art. An engine air handling system presents further challenges. The engine operates over a wide range of parameters including variable engine speeds, variable torque outputs, and variable fueling and timing schedules. In many cases, exact transfer functions for the system are unavailable and/or the computing power needed for a standard decoupling calculation is not available.

Control loops may be used in determining boost air and EGR control commands. Feedback control methods may be utilized in the control loops to minimize error between a measured process variable and a desired setpoint. This minimized error is utilized in feedback control calibration of a system control command, such as a boost air control command and an EGR control command.

SUMMARY

An internal combustion engine includes an air charging system. A method to control the air charging system includes providing a desired operating target command for the air charging system, and monitoring operating parameters of the air charging system. An error between the desired operating target command for the air charging system and the corresponding one of said operating parameters of the air charging system is determined, and scheduled PID gains are determined based on the error utilizing a PID controller. An adaptive algorithm is applied to modify the scheduled PID gains, and a system control command for the air charging system is determined based upon the modified scheduled PID gains. The air charging system is controlled based upon the system control command for the air charging system.

DETAILED DESCRIPTION

Referring now to the drawings, wherein the showings are for the purpose of illustrating certain exemplary embodiments only and not for the purpose of limiting the same,FIG. 1schematically depicts an exemplary internal combustion engine10, control module5, and exhaust aftertreatment system65, in accordance with the present disclosure. The exemplary engine includes a multi-cylinder, direct-injection, compression-ignition internal combustion engine having reciprocating pistons22attached to a crankshaft24and movable in cylinders20which define variable volume combustion chambers34. The crankshaft24is operably attached to a vehicle transmission and driveline to deliver tractive torque thereto, in response to an operator torque request, TO_REQ. The engine preferably employs a four-stroke operation wherein each engine combustion cycle includes 720 degrees of angular rotation of crankshaft24divided into four 180-degree stages (intake-compression-expansion-exhaust), which are descriptive of reciprocating movement of the piston22in the engine cylinder20. A multi-tooth target wheel26is attached to the crankshaft and rotates therewith. The engine includes sensors to monitor engine operation, and actuators which control engine operation. The sensors and actuators are signally or operatively connected to control module5.

The engine is preferably a direct-injection, four-stroke, internal combustion engine including a variable volume combustion chamber defined by the piston reciprocating within the cylinder between top-dead-center and bottom-dead-center points and a cylinder head including an intake valve and an exhaust valve. The piston reciprocates in repetitive cycles each cycle including intake, compression, expansion, and exhaust strokes.

The engine preferably has an air/fuel operating regime that is primarily lean of stoichiometry. One having ordinary skill in the art understands that aspects of the disclosure are applicable to other engine configurations that operate either at stoichiometry or primarily lean of stoichiometry, e.g., lean-burn spark-ignition engines or the conventional gasoline engines. During normal operation of the compression-ignition engine, a combustion event occurs during each engine cycle when a fuel charge is injected into the combustion chamber to form, with the intake air, the cylinder charge. The charge is subsequently combusted by action of compression thereof during the compression stroke.

The engine is adapted to operate over a broad range of temperatures, cylinder charge (air, fuel, and EGR) and injection events. The methods disclosed herein are particularly suited to operation with direct-injection compression-ignition engines operating lean of stoichiometry to determine parameters which correlate to heat release in each of the combustion chambers during ongoing operation. The methods are further applicable to other engine configurations and their subsystems, including spark-ignition engines, including those adapted to use homogeneous charge compression ignition (HCCI) strategies. The methods are applicable to systems utilizing multi-pulse fuel injection events per cylinder per engine cycle, e.g., a system employing a pilot injection for fuel reforming, a main injection event for engine power, and where applicable, a post-combustion fuel injection event for aftertreatment management, each which affects cylinder pressure.

Sensors are installed on or near the engine to monitor physical characteristics and generate signals which are correlatable to engine and ambient parameters. The sensors include a crankshaft rotation sensor, including a crank sensor44for monitoring crankshaft (i.e. engine) speed (RPM) through sensing edges on the teeth of the multi-tooth target wheel26. The crank sensor is known, and may include, e.g., a Hall-effect sensor, an inductive sensor, or a magnetoresistive sensor. Signal output from the crank sensor44is input to the control module5. A combustion pressure sensor30is adapted to monitor in-cylinder pressure (COMB_PR). The combustion pressure sensor30is preferably non-intrusive and includes a force transducer having an annular cross-section that is adapted to be installed into the cylinder head at an opening for a glow-plug28. The combustion pressure sensor30is installed in conjunction with the glow-plug28, with combustion pressure mechanically transmitted through the glow-plug to the pressure sensor30. The output signal, COMB_PR, of the pressure sensor30is proportional to cylinder pressure. The pressure sensor30includes a piezoceramic or other device adaptable as such. Other sensors preferably include a manifold pressure sensor for monitoring manifold pressure (MAP) and ambient barometric pressure (BARO), a mass air flow sensor for monitoring intake mass air flow (MAF) and intake air temperature (TIN), and a coolant sensor35monitoring engine coolant temperature (COOLANT). The system may include an exhaust gas sensor for monitoring states of one or more exhaust gas parameters, e.g., temperature, air/fuel ratio, and constituents. One skilled in the art understands that there may be other sensors and methods for purposes of control and diagnostics. The operator input, in the form of the operator torque request, TO_REQ, is typically obtained through a throttle pedal and a brake pedal, among other devices. The engine is preferably equipped with other sensors for monitoring operation and for purposes of system control. Each of the sensors is signally connected to the control module5to provide signal information which is transformed by the control module to information representative of the respective monitored parameter. It is understood that this configuration is illustrative, not restrictive, including the various sensors being replaceable with functionally equivalent devices and routines.

The actuators are installed on the engine and controlled by the control module5in response to operator inputs to achieve various performance goals. Actuators include an electronically-controlled throttle valve which controls throttle opening in response to a control signal (ETC), and a plurality of fuel injectors12for directly injecting fuel into each of the combustion chambers in response to a control signal (INJ_PW), all of which are controlled in response to the operator torque request, TO_REQ. An exhaust gas recirculation valve32and cooler control flow of externally recirculated exhaust gas to the engine intake, in response to a control signal (EGR) from the control module. A glow-plug28is installed in each of the combustion chambers and adapted for use with the combustion pressure sensor30. Additionally, a charging system can be employed in some embodiments supplying boost air according to a desired manifold air pressure.

Fuel injector12is a high-pressure fuel injector adapted to directly inject a fuel charge into one of the combustion chambers in response to the command signal, INJ_PW, from the control module. Each of the fuel injectors12is supplied pressurized fuel from a fuel distribution system, and has operating characteristics including a minimum pulsewidth and an associated minimum controllable fuel flow rate, and a maximum fuel flow rate.

The engine may be equipped with a controllable valvetrain operative to adjust openings and closings of intake and exhaust valves of each of the cylinders, including any one or more of valve timing, phasing (i.e., timing relative to crank angle and piston position), and magnitude of lift of valve openings. One exemplary system includes variable cam phasing, which is applicable to compression-ignition engines, spark-ignition engines, and homogeneous-charge compression ignition engines.

The control module5executes routines stored therein to control the aforementioned actuators to control engine operation, including throttle position, fuel injection mass and timing, EGR valve position to control flow of recirculated exhaust gases, glow-plug operation, and control of intake and/or exhaust valve timing, phasing, and lift on systems so equipped. The control module is configured to receive input signals from the operator (e.g., a throttle pedal position and a brake pedal position) to determine the operator torque request, TO_REQ, and from the sensors indicating the engine speed (RPM) and intake air temperature (Tin), and coolant temperature and other ambient conditions.

Control module, module, controller, control unit, processor and similar terms mean any suitable one or various combinations of one or more of Application Specific Integrated Circuit(s) (ASIC), electronic circuit(s), central processing unit(s) (preferably microprocessor(s)) and associated memory and storage (read only, programmable read only, random access, hard drive, etc.) executing one or more software or firmware programs, combinational logic circuit(s), input/output circuit(s) and devices, appropriate signal conditioning and buffer circuitry, and other suitable components to provide the desired functionality. The control module has a set of control routines, including resident software program instructions and calibrations stored in memory and executed to provide the desired functions. The routines are preferably executed during preset loop cycles. Routines are executed, such as by a central processing unit, and are operable to monitor inputs from sensors and other networked control modules, and execute control and diagnostic routines to control operation of actuators. Loop cycles may be executed at regular intervals, for example each 3.125, 6.25, 12.5, 25 and 100 milliseconds during ongoing engine and vehicle operation. Alternatively, routines may be executed in response to occurrence of an event.

FIG. 1depicts an exemplary diesel engine, however, the present disclosure can be utilized on other engine configurations, for example, including gasoline-fueled engines, ethanol or E85 fueled engines, or other similar known designs. The disclosure is not intended to be limited to the particular exemplary embodiments disclosed herein.

FIG. 2schematically depicts an exemplary engine configuration including a turbocharger, in accordance with the present disclosure. The exemplary engine is multi-cylinder and includes a variety of fueling types and combustion strategies known in the art. Engine system components include an intake air compressor40including a turbine46and an air compressor45, an air throttle valve136, a charge air cooler142, an EGR valve132and cooler152, an intake manifold50, and exhaust manifold60. Ambient intake air is drawn into compressor45through intake171. Pressurized intake air and EGR flow are delivered to intake manifold50for use in engine10. Exhaust gas flow exits engine10through exhaust manifold60, drives turbine46, and exits through exhaust tube170. The depicted EGR system is a high pressure EGR system, delivering pressurized exhaust gas from exhaust manifold60to intake manifold50. An alternative configuration, a low pressure EGR system, can deliver low pressure exhaust gas from exhaust tube170to intake171. Sensors are installed on the engine to monitor physical characteristics and generate signals which are correlatable to engine and ambient parameters. The sensors preferably include an ambient air pressure sensor112, an ambient or intake air temperature sensor114, and a mass air flow sensor116(all which can be configured individually or as a single integrated device), an intake manifold air temperature sensor118, an MAP sensor120, an exhaust gas temperature sensor124, an air throttle valve position sensor134and an EGR valve position sensor130, and a turbine vane position sensor138. Engine speed sensor44monitors rotational speed of the engine. Each of the sensors is signally connected to the control module5to provide signal information which is transformed by the control module5to information representative of the respective monitored parameter. It is understood that this configuration is illustrative, not restrictive, including the various sensors being replaceable within functionally equivalent devices and routines and still fall within the scope of the disclosure. Furthermore, the intake air compressor40may include alternative turbocharger configurations within the scope of this disclosure.

The intake air compressor40includes a turbocharger including an air compressor45positioned in the air intake of the engine which is driven by turbine46that is positioned in the exhaust gas flowstream. Turbine46can include a number of embodiments, including a device with fixed vane orientations or variable vane orientations. Further, a turbocharger can be used as a single device, or multiple turbochargers can be used to supply boost air to the same engine.

The engine configuration, such as the exemplary engine configuration, including a turbocharger, as is schematically depicted inFIG. 2may be represented by a mathematical model. Model-based boost control algorithms using physics-based energy balance relation of a turbocharger may be used to decouple the design of boost or turbocharger control from air and EGR system controls. By using the physics-based turbocharger energy balance model with feedback linearization or feedforward control architectures a nonlinear control problem may be transformed into an approximately linearized feedback system. This decoupled boost control may reduce vehicle calibration work for altitude and extreme ambient operating conditions. The decoupled control simplifies design work, and model based control design can be calibrated at dyno test cell, which works for varying operating conditions with reduced vehicle calibrations.

An inverse flow model or an inverse of a physical model of a system can be useful in determining settings required to achieve a desired flow through an orifice in the system. Flow through a system can be modeled as a function of a pressure difference across the system and a flow restriction in the system. Known or determinable terms can be substituted and the functional relationship manipulated to make an inverse flow model of the system useful to determine a desired system setting to achieve a desired flow. Exemplary methods disclosed herein utilize a first input of an effective flow area or of a flow restriction for the system being modeled, and a second input including a pressure value for the system of pressure moving the flow through the system. One exemplary method of decoupled feed forward control of an EGR valve can include utilizing an inverse flow model of the system embodied in a mixed polynomial based upon the inverse model and calibrated terms. Another exemplary method of decoupled feed forward control of an EGR valve can include utilizing a dimensional table-based approach. Another exemplary method of decoupled feed forward control of an EGR valve can include utilizing an exponential polyfit model. An exemplary method of decoupled feed forward control of air throttle can utilize an inverse of the physical model of the system, a dimensional table approach, or an exponential polyfit model. An exemplary method of decoupled feed forward control of a charging system, such as a turbocharger equipped with a VGT, can utilize an inverse of the physical model of the system, a dimensional table approach, or an exponential polyfit model.

These methods can be utilized individually or in combination, and different methods can be utilized for the same system for different conditions and operating ranges. A control method can utilize an inverse flow model to determine a feed forward control command for a first selection including one of the EGR circuit, the air throttle system, and the charging system. The control method can additionally utilize a second inverse flow model to determine a second feed forward control command for a second selection including another of the EGR circuit, the air throttle system, and the charging system. The control method can additionally utilize a third inverse flow model to determine a third feed forward control command for a third selection including another of the EGR circuit, the air throttle system, and the charging system. In this way, a control method can control any or all of the EGR circuit, the air throttle system, and the charging system.

A method to control EGR flow by an inverse control method according to an inverse model of EGR flow is disclosed in co-pending and commonly assigned application Ser. No. 12/982,994, corresponding to publication US 2012-0173118 A1, which is incorporated herein by reference.

Feedback control modules are implemented in linear control strategies to determine feedback control commands using feedback control methods. Exemplary feedback control methods used by feedback control modules can include proportional-integral-derivative (PID) feedback control methods. In an exemplary embodiment PID control modules can be designed individually to output decoupled feedback control signals for each system to be controlled in a MIMO system.

A method of applying automated algorithms for on-line fine tuning of PID gains may be applied to feedback control systems using PID feedback control methods to reduce feedback control calibration and optimize the feedback control system. In addition to reducing feedback control calibration and improving transient responses, this method may compensate system performances due to aging, such as EGR cooler fouling. This method may further enhance robustness against plant uncertainties, such as operating temperature and pressure changes, and may also reduce the number of PID calibrations required in gain scheduling.

FIG. 3schematically depicts an exemplary closed-loop feedback control system, in accordance with the present disclosure. A system to be controlled302is represented by a mathematical model, time-varying plant function F(u(t)). A reference setpoint r(t)320is input into the closed-loop feedback system. The reference setpoint320is then compared with a measured output of the system to be controlled y(t)323and the difference is the error value e(t)321. Error value e(t) is input into a PID controller301. The PID controller attempts to minimize the error value e(t) by manipulating the value. This includes the use of three correcting terms, the proportional, integral and derivative terms. These terms are summed up to calculate the output of the PID controller, feedback control command u(t)322. Feedback control command322is then input into the time-varying plant F(u(t)) of the system to be controlled302, thus controlling the system302. As the time-varying plant F(u(t)) inputs feedback control command u(t)322and outputs y(t)323, the plant may be represented by the following relationship:
y(t)=F(u(t))  [1]
PID controller output u(t)322is equivalent to the algorithm implemented by the PID controller301and may be represented by the following relationship:
u(t)=Kpe(t)+KI∫e(t)dt+KDė(t)  [2]
wherein Kpis a proportional gain,

KIis an integral gain,

KDis a derivative gain,

t is time, and

e(t) is an error function determining an error value between a setpoint and a monitored system parameter.

A time-varying plant of system to be controlled302may alternatively include a disturbance, which would impact the measured output of the system to be controlled y(t)323. In this case, the time-varying plant with disturbance may be represented by the following relationship:
y(t)=f(t)+g(t)*u(t)=f(t)+θTØ  [3]
wherein f(t) is a disturbance function,

g(t) is a system gain function,

u(t) is a PID controller algorithm,

θTis a vector of PID gains, and

Ø is the product of the system gain function and the error vector.

θTcan be expressed by the following relationship:

Ø can be expressed by the following relationship:

A closed-loop feedback system, such as the one schematically depicted inFIG. 3may be represented as a cost function J which seeks to minimize the error term ε which is equivalent to the error function e(t). This cost function J may be represented by the following relationship:

J=12⁢ɛ2=(r⁡(t)-y⁡(t))22=(r⁡(t)-f⁡(t)-θT⁢ϕ)22[6]
As J has a global minimum, an adaptive PID control algorithm using a gradient search for the global minimum may be represented by the following relationship:

θ.=-Γ*∇Jθ=Γ*ɛ*g⁡(t)*[e⁡(t)∫e⁡(t)⁢ⅆte.⁡(t)][7]
wherein Γ is an adaptive gain,

∇Jθis a gradient of the cost function with respect to theta,

ε is an error value equivalent to e(t).

For a discrete algorithm, this relationship may be expressed by the following relationship:

[Kp⁡(k+1)KI⁡(k+1)KD⁡(k+1)]=[Kp⁡(k)KI⁡(k)KD⁡(k)]+Δ⁢⁢T*Γ*g⁡(t)*[e⁡(t)∫e⁡(t)⁢ⅆte.⁡(t)]⁢ɛ[8]
wherein k is a present iteration of a scheduled gain, and

ΔT is a discrete sampling rate.

For a general nonlinear system, the adaptive algorithm may find a local minimum. The adaptive algorithm for adaptive PID control of a general nonlinear system may be expressed by the following relationships:

y=F⁡(u⁡(t))=F⁡(Kp⁢e⁡(t)+KI⁢∫e⁡(t)⁢ⅆt+KD⁢e.⁡(t))[9][Kp⁡(k+1)KI⁡(k+1)KD⁡(k+1)]=[Kp⁡(k)KI⁡(k)KD⁡(k)]+Δ⁢⁢T*Γ*Fu′⁡(t)*[e⁡(t)∫e⁡(t)⁢ⅆte.⁡(t)]⁢ɛ[10]
wherein F′u(t) is a partial derivative of the plant function.

Since gain scheduling is based on PID designs for linearized systems at each operating condition, this adaptive algorithm can modify the scheduled gains to improve system transient responses and robustness to system uncertainties, and can additionally compensate system performances due to aging.

FIG. 4schematically depicts an exemplary adaptive PID control system flowchart, in accordance with the present disclosure. As with the exemplary closed-loop feedback system depicted byFIG. 3, a system to be controlled404is represented by a mathematical model, time-varying plant function F(u(t)). A reference setpoint r(t)420is input into the feedback system. The reference setpoint420is then compared with a measured output of the system to be controlled y(t)423and the difference is the error value e421, determined by error function e(t). Error value421is input into a PID controller401. In this adaptive PID control method, the PID controller determines proportional gain402and integral gain403based on the error value421, and further adds a determined change in proportional gain ΔKp431and a determined change in integral gain ΔKI432to modify the scheduled gains. The determined proportional gain402and integral gain403terms are summed up to calculate the output of the PID controller, feedback control command u(t)422. Feedback control command422is then input into the time-varying plant F(u(t)) of the system to be controlled404, thus controlling the system404.

The determined change in proportional gain ΔKp431and the determined change in integral gain ΔKI432are calculated using the following process, as is depicted in the flowchart ofFIG. 4. Error value421is input into module406which squares error value421, determining an output value424. Error value421is additionally input into module407which executes a function that is the integral of the error value421. The integral of the error value421is multiplied by the actual error value421to determine an output value425as is expressed by equation 10. Feedback control command422, and measured system output423are input into derivative module405. Additional enabling conditions412are additionally considered by derivative module405to determine whether to calculate the derivative, thus providing for an adaptation to the PID control, or to set the output to zero, meaning no adaptation is provided. The additional enabling conditions412may be expressed by the following relationship:

∂y∂u={∂y∂u,Δ⁢⁢y≥ɛ1&⁢⁢Δ⁢⁢u≥ɛ20,otherwise[11]
wherein ε1is a threshold minimum error value relative to the y(t), and

ε2 is a threshold minimum error value relative to u(t).

The output426of derivative module405is multiplied by the squared error value424to determine the term e(t)*ε*F′u(t) as expressed by equation 10. This term is output as value427, which is then multiplied by adaptation rate ΓΔT408in accordance with equation 10, and this term is output429. Output term429is then integrated at integration module410to determine ΔKp431, as is expressed by equation 10. The output426of derivative module405is also multiplied by output term425to determine term428, which may be expressed as ∫e(t)dt*ε*F′u(t). Term428is then multiplied by adaptation rate ΓΔT409to determine output term430. Output term430is then integrated at integration module411to determine ΔKI432. This method may additionally be implemented to modify the scheduled derivative gain KDas is expressed by equation 10.

FIG. 5graphically depicts a comparison of adaptive PID control and default PID control, in accordance with the present disclosure. This depiction of adaptive tuning is in relation to a numerative example of a second order system. Default PID control530is compared to adaptive PID control531. Over time510, the output520of adaptive PID control531is shown to have an improved response time, as well as reduced overshoot and settling time.

FIG. 6-1graphically depicts a comparison of adaptive PID control and default PID control with a damping decrease of 50%, in accordance with the present disclosure. This depiction of adaptive tuning is in relation to a numerative example of a second order system. Default PID control630is compared to adaptive PID control631. Over time610, the output620of adaptive PID control631is shown to have an improved response time, as well as reduced overshoot and settling time. The increase in oscillation of the PID control would be stabilized with damping to prevent oscillation. The adaptive PID control has improved robustness of the PID control.

FIG. 6-2graphically depicts a comparison of adaptive PID control and default PID control with a damping increase of 100%, in accordance with the present disclosure. This depiction of adaptive tuning is in relation to a numerative example of a second order system. Default PID control632is compared to adaptive PID control633. Over time610, the output620of adaptive PID633is shown to have an improved response time, as well as reduced overshoot and settling time. In both instances of plant damping coefficient changes adaptive PID control improved robustness of the control.

FIG. 7graphically depicts potential calibration reduction for gain-scheduling with adaptive PID control, in accordance with the present disclosure. Scheduled gain730is a function of rpm710and fuel720. Where gain-scheduling with default PID control would require calibration at each matrix point illustrated inFIG. 7, gain-scheduling with adaptive PID control allows for calibration to only be required at select points740. Through adaptation, points750may be determined without additional calibration resulting in a reduced number of PID calibrations required in gain-scheduling.

FIG. 8schematically depicts an exemplary adaptive PID control for a model-based MIMO airpath system, in accordance with the present disclosure. This exemplary air charging multivariable control system, uses model-based feedforward control and PID feedback control methods. Air charging system810receives commands and produces outputs. A number of modules and control strategies are depicted developing the commands, including the feedback control modules807and808, and the feedforward control modules805and806. Target EGR value824and boost target825may be determined by lookup table801and802respectively as a function of a monitored fuel value822and a monitored rpm value823. These target values824and825are compared with corresponding feedback signals838and839which are determined by either direct sensor measurements or may alternatively be estimated by a state variable observer based on the actual operating parameters of the air charging system810. These operating parameters may include, for example, intake manifold pressure, intake manifold temperature, air mass, ambient pressure, and ambient temperature.

The air charging system parameters may be monitored by sensors or alternatively estimated by a state variable observer. Exemplary estimated air charging system parameters may include actual compressor pressure ratio, and exhaust manifold pressure. The monitored and estimated system operating parameters may be used to determine feedback signals. The feedback signals describe actual EGR838and actual boost pressure839. The comparison of the desired operating parameters and the respective actual operating parameters determines error terms for each parameter including an EGR error term830and a boost pressure error term831. These error terms are then input into the feedback control modules807and808respectively. The adaptive transient control methods, in accordance with the present disclosure, may be implemented by control modules807and808such that scheduled gains determined by the PID feedback control methods of modules807and808are modified by the adaptive transient values841and840. Adaptive transient values841and840are determined by module803as a function of a monitored fuel value822and a monitored rpm value823. The PID feedback control method, including adaptive transient control, implemented by each of feedback control modules807and808determines feedback control signals832and833. Feedback control signals832and833are input into module809where the control signals are inverted to determine feedback signals834and835.

Desired operating parameter points, including target EGR824and target boost pressure825are additionally input into feedforward control modules805and806respectively, in addition to monitored operating parameters820and821. Feedforward control module805utilizes a physical VGT inverse model to determine feedforward VGT control command827and feedforward control module806utilizes a physical EGR inverse model to determine feedforward EGR control command829. Feedback signals834and835are then combined with feedforward control commands827and829respectively to determine EGR control command836and VGT control command837which are used to control air charging system810.

FIG. 9graphically depicts a comparison of commanded boost pressure and actual boost pressure over time with adaptive PID tuning, in accordance with the present disclosure. Commanded boost pressure930and actual boost pressure931are plotted with boost pressure920over time910. Adaptive PID tuning begins at 78 seconds. Upon initiation of the adaptive tuning commanded boost pressure is shown to track actual boost pressure with decreased error.

FIG. 10depicts an exemplary process of adaptive PID control, in accordance with the present disclosure. Table 1 is provided as a key wherein the numerically labeled blocks and the corresponding functions are set forth as follows.

TABLE 1BLOCKBLOCK CONTENTS1001Monitor a desired operating target command for the aircharging system1002Monitor operating parameters of the air charging system1003Determine an error between the monitored desiredoperating target command for the air charging system andthe monitored operating parameter of the air chargingsystem1004Determine scheduled PID gains based on the determinederror utilizing a PID controller1005Apply an adaptive algorithm to modify the determinedscheduled PID gains;1006Determine a system control command for the air chargingsystem based upon the modified scheduled PID gains; and1007Control the air charging system based upon the systemcontrol command for the air charging system.