CN117348412A - Model prediction control method and device, electronic equipment and storage medium - Google Patents
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Abstract
The invention discloses a model predictive control method, a device, electronic equipment and a storage medium, wherein the model predictive control method comprises the following steps: constructing an air system state equation based on operating parameters of the engine, wherein the operating parameters at least comprise parameters of an air inlet throttle valve, parameters of a turbocharger and parameters of an exhaust gas recirculation valve; constructing an extended state observer based on an air system state equation and performing disturbance estimation to obtain a disturbance value; based on the air system state equation and the disturbance value, a predictive model controller is constructed, and the predictive model controller comprises a feedforward control term based on the disturbance value and a feedback control term based on the measured value. In the invention, the closed-loop control and the high-efficiency control of the boost pressure, the exhaust pressure and the EGR rate can be realized through the model predictive control and the extended state observer, the responsiveness and the control precision of the air system are improved, and the required parameters are mostly inherent parameters of the air system, so that the calibration workload is reduced, and the control performance is provided.
Description
Technical Field
The present invention relates to the field of engine technologies, and in particular, to a model prediction control method, a device, an electronic apparatus, and a storage medium.
Background
Model Predictive Control (MPC) is widely applied to process control in the later industrial field of the 20 th century 70, and is widely applied to the process industry application industries such as chemical engineering and the like in early stages. Model predictive control was applied to the field of power electronics in the 90 s of the 20 th century.
In recent years, with the improvement of the performance of a digital processor and the reduction of the cost thereof, the calculation amount is large and is no longer an obstacle for limiting the development of MPC, and MPC is becoming a hot spot of research.
MPC, however, relies on the mathematical model of the controlled object and parameter accuracy. In practical application, the change of the running working parameters of the motor and the inaccuracy of the parameters can influence the performance of the control algorithm.
Disclosure of Invention
The invention provides a model predictive control method, a model predictive control device, electronic equipment and a storage medium, which are used for solving the problems of poor control performance and the like of the existing model predictive control.
According to an aspect of the present invention, there is provided a model predictive control method including:
constructing an air system state equation based on operating parameters of the engine, the operating parameters including at least parameters of the intake throttle valve, parameters of the turbocharger, and parameters of the exhaust gas recirculation valve;
constructing an extended state observer based on the air system state equation and performing disturbance estimation to obtain a disturbance value;
and constructing a predictive model controller based on the air system state equation and the disturbance value, wherein the predictive model controller comprises a feedforward control term based on the disturbance value and a feedback control term based on a measured value.
Further, constructing an air system state equation includes:
acquiring the pressure, temperature and volume of the air inlet throttle valve, acquiring the pressure, temperature and volume of the turbocharger, and acquiring the volume of the exhaust gas recirculation valve;
and constructing the air system state equation containing the total disturbance according to the working parameters of the engine.
Further, constructing the extended state observer and performing disturbance estimation to obtain a disturbance value includes:
expanding disturbance in the air system state equation into state quantity, and constructing to obtain the extended state observer;
solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer;
and carrying out disturbance estimation on the extended state observer based on the gain to obtain the disturbance value.
Further, constructing the predictive model controller includes:
performing discrete and incremental changes on the air system state equation to obtain p step-length prediction models;
constructing a cost function based on the prediction models of the p step sizes;
and solving the cost function by adopting an unconstrained solving algorithm to obtain the predictive model controller.
According to another aspect of the present invention, there is provided a model predictive control apparatus including:
the system comprises a building equation module, a control module and a control module, wherein the building equation module is used for building an air system state equation based on the working parameters of an engine, and the working parameters at least comprise the parameters of an air inlet throttle valve, the parameters of a turbocharger and the parameters of an exhaust gas recirculation valve;
the state observation module is used for constructing an extended state observer based on the air system state equation and carrying out disturbance estimation to obtain a disturbance value;
and the prediction model module is used for constructing a prediction model controller based on the air system state equation and the disturbance value, wherein the prediction model controller comprises a feedforward control term based on the disturbance value and a feedback control term based on a measured value.
Further, the building equation module includes:
a parameter acquisition unit configured to acquire a pressure, a temperature, and a volume of the intake throttle valve, acquire a pressure, a temperature, and a volume of the turbocharger, and acquire a volume of the exhaust gas recirculation valve;
and constructing an equation unit for constructing the air system state equation containing the total disturbance according to the working parameters of the engine.
Further, the state observation module includes:
the state expansion unit is used for expanding disturbance in the air system state equation into state quantity, and constructing and obtaining the expansion state observer;
the observation solving unit is used for solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer;
and the disturbance estimation unit is used for carrying out disturbance estimation on the extended state observer based on the gain to obtain the disturbance value.
Further, the prediction model module includes:
the discrete change unit is used for carrying out discrete and incremental changes on the air system state equation to obtain p step-length prediction models;
a cost function unit, configured to construct a cost function based on the prediction models of the p step sizes;
and the prediction model unit is used for solving the cost function by adopting an unconstrained solving algorithm to obtain the prediction model controller.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program for execution by the at least one processor to cause the at least one processor to perform the model predictive control method described previously.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to execute the aforementioned model predictive control method.
According to the model predictive control method, closed-loop control and efficient control of boost pressure, exhaust pressure and EGR rate can be realized through the model predictive control and the extended state observer, decoupling of control parameters of an air system can be realized, the responsiveness and control precision of the air system are improved, most of required parameters are inherent parameters of the air system, the parameters can be directly obtained, the calibration workload is reduced, and the control performance is provided.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of a model predictive control method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of another model predictive control method according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a model predictive control apparatus according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 is a schematic diagram of a model predictive control method according to an embodiment of the present invention, which is applicable to a case where decoupling control of an air system of an engine is achieved by model predictive control, the model predictive control method may be performed by a model predictive control device, which may be implemented in the form of hardware and/or software, and the model predictive control device may be configured in the engine.
As shown in fig. 1, the model predictive control method includes:
step 110, constructing an air system state equation based on working parameters of the engine, wherein the working parameters at least comprise parameters of an air inlet throttle valve, parameters of a turbocharger and parameters of an exhaust gas recirculation valve;
step 120, constructing an extended state observer based on an air system state equation and performing disturbance estimation to obtain a disturbance value;
and 130, constructing a predictive model controller based on the air system state equation and the disturbance value, wherein the predictive model controller comprises a feedforward control term based on the disturbance value and a feedback control term based on the measured value.
In this embodiment, the control amounts of the air intake and exhaust system of the engine include boost pressure, exhaust pressure and EGR rate, and the model predictive control method can realize efficient control of the boost pressure, the exhaust pressure and the EGR rate, where the boost pressure is the pressure behind the air intake throttle valve, and the exhaust pressure is the pressure in front of the turbocharger.
Some knowledge profiles associated with this model predictive control method are as follows: PID is a common control method in classical control theory, and comprises a proportional link P, an integral link I and a differential link D, so as to construct closed-loop control of the system. The MPC is model predictive control, mainly comprises three aspects of predictive model, rolling optimization and feedback correction, and realizes optimal control of the system by on-line optimizing a cost function and constraint conditions. ESO is an extended state observer, and is a full-order state observer constructed based on a state space equation, and is characterized in that besides an original state variable, a disturbance part which cannot be modeled is also changed into the state variable, and estimation is performed simultaneously. VGT is a variable cross-section turbocharger, which can dynamically adjust the supercharging pressure of an engine by adjusting the swing angle of a supercharger blade through a motor, and generally takes the supercharging pressure of an intake manifold as a control target. EGR is an exhaust gas recirculation valve, and the angle of the valve can be adjusted through a motor, so that the control of the introduced amount of exhaust gas is realized, and the NOx emission is reduced. TVA is the throttle valve that admits air, can adjust the valve aperture through the motor, realizes the throttle control of air inlet flow, can cooperate the regulation EGR rate.
The first step of the model predictive control method is to establish an air system state equation based on the working parameters of the engine, wherein the air system is an air intake and exhaust system of the engine, and the air system state equation is also an air intake and exhaust system state space equation of the engine. The engine comprises at least an intake throttle valve, a turbocharger and an exhaust gas recirculation valve, and the operating parameters of the engine comprise at least the relevant operating parameters of the intake throttle valve, the relevant operating parameters of the turbocharger and the relevant operating parameters of the exhaust gas recirculation valve. The air system state equation is therefore established based on the parameters of the intake throttle, the turbocharger and the EGR valve, the relevant operating parameters of the intake throttle including boost pressure, the relevant operating parameters of the turbocharger including exhaust pressure, and the relevant operating parameters of the EGR valve including EGR rate. In this embodiment, the air system state equation is constructed based on different control amounts, so that the model predictive control method considers the relationship between the different control amounts, and the subsequent control process also considers the coupling control between the different control amounts. Compared with the independent control of each control quantity, the parameter calibration work can be reduced, and the control precision is improved.
The second step of the model predictive control method is to construct an extended state observer based on the constructed air system state equation and perform disturbance estimation to obtain a disturbance value. The air system state equation constructed based on different control amounts comprises a disturbance part F, the disturbance part F is expanded into state amounts, and an expanded state observer is constructed. The extended state observer is a full-order state observer constructed based on a state space equation, and is characterized in that besides the original state variable, a disturbance part which cannot be modeled is also changed into the state variable for estimation. The disturbance value can be obtained by state estimation of the extended state observer, which is substantially a disturbance estimated value.
The third step of the model predictive control method is to construct a predictive model controller based on the air system state equation constructed in the first step and the disturbance value obtained in the second step. The predictive model controller comprises a feedforward control item based on disturbance values and a feedback control item based on measured values, the meaning of feedback correction in a model predictive control algorithm is embodied, the algorithm only takes a first group of numerical values as control output, and then continuous iterative solution is carried out in each control period, so that rolling optimization updating is realized. Optimal control of the air system is achieved.
According to the model predictive control method, closed-loop control and efficient control of boost pressure, exhaust pressure and EGR rate can be realized through the model predictive control and the extended state observer, decoupling of control parameters of an air system can be realized, the responsiveness and control precision of the air system are improved, most of required parameters are inherent parameters of the air system, the parameters can be directly obtained, the calibration workload is reduced, and the control performance is provided.
Optional construction of the air system state equation includes: acquiring pressure, temperature and volume of an air inlet throttle valve, acquiring pressure, temperature and volume of a turbocharger, and acquiring volume of an exhaust gas recirculation valve; and constructing an air system state equation containing the total disturbance according to the working parameters of the engine.
Optionally constructing the extended state observer and performing disturbance estimation to obtain a disturbance value includes: expanding disturbance in an air system state equation into state quantity, and constructing to obtain an expanded state observer; solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer; and carrying out disturbance estimation on the extended state observer based on the gain to obtain a disturbance value.
The optional build prediction model controller includes: discrete and incremental changes are carried out on the state equation of the air system, and a prediction model with p step sizes is obtained; constructing a cost function based on the prediction model of p step sizes; and solving the cost function by adopting an unconstrained solving algorithm to obtain the predictive model controller.
The foregoing is the main idea of the present invention, and based on the above technical solutions, the model prediction method will be described in detail by specific formulas.
Fig. 2 is a schematic diagram of another model predictive control method according to an embodiment of the invention. Fig. 2 is described in conjunction with the following.
Optional construction of the air system state equation includes: acquiring pressure, temperature and volume of an air inlet throttle valve, acquiring pressure, temperature and volume of a turbocharger, and acquiring volume of an exhaust gas recirculation valve; and constructing an air system state equation containing the total disturbance according to the working parameters of the engine.
In this embodiment, working parameters such as front pressure, rear pressure, front temperature, rear temperature, volume, effective flow area and the like of the air intake throttle valve are directly obtained from an air intake and exhaust system of the engine. Working parameters such as front pressure, rear pressure, front temperature, volume, effective flow area and the like of the turbocharger are directly obtained from an air inlet and exhaust system of the engine. Working parameters such as the volume and the effective flow area of the EGR valve are directly obtained from an air inlet and outlet system of the engine. Without being limited thereto, corresponding operating parameters are directly obtained from the engine intake and exhaust system according to the construction requirements of the state space equation.
Based on this, the air system state equation is constructed as the following formula (1):
in the formula (1):
wherein p is 22 The pressure after the air inlet throttle valve is the boost pressure; p is p 3 Is the turbocharger front pressure, i.e., the displacement pressure; x is X EGR EGR rate, which is the exhaust gas recirculation rate; a, a 1 ，a 2 ，a 3 Coefficients that are state transition matrices; b 1 ，b 2 ，b 3 ，b 4 ，b 5 Coefficients for an input matrix; a is that TVA An effective flow area of the air inlet throttle valve A VGT Is the effective flow area of the turbocharger, A EGR An effective flow area for an exhaust gas recirculation valve; f is disturbance, F 1 、f 2 、f 3 Is the total perturbation of the equations.
The coefficients in each matrix are as follows:
wherein eta vol For the air charging efficiency, the ratio of the actual air inflow to the theoretical air inflow of the cylinder is represented; v (V) d Is the cylinder volume; n (N) Eng Engine speed; v (V) 22 Is the volume of the intake manifold; t (T) 3 Is the turbocharger front temperature; t (T) 22 Is the temperature after the air inlet throttle valve; v (V) 3 Is the volume of the exhaust manifold; r is a gas constant; p is p 21 Is the inlet throttle pre-valve pressure; t (T) 21 Is the intake throttle front temperature; p is p 4 Is turbocharger post pressure; ρ 22 Is the air flow density at the intake manifold;is the fuel injection quantity.
Optionally constructing the extended state observer and performing disturbance estimation to obtain a disturbance value includes: expanding disturbance in an air system state equation into state quantity, and constructing to obtain an expanded state observer; solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer; and carrying out disturbance estimation on the extended state observer based on the gain to obtain a disturbance value.
In this embodiment, the air system state equation includes a disturbance F, where the disturbance F includes F 1 、f 2 、f 3 ，f 1 、f 2 、f 3 Is the total perturbation of the equations. Expanding disturbance F in an air system state equation into a state quantity, constructing to obtain an extended state observer, and obtaining a state quantity observation error equation of the extended state observer; observer based on expansion stateA state quantity observation error equation of (2) is calculated to obtain a characteristic equation; the poles of the characteristic equation of the extended state observer are configured by a pole configuration method, and the poles are configured at-w E Where the gain of the extended state observer can be obtained and used with a bandwidth w containing the extended state observer E Is represented by the expression of (2); and carrying out disturbance estimation on the extended state observer based on the gain to obtain a disturbance value.
Based on this, if the disturbance F in the air system state equation (1) is expanded to a state quantity and the derivative of the disturbance F is set to 0 for the sake of simplicity of calculation, the following equation (2) is obtained by conversion according to equation (1):
in the formula (2):
on the basis, constructing and obtaining an extended state observer, wherein the deviation correction term can compensate the model precision error, and the extended state observer is represented by the following formula (3):
in the formula (3):
wherein L is E Is a gain matrix of the extended state observer.
The state quantity observation error equation of the extended state observer is:
combining formula (2) and formula (3) to obtain:
from the pole allocation principle in modern control theory, it is known that the system performance is related to the characteristic root, i.e. the pole of the system transfer function. That is, when the system characteristic equation (A E -L E ) When the characteristic roots of (a) fall to the left of the complex plane, the initial state vector error can be satisfied according to the exponential decay lawThe system is stable. Therefore, the characteristic equation (A E -L E ) The pole of the pole is arranged at-w E Here, it is possible to obtain:
λ E (s)＝|sI-(A E -L E )|＝(s+ω E ) 6 。
the extended state observer ESO gains are all controlled by the method of pole allocation by using the bandwidth w containing ESO E The expression of (2) can ensure the stability of the system and reduce the calibration workload.
The disturbance F of each equation can be obtained by state estimation of the extended state observer.
The optional build prediction model controller includes: discrete and incremental changes are carried out on the state equation of the air system, and a prediction model with p step sizes is obtained; constructing a cost function based on the prediction model of p step sizes; and solving the cost function by adopting an unconstrained solving algorithm to obtain the predictive model controller.
In this embodiment, since the prediction model controller MPC needs to perform discretization, and meanwhile, in order to eliminate steady-state error, incremental control is performed, discrete and incremental changes are performed by the air system state equation, so as to obtain a prediction model of p steps in the future. And reconstructing the cost function, adopting unconstrained solution and then carrying out saturation value limit on the control value to obtain the predictive model controller for facilitating engineering.
Based on the above, discrete and incremental changes are performed by the air system state equation (1), and a prediction model of p steps in the future is obtained, which is represented by the following formula (4):
Y p ＝I p Y(k)+B p ΔU p +A p ΔX(k)+H p ΔF(k) (4)；
in the formula (4):
on this basis, the construction cost function is the following equation (5), and it can be known that J is a function of Δup:
wherein Rp is the set value of p step sizes in the future; q is a deviation weight matrix of a set value and an actual value, and the larger the weight is, the closer the control output corresponding to the expected value is to a given reference input; r is a control quantity weight matrix, and the larger the weight is, the smaller the corresponding control action change is expected.
For engineering convenience, unconstrained solution is adopted, then saturation value limiting is carried out on the control value, the equation (5) is used for solving the first derivative of DeltaUp, and the minimum value is obtained as shown in the following equation (6):
ΔU p * ＝(B p T Q T QB p +R T R) -1 B p T Q T Q(R p -H p ΔF(k)-I p Y(k)-A p ΔX(k)) (6)；
the prediction model controller MPC can be constructed by the above formula (6), and is related to the extended state observer ESO, so the above formula (6) is also an MPC-ESO controller.
Furthermore, ΔUp * The first two items in the brackets are feedforward control items based on a set value and a disturbance value, wherein the set value is a known quantity set according to the performance of the whole machine, and the disturbance value is estimated by an extended state observer ESO. ΔUp * The last two items in the brackets are feedback control items based on output measurement quantity, wherein the measurement quantity comprises the measurement value of the current step length and the last step length, and the meaning of feedback correction in the MPC control algorithm is embodied. Meanwhile, deltaUp * For a group of vectors, the algorithm only takes a first group of numerical values as control output, and then continuously and iteratively solves in each control period to realize rolling optimization updating.
In the embodiment, the closed-loop feedback and the high-efficiency control of the boost pressure, the exhaust pressure and the EGR rate are realized through the model predictive control and the extended state observer, the decoupling of the control parameters of the system is realized, and the responsiveness of the system is improved. In addition, most of parameters are intrinsic parameters of the system, and the parameters can be directly obtained, so that the calibration workload is reduced. In the traditional scheme, closed-loop control of boost pressure, exhaust pressure and EGR rate is realized through three independent classical PID control, a plurality of groups of PID control parameters are added for control effect sometimes, and feedforward control based on engine speed and fuel injection quantity is added at the same time. Compared with the traditional scheme, the method has the advantages that no matter the feedforward or feedback part does not need to do a large amount of parameter calibration work, and meanwhile, the coupling control among three control quantities is considered, so that the control precision and the control efficiency can be improved.
Based on the same inventive concept, the embodiment of the invention provides a model predictive control device. Fig. 3 is a schematic diagram of a model predictive control apparatus according to an embodiment of the present invention, which is applicable to a case where decoupling control of an air system of an engine is implemented by model predictive control, the model predictive control apparatus may perform the model predictive control method according to any of the above embodiments, the model predictive control apparatus may be implemented in the form of hardware and/or software, and the model predictive control apparatus may be configured in the engine.
As shown in fig. 3, the model predictive control apparatus includes: an equation building module 310, a state observation module 320 and a prediction model module 330; the build equation module 310 is configured to build an air system state equation based on engine operating parameters including at least parameters of an intake throttle, a turbocharger, and an exhaust gas recirculation valve; the state observation module 320 is configured to construct an extended state observer based on an air system state equation and perform disturbance estimation to obtain a disturbance value; the predictive model module 330 is configured to construct a predictive model controller based on the air system state equation and the disturbance value, wherein the predictive model controller includes a feedforward control term based on the disturbance value and a feedback control term based on the measured value.
The optional build equation module 310 includes: a parameter acquisition unit and a construction equation unit; the parameter acquisition unit is used for acquiring the pressure, the temperature and the volume of the air inlet throttle valve, acquiring the pressure, the temperature and the volume of the turbocharger and acquiring the volume of the exhaust gas recirculation valve; the construction equation unit is used for constructing an air system state equation containing total disturbance according to the working parameters of the engine.
The optional state observation module 320 includes: the system comprises a state expansion unit, an observation solving unit and a disturbance estimating unit; the state expansion unit is used for expanding disturbance in an air system state equation into state quantity, and constructing an expanded state observer; the observation solving unit is used for solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer; the disturbance estimation unit is used for carrying out disturbance estimation on the extended state observer based on the gain to obtain a disturbance value.
The optional predictive model module 330 includes: a discrete change unit, a cost function unit and a prediction model unit; the discrete change unit is used for carrying out discrete and incremental changes on the state equation of the air system to obtain a prediction model with p step sizes; the cost function unit is used for constructing a cost function based on the prediction models of p step sizes; and the prediction model unit is used for solving the cost function by adopting an unconstrained solving algorithm to obtain a prediction model controller.
In the embodiment, the closed-loop feedback and the high-efficiency control of the boost pressure, the exhaust pressure and the EGR rate are realized through the model predictive control and the extended state observer, the decoupling of the control parameters of the system is realized, and the responsiveness of the system is improved. In addition, most of parameters are intrinsic parameters of the system, and the parameters can be directly obtained, so that the calibration workload is reduced. Compared with the traditional scheme, the device does not need to do a large number of parameter calibration work in the feedforward or feedback part, and meanwhile, the coupling control among three control quantities is considered, so that the control precision and the control efficiency can be improved.
The model prediction control device provided by the embodiment of the invention can execute the model prediction control method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Based on the same inventive concept, an embodiment of the present invention provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores a computer program for execution by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the model predictive control method of any of the embodiments described above.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores computer instructions, wherein the computer instructions are used for enabling a processor to implement the model predictive control method according to any embodiment.
Fig. 4 is a schematic diagram of an electronic device according to an embodiment of the present invention. As shown in FIG. 4, electronic device 410 is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device 410 may also represent various forms of mobile equipment, such as personal digital processing, cellular telephones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing equipment. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
The electronic device 410 comprises at least one processor 411, and a memory communicatively coupled to the at least one processor 411, such as a Read Only Memory (ROM) 412, a Random Access Memory (RAM) 413, etc., wherein the memory stores computer programs executable by the at least one processor 411, and the processor 411 may perform various suitable actions and processes in accordance with the computer programs stored in the Read Only Memory (ROM) 412 or the computer programs loaded from the storage unit 418 into the Random Access Memory (RAM) 413. In the RAM413, various programs and data required for the operation of the electronic device 410 may also be stored. The processor 411, the ROM412, and the RAM413 are connected to each other through a bus 414. An input/output (I/O) interface 415 is also connected to bus 414.
Various components in the electronic device 410 are connected to the I/O interface 415, including: an input unit 416 such as a keyboard, a mouse, etc.; an output unit 417 such as various types of displays, speakers, and the like; a storage unit 418, such as a magnetic disk, optical disk, or the like; and a communication unit 419 such as a network card, modem, wireless communication transceiver, etc. The communication unit 419 allows the electronic device 410 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The processor 411 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 411 performs the various methods and processes described above, such as the model predictive control method described in any of the embodiments described above.
In some embodiments, the model predictive control method described in any of the embodiments above may be implemented as a computer program, which is tangibly embodied on a computer-readable storage medium, such as the storage unit 418. In some embodiments, some or all of the computer program may be loaded and/or installed onto the electronic device 410 via the ROM412 and/or the communication unit 419. When the computer program is loaded into RAM413 and executed by processor 411, one or more steps of the model predictive control method described in any of the above embodiments described above may be performed. Alternatively, in other embodiments, processor 411 may be configured by any other suitable means (e.g., by means of firmware) to perform the model predictive control method described in any of the embodiments above.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A model predictive control method, characterized by comprising:
constructing an air system state equation based on operating parameters of the engine, the operating parameters including at least parameters of the intake throttle valve, parameters of the turbocharger, and parameters of the exhaust gas recirculation valve;
constructing an extended state observer based on the air system state equation and performing disturbance estimation to obtain a disturbance value;
and constructing a predictive model controller based on the air system state equation and the disturbance value, wherein the predictive model controller comprises a feedforward control term based on the disturbance value and a feedback control term based on a measured value.
2. The model predictive control method according to claim 1, wherein constructing an air system state equation includes:
acquiring the pressure, temperature and volume of the air inlet throttle valve, acquiring the pressure, temperature and volume of the turbocharger, and acquiring the volume of the exhaust gas recirculation valve;
and constructing the air system state equation containing the total disturbance according to the working parameters of the engine.
3. The model predictive control method according to claim 1, wherein constructing an extended state observer and performing disturbance estimation to obtain a disturbance value includes:
expanding disturbance in the air system state equation into state quantity, and constructing to obtain the extended state observer;
solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer;
and carrying out disturbance estimation on the extended state observer based on the gain to obtain the disturbance value.
4. The model predictive control method as set forth in claim 1, wherein constructing a predictive model controller includes:
performing discrete and incremental changes on the air system state equation to obtain p step-length prediction models;
constructing a cost function based on the prediction models of the p step sizes;
and solving the cost function by adopting an unconstrained solving algorithm to obtain the predictive model controller.
5. A model predictive control apparatus, comprising:
the system comprises a building equation module, a control module and a control module, wherein the building equation module is used for building an air system state equation based on the working parameters of an engine, and the working parameters at least comprise the parameters of an air inlet throttle valve, the parameters of a turbocharger and the parameters of an exhaust gas recirculation valve;
the state observation module is used for constructing an extended state observer based on the air system state equation and carrying out disturbance estimation to obtain a disturbance value;
and the prediction model module is used for constructing a prediction model controller based on the air system state equation and the disturbance value, wherein the prediction model controller comprises a feedforward control term based on the disturbance value and a feedback control term based on a measured value.
6. The model predictive control device of claim 5, wherein said build equation module comprises:
a parameter acquisition unit configured to acquire a pressure, a temperature, and a volume of the intake throttle valve, acquire a pressure, a temperature, and a volume of the turbocharger, and acquire a volume of the exhaust gas recirculation valve;
and constructing an equation unit for constructing the air system state equation containing the total disturbance according to the working parameters of the engine.
7. The model predictive control device according to claim 5, wherein the state observation module includes:
the state expansion unit is used for expanding disturbance in the air system state equation into state quantity, and constructing and obtaining the expansion state observer;
the observation solving unit is used for solving the extended state observer through a pole allocation method to obtain the gain of the extended state observer;
and the disturbance estimation unit is used for carrying out disturbance estimation on the extended state observer based on the gain to obtain the disturbance value.
8. The model predictive control apparatus according to claim 5, wherein the predictive model module includes:
the discrete change unit is used for carrying out discrete and incremental changes on the air system state equation to obtain p step-length prediction models;
a cost function unit, configured to construct a cost function based on the prediction models of the p step sizes;
and the prediction model unit is used for solving the cost function by adopting an unconstrained solving algorithm to obtain the prediction model controller.
9. An electronic device, comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
the memory stores a computer program for execution by the at least one processor, the computer program being executable by the at least one processor to cause the at least one processor to perform the model predictive control method of any one of claims 1-4.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores computer instructions for causing a processor to implement the model predictive control method according to any one of claims 1-4 when executed.
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