CN115562261B - Workflow-based prediction control method for vehicle kinematic trajectory tracking model - Google Patents
Workflow-based prediction control method for vehicle kinematic trajectory tracking model Download PDFInfo
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Abstract
The invention discloses a workflow-based vehicle kinematic trajectory tracking model predictive control method, which establishes an unmanned vehicle trajectory tracking optimization problem model aiming at unmanned vehicle kinematic characteristics, solves the optimization problem by adopting an alternate direction multiplier method, and completes a solving process by utilizing a cloud computing distributed processing structure, thereby accelerating the computing speed of a model predictive control algorithm, ensuring the instantaneity of the control algorithm, realizing the dispersion of finer granularity of the model, effectively reducing the dispersion period and further improving the control quality.
Description
Technical Field
The invention belongs to the technical field of automatic control, and particularly relates to a vehicle kinematic trajectory tracking model predictive control method based on workflow.
Background
In recent years, cloud computing is considered as one of the best computing paradigms in the field of information technology, and has a powerful computing power. As the complexity and scale of control systems increase, cloud computing is applied in control systems to address complex control problems, and thus the concept of cloud control is also proposed. In order to fully utilize cloud resources, the workflow-based cloud control method utilizes a distributed processing structure of cloud computing, and the computing speed of a control algorithm is improved.
Model predictive control is a control algorithm for solving an optimization problem, and because multi-step prediction is required, the calculation amount of the algorithm is usually large, and the real-time requirement is usually difficult to meet. Even with gradient descent or alternate direction multiplication, computation time is limited by the fixed computing resources of the local device. Although the existing cloud control method is widely used for solving the control of systems such as robots and unmanned aerial vehicles, particularly for controlling vehicles, the application mode is generally to directly deploy control tasks on a cloud platform, and resources in a cloud environment are not fully utilized, so that the existing control method is low in calculation speed and poor in instantaneity.
Disclosure of Invention
In view of the above, the invention provides a workflow-based prediction control method for a vehicle kinematic trajectory tracking model, which realizes model transmission compression with higher training convergence speed and model precision.
The invention provides a workflow-based vehicle kinematic trajectory tracking model prediction control method, which comprises the following steps:
Step1, establishing a workflow-based unmanned vehicle track tracking optimization problem model by using a model predictive control method, wherein the model is shown in the following formula;
Constraints on the status and inputs of the unmanned vehicle are as follows:
Wherein V N is an objective function of the optimization problem, ζ k is a state quantity ζ of the unmanned vehicle at the k moment, and the state quantity ζ is expressed as Xi is N x,(Xr,Yr) is the coordinates of the center of the rear axle of the unmanned vehicle,/>For yaw angle, u k-1 is the control quantity u of the unmanned vehicle at time k-1, the expression of the control quantity u is u= [ v r,ω]T, the dimension of u is N u,vr, the speed of the center of the rear axle of the vehicle, ω is the angular speed of the vehicle, and/>Predicting the state at time k+N for time k,/>Control input for predicting time k+j for time k,/>Q is a weight matrix of state quantity, R is a weight matrix of control input, N is a prediction time domain, A k is a system matrix, and B k is a control matrix; and xi, the method comprises the steps of (a) mixing,u,/>Boundaries of state constraints and control quantity constraints, respectively;
and solving the optimization problem by adopting an alternate direction multiplier method to obtain an updated expression of the parameters y, z and mu, wherein the updated expression is shown in the following formula:
y(i+1)＝E11(ρz(i)-μ(i))+E12Fξ
μ(i+1):＝μ(i)+ρ(E11(ρz(i+1)-μ(i))+E12Fξ-z(i+1))
Wherein i is the number of iterations, y (i+1) is the value of parameter y for the (i+1) th iteration, z (i+1) is the value of parameter z for the (i+1) th iteration, μ (i+1) is the value of parameter μ for the (i+1) th iteration, and E matrix is defined as
Wherein the method comprises the steps ofE 11＝H+ρIq,E12＝GT,projZ () is the projection of parameter Z onto constraint set Z,/>As constraint set, ρ is penalty parameter;
Step 2, a first task node in the workflow calculates a constraint boundary of a matrix E, G and a parameter z by adopting the unmanned vehicle track tracking optimization problem model according to the output data of the unmanned vehicle at the kth moment, and sets a weight parameter matrix H; then, the constraint boundaries of the matrix E, G, H and the parameter z are respectively sent to a second task node, a third task node and a fourth task node in the workflow, and output data is sent to a fifth task node; the output data comprises a vehicle rear axle center coordinate and a vehicle yaw angle;
Step 3, the second task node completes the update calculation of the parameter y by adopting a formula (3) according to a matrix E, G, H, the third task node completes the update calculation of the parameter mu by adopting a formula (5) according to a matrix E, G, H, and the fourth task node completes the update calculation of the parameter z by adopting a formula (4) according to a constraint boundary of the parameter z;
Step 4, after the second task node, the third task node and the fourth task node finish updating calculation of the parameters, respectively sending the updated parameters to the other two nodes, and enabling the iteration times to be added by 1;
Step 5, executing the step 3 when the iteration times are not greater than a threshold value; otherwise, the fourth task node sends the parameter z to the fifth task node, and the step 6 is executed;
And 6, the fifth task node extracts the control quantity u from the parameter z, sends the control quantity u to the unmanned vehicle, and enables the k to be added with 1 after the unmanned vehicle runs to the k+1st moment until the unmanned vehicle stops running.
Further, the updating calculation of the parameter y and the parameter μ in the step 3 is completed by using the same task node.
Further, the update calculation of the parameter μ is implemented by the following formula: mu (i+1):＝μ(i)+ρ(E11(ρzi-μ(i))+E12Fξ-zi).
The beneficial effects are that:
The invention establishes an unmanned vehicle track tracking optimization problem model aiming at unmanned vehicle kinematic characteristics, solves the optimization problem by adopting an alternate direction multiplier method, and completes the solving process by utilizing a distributed processing structure of cloud computing, thereby accelerating the computing speed of a model predictive control algorithm, ensuring the instantaneity of the control algorithm, realizing the dispersion of finer granularity of the model, effectively reducing the dispersion period and further improving the control quality.
Drawings
Fig. 1 is a control schematic diagram of a vehicle kinematic trajectory tracking model prediction control method based on a workflow.
Detailed Description
The present invention will be described in detail with reference to the following examples.
The invention provides a workflow-based vehicle kinematic trajectory tracking model prediction control method, which specifically comprises the following steps:
step 1, establishing an unmanned vehicle kinematic model, obtaining the unmanned vehicle model by linearizing and discretizing the unmanned vehicle kinematic model, establishing an unmanned vehicle track tracking optimization problem model based on the unmanned vehicle model by using a model predictive control method, and solving the optimization problem by using an alternate direction multiplier method to obtain an updated expression of parameters y, z and mu.
Establishing a kinematic model of the unmanned vehicle, as shown in a formula (1):
Wherein (X r,Yr) is the position coordinate of the center of the rear axle of the unmanned vehicle, v r is the speed of the rear axle of the unmanned vehicle, l is the distance between the front axle and the rear axle of the unmanned vehicle, The yaw angle of the unmanned vehicle, δ f, is the front wheel deflection angle of the unmanned vehicle.
Further, linearizing and discretizing the unmanned vehicle kinematic model to obtain an unmanned vehicle model, as shown in formula (2):
Wherein v ref is a speed reference value of the rear axle center of the unmanned vehicle; t is the sampling time; Yaw angle of the unmanned vehicle at a reference time; /(I) The state quantity xi is the state quantity error between the state quantity xi and the reference value xi ref, and the state quantity xi is formed by the coordinate (X r,Yr) of the center of the rear axle of the unmanned vehicle and the yaw angle/>Composition, noted as/>The state quantity reference value xi ref is formed by a coordinate reference value (X ref,Yref) of the rear axle center of the unmanned vehicle and a yaw angle reference value/>The composition is recorded asThe error between the control amount u (k) at the time of k and the reference control amount u ref (k) is formed by the speed v r of the center of the rear axle of the vehicle and the angular speed ω of the vehicle, and is denoted as u= [ v r,ω]T ], the reference control amount u ref (k) is artificially given, a k is a system matrix of the vehicle kinematic model, and B k is a control matrix of the vehicle kinematic model.
On the basis, an unmanned vehicle track tracking optimization problem model is established by using a model prediction control method, and the model is shown as a formula (3):
Wherein N is the prediction time domain,
Constraints on vehicle state and inputs are expressed as:
Wherein, xi, u,/>The boundaries of the state constraint and the control quantity constraint are respectively represented, the dimension of the state quantity xi is N x, and the dimension of the control quantity u is N u.
Further, the unmanned vehicle track tracking optimization problem model is rewritten into a standard quadratic form, as shown in formula (4):
Wherein,
The constraint set is expressed as:
Defining a matrix:
The updated expression of each variable in solving the optimization problem by using the alternate direction multiplier method is given below:
y(i+1)＝E11(ρz(i)-μ(i))+E12Fξ
μ(i+1):＝μ(i)+ρ(E11(ρz(i+1)-μ(i))+E12Fξ-z(i+1))
Where proj Z represents the projection onto constraint set Z.
Step 2, determining a first task node in the workflow, wherein the first task node is used for acquiring output data of the unmanned vehicle at a kth moment, calculating a constraint boundary of a matrix E, G and a parameter z by adopting an unmanned vehicle track tracking optimization problem model according to the output data, setting a weight parameter matrix H, respectively transmitting the constraint boundary of the matrix E, G, H and the constraint boundary of the parameter z to a second task node, a third task node and a fourth task node in the workflow, and transmitting the output data to a fifth task node. Wherein the output data includes a vehicle rear axle center coordinate and a yaw angle of the vehicle.
Step3, the second task node adopts an iterative calculation formula for solving the optimization problem by adopting an alternate direction multiplier method according to the received matrix E, G, H:
y(i+1)＝E11(ρz(i)-μ(i))+E12Fξ
Completing updating calculation of the parameter y;
And the third task node adopts an iterative calculation formula in the unmanned vehicle track tracking optimization problem model according to the received matrix E, G, H:
μ(i+1):＝μ(i)+ρ(E11(ρz(i+1)-μ(i))+E12Fξ-z(i+1))
Completing updating calculation of the parameter mu;
Meanwhile, the fourth task node adopts an iterative calculation formula in the unmanned vehicle track tracking optimization problem model according to the constraint boundary of the received parameter z:
Completing updating calculation of the parameter z;
After the second task node, the third task node and the fourth task node finish updating calculation of the parameters, respectively sending the updated parameters to the other two nodes, and enabling the iteration times to be added by 1.
At the end of each round of optimization update, there is information interaction between the three nodes, which will result in an increase in the total time the algorithm calculates. Therefore, it is considered to combine container nodes whose calculation tasks are relatively simple, and further reduce the number of interactions and communication time between the nodes. Because the iterative updating calculation process of the constraint condition parameter z is relatively complex, and the updating calculation of the parameters y and mu is relatively simple, the invention completes the updating calculation of the parameters y and mu by adopting the same task node.
In addition, in order to further improve the efficiency of parallel computing, the invention can change z i+1 in the parameter μ update formula to z i, namely update the computing formula as follows: mu (i+1):＝μ(i)+ρ(E11(ρzi-μ(i))+E12Fξ-zi).
Step 4, executing the step 3 when the iteration times are not greater than a threshold value; otherwise, the fourth task node sends the parameter z to the fifth task node, and step 5 is executed.
And 5, the fifth task node extracts the control quantity u from the parameter z according to the output data and the parameter z, and sends the control quantity u to the unmanned vehicle to realize the control of the unmanned vehicle, and the unmanned vehicle runs to the k+1 moment, so that the k self-adding 1 executes the step 2 to complete the closed-loop control of the unmanned vehicle until the unmanned vehicle stops running.
Because the parameter z includes the state quantity and the control input quantity of the unmanned vehicle, the fourth task node needs to process and transform the data structure of the received parameter z to extract the control quantity u required for controlling the unmanned vehicle, i.e. the first item in the control sequence, and send the control quantity u to the controlled object, i.e. the unmanned vehicle.
Examples:
The embodiment is a control method for predicting and controlling a vehicle kinematic trajectory tracking model based on a workflow, which is implemented by a cloud control platform, wherein the control process is shown in fig. 1, and the specific implementation steps are as follows:
s1, at a sampling time k, the unmanned vehicle of the controlled object outputs data, including information such as a vehicle rear axle coordinate, a front wheel deflection angle and the like, to a task node I in a workflow on a container network of a cloud control platform.
S2, a task node I is used as a routing node for preparing initial data, receives the central coordinate and steering angle information of a rear axle of the vehicle sent by a controlled object, and calculates data required by a subsequent container node, wherein the data comprises a matrix E, G, H and a constraint boundary of a parameter z; and then the calculated matrix is sent to a task node II, the constraint boundary of the parameter z is sent to a task node III, and the data obtained from the controlled object is sent to a task node IV.
S3, the task node II receives matrix data sent by the task node I, and performs update calculation of parameters y and mu according to an iteration formula; after the update calculation is completed once, information interaction is carried out with the task node III, namely, updated values of the parameters y and mu are sent to the task node III, and meanwhile, the parameter z sent by the node III is received. Repeating the above process, and calculating parameters y and mu and interacting with the node three in the preset cycle times.
S4, the task node III receives the constraint boundary of the parameter z sent by the task node I, and performs updating calculation of the parameter z according to an iteration formula; after the update calculation is completed once, information interaction is carried out with the task node II. In the interaction process, firstly, parameters y and mu sent by a task two node are received, and then the updated parameter z is sent to the node two. Repeating the above process, and calculating the parameter z and interacting with the second node in the preset cycle times. And after all the loops are completed, the result z of the last optimization calculation is sent to a task node four.
S5, the fourth task node receives data sent by the third task node, and the fourth task node processes the received parameter z and converts a data structure to obtain a control quantity u required by a controlled object, namely a first item in a control sequence, and sends the control quantity u to the controlled object, namely the unmanned vehicle, because the parameter z comprises the state quantity and the control input quantity of the vehicle.
And S6, the unmanned vehicle obtains a control quantity u sent by a task node IV in the cloud platform container network, moves to the moment k+1 according to the value of the control quantity, and repeats the step S1 to complete closed-loop control.
In summary, the above embodiments are only preferred embodiments of the present invention, and are not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (3)
1. The vehicle kinematic trajectory tracking model prediction control method based on the workflow is characterized by comprising the following steps of:
Step 1, establishing a workflow-based unmanned vehicle track tracking optimization problem model by using a model prediction control method, solving the optimization problem by adopting an alternate direction multiplier method as shown in a formula (1), and obtaining updated expressions of parameters y, z and mu as shown in formulas (3), (4) and (5);
Constraints on the state and inputs of the unmanned vehicle are as shown in equation (2):
Wherein V N is an objective function of the optimization problem, ζ k is a state quantity ζ of the unmanned vehicle at the k moment, and the state quantity ζ is expressed as Xi is N x,(Xr,Yr) is the coordinates of the center of the rear axle of the unmanned vehicle,/>For yaw angle, u k-1 is the control quantity u of the unmanned vehicle at time k-1, the expression of the control quantity u is u= [ v r,ω]T, the dimension of u is N u,vr, the speed of the center of the rear axle of the vehicle, ω is the angular speed of the vehicle, and/>Predicting the state at time k+N for time k,/>Control input for predicting time k+j for time k,/>Q is a weight matrix of state quantity, R is a weight matrix of control input, N is a prediction time domain, A k is a system matrix, and B k is a control matrix; /(I)Boundaries of state constraints and control quantity constraints, respectively;
y(i+1)＝E11(ρz(i)-μ(i))+E12Fξ (3)
μ(i+1):＝μ(i)+ρ(E11(ρz(i+1)-μ(i))+E12Fξ-z(i+1)) (5)
Wherein i is the number of iterations, y (i+1) is the value of parameter y for the (i+1) th iteration, z (i+1) is the value of parameter z for the (i+1) th iteration, μ (i+1) is the value of parameter μ for the (i+1) th iteration, and E matrix is defined as Wherein/> E 11＝H+ρIq,E12＝GT,projZ () is the projection of parameter Z onto constraint set Z,/>As constraint set, ρ is penalty parameter;
Step 2, a first task node in the workflow calculates a constraint boundary of a matrix E, G and a parameter z by adopting the unmanned vehicle track tracking optimization problem model according to the output data of the unmanned vehicle at the kth moment, and sets a weight parameter matrix H; then, the constraint boundaries of the matrix E, G, H and the parameter z are respectively sent to a second task node, a third task node and a fourth task node in the workflow, and output data is sent to a fifth task node; the output data comprises a vehicle rear axle center coordinate and a vehicle yaw angle;
Step 3, the second task node completes the update calculation of the parameter y by adopting a formula (3) according to a matrix E, G, H, the third task node completes the update calculation of the parameter mu by adopting a formula (5) according to a matrix E, G, H, and the fourth task node completes the update calculation of the parameter z by adopting a formula (4) according to a constraint boundary of the parameter z;
Step 4, after the second task node, the third task node and the fourth task node finish updating calculation of the parameters, respectively sending the updated parameters to the other two nodes, and enabling the iteration times to be added by 1;
Step 5, executing the step 3 when the iteration times are not greater than a threshold value; otherwise, the fourth task node sends the parameter z to the fifth task node, and the step 6 is executed;
And 6, the fifth task node extracts the control quantity u from the parameter z, sends the control quantity u to the unmanned vehicle, and enables the k to be added with 1 after the unmanned vehicle runs to the k+1st moment until the unmanned vehicle stops running.
2. The prediction control method of a vehicle kinematic trajectory tracking model according to claim 1, wherein the updating calculation of the parameter y and the parameter μ in the step 3 is completed by using the same task node.
3. The prediction control method of a vehicle kinematic trajectory tracking model according to claim 1, characterized in that the update calculation of the parameter μ is implemented by adopting the following formula: mu (i+1):＝μ(i)+ρ(E11(ρzi-μ(i))+E12Fξ-zi).
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