CN117434838A - Variable time domain event trigger intersection butt joint collaborative prediction control method - Google Patents
Variable time domain event trigger intersection butt joint collaborative prediction control method Download PDFInfo
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Abstract
The invention relates to a variable time domain event trigger intersection butting collaborative prediction control method, which respectively establishes discrete models of a state equation and an observation equation of an unmanned aerial vehicle and an unmanned aerial vehicle according to an Euler method, establishes state constraint and control constraint on the unmanned aerial vehicle and the unmanned aerial vehicle, establishes an adaptive event trigger mechanism for automatically adjusting a trigger threshold along with state change and defines an adaptive prediction time domain update strategy; and controlling the unmanned aerial vehicle to run in a certain track, determining the self-adaptive predicted track of the unmanned aerial vehicle according to the geometric relationship between the unmanned aerial vehicle and the movement process of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to fly according to the track so as to realize control. According to the invention, the fixed wing unmanned aerial vehicle can be precisely controlled under the constraint condition, and the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle are cooperatively controlled, so that the method is more efficient and precise, the problem of optimization is not required to be solved at each moment, and the calculation time is reduced; the method has the advantages of considering the resolvability of the optimization problem, reducing the prediction error as much as possible, dynamically adjusting the prediction time domain at each moment and reducing the calculation load.
Description
Technical Field
The invention belongs to the technical field of non-electric variable control or regulation systems, and particularly relates to a variable time domain event trigger intersection butt joint collaborative prediction control method for controlling the position, channel, altitude or attitude of a carrier on land, water, air or in space.
Background
With the arrival of the information age, the air-ground cooperative work is widely applied to various task scenes, such as in tasks of searching and rescuing in dangerous complex environments, and autonomous unmanned equipment such as unmanned aerial vehicles, unmanned vehicles and the like realizes air-ground cooperative work and plays an important role.
The autonomous unmanned equipment realizes air-ground coordination and requires that the unmanned equipment can independently fall on a stationary or moving unmanned vehicle to charge, and then take off to execute a new task. Unlike traditional single unmanned aerial vehicle control, unmanned aerial vehicle and unmanned aerial vehicle carry out cooperative control and have very big degree of difficulty and challenge, especially how high-efficient, accurate realization includes unmanned aerial vehicle and unmanned aerial vehicle of types such as fixed wing, four rotors and six rotors and cooperative control in order to accomplish the meeting and dock more challenging.
For the autonomous unmanned equipment, the traditional control mainly adopts a PID control method, and the problems of constraint and the like caused by actual situations cannot be solved although the principle is simple and the implementation is easy; while the model prediction control method can simultaneously ensure the optimal performance of the system and process constraint problems, because the optimization problem in the model prediction control needs to be solved on line at each sampling moment, a plurality of actual systems with smaller sampling intervals cannot meet the requirement of real-time calculation, and therefore, the model prediction control method is not suitable for a dynamic system with quick response. The optimization problem that is repeatedly solved at each sampling time also results in an increase in the amount of computation and a waste of computation resources.
Disclosure of Invention
The invention provides a variable time domain event trigger intersection butt joint collaborative prediction control method for a fixed wing unmanned aerial vehicle and an unmanned aerial vehicle, aiming at solving the problems existing in the prior art.
The technical scheme adopted by the invention is that a variable time domain event triggering intersection butt joint collaborative prediction control method is used for intersection butt joint collaboration of an unmanned aerial vehicle and an unmanned aerial vehicle, and comprises the following steps of:
step 1: respectively establishing discrete models of a state equation and an observation equation of the unmanned aerial vehicle and the unmanned aerial vehicle according to an Euler method;
step 2: establishing state constraint and control constraint for the unmanned aerial vehicle and the unmanned aerial vehicle;
step 3: establishing a self-adaptive event trigger mechanism for automatically adjusting a trigger threshold value along with state change;
step 4: defining a self-adaptive prediction time domain updating strategy;
step 5: and controlling the unmanned aerial vehicle to run in a certain track, determining the self-adaptive predicted track of the unmanned aerial vehicle according to the geometric relationship between the unmanned aerial vehicle and the movement process of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to fly according to the track so as to realize control.
Preferably, in step 2, the state constraint and the control constraint of the unmanned aerial vehicle are as follows
V a,min ≤V a ≤V a,max ,h a,min ≤h a ≤h a,max
γ a,min ≤γ a ≤γ a,max ,n x,min ≤n x ≤n x,max
n y,min ≤n y ≤n y,max ,n h,min ≤n h ≤n h,max
Wherein V is a Is the flying speed of the unmanned aerial vehicle, h a Is the flying height of the unmanned plane, gamma a Is the path angle of the unmanned aerial vehicle, n x 、n y 、n z Tangential overload control quantity, normal overload control quantity and vertical overload control quantity of unmanned aerial vehicle respectively a,min And · a,max And the constraint lower bound and the constraint upper bound of the corresponding state quantity of the unmanned aerial vehicle are respectively adopted.
Preferably, in step 2, the state constraint and the control constraint of the unmanned vehicle are as follows
V g,min ≤V g ≤V g,max ,δ min ≤δ≤δ max
Wherein V is g Is the speed of the unmanned vehicle, delta is the control quantity, and (V) g,min And · g,max The constraint lower bound and the constraint upper bound of corresponding variables of the unmanned vehicle are respectively adopted.
Preferably, in step 3, the event triggering condition is that
Wherein, xi is epsilon%t l ,t l+1 ]，x(ξ|t l ) For t under the actual system l The state value of the moment predicted by the moment,for t under nominal system l The predicted state value of the moment zeta is P, a weight matrix, delta (zeta) is a trigger threshold of the moment zeta, and x is a state quantity; the actual system refers to a system requiring quick solution control, which has high calculation time requirements, including but not limited to a quick response system;
defining an adaptive lawObtaining a lower bound and an upper bound of a trigger threshold;
the trigger time of the next moment is metWherein N is l At t l Prediction time domain of time.
Preferably, θ is satisfied,
wherein,xi is the maximum allowable error of customization, sigma 1 、σ 2 、ρ 1 、ρ 2 For adaptive parameters, sigma 1 、σ 2 Are all greater than 0, e (·) As an exponential function, delta l min And delta l max Respectively (t) l ,t l+1 ]Lower and upper bounds of trigger threshold in time satisfy
Wherein, alpha is E (0, 1),N l at t l The predicted time domain of the moment, η, is the upper bound of the disturbance, here due to δ l max Increment, delta l min Decrementing in order to ensure delta l min Always less than delta l max Therefore, a proper one is selectedThe parameters being such that the initial moment delta l min Less than delta l max ；L s Is Li Puxi z constant, r f R are terminal constraint set parameters of a nominal system and an actual system respectively, lambda max (. Cndot.) is the maximum eigenvalue of the matrix, P is the weight matrix; the actual system is a model with disturbances, while the nominal system does not take disturbances into account.
Preferably, when k=t l +N l The trigger condition is not reached at the moment, at k=t l +N l And solving the optimization problem again at any time.
Preferably, in step 4, the adaptive prediction time domain update strategy is
Wherein N is l At t l Predicted time domain, μ of trigger time l At t l The time-domain contraction factor is predicted from time to time,for the time interval to reach the end domain boundary, α ε (0, 1), in (·) is a logarithmic function with base 10, r f R is terminal constraint set parameter of nominal system and actual system, Q, P, R is weight matrix, which can be adjusted by itself, K is state feedback matrix, Q * For matrices Q and K T RK sum, (. Cndot.) of T Is transposed of matrix lambda max (. Cndot.) and lambda min (. Cndot.) are the maximum and minimum eigenvalues of the matrix, respectively.
Preferably, in step 5, the unmanned vehicle runs on a certain track, and the control includes each timeHorizontal plane position of unmanned vehicle under ground fixed coordinate systemDesired course angle +.>Desired speed->Desired state quantity for unmanned vehicleTerminal state quantity->Designing a target cost function of the unmanned aerial vehicle, and controlling and optimizing the unmanned aerial vehicle; here des refers to design.
Preferably, in combination with the motion trail of the unmanned vehicle, the flying path angle gamma is fixed * Under the condition of (1), according to the relation between the relative track and the absolute track of the unmanned aerial vehicle and the state information of the unmanned aerial vehicle and the unmanned aerial vehicle at the current moment, updating in real time to obtain the self-adaptive prediction track of the unmanned aerial vehicle, wherein the self-adaptive prediction track comprises the position and the flying height of each moment of the unmanned aerial vehicle in the horizontal plane under the ground fixed coordinate systemDesigning a desired path angle +.>And heading angle->Desired speed->Let the desired state quantity of the fixed wing unmanned aerial vehicle +.>Terminal state quantityAnd designing a target cost function of the unmanned aerial vehicle, and controlling and optimizing the unmanned aerial vehicle.
Preferably, when a trigger mechanism is met, solving to obtain a control sequence, a state sequence and a prediction time domain of the unmanned aerial vehicle and/or the unmanned aerial vehicle, and respectively updating the lower bound and the upper bound of a trigger threshold and the prediction time domain of the next trigger moment by adopting a first element of the control sequence; under other conditions, solving the control quantity at the corresponding moment in the obtained control sequence by adopting the last trigger moment;
calculating a trigger threshold value from the current trigger time to the next trigger time, repeatedly judging whether a trigger mechanism is met, when the state of the unmanned aerial vehicle or the unmanned aerial vehicle enters a terminal set, combining a state feedback control law to obtain a corresponding control quantity, acting a complete control sequence on the executors of the unmanned aerial vehicle and the unmanned aerial vehicle, and completing a meeting butt joint task through cooperative control; the control law, i.e., u=kx, K is the state feedback matrix, and x is the state quantity.
The invention relates to a variable time domain event trigger intersection butting collaborative prediction control method, which is used for intersection butting collaborative of an unmanned aerial vehicle and an unmanned aerial vehicle, wherein discrete models of state equations and observation equations of the unmanned aerial vehicle and the unmanned aerial vehicle are respectively established according to an Euler method, state constraint and control constraint of the unmanned aerial vehicle and the unmanned aerial vehicle are established, an adaptive event trigger mechanism which automatically adjusts a trigger threshold along with state change is established, and an adaptive prediction time domain update strategy is defined; and controlling the unmanned aerial vehicle to run in a certain track, determining the self-adaptive predicted track of the unmanned aerial vehicle according to the geometric relationship between the unmanned aerial vehicle and the movement process of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to fly according to the track so as to realize control.
The beneficial effects of the invention are mainly shown in the following steps:
(1) Compared with the traditional PID control, the model predictive control algorithm is adopted, so that the fixed wing unmanned aerial vehicle can be precisely controlled under the constraint condition;
(2) Compared with the traditional independent control of the fixed wing unmanned aerial vehicle, the method has the advantages that the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle are controlled in a cooperative mode, and the method is more efficient and accurate;
(3) An event triggering algorithm of a self-adaptive threshold is adopted, dynamic threshold adjustment is carried out for different moments, and the optimization problem is not required to be solved at each moment, so that the calculation time is reduced;
(4) The adaptive prediction time domain design is adopted, the dissolubility of the optimization problem is considered, the prediction error is reduced as much as possible, the prediction time domain at each moment is dynamically adjusted, and the calculation load is reduced.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
The specific implementation part of the invention is mainly divided into two parts of parameter setting and online operation, and the operation is implemented on a control computer of a fixed-wing unmanned aerial vehicle and an unmanned aerial vehicle, and the application process of the invention is further described and illustrated by combining the specification and the drawings.
Parameter setting: comprises self-adaptive threshold parameters, self-adaptive prediction time domain parameters and prediction controller parameters of both the fixed-wing unmanned aerial vehicle and the unmanned aerial vehicle, wherein the self-adaptive threshold parameters comprise self-adaptive parameters sigma 1 ,σ 2 ,ρ 1 ,ρ 2 Xi, li Puxi z constant L s Initial threshold delta 0 Maximum minimum threshold delta l max 、δ l min . The adaptive prediction time domain parameters include an initial prediction time domain N 0 Predicting time domain contraction factor mu l . The predictive controller parameters include a weight matrix Q, R, P, a state feedback control rate K, a terminal constraint set phi (r), phi (r) f )。
And (3) online operation: the method comprises the steps of online updating of the self-adaptive prediction track of the fixed-wing unmanned aerial vehicle, and control solving of the fixed-wing unmanned aerial vehicle and the unmanned aerial vehicle. And starting the CPU of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle control computer, and reading the initialized parameters and the state information of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle. The unmanned aerial vehicle runs at a constant speed, and the self-adaptive prediction track of the fixed-wing unmanned aerial vehicle is updated on line by the geometric relationship between the relative track and the absolute track of the fixed-wing unmanned aerial vehicle. And executing corresponding algorithm steps, calculating optimal control quantity of each sampling period, and regulating the control quantity of the fixed-wing unmanned aerial vehicle and the unmanned aerial vehicle in real time by a computer according to the calculated optimal control quantity, so as to repeatedly perform the steps, thereby realizing autonomous cooperative control in the process of the intersection and the butt joint of the fixed-wing unmanned aerial vehicle and the unmanned aerial vehicle.
Referring to fig. 1 and 2, the present invention relates to a variable time domain event trigger rendezvous and docking cooperative control method based on an adaptive threshold, and a specific embodiment includes the following steps:
1) And (3) establishing a fixed wing unmanned aerial vehicle model:
considering that the fixed-wing unmanned aerial vehicle carries out sideslip-free flight, ignoring the influence of the earth curvature, and defining a state quantity X of the fixed-wing unmanned aerial vehicle a ＝(x a ,y a ,h a ,V a ,χ a ,γ a ) T Observed quantity z a ＝(x a ,y a ,h a ) T Control amount u a ＝(n x ,n y ,n z ) T . Sampling time T s The method comprises the steps of establishing a discrete model of a state equation and an observation equation of the fixed-wing unmanned aerial vehicle according to an Euler method, wherein the discrete model of the fixed-wing unmanned aerial vehicle can be described as:
z a (k)＝C 1 X a (k) (2)
wherein k and k+1 respectively represent the k and k+1 times, V a Representing the flying speed of a fixed wing unmanned aerial vehicle, χ a Indicating course angle gamma of fixed wing unmanned aerial vehicle a Representing the path angle, x of a fixed wing unmanned aerial vehicle a 、y a Indicating the position of the fixed wing unmanned aerial vehicle in the horizontal plane under the ground fixed coordinate system, h a Representing the flying height of the fixed wing unmanned aerial vehicle, v 1～6 Representing disturbance, wherein the disturbance mainly comes from wind resistance in the actual intersecting and docking process of the fixed-wing unmanned aerial vehicle, and testing and controlling the wind resistanceAnd acquiring data, and modeling the wind resistance model by means of a neural network and the like to obtain a disturbance expression.
And the same can be used for obtaining a discrete expression of a nominal model of the fixed-wing unmanned aerial vehicle:
wherein,state quantity representing nominal model of fixed wing unmanned aerial vehicle, < ->Representing control quantity of nominal model of fixed wing unmanned aerial vehicle, f 1 (. Cndot.) represents the corresponding functional relationship of the fixed-wing unmanned aerial vehicle nominal model state equation.
2) Establishing an unmanned vehicle model:
sampling time T s Defining the state quantity X of the unmanned vehicle g ＝(x g ,y g ,V a ,χ a ) T Observed quantity z g ＝(x g ,y g ) T Control amount u g =δ, a discrete model of the state equation and the observation equation of the unmanned vehicle is established according to the euler method:
z g (k)＝C 2 X g (k) (6)
wherein k and k+1 respectively represent the k and k+1 times, V g Indicating the speed of the unmanned vehicle, χ g Indicating heading angle, x of unmanned vehicle g 、y g Respectively represent the horizontal plane positions of the unmanned vehicles under the ground fixed coordinate systemDelta represents the steering angle of the front wheels of the unmanned aerial vehicle, L represents the wheelbase, R represents the radius of a circle formed by the movement of the unmanned aerial vehicle under the steering angle delta, and w 1～3 Representing the disturbance.
And the discrete expression of the unmanned vehicle nominal model is obtained in the same way:
wherein,state quantity representing nominal model of unmanned vehicle, < ->Representing control quantity of nominal model of unmanned vehicle, f 2 (. Cndot.) represents the corresponding functional relationship of the unmanned vehicle nominal model state equation.
3) Fixed wing unmanned aerial vehicle and unmanned aerial vehicle performance constraint:
in order to ensure that the fixed wing unmanned aerial vehicle can fly safely, the lift force required by the fixed wing unmanned aerial vehicle is required to be ensured, so that the minimum speed lower limit exists in the flying speed, the upper limit of the flying speed is limited by the maximum flying speed designed by the fixed wing unmanned aerial vehicle, and the path angle is limited in a certain range. The flying height of the fixed wing unmanned aerial vehicle must be positive and kept a certain distance to the ground to ensure safety, and the maximum flying height is limited by the maximum control range of the ground station. Meanwhile, the control input of the fixed wing unmanned aerial vehicle is influenced by the design of the fixed wing unmanned aerial vehicle, and corresponding upper and lower bounds exist. Therefore, the state constraint and the control constraint of the fixed wing unmanned aerial vehicle comprise the constraint on the state quantity such as the speed, the height and the like and the control quantity of overload in three directions:
V a,min ≤V a ≤V a,max ,h a,min ≤h a ≤h a,max
γ a,min ≤γ a ≤γ a,max ,n x,min ≤n x ≤n x,max (9)
n y,min ≤n y ≤n y,max ,n h,min ≤n h ≤n h,max
wherein, the state quantity flying speed V of the unmanned plane with the fixed wing is represented a Flying height h a Path angle gamma a Control amount tangential overload n x Normal overload n y Vertical overload n z Constraint of (C) a,min And · a,max And respectively representing a lower bound and an upper bound of the fixed wing unmanned aerial vehicle on the strain quantity.
Similarly, unmanned vehicles are constrained by the conditions in their design and actual intersection interfacing, with constraints:
V g,min ≤V g ≤V g,max ,δ min ≤δ≤δ max (10)
wherein, represents the speed V of the unmanned vehicle g Constraint of control quantity delta · g,min And · g,max Representing the constraint lower bound and upper bound of the corresponding variable of the unmanned vehicle respectively.
4) Event trigger mechanism design based on adaptive threshold:
in order to reduce the computational load, an adaptive event trigger mechanism is designed that can continuously adjust the trigger threshold as the state changes to ensure a better tradeoff between computing resources and system performance.
The design event triggering conditions are as follows:
where δ (ζ) represents the trigger threshold at ζ, x (ζ|t) l ) Representing t under an actual system l The state value of the moment predicted by the moment,representing under nominal systemt l State value of the moment predicted xi moment.
The adaptive law design of the trigger threshold is as follows:
wherein θ satisfies the following condition:
wherein,σ 1 、σ 2 、ρ 1 、ρ 2 representing the adaptive parameter, delta (ζ) representing the trigger threshold at ζ, delta l min And delta l max Are respectively shown in (t l ,t l+1 ]The lower and upper bounds of the trigger threshold within a time of day.
Wherein, alpha is E (0, 1), N l Represents the predicted time domain at time L, eta represents the disturbance upper bound, L s Represent Li Puxi z constant, r f R respectively represent terminal constraint set parameters of a nominal system and an actual system, lambda max (. Cndot.) represents the maximum eigenvalue of the matrix and P represents the weight matrix.
To prevent k=t l +N l The moment is not triggered yet, resulting in t l Control sequence U obtained by time solving l * The subsequent failure to act on the system, therefore, the next trigger time t l+1 The conditions are satisfied:
if it isWhen k=t l +N l When the trigger condition is not reached at the moment, the design is that k=t l +N l And solving the optimization problem again at any time.
5) Adaptive prediction time domain design:
in model predictive control, in order to ensure that the optimization problem is solvable, the prediction time domain should not be too small. Meanwhile, as the prediction error can be gradually amplified along with the increase of the prediction time domain, the prediction information exceeding a certain prediction step length is unreliable, so that the performance of the controller is reduced, and the prediction time domain is not too large. Therefore, in the intersection control process of the fixed wing unmanned aerial vehicle, the control requirement can be completed under the constraint condition, and the prediction error and the calculation time are reduced. Assuming that an initial prediction time domain N that satisfies the optimization problem is solvable 0 Solving the optimization problem on line to obtainDesigning an adaptive prediction time domain updating strategy:
wherein N is l Representing t l Predicted time domain, μ of time instant l Representing t l Time prediction time domain contraction factor, alpha E (0, 1), r f R respectively represents terminal constraint set parameters of a nominal system and an actual system, Q, P, R is a weight matrix, K represents a state feedback matrix, Q * Defined as matrices Q and K T RK sum, (. Cndot.) of T Representing the transpose of the matrix lambda max (. Cndot.) and lambda min (. Cndot.) represents the maximum and minimum eigenvalues of the matrix, respectively.
6) Event-triggered mating co-control:
in the invention, the state information acquired by the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle through the sensor is assumed to be accurate, and the wireless communication process is an ideal condition, namely, the conditions of communication delay, data packet loss and the like are not existed. The problem of meeting and docking can be described as that the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle are cooperatively controlled, so that the fixed wing unmanned aerial vehicle can safely fall onto the unmanned aerial vehicle and complete the task of docking. According to the method, the unmanned aerial vehicle is controlled to run in a certain track, the situation that the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle meet in the horizontal and vertical directions is considered to meet and butt, the meeting point is required to be achieved simultaneously, and the terminal states of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle are required to be kept consistent, so that the self-adaptive prediction track of the fixed wing unmanned aerial vehicle is determined according to the geometric relation between the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle in the movement process, and the fixed wing unmanned aerial vehicle is controlled to fly according to the track.
In the invention, the unmanned aerial vehicle is controlled to move according to the set track, wherein the movement comprises each moment of the unmanned aerial vehicleDesigning a desired heading angle +.>Desired speed->Desired state quantity for unmanned vehicleTerminal state quantity->Thus, the objective cost function of the drone is designed:
wherein,a sequence of states of the unmanned vehicle is represented,control sequence representing an unmanned vehicle, +.>Representing the state quantity of the unmanned vehicle at the moment k+i at the moment k,/>Representing the control input quantity of the unmanned vehicle at the moment k+i under the moment k, and +.>Indicating that the unmanned vehicle is at k+N at time k g (k) State quantity of time->Indicating the predicted expected target state of the unmanned vehicle at time i+k, +.>Indicating the terminal state of the unmanned vehicle, N g (k) Representing the predicted time domain at time k, Q 1 、R 1 、P 1 Representing the corresponding weight matrix.
Control of the drone is achieved by solving the optimization problem of equations (18 a) - (18 g):
wherein s.t. represents constraint symbols, · (i|k) represents values of corresponding variables at i time in the k time optimization problem, equation (18 b) and equation (18 c) represent discrete models of the unmanned vehicle state equation and the observation equation, respectively, equation (18 d) represents initial state conditions for solving the optimization problem, the unmanned vehicle measures the current state through sensors and performs real-time update, and equation (18 e) and equation (18 f) represent state constraint and control constraint of the unmanned vehicle, respectively, wherein U g 、X g The corresponding constraint feasible sets are respectively represented, the specific constraint is shown as a formula (10), the formula (18 g) is a terminal state constraint set of the unmanned aerial vehicle, and in the intersecting butt joint, the terminal constraint set is set so that the relative positions and the relative speeds of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle in all directions are kept in a small deviation range.
Combining the motion trail of the unmanned vehicle, and fixing the flying path angle gamma * Under the condition of (1), according to the relation between the relative track and the absolute track and the state information of the two at the current moment, the self-adaptive prediction track of the fixed wing unmanned aerial vehicle can be obtained:
wherein Deltax represents the relative forward distance between the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle at time t, Θ (t) represents the relative flight path angle at time t, Θ 2 (t) represents the relative heading angle at time t, V a (t) represents the speed of the fixed wing unmanned aerial vehicle at the moment t, V g (t) represents the speed of the unmanned vehicle at time t, h a (t) represents the height, x of the fixed wing unmanned aerial vehicle at time t a (t) represents the horizontal position, χ of the fixed wing unmanned aerial vehicle at the moment t * And the included angle between the central line of the fixed wing unmanned aerial vehicle and the running direction of the unmanned aerial vehicle is represented.
Obtaining self-adaptive track of the fixed-wing unmanned aerial vehicle, wherein the self-adaptive track comprises each moment of the fixed-wing unmanned aerial vehicleDesigning a desired path angle +.>And heading angle->Desired speed->Let the desired state quantity of the fixed wing unmanned aerial vehicle +.> For the terminal state quantity of the fixed-wing unmanned aerial vehicle, designing a target cost function of the fixed-wing unmanned aerial vehicle:
wherein,representing a state sequence of the fixed wing unmanned aerial vehicle, +.>Representing the control sequence of the fixed wing drone,representing the state quantity of the fixed wing unmanned aerial vehicle at the moment k and the moment k+i at the moment k, and the +.>Representing the control input quantity of the fixed wing unmanned aerial vehicle at the moment k and the moment k+i at the moment k, and the +.>Representing k time and k+N of fixed wing unmanned aerial vehicle a (k) State quantity of time->Representing the expected target state of the fixed wing unmanned aerial vehicle at the i+k moment predicted at the k moment,indicating the terminal state of the fixed wing unmanned plane, N a (k) Representing the predicted time domain at time k, Q 2 、R 2 、P 2 Representing the corresponding weight matrix.
Control of the fixed wing drone is achieved by solving the optimization problem of equations (21 a) - (21 g):
wherein s.t. represents constraint symbols, · (i|k) represents values of corresponding variables at i time in a k time optimization problem, formula (21 b) and formula (21 c) represent discrete models of a fixed wing unmanned aerial vehicle state equation and an observation equation, formula (21 d) represents initial state conditions for solving the optimization problem, the fixed wing unmanned aerial vehicle measures a current state through a sensor and performs real-time update, and formula (21 e) and formula (21 f) represent state constraints and control constraints of the fixed wing unmanned aerial vehicle respectively, wherein U is a 、X a The corresponding constraint feasible sets are respectively represented, the specific constraint is shown as a formula (9), the formula (21 g) is a terminal state constraint set of the fixed wing unmanned aerial vehicle, and in the intersecting butt joint, the terminal constraint set is set so that the relative positions and the relative speeds of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle in all directions are kept in a small deviation range.
The invention adopts a model prediction control method based on event triggering, combines a self-adaptive threshold value and a self-adaptive prediction time domain design, applies the algorithm to the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle to solve so as to realize the cooperative control of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle, and comprises the following steps:
when the number of triggers is l=0 or when the time of k (noted as t l ) When the triggering condition formula (11) is met, solving the optimization problems (18 a) - (18 g) or (21 a) - (21 g) to obtain a control sequence of the fixed-wing unmanned aerial vehicle or the unmanned aerial vehicleStatus sequence->And prediction time domain +.>Use of the control sequence->Is->
Calculating the upper and lower bounds of the trigger threshold by equation (14), calculating the predicted time domain contraction factor and the predicted time domain N of the next trigger time by equation (16) a (t l+1 )、N g (t l+1 ) Let the trigger times l=l+1;
under other conditions, the last trigger time (denoted as t l-1 ) Solving the resulting control sequence, i.eCorresponding t in the control sequence l-1 Control amount at +i time ∈ ->
Updating the triggering threshold values of the fixed wing unmanned aerial vehicle and the unmanned aerial vehicle from the current triggering time to the next triggering time in real time through the formula (12) and the formula (13);
when the state of the fixed wing unmanned aerial vehicle or the unmanned aerial vehicle is in the terminal set, the control input of the input u (k) =Kx (k) is applied to ensure that the fixed wing unmanned aerial vehicle or the unmanned aerial vehicle is stable;
the complete optimal control sequence corresponding to each moment is obtained through the event trigger model predictive control based on the self-adaptive threshold and the predictive time domain, and is acted on the fixed wing unmanned aerial vehicle and the executor of the unmanned aerial vehicle, and the meeting and docking tasks are completed through cooperative control.
The invention also relates to a computer readable storage medium, on which a variable time domain event trigger rendezvous and docking cooperation prediction control program is stored, which when executed by a processor implements the above-mentioned variable time domain event trigger rendezvous and docking cooperation prediction control method.
The invention also relates to a computer device in application, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the variable time domain event trigger intersection butt joint collaborative prediction control method when executing the program.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the invention.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. A variable time domain event triggering intersection butt joint collaborative prediction control method is characterized by comprising the following steps of: the method is used for the intersection, docking and coordination of the unmanned aerial vehicle and the unmanned aerial vehicle, and comprises the following steps of:
step 1: respectively establishing discrete models of a state equation and an observation equation of the unmanned aerial vehicle and the unmanned aerial vehicle according to an Euler method;
step 2: establishing state constraint and control constraint for the unmanned aerial vehicle and the unmanned aerial vehicle;
step 3: establishing a self-adaptive event trigger mechanism for automatically adjusting a trigger threshold value along with state change;
step 4: defining a self-adaptive prediction time domain updating strategy;
step 5: and controlling the unmanned aerial vehicle to run in a certain track, determining the self-adaptive predicted track of the unmanned aerial vehicle according to the geometric relationship between the unmanned aerial vehicle and the movement process of the unmanned aerial vehicle, and controlling the unmanned aerial vehicle to fly according to the track so as to realize control.
2. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 1, wherein the method comprises the following steps: in step 2, the state constraint and the control constraint of the unmanned aerial vehicle are as follows
V a,min ≤V a ≤V a,max ,h a,min ≤h a ≤h a,max
γ a,min ≤γ a ≤γ a,max ,n x,min ≤n x ≤n x,max
n y,min ≤n y ≤n y,max ,n h,min ≤n h ≤n h,max
Wherein V is a Is the flying speed of the unmanned aerial vehicle, h a Is the flying height of the unmanned plane, gamma a Is the path angle of the unmanned aerial vehicle, n x 、n y 、n z Tangential overload control quantity, normal overload control quantity and vertical overload control quantity of unmanned aerial vehicle respectively a,min And · a,max And the constraint lower bound and the constraint upper bound of the corresponding state quantity of the unmanned aerial vehicle are respectively adopted.
3. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 1, wherein the method comprises the following steps: in step 2, the state constraint and the control constraint of the unmanned vehicle are as follows
V g,min ≤V g ≤V g,max ,δ min ≤δ≤δ max
Wherein V is g Is the speed of the unmanned vehicle, delta is the control quantity, and (V) g,min And · g,max The constraint lower bound and the constraint upper bound of corresponding variables of the unmanned vehicle are respectively adopted.
4. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 1, wherein the method comprises the following steps: in step 3, the event triggering condition is that
Wherein, xi epsilon (t) l ,t l+1 ]，x(ξ|t l ) For t under the actual system l State of zeta moment predicted by momentThe value of the sum of the values,for t under nominal system l The predicted state value of the moment zeta is P, a weight matrix, delta (zeta) is a trigger threshold of the moment zeta, and x is a state quantity;
defining an adaptive lawObtaining a lower bound and an upper bound of a trigger threshold;
the trigger time of the next moment is metWherein N is l At t l Prediction time domain of time.
5. The method for controlling collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 4, wherein: the theta is satisfied and the number of the components is equal to the theta,
wherein,xi is the maximum allowable error of customization, sigma 1 、σ 2 、ρ 1 、ρ 2 For adaptive parameters, sigma 1 、σ 2 Are all greater than 0, e (·) As an exponential function +.>And->Respectively (t) l ,t l+1 ]Lower and upper bounds of trigger threshold in time satisfy
Wherein, alpha is E (0, 1), N l For the predicted time domain at time L, η is the upper bound of the disturbance, L s Is Li Puxi z constant, r f R are terminal constraint set parameters of a nominal system and an actual system respectively, lambda max (. Cndot.) is the maximum eigenvalue of the matrix, and P is the weight matrix.
6. The method for controlling collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 4, wherein: when k=t l +N l The trigger condition is not reached at the moment, at k=t l +N l And solving the optimization problem again at any time.
7. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 1, wherein the method comprises the following steps: in step 4, the adaptive prediction time domain update strategy is
Wherein N is l At t l Predicted time domain, μ of trigger time l At t l The time-domain contraction factor is predicted from time to time,for the time interval to reach the end domain boundary, α ε (0, 1), in (·) is a logarithmic function with base 10, r f R is terminal constraint set parameter of nominal system and actual system, Q, P, R is weight matrix, K is state feedback matrix, Q * For matrices Q and K T RK sum, (. Cndot.) of T Is transposed of matrix lambda max (. Cndot.) and lambda min (. Cndot.) are the maximum and minimum eigenvalues of the matrix, respectively.
8. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 1, wherein the method comprises the following steps: in step 5, the unmanned vehicle travels along a certain track, and the control includes the horizontal plane position of the unmanned vehicle under the ground fixed coordinate system at each momentDesired course angle +.>Desired speed->Desired state quantity for unmanned vehicleTerminal state quantity->And designing a target cost function of the unmanned aerial vehicle, and controlling and optimizing the unmanned aerial vehicle.
9. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 8, wherein the method comprises the steps of: combining the motion trail of the unmanned vehicle, and fixing the flying path angle gamma * Under the condition of (1), according to the relation between the relative track and the absolute track of the unmanned aerial vehicle and the state information of the unmanned aerial vehicle and the unmanned aerial vehicle at the current moment, updating in real time to obtain the self-adaptive prediction track of the unmanned aerial vehicle, wherein the self-adaptive prediction track comprises the position and the flying height of each moment of the unmanned aerial vehicle in the horizontal plane under the ground fixed coordinate systemDesigning a desired path angle +.>And heading angle->Desired speed->Let the desired state quantity of the fixed wing unmanned aerial vehicle +.>Terminal state quantityAnd designing a target cost function of the unmanned aerial vehicle, and controlling and optimizing the unmanned aerial vehicle.
10. The method for controlling the collaborative prediction of variable time domain event-triggered rendezvous and docking according to claim 9, wherein the method comprises the steps of: when a trigger mechanism is met, solving to obtain a control sequence, a state sequence and a prediction time domain of the unmanned aerial vehicle and/or the unmanned aerial vehicle, and respectively updating the lower bound and the upper bound of a trigger threshold and the prediction time domain of the next trigger moment by adopting a first element of the control sequence; under other conditions, solving the control quantity at the corresponding moment in the obtained control sequence by adopting the last trigger moment;
and calculating a trigger threshold value from the current trigger time to the next trigger time, repeatedly judging whether a trigger mechanism is met, when the state of the unmanned aerial vehicle or the unmanned aerial vehicle enters a terminal set, obtaining corresponding control quantity by combining a state feedback control law, acting a complete control sequence on the executors of the unmanned aerial vehicle and the unmanned aerial vehicle, and finishing the meeting butt joint task through cooperative control.
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