CN116301023A - Aircraft track tracking method and device based on data driving model predictive control - Google Patents
Aircraft track tracking method and device based on data driving model predictive control Download PDFInfo
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Abstract
The invention provides an aircraft track tracking method and device based on data driving model predictive control. Firstly, capturing the latest aircraft state response data in real time in the flight process of an aircraft, and utilizing an expected roll angle reference signal at the next moment obtained by the optimization solution of a data driving model predictive controller as the flight control input of the aircraft to realize the plane transverse track tracking of the aircraft; meanwhile, model parameters used by the data-driven model predictive controller are dynamically adjusted according to the on-line identification of the real-time captured and stored aircraft state response data. The invention can capture the latest response data in real time in the flight process of the aircraft, and can identify and dynamically adjust the model parameters used by the model predictive controller on line so as to improve the flight robustness of the aircraft under different environmental working conditions. The model predictive control method based on data driving can fully utilize the input and output data of the controlled system, and enhance the self-adaptive capacity of the controller to the system.
Description
Technical Field
The invention relates to the field of aircraft track tracking, in particular to an aircraft track tracking method and device based on data driving model predictive control.
Background
Model predictive control has become one of the more common control strategies in the industry, thanks to the improvement of the computing power of the terminal equipment. The principle is that a mathematical model is utilized, future output is predicted according to the system state and the control input at the current moment, a future optimal control sequence is obtained by solving, and the process is continuously repeated to perform rolling optimization. The method is characterized by comprising the following steps: the method can solve the problem of multivariable control, can solve the physical constraints of input and output, can adapt to structural changes and the like. As a control algorithm widely used in the field of real-time control, model predictive control has a control effect close to optimum in many cases.
With the wide application of unmanned aerial vehicles with the advantages of low cost, small volume, easy maneuver and the like in the field of army and civilian, people put forward higher requirements on the autonomous control capability of a flight controller, and the frequency of model predictive control technology in the design of a domestic and foreign flight control system is increased year by year. However, in the existing related research results, one always only performs a single system parameter identification on the controlled system, and then uses the obtained model as an inherent attribute of the system to be not changed any more, but the aircraft system may need to face a changeable flight environment in the process of performing a flight task: under different working conditions, the aerodynamic response parameters of the aircraft are changed difficultly to predict, so that the aerodynamic characteristics of the aircraft in actual flight are different from the aerodynamic calculation estimated value or the ground wind tunnel test measured value, and the performance of a control system designed based on the nominal dynamics model is degraded. Therefore, in order to implement a stable and adaptive control algorithm, a concept similar to the incremental training in the machine learning field needs to be proposed for the traditional model predictive controller, and a technical framework for dynamically updating the model needs to be provided.
Disclosure of Invention
The invention aims to solve the technical problems and provide an aircraft track tracking method and device based on data driving model predictive control. The aerodynamic response characteristics of the aircraft are acquired through a pre-flight task, a dynamic model is identified by using a sparse identification method with control, and model parameters of a model predictive controller are identified on line and dynamically adjusted according to real-time data of the flight process, so that the aircraft can better complete a target track tracking task. The invention can fully utilize the input and output data of the controlled system, and enhance the self-adaptive capacity of the controller to the system.
The technical scheme adopted by the invention is as follows:
an aircraft track tracking method based on data driving model predictive control comprises the following steps: capturing and storing up-to-date aircraft state response data in real time during the flight of the aircraft; the aircraft state response data comprises a north position coordinate n, an east position coordinate e and a yaw angle psi of the aircraft g Roll angle phi, roll rate p, lateral tracking error l e Heading tracking error psi e ；
Inputting the state response data of the aircraft at the current moment as a data driving model prediction controller, continuously predicting the state response data of the aircraft in a period of time after the current moment in real time based on a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a course tracking error equation of the aircraft, establishing an objective function for optimization based on the state response data of the aircraft in the period of time after the predicted current moment, obtaining an optimal expected roll angle reference signal at the next moment, and executing a flight task by the aircraft according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft;
meanwhile, model parameters used by the data-driven model predictive controller are dynamically adjusted according to the on-line identification of the real-time captured and stored aircraft state response data.
Further, the aircraft is a fixed-wing aircraft, and the plane transverse dynamics equation of the fixed-wing aircraft is expressed as:
n k+1 ＝n k +V g cosψ gk δt
e k+1 ＝e k +V g sinψ gk δt
ψ gk+1 ＝ψ gk +(gtanφ k /V)δt
φ k+1 ＝φ k +p k δt
the roll dimensional response equation is expressed as:
p k+1 ＝p k +(b 0 φ rk -a 1 p k -a 0 φ k )δt
the lateral tracking error equation is expressed as:
l ek+1 ＝l ek +V g sinψ g δt
the heading tracking error equation represents:
ψ ek+1 ＝ψ ek +(gtanφ k /V)δt
wherein n and e respectively represent the north position and the east position of the aircraft, and take the take-off coordinates as the origin; v (V) g Representing a ground speed vector; psi phi type g A plane included angle between the ground speed vector and the north direction is represented; g is the local gravitational acceleration; phi represents a roll angle; v represents airspeed vector, p represents roll angle velocity; phi (phi) r Is the desired roll angle reference signal input to the aircraft; a, a 0 、a 1 、b 0 Is a constant parameter of the model; k is the index of the sampling instant and δt represents the time difference of the sampling interval.
Further, model parameters used by the data-driven model predictive controller are determined in advance through an optimizer through collected flight data by using a sparse identification method with control.
Further, the flight data is collected by the following method: the method comprises the steps of continuously inputting control excitation to an aircraft in a windless environment, wherein data of the input control comprise a plurality of 2-1-1 double cascade maneuvers with different magnitudes, and recording flight data after each input control reaches a stable state, wherein the three-dimensional vector sequences comprise a roll angle, a roll rate and an expected roll angle reference signal of the input aircraft.
Further, the expected roll angle reference signal output by the data driving model predictive controller is provided with a hard constraint condition, so that the roll angle output by the controller is set within a safety range.
Further, the objective function comprises a cost generated by a transverse tracking error and a heading tracking error, a roll angle penalty term and a roll maneuver penalty term.
Further, the method also comprises using a PID controller to track the longitudinal altitude of the aircraft.
An aircraft trajectory tracking device based on data-driven model predictive control, comprising:
the data acquisition and storage unit is used for capturing and storing the latest aircraft state response data in real time in the flight process of the aircraft; the aircraft state response data comprises a north position coordinate n, an east position coordinate e and a yaw angle psi of the aircraft g Roll angle phi, roll rate p, lateral tracking error l e Heading tracking error psi e ；
The data driving model prediction controller takes the state response data of the aircraft at the current moment as input, continuously predicts the state response data of the aircraft in a period of time after the current moment in real time based on a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a course tracking error equation of the aircraft, establishes a target function to optimize based on the state response data of the aircraft in the period of time after the predicted current moment, obtains an optimal expected roll angle reference signal at the next moment, and executes a flight task according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft;
and the model parameter dynamic adjustment unit is used for dynamically adjusting the model parameters used by the data-driven model prediction controller according to the on-line identification of the aircraft state response data stored by the data acquisition and storage unit.
Further, a PID controller is included for longitudinal altitude tracking of the aircraft.
Further, in the PID controller for longitudinal altitude control, a limit may be made to the target pitch angle output by the controller such that both the aircraft climb angle and the glide angle are less than 10 degrees.
The beneficial effects of the invention are as follows:
1. aiming at a dynamic model of an aircraft, the method of combining 2-1-1 maneuver with sparse identification can quickly identify model parameters from data, and has high accuracy and Jiang Pushi property.
2. The invention provides an idea similar to the incremental training in the machine learning field on the basis of the traditional model predictive controller, provides a technical framework for dynamic model updating, and can fully utilize the input and output data of a controlled system to realize more robust self-adaptive control behaviors.
3. According to the tracking control framework provided by the invention, the data-driven model predictive controller is combined with the PID controller, so that a three-dimensional track tracking task of the fixed-wing aircraft in a three-dimensional space can be well completed, the space track tracking task of the aircraft is realized, the flight track of the aircraft and a target path can keep good convergence, and the control performance is excellent.
4. The aircraft track tracking method based on the data-driven model predictive control can be popularized and applied to more control strategies.
Drawings
FIG. 1 is a control block diagram of a data-driven model predictive control method for a fixed wing track following task of the present invention;
FIG. 2 is a schematic diagram of the coordinate system and parameters used to model the dynamics of an aircraft;
FIG. 3 is a diagram of the relationship between various software in a simulation environment;
FIG. 4 illustrates a partial double cascade "2-1-1" maneuver input and corresponding output of aircraft roll during the flight data acquisition step;
FIG. 5 is a schematic representation of the relationship of an aircraft to waypoints and error terms;
FIG. 6 is a graph comparing data recovered using a model obtained by recognition with actual flight data;
FIG. 7 is a graph comparing predicted data with actual flight data using a model derived from recognition;
FIG. 8 is a graph of the results of a fixed wing unmanned aerial vehicle block tracking flight test;
fig. 9 is a graph of the results of a fixed wing unmanned aerial vehicle space trajectory tracking flight test.
Detailed Description
The invention provides an aircraft track tracking method based on a data driving model predictive control method, which comprises the following steps: capturing and storing up-to-date aircraft state response data in real time during the flight of the aircraft;
inputting the state response data of the aircraft at the current moment as a data driving model prediction controller, continuously predicting the state response data of the aircraft in a period of time after the current moment in real time based on a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a course tracking error equation of the aircraft, establishing an objective function for optimization based on the state response data of the aircraft in the period of time after the predicted current moment, obtaining an optimal expected roll angle reference signal at the next moment, and executing a flight task by the aircraft according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft;
meanwhile, model parameters used by the data-driven model predictive controller are dynamically adjusted according to the on-line identification of the stored aircraft state response data.
According to the tracking method provided by the invention, the data-driven model prediction controller is constructed based on the plane transverse dynamics equation, the roll dimension response equation, the transverse tracking error and the course tracking error equation and the like of the aircraft, so that the space track tracking task of the aircraft is realized, the flight track of the aircraft and the target path can keep good convergence, and the control performance is excellent. The method of the present invention is applicable to various aircraft, and the present invention will be described in detail below with reference to the accompanying drawings by taking a fixed wing aircraft as an example.
The right side of fig. 1 is a control block diagram of a data driving model prediction control method for tracking the track of a fixed wing aircraft, wherein the implementation of the block diagram comprises the following steps:
and S1, establishing a basic model of the model predictive controller. First, the plane transverse dynamics equation of the aircraft is established, and fig. 2 is an illustration of the establishment of the aircraftThe dynamic model is a schematic diagram of the coordinate system and parameters used. The planar transverse dynamics equation for a fixed wing aircraft may use a local inertial coordinate system consisting of north and east
Body coordinate system->
The following parameters are collectively defined and the planar transverse dynamics equation is expressed as follows:
wherein n and e respectively represent the north position and the east position of the aircraft, and take the take-off coordinates as the origin; superscript. Indicates the derivative of the corresponding physical quantity,
and->
Respectively representing the north speed and the east speed of the aircraft, V g Representing a ground speed vector; psi phi type g The plane included angle between the ground speed vector and the north direction is defined as positive by clockwise rotation based on the north direction, and the value range is [ -pi, pi]；/>
The angular velocity of the included angle between the ground velocity vector and the plane in the north direction is represented, and g is the local gravity acceleration; phi represents the roll angle, and the right roll is defined as positive; v represents the airspeed vector; />
p represents the roll angle speed. In the drawing, W represents a wind speed. The following roll dimensional response equation is additionally added for the roll channel of the fixed wing aircraft:
wherein,,
is roll angle acceleration; phi (phi) r Is the desired roll angle reference signal input to the aircraft; a, a 0 、a 1 、b 0 Is a constant parameter of the model.
When the aircraft executes a track tracking task, a target track designated by a user is given in a discrete waypoint sequence form, the waypoint information can be degenerated into two-dimensional plane coordinates in a transverse track tracking task, K-D tree space division is carried out on the two-dimensional plane coordinates, then a plurality of waypoint information taking the nearest target point as a starting point is extracted from the two-dimensional plane coordinates, a plane track coordinate system of the aircraft is projected, and high-order polynomial fitting is carried out under the axis system to obtain a curve F (x); FIG. 5 is a schematic representation of the relationship of an aircraft to waypoints and error terms. The subgraph (a) is a space division K-D tree constructed for the target track waypoints, and the program extracts a plurality of waypoint information taking the nearest target point as a starting point. Sub-graph (b) presents a curve F (x) obtained by performing high-order polynomial fitting on selected route points under an unmanned plane track coordinate system, and gives a transverse tracking error term l in a controller state quantity e And heading tracking error term ψ e Is a geometric representation of (c).
Finally, based on tracking purpose, establishing a specific expression of a transverse tracking error and heading tracking error equation:
l e ＝F(x)-y
Ψ e ＝arctan(F′(x))-Ψ g
the x and y in the formula are the space position parameters of the waypoints under the navigation coordinate system. F' (x) is the derivative of F (x).
Wherein the north position coordinates n, the east position coordinates e, the acquired yaw angle ψ of the aircraft are calculated g Roll angle phi, roll rate p, lateral tracking error l e Heading tracking error psi e As a state vector, the control quantity is determined as phi of the input flight control r Values.
Step S3, in order to set an objective function in a computer and perform optimization solution, a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a heading tracking error equation of the aircraft which are given previously need to be discretized into a differential form:
n k+1 ＝n k +V g cosψ gk δt
e k+1 ＝e k +V g sinψ gk δt
ψ gk+1 ＝ψ gk +(gtanφ k /V)δt
φ k+1 ＝φ k +p k δt
p k+1 ＝p k +(b 0 φ rk -a 1 p k -a 0 φ k )δt
l ek+1 ＝l ek +V g sinψ g δt
ψ ek+1 ＝ψ ek +(gtanφ k /V)δt
where k is the index of the sampling instant and δt represents the time difference of the sampling interval.
Based on the differential form, the data-driven model prediction controller can continuously predict the aircraft state response data in a period of time after the current moment in real time according to the input aircraft state response data in the current moment, establish an objective function to optimize based on the aircraft state response data in the period of time after the predicted current moment, obtain an optimal expected roll angle reference signal in the next moment, and the aircraft executes a flight task according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft.
Where minimizing the objective function is a critical part of the model predictive controller, this objective determines which control actions and flight conditions are desirable and which flight actions are best avoided in the process for better tracking the reference. In the transverse track following problem, the invention provides an optimized objective function:
wherein the method comprises the steps of
ω φr 、/>
Representing the weight coefficients of the terms, for adjusting the importance of the terms in the optimization problem. N represents the length of a prediction interval of the model prediction controller, wherein the first line describes the cost generated by the transverse tracking error and the heading tracking error, so that the minimum cost can enable the aircraft to track the reference track more accurately; the second line describes a penalty term for roll angle, reducing the term to enable the aircraft to fly in a stable attitude as much as possible while tracking the reference; the third line describes a roll maneuver penalty term, and minimizing the term can ensure that the transition process of the controller output control quantity is smoother, so that the aircraft is prevented from frequent and large maneuver in the mission process, thereby increasing the flight stability and reducing the energy consumption.
As a preferred solution, the objective function weights may be given as follows:
through testing, the indexes of a data driving model predictive controller (MPC controller) can be regulated under the weight coefficient, and the aircraft can have better planar track tracking capability with fewer and more mild maneuvers by minimizing an objective function, wherein the optimization solving step can be realized by using an Ipopt open source solver in real time.
And S4, in the process of tracking the real-time aircraft track, identifying the model parameters used by the data-driven model prediction controller according to the real-time captured and stored aircraft state response data by using a power system sparse identification method (Sparse Identification of Nonlinear Dynamics with Control, SINDYC) with control, and dynamically adjusting. Based on the foregoing description, the model parameters used by the data-driven model predictive controller are mainly a in the roll dimension response equation 0 、a 1 、b 0 Three constant parameters. For a roll channel, the stored aircraft state response data comprises a three-dimensional vector sequence of roll angle, roll rate, and an input roll angle reference value; in the configuration of the candidate library, step S1 has given a determined second order model form-roll dimensional response equation, so that the corresponding basis functions can be set according to this equation. Can then be solved by an optimizer to obtain a 0 、a 1 、b 0 And corresponding specific parameter values are identified on line and dynamically adjusted.
Further, before the initial use, model parameters used by the data-driven model predictive controller can be determined in advance through an optimizer through the collected flight data by using a sparse identification method with control.
Flight data is collected by performing programmed flight tasks, and a fixed-wing aircraft needs to continuously input control excitation and record all flight data in a windless environment, wherein the data should comprise 2-1-1 double cascade maneuvers with a plurality of different amplitudes, and the input control amplitude should be controlled in a proper range. FIG. 4 is a partial double cascade "2-1-1" maneuver input and corresponding output of aircraft roll used in the flight data acquisition step. "2-1-1" maneuver is an alternating pulse signal input of the same amplitude and with a duty cycle of 2:1:1, and the present invention uses the modified dual cascade "2-1-1" maneuver as a primary control input combination, as indicated by the dashed line in the figure; the solid line in the figure is the response of the fixed-wing unmanned aerial vehicle under the excitation input. It should be noted that, the process may adopt actual flight collection or simulation collection, in this embodiment, the simulation platform is built by using a mainstream open source tool rack such as PX4, gazebo, ROS, etc., so as to implement real simulation of multiple objects such as an aircraft, a flight control, an onboard computer, an atmospheric environment, etc., where the program control part is completed by an ROS node program, and each software block diagram is shown in fig. 3. The platform realizes the real simulation of a plurality of objects such as an aircraft, a flight control system, an onboard computer, an atmospheric environment and the like. The Gazebo is a set of robot simulation tool set, a high-performance real-time physical engine is built in the Gazebo, a real three-dimensional object understanding function is provided, a virtual fixed wing unmanned aerial vehicle can be created in the Gazebo, environmental parameters including wind field speed and turbulence are configured, the dynamic behavior of an aircraft can be simulated through a simulator, and airborne sensor data with simulated noise can be output. PX4 is a solution of open source unmanned system control software, and can be connected with a sensor and an actuator of a virtual aircraft to realize low-level control. The data-driven model predictive controller provided by the invention is used as an advanced control loop to run on a robot operating system (Robot Operating System, ROS), which is a universal robot development tool set, can normalize and coordinate the operation of the controller and various auxiliary processes thereof, and uses a MAVROS software package to access the controller to PX4 flight control software.
As an embodiment, the sampling frequency during the flight data acquisition may be set to 20Hz, wherein the control inputs comprise a double cascade maneuver of different magnitudes "2-1-1" from 0.1 to 0.8 radians and spaced at 0.1 radians, each with a standby gap arranged long enough for steady state after each control unit has completed its input.
Based on the acquired flight data, a power system sparse identification method with control is also used (Sparse Identification of Nonlinear Dynamics with Control, SINDYC) to obtain a by solving through an optimizer 0 、a 1 、b 0 And determining the initial value of the model parameter used by the data driving model predictive controller according to the corresponding specific parameter value.
After the initial value is determined, the basic model is used for carrying out fitting restoration on the flight state of the aircraft under the conditions of the same initial state and the same control sequence in the data acquisition process, the error condition of the real flight record is estimated, if the accuracy meets a certain requirement, the verification is passed, otherwise, whether the flight data acquisition and identification process is improperly operated is checked, and the relevant steps are re-executed.
FIG. 6 is a graph comparing data obtained by recovering a training set using a model obtained by training set identification in the collected flight data with actual flight data of the training set. The figure shows the result of the fitting of the model to the data set with the same control sequence as the training set and the same initial state input, wherein the solid line and the dotted line are the original curve and the fitted curve respectively. It is not difficult to find that under the condition of smaller rolling amplitude, the identification model can well perform fitting reduction on the rolling angle phi item in the data set, and certain fitting precision reduction phenomenon occurs when the rolling amplitude is larger, which is caused by the fact that the low-order model cannot accurately restore the high-frequency signal. Fortunately, the fitting result is completely suitable for the design of a model predictive controller, the unmanned aerial vehicle is in pursuit of stable flight, the actual flight process is often rarely in such high-frequency and large-amplitude maneuver, and the control input falls in a smaller amplitude interval under most conditions, so that the prediction of the flight state is facilitated.
FIG. 7 is a graph comparing data obtained by predicting a test set using a training set recognition result model in the collected flight data with actual flight data of the test set. To verify the generalizability of the fitting parameters, it is also advantageous to include some form of input in the test set that is not a "2-1-1" maneuver, such as some additional task-oriented process. The solid line in the figure shows the flight data during a section of random guidance mission, and the dotted line is the forward prediction result obtained by inputting the same control sequence as the section of flight into the recognition model. The observation shows that the model prediction results can be well fit with the real flight data in the experimental test for up to 100 seconds, and no obvious prediction deviation appears even after the experimental section. In the model predictive control process, only the state within a few seconds after the current moment of the system is usually predicted, and the performance of the model is far beyond the requirement index of the controller. Meanwhile, the experiment also proves the universality of model parameter identification based on the '2-1-1' double-stage online dynamic data set.
In addition, the stored aircraft state response data can be continuously stored in the actual flight process by maintaining a queue with the length adjustable according to actual conditions, the queue is used as an incremental data set in the model identification process, and the correction weight of the incremental data set on the model basic parameters can be controlled by adjusting the length of the queue. Further, the incremental data set can be subjected to sparse identification together with the previously obtained flying data of the '2-1-1' double-stage online dynamic collection at any moment, so that the model parameters are corrected according to the latest data, dynamic correction is performed on the model, and data-driven dynamic model identification is realized.
As an embodiment, the length of the data queue storing real-time status and control inputs of the aircraft may be set to 10 seconds at a sampling rate of 20Hz, and the correction weights of the incremental dataset to the model base parameters can also be controlled by adjusting the length of the queue.
Further, hard constraints, which typically originate from factory physical constraints of the components of the system and constraints based on safety considerations, may be specified for the model predictive controller. The response of the aircraft is delayed from the control quantity, the corresponding state quantity can be constrained by constraining the control quantity, and any form of hard constraint can be added to the state quantity of the aircraft, and the rolling constraint is set for the output of the controller only for the consideration of the stable flight and the autopilot safety of the aircraft.
As one embodiment, the output hard constraint of the model predictive controller may set the roll angle between-40 degrees and 40 degrees.
Further, a PID controller may also be used to perfect the longitudinal altitude control of the aircraft. Specifically, multiplexing a plurality of route point information which is acquired from the MPC controller and takes the nearest point of the current aircraft position as a starting point, and performing interpolation fitting on the altitude of the route point information, so that a smoothly-changing target altitude reference value is calculated in the flight process and is input into the controller. The output of the PID controller is set as a pitch angle reference value to approach the target height, and limitation can be made for the target pitch angle output by the controller for safety, so that the climbing angle and the downslide angle of the aircraft are smaller than 10 degrees; and meanwhile, a piecewise function curve is used for mapping and obtaining a proper accelerator output set value according to a pitching reference value, so that an energy management strategy is provided for the system. The accelerator output value and the pitching control quantity are sent to the flight control, so that the longitudinal height tracking function is realized.
The aircraft is instructed to trace a square path of 100 meters on a side, which is already a relatively small value for a fixed wing aircraft, which is challenging. The method is utilized to track, model parameters used by the data driving model predictive controller are identified by using a power system sparse identification method (Sparse Identification of Nonlinear Dynamics with Control, SINDYC) with control according to stored aircraft state response data in the tracking flight test process, and the dynamic adjustment is carried out, wherein the adjustment frequency is once every 10 seconds. Fig. 8 is a graph of the results of a fixed wing unmanned aerial vehicle block tracking flight test. The sub-graph (a) is a control effect achieved by using the control method provided by the invention under the condition of no wind, the sub-graph (b) is a tracking effect of the aircraft after southwest wind balancing with the wind speed of 4 meters per second is added, and the sub-graph (c) is a track tracking result under southwest gust environment with the average wind speed of 4 meters per second and the maximum wind speed of 8 meters per second, which generally shows that the used controller has excellent performance, can well complete a planar track tracking task, and can realize more robust self-adaptive control behavior.
Fig. 9 is a graph of the results of a fixed wing unmanned aerial vehicle space trajectory tracking flight test. The space track simultaneously comprises a plane track tracking instruction and a height tracking instruction, the difference between plane coordinates, height coordinates, rolling angles and actual reference values in the process of executing the task of the aircraft is given in the figure, and the result shows that the controller has very good performance.
Corresponding to the method for tracking the aircraft track based on the data driving model predictive control, the invention also provides an aircraft track tracking device based on the data driving model predictive control.
The embodiment of the invention provides an aircraft track tracking device based on data-driven model predictive control, which comprises:
the data acquisition and storage unit is used for capturing and storing the latest aircraft state response data in real time in the flight process of the aircraft; the aircraft state response data comprises a north position coordinate n, an east position coordinate e and a yaw angle psi of the aircraft g Roll angle phi, roll rate p, lateral tracking error l e Heading tracking error psi e ；
The data driving model prediction controller takes the state response data of the aircraft at the current moment as input, continuously predicts the state response data of the aircraft in a period of time after the current moment in real time based on a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a course tracking error equation of the aircraft, establishes a target function to optimize based on the state response data of the aircraft in the period of time after the predicted current moment, obtains an optimal expected roll angle reference signal at the next moment, and executes a flight task according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft;
and the model parameter dynamic adjustment unit is used for dynamically adjusting the model parameters used by the data-driven model prediction controller according to the on-line identification of the aircraft state response data stored by the data acquisition and storage unit.
Further, a PID controller is included for longitudinal altitude tracking of the aircraft.
The apparatus embodiments may be implemented in software, or in hardware or a combination of hardware and software.
For the device embodiments, reference is made to the description of the method embodiments for the relevant points, since they essentially correspond to the method embodiments. The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purposes of the present invention. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
It is apparent that the above examples are given by way of illustration only and are not limiting of the embodiments. Other variations or modifications of the above teachings will be apparent to those of ordinary skill in the art. It is not necessary or exhaustive of all embodiments. And obvious variations or modifications thereof are contemplated as falling within the scope of the present invention.
Claims (9)
1. An aircraft track tracking method based on data driving model predictive control is characterized by comprising the following steps: capturing the latest aircraft state response data in real time during the flight of the aircraft; the aircraft state response data comprises a north position coordinate n, an east position coordinate e and a yaw angle psi of the aircraft g Roll angle phi, roll rate p, lateral tracking error l e Heading tracking error psi e ；
Inputting the state response data of the aircraft at the current moment as a data driving model prediction controller, continuously predicting the state response data of the aircraft in a period of time after the current moment in real time based on a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a course tracking error equation of the aircraft, establishing an objective function for optimization based on the state response data of the aircraft in the period of time after the predicted current moment, obtaining an optimal expected roll angle reference signal at the next moment, and executing a flight task by the aircraft according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft;
meanwhile, model parameters used by the data-driven model predictive controller are dynamically adjusted according to the on-line identification of the real-time captured and stored aircraft state response data.
2. The method of claim 1, wherein the aircraft is a fixed-wing aircraft, and wherein the planar transverse dynamics equation for the fixed-wing aircraft is expressed as:
n k+1 ＝n k +V g cosψ gk δt
e k+1 ＝e k +V g sinψ gk δt
ψ gk+1 ＝ψ gk +(gtanφ k /V)δt
φ k+1 ＝φ k +p k δt
the roll dimensional response equation is expressed as:
p k+1 ＝p k +(b 0 φ rk -a 1 p k -a 0 φ k )δt
the lateral tracking error equation is expressed as:
l ek+1 ＝l ek +V g sinψ g δt
the heading tracking error equation represents:
ψ ek+1 ＝ψ ek +(gtanφ k /V)δt
wherein n and e respectively represent the north position and the east position of the aircraft, and take the take-off coordinates as the origin; v (V) g Representing a ground speed vector; psi phi type g A plane included angle between the ground speed vector and the north direction is represented; g is the local gravitational acceleration; phi represents a roll angle; v represents airspeed vector, p represents roll angle velocity; phi (phi) r Is the desired roll angle reference signal input to the aircraft; a, a 0 、a 1 、b 0 Is a constant parameter of the model; k is the index of the sampling instant and δt represents the time difference of the sampling interval.
3. The method of claim 1, wherein model parameters used by the data-driven model predictive controller are determined by an optimizer solution using a sparse identification method with control over collected flight data.
4. A method according to claim 3, wherein the flight data is collected by: the method comprises the steps of continuously inputting control excitation to an aircraft in a windless environment, wherein data of the input control comprise a plurality of 2-1-1 double cascade maneuvers with different magnitudes, and recording flight data after each input control reaches a stable state, wherein the three-dimensional vector sequences comprise a roll angle, a roll rate and an expected roll angle reference signal of the input aircraft.
5. The method of claim 1, wherein the data-driven model predicts that the desired roll angle reference signal output by the controller is provided with a hard constraint that sets the roll angle output by the controller to be within a safe range.
6. The method of claim 1, wherein the objective function comprises a cost of lateral tracking error versus heading tracking error, a roll angle penalty term, and a roll maneuver penalty term.
7. The method of claim 1, further comprising using a PID controller for longitudinal altitude tracking of the aircraft.
8. An aircraft trajectory tracking device based on data-driven model predictive control, comprising:
the data acquisition and storage unit is used for capturing and storing the latest aircraft state response data in real time in the flight process of the aircraft; the aircraft state response data comprises a north position coordinate n, an east position coordinate e and a yaw angle psi of the aircraft g Roll angle phi, roll rate p, lateral tracking error l e Heading tracking error psi e ；
The data driving model prediction controller takes the state response data of the aircraft at the current moment as input, continuously predicts the state response data of the aircraft in a period of time after the current moment in real time based on a plane transverse dynamics equation, a roll dimension response equation, a transverse tracking error and a course tracking error equation of the aircraft, establishes a target function to optimize based on the state response data of the aircraft in the period of time after the predicted current moment, obtains an optimal expected roll angle reference signal at the next moment, and executes a flight task according to the predicted expected roll angle reference signal to realize plane transverse track tracking of the aircraft;
and the model parameter dynamic adjustment unit is used for dynamically adjusting the model parameters used by the data-driven model prediction controller according to the on-line identification of the aircraft state response data stored by the data acquisition and storage unit.
9. The apparatus of claim 8, further comprising a PID controller for performing longitudinal altitude tracking of the aircraft.
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