A maglev vehicle control method and system based on distributed model predictive control

Abstract

The present invention provides a maglev vehicle control method and system based on distributed model predictive control, including: establishing a state space equation of a two-point suspension system according to the electromagnet parameters of the maglev vehicle; splitting the state space equation of the two-point suspension system into a discretized state space equation of a single-point suspension model with coupling information based on the planned state trajectory information interaction; calculating the robust positive invariant set of the system state based on the discretized state space equation and the preset interference range; performing centralized optimization and solving the initial optimization problem according to the robust positive invariant set, and calculating the initial planned state trajectory; updating the initial planned state trajectory in real time to obtain the current planned state trajectory; solving the target optimization problem according to the current planned state trajectory to obtain the current optimal solution; updating the preset control amount in real time according to the current optimal solution, and performing suspension control on the maglev vehicle according to the updated control amount. The present invention improves the stability of train operation.

Description

Technical Field

[0001] The present invention relates to the field of magnetic levitation of vehicles, and in particular to a magnetic levitation vehicle control method and system based on distributed model predictive control. Background Art

[0002] The speed domain of high-speed maglev transportation fills the gap between wheel-rail rail transportation and aviation. It is of great significance to shorten intercity travel time and promote regional integrated development. It is a new commanding height and innovation highland for the development of public transportation in the future. The suspension system of the maglev train is a typical open-loop unstable system, which requires active control to maintain stable suspension. The electromagnetic attraction inside the suspension system has a unidirectional characteristic. At the same time, the working environment of the maglev train suspension system is complex and is affected by external interference such as track unevenness. There are constraints such as extremely small suspension air gap and bounded control current. In addition, the suspension of the maglev train is achieved through the joint action of multiple suspension points, which are coupled with each other, causing mutual interference during operation. When the train speed increases to 600km / h and above, external interference and coupling interference will become more severe. It is necessary to design a suspension control method that can adapt to new types of suspension to ensure the stability and reliability of the suspension system. Summary of the invention

[0003] In view of this, the present invention provides a maglev vehicle control method and system based on distributed model predictive control to solve the above problems.

[0004] The present invention provides a maglev vehicle control method based on distributed model predictive control, comprising: establishing a state space equation of a two-point suspension system according to the electromagnet parameters of the maglev vehicle; splitting the state space equation of the two-point suspension system into a discretized state space equation of a single-point suspension model with coupling information based on the interaction of planned state trajectory information; calculating a robust positive invariant set of system states based on the discretized state space equation and a preset interference range; performing centralized optimization and solving an initial optimization problem according to the robust positive invariant set, and calculating an initial planned state trajectory; updating the initial planned state trajectory in real time to obtain a current planned state trajectory; solving a target optimization problem according to the current planned state trajectory to obtain a current optimal solution; updating a preset control amount in real time according to the current optimal solution, and performing suspension control on the maglev vehicle according to the updated control amount.

[0005] In another implementation of the present invention, the state quantity and control quantity of the two-point suspension system are expressed as:

[0006]

[0007] Where x represents the state quantity; x1, x2, ..., x6 represent the first to sixth components of the state quantity respectively; u1, u2 represent the first and second components of the control quantity respectively; x b Indicates the displacement of the suspension frame, in m; Indicates the suspension frame speed, in m / s; i f and i r Respectively represent the coil current of the front and rear electromagnets, in A; δ f and δ r They represent the suspension gaps at the front and rear suspension points, in m; and Respectively represent the suspension gap speed of the front and rear suspension points, in m / s.

[0008] The state space equation of the two-point suspension system is expressed as:

[0009]

[0010] in, , , ..., They represent the derivatives of the 1st to 6th components of the state quantity respectively; g represents the acceleration due to gravity, in N / kg; k e Represents the electromagnetic force coefficient, in units of ;k s is the stiffness coefficient of the suspension spring, in N / m; m e Indicates the mass of the electromagnet in kg; m c and m b Respectively represent the mass of the suspension frame and the vehicle body, in kg; c s Represents the damping coefficient of the suspension spring in units of ;x gf and x gr They represent the deviation between the actual position and the ideal position of the track corresponding to the front and rear suspension points, respectively, in m; and They represent the speed of change of the deviation between the actual position of the track and the ideal position corresponding to the front and rear suspension points, respectively, in m / s; and Respectively represent the change in acceleration of the deviation between the actual position of the track corresponding to the front and rear suspension points and the ideal position, in units of .

[0011] In another implementation of the present invention, the discretized state space equation of the single-point suspension model includes the discretized state space equation of the front suspension point and the discretized state space equation of the rear suspension point; the discretized state space equation of the front suspension point is expressed as:

[0012]

[0013] Among them, x f (t) represents the state variable of the previous suspension point; u f (t) represents the control current of the front suspension point, in A; w1(t) represents the interference of the front suspension point; Respectively represent the planning state of the rear suspension point at time t; A sT represents the state transfer matrix of a single suspended point after discretization; B sT A represents the discretized single floating point input matrix; cT Represents the discretized single suspended point perturbation matrix.

[0014] The discretized state space equation of the post-suspension point is expressed as:

[0015]

[0016] Among them, x r (t) represents the state variable of the rear suspension point; u r (t) represents the control current of the rear suspension point, in A; w2(t) represents the interference of the rear suspension point; Represents the planning state of the previous suspension point at time t.

[0017] ;

[0018] ;

[0019] ;

[0020] ;

[0021] ;

[0022] Among them, w f (t) and w r (t) represents the disturbance of the front and rear suspension points due to the unevenness of the track; T is the sampling time, in seconds;

[0023] A s represents the state transfer matrix, ;

[0024] B s represents the input matrix, ;

[0025] A c represents the perturbation matrix, .

[0026] In another implementation of the present invention, the initial optimization problem is expressed as:

[0027]

[0028] Where N is the time domain length of predictive control; t represents the prediction time step; k represents the current time step; is the nominal state quantity; Represents the prediction of the nominal state quantity from k to k+N-1 time steps at time step k; is the nominal control quantity; It represents the prediction of the nominal control quantity from k to k+N-1 time steps at time step k; represents the prediction of the nominal state quantity at time step t at time step k; represents the prediction of the nominal control quantity at time step t at time step k; A represents the nominal state prediction of k time step to k+N time step; T represents the state transfer matrix of the discretized two-point suspension system, ; B T represents the discretized input matrix of the two-point suspension system, ;Q x , Q u , Q ter Respectively represent the weight coefficients of predicted transition state, predicted control amount and predicted terminal state; x f (k) represents the measurement state of the previous suspension point at time step k; represents the nominal state of the previous suspension point at time step k; x r (k) represents the post-suspension point measurement state at time step k; Represents the nominal state of the post-suspension point at k time steps; , , , , K s is the state feedback matrix, In order to ensure the state constraint range of the safe operation of the single suspension point, To consider the control quantity constraint range of the electromagnetic coil output capacity; To ensure the terminal state constraints for stability, is a robust invariant set.

[0029] In another implementation of the present invention, the initial planning state trajectory is expressed as:

[0030]

[0031] in, and Respectively represent the initial planning state trajectory of the front and rear suspension points, and They represent the optimal solutions of the initial optimization problem at the front and rear suspension points respectively.

[0032] In another implementation of the present invention, the preset control amount is expressed as:

[0033]

[0034] Among them, i f (0) and i r (0) represents the initial control current of the front and rear suspension points, in A; δ f (0) and δ r (0) represents the initial suspension gap between the front and rear suspension points, in m; F eo To balance the electromagnetic force, the unit is N; k e is a constant coefficient.

[0035] In another implementation of the present invention, the control amount includes a front suspension point output control amount and a rear suspension point output control amount.

[0036] The output control amount of the front suspension point is:

[0037]

[0038] in, , is the optimal control solution of the front suspension point; K s is the state feedback matrix; δ f (k) represents the suspension gap of the previous suspension point at time step k, in m.

[0039] The output control amount of the rear suspension point is:

[0040]

[0041] in, , is the optimal control solution of the rear suspension point; δ r (k) represents the suspension gap of the post-suspension point at time step k, in m.

[0042] Another aspect of the present invention provides a maglev vehicle control system based on distributed model predictive control, including: a model building module: establishing a state space equation of a two-point suspension system according to the electromagnet parameters of the maglev vehicle; splitting the state space equation of the two-point suspension system into a discretized state space equation of a single-point suspension model with coupling information based on the interaction of planned state trajectory information; a data processing module: calculating a robust positive invariant set of system states based on the discretized state space equation and a preset interference range; performing centralized optimization and solving an initial optimization problem according to the robust positive invariant set, and calculating an initial planned state trajectory; updating the initial planned state trajectory in real time to obtain a current planned state trajectory; solving a target optimization problem according to the current planned state trajectory to obtain a current optimal solution; a control module: updating a preset control amount in real time according to the current optimal solution, and performing suspension control on the maglev vehicle according to the updated control amount.

[0043] The maglev vehicle control method based on distributed model predictive control of the present invention establishes a single-point suspension system model containing coupling information through the interaction of the front and rear suspension point planning state trajectories, thereby realizing the processing of the coupling disturbance between the suspension points; the initial reference trajectory is solved by centralized optimization, the control signal is designed based on the robust model predictive control method, and the control quantity is updated in real time by online optimization, so as to realize the high robustness and stability of the system and ensure the smooth operation of the system under external disturbance; only a communication connection needs to be established between the two suspension point controllers to transmit a small amount of information, the structure is simple, and less resources are occupied; the stability and reliability of the maglev vehicle suspension system at high operating speed are guaranteed. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art description. By reading the detailed description of the following implementation, the advantages and benefits of the solutions become clear to those skilled in the art. The drawings are only used to illustrate the preferred implementation and are not considered to be limitations of the present invention. In the drawings:

[0045] Figure 1 The present invention is a flowchart of a method for controlling a maglev vehicle based on distributed model predictive control according to an embodiment of the present invention.

[0046] Figure 2 The figure is a schematic diagram of a distributed suspension control structure of a maglev train according to an embodiment of the present invention.

[0047] Figure 3 The diagram is a schematic diagram of set calculation of a robust positive invariant set according to an embodiment of the present invention.

[0048] Figure 4FIG. 1 is a time curve diagram of the front and rear suspension gap error in the simulation of an embodiment of the present invention.

[0049] Figure 5 The figure is a time curve diagram of the front and rear suspension gap error in a test of an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in the field based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0051] Figure 1 A flow chart of a maglev vehicle control method based on distributed model predictive control provided by an embodiment of the present invention is as follows: Figure 1 As shown, this embodiment mainly includes:

[0052] S101. Establish a state space equation of a two-point suspension system according to the electromagnet parameters of the maglev vehicle.

[0053] S102. Based on the interaction of planned state trajectory information, the state space equation of the two-point suspension system is split into a discretized state space equation of a single-point suspension model with coupling information.

[0054] S103. Calculate a robust positive invariant set of system states based on the discretized state space equation and a preset interference range.

[0055] S104. Perform centralized optimization to solve the initial optimization problem according to the robust positive invariant set, and calculate the initial planning state trajectory.

[0056] S105: Update the initial planning state trajectory in real time to obtain the current planning state trajectory.

[0057] S106. Solve the target optimization problem according to the current planning state trajectory to obtain the current optimal solution.

[0058] S107. Update the preset control amount in real time according to the current optimal solution, and perform suspension control on the maglev vehicle according to the updated control amount.

[0059] The maglev vehicle control method based on distributed model predictive control of the present invention establishes a single-point suspension system model containing coupling information through the interaction of the front and rear suspension point planning state trajectories, thereby realizing the processing of the coupling disturbance between the suspension points; the initial reference trajectory is solved by centralized optimization, the control signal is designed based on the robust model predictive control method, and the control quantity is updated in real time by online optimization, so as to realize the high robustness and stability of the system and ensure the smooth operation of the system under external disturbance; only a communication connection needs to be established between the two suspension point controllers to transmit a small amount of information, the structure is simple, and less resources are occupied; the stability and reliability of the maglev vehicle suspension system at high operating speed are guaranteed.

[0060] In another implementation of the present invention, the state quantity and control quantity of the two-point suspension system are expressed as:

[0061]

[0062] Where x represents the state quantity; x1, x2, ..., x6 represent the first to sixth components of the state quantity respectively; u1, u2 represent the first and second components of the control quantity respectively; x b Indicates the displacement of the suspension frame, in m; Indicates the suspension frame speed, in m / s; i f and i r Respectively represent the coil current of the front and rear electromagnets, in A; δ f and δ r They represent the suspension gaps at the front and rear suspension points, in m; and Respectively represent the suspension gap speed of the front and rear suspension points, in m / s.

[0063] Considering only the degrees of freedom of up and down movement, the state space equation of the two-point suspension system is expressed as:

[0064]

[0065] in, , , ..., They represent the derivatives of the 1st to 6th components of the state quantity respectively; g represents the acceleration due to gravity, in N / kg; k e Represents the electromagnetic force coefficient, in units of ;k s is the stiffness coefficient of the suspension spring, in N / m; m e Indicates the mass of the electromagnet in kg; m c and m b Respectively represent the mass of the suspension frame and the vehicle body, in kg; c s Represents the damping coefficient of the suspension spring in units of ;x gf and x gr They represent the deviation between the actual position and the ideal position of the track corresponding to the front and rear suspension points, respectively, in m; and They represent the speed of change of the deviation between the actual position of the track and the ideal position corresponding to the front and rear suspension points, respectively, in m / s; and Respectively represent the change in acceleration of the deviation between the actual position of the track corresponding to the front and rear suspension points and the ideal position, in units of .

[0066] In another implementation of the present invention, the discretized state space equation of the single-point suspension model includes a discretized state space equation of a front suspension point and a discretized state space equation of a rear suspension point.

[0067] The discretized state space equation of the front suspension point is expressed as:

[0068]

[0069] Among them, x f (t) represents the state variable of the previous suspension point; u f (t) represents the control current of the front suspension point, in A; w1(t) represents the interference of the front suspension point; Respectively represent the planning state of the rear suspension point at time t; A sT represents the state transfer matrix of a single suspended point after discretization; B sT A represents the discretized single floating point input matrix; cT Represents the discretized single suspended point perturbation matrix.

[0070] The discretized state space equation of the post-suspension point is expressed as:

[0071]

[0072] Among them, x r (t) represents the state variable of the rear suspension point; u r (t) represents the control current of the rear suspension point, in A; w2(t) represents the interference of the rear suspension point; Represents the planning state of the previous suspension point at time t.

[0073] ;

[0074] ;

[0075] ;

[0076] ;

[0077] ;

[0078] Among them, w f (t) and w r (t) represents the disturbance of the front and rear suspension points due to the unevenness of the track; T is the sampling time, in seconds;

[0079] A s represents the state transfer matrix, ;

[0080] B s represents the input matrix, ;

[0081] A c represents the perturbation matrix, .

[0082] For example, Figure 2 As shown, in order to retain the coupling information, the displacement of the floating frame is approximated as:

[0083]

[0084] Among them, x f Indicates the displacement of the front suspension electromagnet, in m, x r Represents the displacement of the rear electromagnet in m.

[0085] At the same time, in order to avoid linearization, the control quantity is redefined as:

[0086]

[0087] Among them, F ef Represents the electromagnetic force of the front suspension point, in N; F er Represents the electromagnetic force at the rear suspension point, in N; , and redefine the state quantity as:

[0088]

[0089] Among them, δ0 represents the target suspension gap, in m;

[0090] The newly defined state quantity is the error of the suspension system from the equilibrium point, thus defining a two-point suspension system error model:

[0091]

[0092]

[0093]

[0094] Where x gf and x gr They represent the deviation between the actual position and the ideal position of the track corresponding to the front and rear suspension points, respectively, in m.

[0095] The corresponding state space equation of the front suspension point is:

[0096]

[0097] in,

[0098]

[0099] Correspondingly, the state space equation of the post-suspension point is expressed as:

[0100]

[0101] in, .

[0102] The state space equations of the front and rear suspension points are discretized to obtain the following equations. Considering the communication between the front and rear suspension points and sending the planned state trajectory information, the discretized state space equations of the front and rear suspension points are:

[0103]

[0104] in, and They represent the planning states of the front and rear suspension points at time t respectively, , , T is the sampling time, , .

[0105] The corresponding discretized state space equation of the two-point suspension system is:

[0106]

[0107] in, .

[0108] In another implementation of the present invention, a suitable constraint range is set , assuming .

[0109] At the same time, the disturbance w caused by track irregularity can be obtained by measurement f (t) and w r (t) range, so that .

[0110] Then you can confirm the overall interference range , so that ,in , .

[0111] Confirm a state feedback coefficient so that holds.

[0112] Then the robust positive invariant set is approximately calculated .

[0113] examine Is it empty? , then a suitable ;like , then adjust Repeat the above steps. A set of suitable calculation results is as follows Figure 3 shown.

[0114] In another implementation of the present invention, the initial optimization problem is expressed as:

[0115]

[0116]

[0117] Where N is the time domain length of predictive control; t represents the prediction time step; k represents the current time step; is the nominal state quantity; Represents the prediction of the nominal state quantity from k to k+N-1 time steps at time step k; is the nominal control quantity; It represents the prediction of the nominal control quantity from k to k+N-1 time steps at time step k; represents the prediction of the nominal state quantity at time step t at time step k; represents the prediction of the nominal control quantity at time step t at time step k; A represents the nominal state prediction of k time step to k+N time step; T represents the state transfer matrix of the discretized two-point suspension system, ; B T represents the discretized input matrix of the two-point suspension system, ;Q x , Q u , Q ter Respectively represent the weight coefficients of predicted transition state, predicted control amount and predicted terminal state; x f (k) represents the measurement state of the previous suspension point at time step k; represents the nominal state of the previous suspension point at time step k; x r (k) represents the post-suspension point measurement state at time step k; Represents the nominal state of the post-suspension point at k time steps; , , , , K s is the state feedback matrix, In order to ensure the state constraint range of the safe operation of the single suspension point, To consider the control quantity constraint range of the electromagnetic coil output capacity; To ensure the terminal state constraints for stability, is a robust invariant set.

[0118] For example, at the initial moment when the suspension control system is started, the above optimization problem is solved, and the optimal state trajectory is calculated and set as the planned state trajectory, that is, , , sent to the front and rear suspension point controllers, and set the control current to , based on the stability of the suspension control system at the initial moment of activation.

[0119] In another implementation of the present invention, the initial planning state trajectory is expressed as:

[0120]

[0121] in, and Respectively represent the initial planning state trajectory of the front and rear suspension points, and They represent the optimal solutions of the initial optimization problem at the front and rear suspension points respectively.

[0122] In another implementation of the present invention, the preset control amount is expressed as:

[0123]

[0124] Among them, i f (0) and i r (0) represents the initial control current of the front and rear suspension points, in A; δ f (0) and δ r (0) represents the initial suspension gap between the front and rear suspension points, in m; F eo To balance the electromagnetic force, the unit is N; k e is a constant coefficient.

[0125] In another implementation of the present invention, the control amount includes a front suspension point output control amount and a rear suspension point output control amount; the front suspension point output control amount is:

[0126]

[0127] in, , is the optimal control solution of the front suspension point; K s is the state feedback matrix; δ f (k) represents the suspension gap of the previous suspension point at time step k, in m;

[0128] The output control amount of the rear suspension point is:

[0129]

[0130] in, , is the optimal control solution of the rear suspension point; δ r (k) represents the suspension gap of the post-suspension point at time step k, in m.

[0131] For example, for the previous suspension point, the previous suspension point state x is measured at time k. f (k), and receive the updated value of the planned trajectory of the rear suspension point at the same time , the updated stored post-suspension point planning state trajectory is:

[0132]

[0133] Solve the following optimization problem:

[0134]

[0135] in,

[0136]

[0137] in, , K s is the state feedback matrix, In order to ensure the state constraint range of the safe operation of the suspension system, The control quantity constraint range is considered to take into account the output capacity of the electromagnetic coil.

[0138] The cost function J is defined as:

[0139]

[0140] Among them, Q sx With Q su They represent the penalty coefficients for state deviation and control deviation, Q ster Represents the penalty coefficient of the terminal in the prediction time domain, and the terminal constraint Together they serve as a condition to ensure system stability.

[0141] Solving the above optimization problem, the optimal solution at time k is:

[0142]

[0143] Update the self-planning state trajectory as follows:

[0144]

[0145] And will Send to the rear suspension point.

[0146] The output control quantity is:

[0147]

[0148] in, .

[0149] For the rear suspension point, measure the state x of the front suspension point at time k r (k), while receiving the updated value of the planned trajectory of the previous suspension point , the updated stored post-suspension point planning state trajectory is:

[0150]

[0151] Solve the following optimization problem:

[0152]

[0153] in,

[0154]

[0155]

[0156] Solving the above optimization problem, the optimal solution at time k is:

[0157]

[0158] Update the self-planning state trajectory as follows:

[0159]

[0160] And will Send to the front suspension point.

[0161] The output control quantity is:

[0162]

[0163] in, .

[0164] The above front and rear suspension point optimization solution processes are carried out in the front and rear suspension controllers respectively, and synchronization is maintained by sending and receiving planning states; real-time optimization and online adjustment are achieved through distributed control to ensure that the system can still maintain stable operation when subjected to external disturbances.

[0165] The method of the present invention can discretely adjust the range of the constraint set according to the line conditions, can adapt to different operating conditions and environmental changes, ensure that the system can still maintain an efficient and stable operating state under various complex situations, and enhance the adaptability and reliability of the system.

[0166] In another implementation of the present invention, the numerical simulation results of the distributed suspension prediction control method proposed in the present invention under track irregularity excitation are as follows: Figure 4 The test bench test results under track excitation conditions are shown in Figure 5 The results show that under the interference of track unevenness, the method proposed in the present invention can maintain the suspension gap within a smaller range of the target gap.

[0167] Another aspect of the present invention provides a maglev vehicle control system based on distributed model predictive control, comprising:

[0168] Model building module: According to the electromagnet parameters of the maglev vehicle, the state space equation of the two-point suspension system is established; based on the interaction of planned state trajectory information, the state space equation of the two-point suspension system is split into a discretized state space equation of a single-point suspension model with coupling information.

[0169] Data processing module: Based on the discretized state space equation and the preset interference range, the robust positive invariant set of the system state is calculated; the initial optimization problem is centrally optimized and solved according to the robust positive invariant set, and the initial planning state trajectory is calculated; the initial planning state trajectory is updated in real time to obtain the current planning state trajectory; the target optimization problem is solved according to the current planning state trajectory to obtain the current optimal solution.

[0170] Control module: update the preset control amount in real time according to the current optimal solution, and perform suspension control on the maglev vehicle according to the updated control amount.

[0171] The maglev vehicle control system based on distributed model predictive control of the present invention establishes a single-point suspension system model containing coupling information through the interaction of the front and rear suspension point planning state trajectories, thereby realizing the processing of the coupling disturbance between the suspension points; the initial reference trajectory is solved by centralized optimization, the control signal is designed based on the robust model predictive control method, and the control quantity is updated in real time by online optimization, so as to realize the high robustness and stability of the system and ensure the smooth operation of the system under external disturbance; only a communication connection needs to be established between the two suspension point controllers to transmit a small amount of information, the structure is simple, and less resources are occupied; the stability and reliability of the maglev vehicle suspension system at high operating speed are guaranteed.

[0172] In another aspect of the present invention, an electronic device includes: a processor, a memory, a communication bus, and a communication interface.

[0173] in:

[0174] The processor, memory and communication interface communicate with each other through a communication bus.

[0175] Communication interface, used to communicate with other electronic devices or servers.

[0176] The processor is used to execute the program, and specifically can execute the steps of any one of the methods for controlling a maglev vehicle based on distributed model predictive control in the above-mentioned embodiments.

[0177] Specifically, the program may include program codes including computer operation instructions.

[0178] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0179] Memory is used to store programs. The memory may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk storage.

[0180] The program can be specifically used to enable the processor to execute to implement the steps of any one of the methods for controlling a maglev vehicle based on distributed model predictive control described in the embodiments. The specific implementation of each step in the program can refer to the corresponding descriptions in the steps and units executed by any one of the methods for controlling a maglev vehicle based on distributed model predictive control in the above steps, which will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiments.

[0181] The exemplary embodiments of the present application also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute the methods of the various embodiments of the present application.

[0182] The method according to the embodiment of the present invention described above may be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD ROM, RAM, floppy disk, hard disk or magneto-optical disk), or as computer code that is originally stored in a remote recording medium or a non-temporary machine-readable medium downloaded through a network and will be stored in a local recording medium, so that the method described herein can be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code, and when the software or computer code is accessed and executed by a computer, a processor or hardware, the method described herein is implemented. In addition, when a general-purpose computer accesses the code for implementing the method shown herein, the execution of the code converts the general-purpose computer into a dedicated computer for executing the method shown herein.

[0183] Thus far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing may be advantageous.

[0184] It should be noted that all directional indications (such as up, down, left, right, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0185] In the description of the present invention, the terms "first" and "second" are only used to facilitate the description of different components or names, and cannot be understood as indicating or implying a sequential relationship, relative importance, or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0186] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0187] It should be noted that although the specific embodiments of the present invention are described in detail in conjunction with the accompanying drawings, it should not be understood as limiting the scope of protection of the present invention. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still belong to the scope of protection of the present invention.

[0188] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be improper limitations of the embodiments of the present invention.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A maglev vehicle control method based on distributed model predictive control, characterized in that: include: According to the electromagnet parameters of the maglev vehicle, the state space equation of the two-point suspension system is established. The state quantity and control quantity of the two-point suspension system are expressed as: Where x represents the state quantity; x1, x2, ..., x6 represent the first to sixth components of the state quantity respectively; u1, u2 represent the first and second components of the control quantity respectively; x b It represents the displacement of the suspension frame, in m; Indicates the suspension frame speed, in m / s; i f and i r Respectively represent the coil current of the front and rear electromagnets, in A; δ f and δ r They represent the suspension gaps at the front and rear suspension points, in m; and They represent the suspension gap speeds of the front and rear suspension points, respectively, in m / s; The state space equation of the two-point suspension system is expressed as: in, They represent the derivatives of the 1st to 6th components of the state quantity respectively; g represents the acceleration due to gravity, in N / kg; k e Indicates the electromagnetic force coefficient, the unit is kg*m 3 / (s 2 *A 2 );k s is the stiffness coefficient of the suspension spring, in N / m; m e Indicates the mass of the electromagnet in kg; m c and m b Respectively represent the mass of the suspension frame and the vehicle body, in kg; c s Indicates the damping coefficient of the suspension spring, in N*s / m; x gf and x gr They represent the deviation between the actual position and the ideal position of the track corresponding to the front and rear suspension points, respectively, in m; and They represent the speed of change of the deviation between the actual position of the track and the ideal position corresponding to the front and rear suspension points, respectively, in m / s; and Respectively represent the change in acceleration of the deviation between the actual position of the track corresponding to the front and rear suspension points and the ideal position, in m / s 2 ; Based on the interaction of planned state trajectory information, the state space equation of the two-point suspension system is split into a discretized state space equation of a single-point suspension model with coupling information, wherein the discretized state space equation of the single-point suspension model includes a discretized state space equation of a front suspension point and a discretized state space equation of a rear suspension point; The discretized state space equation of the front suspension point is expressed as: Among them, x f (t) represents the state variable of the previous suspension point; u f (t) represents the control current of the front suspension point, in A; w1(t) represents the interference of the front suspension point; A represents the planning state of the rear suspension point at time t; sT represents the state transfer matrix of a single suspended point after discretization; B sT A represents the discretized single floating point input matrix; cT represents the discretized single suspended point perturbation matrix; The discretized state space equation of the post-suspension point is expressed as: Among them, x r (t) represents the state variable of the rear suspension point; u r (t) represents the control current of the rear suspension point, in A; w2(t) represents the interference of the rear suspension point; represents the planning state of the front suspension point at time t; Among them, w f (t) and w r (t) represents the disturbance of the front and rear suspension points due to the unevenness of the track; T is the sampling time, in seconds; A s represents the state transfer matrix, B s represents the input matrix, A c represents the perturbation matrix, Based on the discretized state space equation and the preset interference range, calculating the robust positive invariant set of the system state; Performing centralized optimization to solve the initial optimization problem according to the robust positive invariant set, and calculating the initial planning state trajectory; The initial planning state trajectory is updated in real time to obtain the current planning state trajectory, and the initial planning state trajectory is expressed as: in, and Respectively represent the initial planning state trajectory of the front and rear suspension points, and They represent the optimal solutions of the initial optimization problem at the front and rear suspension points respectively; Solving the target optimization problem according to the current planning state trajectory to obtain the current optimal solution; The preset control amount is updated in real time according to the current optimal solution, and the maglev vehicle is suspended and controlled according to the updated control amount. The preset control amount is expressed as: Among them, i f (0) and i r (0) represents the initial control current of the front and rear suspension points, in A; δ f (0) and δ r (0) represents the initial suspension gap between the front and rear suspension points, in m; F eo To balance the electromagnetic force, the unit is N; k e is the constant coefficient; The control amount includes the front suspension point output control amount and the rear suspension point output control amount; The output control amount of the front suspension point is: in, is the optimal control solution of the front suspension point; K s is the state feedback matrix; δ f (k) represents the suspension gap of the previous suspension point at time step k, in m; The output control amount of the rear suspension point is: in, is the optimal control solution of the rear suspension point; δ r (k) represents the suspension gap of the post-suspension point at time step k, in m.

2. The method according to claim 1, characterized in that The initial optimization problem is expressed as: Where N is the time domain length of predictive control; t represents the prediction time step; k represents the current time step; is the nominal state quantity; Represents the prediction of the nominal state quantity from k to k+N-1 time steps at time step k; is the nominal control quantity; It represents the prediction of the nominal control quantity from k to k+N-1 time steps at time step k; represents the prediction of the nominal state quantity at time step t at time step k; represents the prediction of the nominal control quantity at time step t at time step k; A represents the nominal state prediction of k time step to k+N time step; T represents the state transfer matrix of the discretized two-point suspension system, B T represents the discretized input matrix of the two-point suspension system, Q x , Q u , Q ter Respectively represent the weight coefficients of predicted transition state, predicted control amount and predicted terminal state; x f (k) represents the measurement state of the previous suspension point at time step k; represents the nominal state of the previous suspension point at time step k; x r (k) represents the post-suspension point measurement state at time step k; Represents the nominal state of the post-suspension point at k time steps; K s is the state feedback matrix, In order to ensure the state constraint range of the safe operation of the single suspension point, To consider the control quantity constraint range of the electromagnetic coil output capacity; To ensure the terminal state constraints for stability, is a robust invariant set.

3. A maglev vehicle control system based on distributed model predictive control, characterized in that: include: Model building module: According to the electromagnet parameters of the maglev vehicle, the state space equation of the two-point suspension system is established. The state quantity and control quantity of the two-point suspension system are expressed as: Where x represents the state quantity; x1, x2, ..., x6 represent the first to sixth components of the state quantity respectively; u1, u2 represent the first and second components of the control quantity respectively; x b It represents the displacement of the suspension frame, in m; Indicates the suspension frame speed, in m / s; i f and i r Respectively represent the coil current of the front and rear electromagnets, in A; δ f and δ r They represent the suspension gaps at the front and rear suspension points, in m; and They represent the suspension gap speeds of the front and rear suspension points, respectively, in m / s; The state space equation of the two-point suspension system is expressed as: in, They represent the derivatives of the 1st to 6th components of the state quantity respectively; g represents the acceleration due to gravity, in N / kg; k e Indicates the electromagnetic force coefficient, the unit is kg*m 3 / (s 2 *A 2 );k s is the stiffness coefficient of the suspension spring, in N / m; m e Indicates the mass of the electromagnet in kg; m c and m b Respectively represent the mass of the suspension frame and the vehicle body, in kg; c s Indicates the damping coefficient of the suspension spring, in N*s / m; x gf and x gr They represent the deviation between the actual position and the ideal position of the track corresponding to the front and rear suspension points, respectively, in m; and They represent the speed of change of the deviation between the actual position of the track and the ideal position corresponding to the front and rear suspension points, respectively, in m / s; and Respectively represent the change in acceleration of the deviation between the actual position of the track corresponding to the front and rear suspension points and the ideal position, in m / s 2 ; Based on the interaction of planned state trajectory information, the state space equation of the two-point suspension system is split into a discretized state space equation of a single-point suspension model with coupling information, wherein the discretized state space equation of the single-point suspension model includes a discretized state space equation of a front suspension point and a discretized state space equation of a rear suspension point; The discretized state space equation of the front suspension point is expressed as: Among them, x f (t) represents the state variable of the previous suspension point; u f (t) represents the control current of the front suspension point, in A; w1(t) represents the interference of the front suspension point; A represents the planning state of the rear suspension point at time t; sT represents the state transfer matrix of a single suspended point after discretization; B sT A represents the discretized single floating point input matrix; cT represents the discretized single suspended point perturbation matrix; The discretized state space equation of the post-suspension point is expressed as: Among them, x r (t) represents the state variable of the rear suspension point; u r (t) represents the control current of the rear suspension point, in A; w2(t) represents the interference of the rear suspension point; represents the planning state of the front suspension point at time t; Among them, w f (t) and w r (t) represents the disturbance of the front and rear suspension points due to the unevenness of the track; T is the sampling time, in seconds; A s represents the state transfer matrix, B s represents the input matrix, A c represents the perturbation matrix, Data processing module: Based on the discretized state space equation and the preset interference range, the robust positive invariant set of the system state is calculated; the initial optimization problem is centrally optimized and solved according to the robust positive invariant set, and the initial planning state trajectory is calculated; the initial planning state trajectory is updated in real time to obtain the current planning state trajectory, and the initial planning state trajectory is expressed as: in, and Respectively represent the initial planning state trajectory of the front and rear suspension points, and They represent the optimal solutions of the initial optimization problem at the front and rear suspension points respectively; Solving the target optimization problem according to the current planning state trajectory to obtain the current optimal solution; Control module: update the preset control amount in real time according to the current optimal solution, and perform suspension control on the maglev vehicle according to the updated control amount, wherein the preset control amount is expressed as: Among them, i f (0) and i r (0) represents the initial control current of the front and rear suspension points, in A; δ f (0) and δ r (0) represents the initial suspension gap between the front and rear suspension points, in m; F eo To balance the electromagnetic force, the unit is N; k e is the constant coefficient; The control amount includes the front suspension point output control amount and the rear suspension point output control amount; The output control amount of the front suspension point is: in, is the optimal control solution of the front suspension point; K s is the state feedback matrix; δ f (k) represents the suspension gap of the previous suspension point at time step k, in m; The output control amount of the rear suspension point is: in, is the optimal control solution of the rear suspension point; δ r (k) represents the suspension gap of the post-suspension point at time step k, in m.

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