Soft decoupling tracking control method for laterally indeterminate virtual track train

Abstract

The present invention relates to a soft decoupling tracking control method for a laterally hyperstatically stressed virtual track train, comprising a first tracking control sub-method embedded in a tracking controller and a second tracking control sub-method embedded in a steering coordination controller; the first tracking control sub-method uses a diagonal dominance matrix to design a single-input single-output tracking control algorithm to achieve tracking; the second tracking control sub-method achieves tracking by minimizing the lateral force of the articulated system as a control target. Compared with the prior art, the present invention has the advantages of making the vehicle system force more reasonable, improving the curve passing performance of the vehicle system, having a simple algorithm structure, and having strong robustness.

Description

Technical Field

[0001] The present invention relates to the technical field of virtual track trains, and in particular to a soft decoupling tracking control method for a laterally hyperstatically stressed virtual track train. Background Art

[0002] The virtual rail transit system is a vehicle train system that adopts the urban rail transit operation and management model. It has the advantages of large capacity and high running stability of traditional trams, as well as strong adaptability of buses and BRTs and low road construction costs. Among them, multi-wheel coordinated steering control technology is the core technology to enable each vehicle body module to effectively follow the existing line. However, multi-wheel coordinated steering control technology is subject to the type of vehicle structure. Especially for virtual rail trains with laterally indeterminate force structures, their tracking control methods are particularly critical, which directly affects the vehicle's dynamic performance and curve passing performance.

[0003] At present, there have been some studies on the path following control of virtual rail trains with various structures. For example, Chinese patent CN107963125A discloses a virtual rail train trajectory following control method, system and train using a six-axle three-section structure. Chinese patent CN105235758A discloses a rubber-wheeled low-floor intelligent rail train with a three-module five-axle structure and a power-distributed trajectory following. Chinese patent CN110244731A discloses an active tracking control method for a three-section virtual rail train. Chinese patent CN109501830A discloses a trolleybus using a four-module six-axle structure and its steering control articulated system. The various vehicle structures mentioned in the above patents have a common feature, namely, lateral indeterminate force. This means that the above various vehicle systems will have a common problem: if the steering angles of each suspension wheel cannot be well coordinated, there will be a large internal force inside the system, which is mainly manifested in that the articulated system installed between adjacent car bodies is subjected to a large lateral force, and the tire force is also large, while the prior art does not consider lateral indeterminate force. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a soft decoupling tracking control method for a laterally indeterminate virtual track train, which makes the force of the vehicle system more reasonable, improves the curve passing performance of the vehicle system, has a simple algorithm structure and strong robustness.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A soft decoupling tracking control method for a laterally hyperstatically stressed virtual track train, the soft decoupling tracking control method comprising a first tracking control sub-method embedded in a tracking controller and a second tracking control sub-method embedded in a steering coordination controller;

[0007] The first tracking control sub-method adopts a diagonal advantage matrix to design a single-input single-output tracking control algorithm to achieve tracking;

[0008] The second tracking control sub-method achieves tracking by minimizing the lateral force on the articulated system as a control target.

[0009] Preferably, the first tracking control sub-method is specifically:

[0010] Step 1: Establish the lateral kinematic differential equation of the vehicle system consisting of the tracking suspension and the vehicle body, and convert it into the vehicle system transfer function matrix;

[0011] Step 2: Set up the frequency-divided feed-forward compensator based on the system transfer function matrix and decouple it into a diagonal advantage matrix;

[0012] Step 3: Set up a single-input single-output tracking control algorithm based on the diagonal dominance matrix.

[0013] More preferably, the tracking controller is used for kinematic decoupling control of a vehicle system consisting of a tracking suspension and a vehicle body to achieve positioning and tracking travel of the train.

[0014] More preferably, the tracking suspension is selected according to the diagonal dominance of the vehicle system dynamics transfer function matrix, and the number of the diagonal dominance is equal to the sum of the number of left and right yaw and lateral displacement of the virtual track train n f .

[0015] More preferably, the steering coordination controller is used to coordinate the control of the suspension to minimize the lateral force on the articulated system, thereby making the entire train equivalent to multiple laterally statically stressed vehicle modules, achieving soft decoupling between multiple vehicle modules, and thus optimizing the stress state of the train.

[0016] More preferably, the cooperative suspension is selected according to the diagonal advantage of the vehicle system transfer function matrix, and the number n is v Equal to the number of virtual track train suspensions n and the number of train yaw and lateral freedom n f The total difference, that is, n v =nn f .

[0017] More preferably, the method for acquiring the vehicle system transfer function matrix is: obtaining it through Laplace transformation of the vehicle lateral motion differential equation.

[0018] More preferably, the soft decoupling tracking control method further includes:

[0019] Sort the diagonal dominance of the vehicle system transfer function matrix from small to large, and select the first n f The suspension corresponding to the position of the diagonal dominance is the tracking suspension, and the remaining n v The suspension is a cooperative suspension.

[0020] More preferably, step 1 of the first tracking control sub-method is specifically:

[0021] The lateral kinematic differential equation of the vehicle system consisting of the tracking suspension and the vehicle body is established and converted into a system transfer function matrix using mathematical tools. The lateral kinematic differential equation includes the lateral translation and yaw degrees of freedom of each vehicle body.

[0022] More preferably, the second tracking control sub-method is specifically:

[0023] With the minimization of the lateral force on the articulated system as the control goal, the wheel steering angle of the cooperative suspension is adjusted so that the lateral force required for each body module to achieve an ideal tracking state is provided by the tire force of its own suspension.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] 1. Make the force on the vehicle system more reasonable: The tracking control method in the present invention comprehensively considers the tracking performance and dynamic performance of the vehicle, and minimizes the lateral force on the articulated system by reasonably coordinating the wheel steering angles of the cooperative suspension and the tracking suspension, thereby making the lateral over-determined vehicle system approximately equivalent to a statically determinate system.

[0026] 2. Improve the curve passing performance of the vehicle system: Since the tracking control method in the present invention can effectively reduce the stress on the joints and realize the decoupling control of the vehicle system, the motion interference between adjacent vehicle bodies is greatly reduced when the vehicle passes through curves (especially small radius curves), which will be beneficial for the vehicle to pass through curved paths.

[0027] 3. The algorithm has a simple structure and strong robustness: The algorithm structure of the steering tracking control method in the present invention is relatively simple. Through system decoupling, the multi-input multi-output control system is converted into multiple single-input single-output systems, which reduces the complexity of the algorithm, reduces the interconnection between each single variable control system, and improves the robustness of the control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Schematic diagram of the soft decoupling tracking control method of the present invention;

[0029] Figure 2Schematic diagram of the structure of a three-module six-axle virtual track train in an embodiment of the present invention;

[0030] Figure 3 Schematic diagram of the decoupling control principle in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0032] A soft decoupling tracking control method for a laterally indeterminate virtual track train, such as Figure 1 As shown in the figure, the tracking control process is divided into two subtasks: 1) Tracking controller: kinematic decoupling control of the vehicle system consisting of the tracking suspension and the vehicle body to achieve tracking driving of the train; 2) Steering coordination controller: coordinated control of the coordinated suspension to minimize the lateral force of the articulated system, thereby making the entire train equivalent to multiple laterally statically stressed vehicle modules, achieving soft decoupling between multiple vehicle modules, and thus optimizing the force state of the train.

[0033] Therefore, the soft decoupling tracking control method in this embodiment includes a first tracking control sub-method embedded in the tracking controller and a second tracking control sub-method embedded in the steering coordination controller; the first tracking control sub-method uses a diagonal dominance matrix to design a single-input single-output tracking control algorithm to achieve tracking; the second tracking control sub-method achieves tracking with the minimization of the lateral force of the articulated system as the control target.

[0034] The tracking suspension is selected based on the diagonal dominance of the vehicle system dynamics transfer function matrix, and its number is equal to the sum of the number of yaw and lateral freedom degrees of the virtual track train n f ;

[0035] The cooperative suspension is selected based on the diagonal dominance of the vehicle system transfer function matrix, and its number n v Equal to the number of virtual track train suspensions n and the number of train yaw and lateral freedom n f The difference in the sum, that is, n v =nn f ;

[0036] The transfer function matrix of the vehicle dynamics system is obtained by Laplace transformation from the vehicle lateral dynamics differential equation.

[0037] The diagonal dominance of the transfer function matrix of the vehicle dynamics system is calculated by the diagonal dominance theory. The diagonal dominance of the transfer function matrix of the vehicle dynamics system needs to be sorted from small to large, and the first n f The suspension corresponding to the position of the diagonal dominance is the tracking suspension, and the remaining n v The suspension is a cooperative suspension.

[0038] In this embodiment, the lateral dynamics equation of the vehicle system is:

[0039]

[0040] in:

[0041]

[0042]

[0043] Where, m is the vehicle mass, I z is the yaw inertia of the vehicle body, K is the equivalent cornering stiffness of the tires on each axle, and is the hinge lateral force, K h is the lateral stiffness of the articulated joint. δ1~δ6 are the equivalent rotation angles of the wheels on each axle, u1~u n is the longitudinal velocity of each vehicle body, is the lateral velocity of each vehicle body, β1~β3 are the sideslip angles of the center of mass of each vehicle body is the yaw angular velocity of each vehicle body, l is half of the wheelbase, and L is the longitudinal distance between the articulation center and its adjacent axis.

[0044] The system transfer function matrix converted by Laplace transform is:

[0045]

[0046] in:

[0047]

[0048]

[0049]

[0050] The system output and output vector matching the system transfer function matrix are:

[0051]

[0052] Among them, y1~y6 are the lateral positions of the centers of each suspension.

[0053] Through the diagonal dominance theory, the dominance of each axis ND1~ND6 can be calculated. Figure 2The vehicle structure diagram shown is easy to judge. In this embodiment, n=6, n f =4,n v =2.

[0054] Substituting conventional engineering application parameters into all the above equations, it can be calculated that within the conventional speed and frequency range, ND3, ND5 < ND1, ND2, ND4, ND6 generally holds true. Therefore, suspensions 3 and 5 are selected as cooperative suspensions, and suspensions 1, 2, 4, and 6 are selected as tracking suspensions.

[0055] Subtask 1) Taking the vehicle system consisting of the tracking suspension and the vehicle body as the control subject, the decoupling control in the first tracking control sub-method includes the following steps:

[0056] Step 1: Establish the lateral kinematic differential equation of the vehicle system consisting of the tracking suspension and the vehicle body, and convert it into a state space expression through Jacobi linearization, and then convert it into the system kinematic transfer function matrix. The lateral kinematic differential equation includes the lateral and yaw degrees of freedom of the train;

[0057] In this embodiment, the train lateral kinematics differential equation is:

[0058]

[0059] The vehicle yaw angle constraint is:

[0060]

[0061] The vehicle longitudinal velocity constraint is:

[0062]

[0063] The lateral displacement constraint of the hinge point is:

[0064]

[0065] In the above formula, v i is the motion speed of the i-th suspension center, is the lateral position of the i-th hinge.

[0066] Combining the above four kinematic equations, the system state space expression after Jacobi linearization is:

[0067]

[0068] in,

[0069]

[0070] B k =diag{v1} 4×4 , Ck =I 4×4 , D k =0

[0071] Therefore, the system kinematic transfer function matrix is:

[0072]

[0073] The system state vector and input and output variables matching the above system transfer function matrix are:

[0074]

[0075] Step 2: Design a frequency-band feedforward compensator for the system transfer function matrix described in step 1 to decouple it into a diagonal (dominant) system;

[0076] The feedforward compensator considering high and low frequencies is:

[0077]

[0078] in, yes The column coefficient matrix, M h and M l is an arbitrary real constant diagonal matrix. T j is the time constant.

[0079] The system transfer function matrix after compensation is:

[0080] G(s)=W k (s)K phl (s)

[0081] Step 3: Design a single-input single-output tracking control algorithm for the compensated system transfer function matrix with diagonal advantage described in Step 2. It can be an existing mature control algorithm or a new control algorithm designed by yourself. The input and output of the controller are determined according to the requirements of the control algorithm.

[0082] This embodiment takes the PI control algorithm as an example, and its structure can be expressed as:

[0083]

[0084] Among them, k cP1 ~k cP4 and k cI1 ~k cI4 are the PI controller parameters.

[0085] Subtask 2) takes minimizing the lateral force of the articulated system as the control objective. The core idea is to adjust the wheel steering angle of the cooperative suspension so that the lateral force required for each body module to achieve the ideal tracking state is provided by the tire force of its suspension.

[0086] When the articulated lateral force is 0, the lateral motion equations of vehicles 2 and 3 are:

[0087]

[0088] According to the above equation, the analytical expression of the steering angle of the 3rd and 5th suspension wheels can be calculated as:

[0089]

[0090] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A soft decoupling tracking control method for a laterally indeterminate virtual track train, characterized in that: The soft decoupling tracking control method includes a first tracking control sub-method embedded in the tracking controller and a second tracking control sub-method embedded in the steering coordination controller; The first tracking control sub-method adopts a diagonal advantage matrix to design a single-input single-output tracking control algorithm to achieve tracking; The second tracking control sub-method achieves tracking by minimizing the lateral force of the articulated system as a control target; The first tracking control sub-method is specifically as follows: Step 1: Establish the lateral kinematic differential equation of the vehicle system consisting of the tracking suspension and the vehicle body, and convert it into the vehicle system transfer function matrix; Step 2: Set up the frequency-divided feedforward compensator based on the system transfer function matrix and decouple it into a diagonal advantage matrix; Step 3: Set up a single-input single-output tracking control algorithm based on the diagonal advantage matrix; The tracking controller is used for kinematic decoupling control of the vehicle system consisting of the tracking suspension and the vehicle body to achieve positioning and tracking driving of the train; The steering coordination controller is used to coordinate the coordinated control of the suspension to minimize the lateral force of the articulated system, thereby making the entire train equivalent to multiple lateral statically stressed vehicle modules, achieving soft decoupling between multiple vehicle modules, and thus optimizing the force state of the train; The soft decoupling tracking control method further includes: Sort the diagonal dominance of the vehicle system transfer function matrix from large to small, and select the first n f The suspension corresponding to the position of the diagonal dominance is the tracking suspension, and the remaining n v The suspension is a cooperative suspension; The second tracking control sub-method is specifically as follows: With the minimization of the lateral force on the articulated system as the control goal, the wheel steering angle of the cooperative suspension is adjusted so that the lateral force required for each body module to achieve an ideal tracking state is provided by the tire force of its own suspension.

2. The soft decoupling tracking control method for a laterally indeterminate virtual track train according to claim 1, characterized in that: The tracking suspension is selected according to the diagonal dominance of the vehicle system dynamics transfer function matrix, and its number is equal to the sum of the number of yaw and lateral freedom degrees of the virtual track train n f .

3. The soft decoupling tracking control method for a laterally indeterminate virtual track train according to claim 1, characterized in that: The cooperative suspension is selected based on the diagonal advantage of the vehicle system transfer function matrix, and its number n v Equal to the number of virtual track train suspensions n and the number of train yaw and lateral freedom n f The total difference, that is, n v =nn f .

4. The soft decoupling tracking control method for a laterally indeterminate virtual track train according to claim 1, characterized in that: The method for obtaining the vehicle system transfer function matrix is: obtaining it through Laplace transformation through the vehicle lateral motion differential equation.

5. The soft decoupling tracking control method for a laterally indeterminate virtual track train according to claim 1, characterized in that: Step 1 of the first tracking control sub-method is specifically: The lateral kinematic differential equation of the vehicle system consisting of the tracking suspension and the vehicle body is established and converted into a system transfer function matrix using mathematical tools. The lateral kinematic differential equation includes the lateral translation and yaw degrees of freedom of each vehicle body.

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