Model predictive control calculation method and device for suspension system

Abstract

This application relates to the field of suspension control technology, and particularly to a model predictive control (MMC) calculation method and apparatus for a suspension system. The method includes: selecting at least one set of existing MMC calculation results based on the state observation information and mathematical model of the target suspension system; calculating a simplified constraint set for the MMC of the mathematical model using the at least one set of existing MMC calculation results and the state observation information; constructing a simplified quadratic programming problem based on the simplified constraint set and the mathematical model; solving the simplified quadratic programming problem to obtain the optimal solution of the mathematical model; and determining the actual control quantity of the target suspension system based on the optimal solution of the mathematical model. This application can remove redundant constraints by using existing online trajectory calculation results or offline calculation of partial solutions of MMC, thereby effectively improving the calculation speed of MMC without changing the optimal solution.

Description

Technical Field

[0001] This application relates to the field of suspension control technology, and in particular to a model predictive control calculation method and apparatus for a suspension system. Background Technology

[0002] In related technologies, traditional maglev train levitation control methods linearize the system at the equilibrium point and utilize PID controllers. However, maglev systems are complex nonlinear systems, and traditional control methods can lead to train instability and even serious safety accidents at high speeds. With the rapid development of modern control theory, more advanced control methods are being applied to the levitation control of maglev trains. For example, adaptive control, a nonlinear model control method, has a certain degree of anti-interference capability; or linear quadratic regulators, a linear control method, but this method struggles to handle the nonlinearity of the levitation system.

[0003] In recent years, Model Predictive Control (MPC) has made significant progress and has been successfully applied in fields such as unmanned aerial vehicles (UAVs), rail transit, and power systems. Furthermore, its application in suspension control has also yielded good control results. The mechanism of MPC is as follows: at each sampling time, based on the current state observation information of the controlled object and combined with the mathematical model of the controlled system, an open-loop optimization problem in the finite-time domain is solved online. The first element of the obtained optimal control sequence is then applied to the controlled object, and the above process is repeated at the next sampling time. Because MPC requires solving the optimization problem online at every time step, it consumes considerable computational and storage resources, limiting the application of this control method. Therefore, numerous algorithms have been proposed to further accelerate the solution of MPC. For example, the positivistic set method iteratively searches for the positivistic set of the optimization problem, transforming it into a quadratic programming problem with equality constraints; the interior-point method solves the MPC problem, but this method is difficult to implement with a hot start; and the first-order method uses gradient descent to gradually search for the optimal solution. All of these methods...

[0004] However, the methods in related technologies are all focused on improving the solution speed of model predictive control by utilizing the special structure of MPC, neglecting the role of historical solution data. For example, for trains on a circular track, if previous solution results can be utilized, the computation speed of MPC is expected to be further improved. Solving model predictive control still has difficulties and is hard to apply to embedded systems, which urgently need to be solved. Summary of the Invention

[0005] This application provides a model predictive control calculation method and apparatus for a suspension system, which addresses the problems that related technologies focus on improving the solution speed of model predictive control by utilizing the special structure of MPC, neglecting the role of historical solution data, making it difficult to further improve the calculation speed of MPC, and model predictive control still faces difficulties in solution and is difficult to apply to embedded systems.

[0006] The first aspect of this application provides a model predictive control calculation method for a levitation system, characterized by the following steps: screening at least one set of existing model predictive control calculation results based on the state observation information of the target levitation system and the mathematical model of the target levitation system; calculating a simplified constraint set for the model predictive control of the mathematical model using the at least one set of existing model predictive control calculation results and the state observation information, and constructing a simplified quadratic programming problem based on the simplified constraint set and the mathematical model; solving the simplified quadratic programming problem to obtain the optimized solution result of the mathematical model, and determining the actual control quantity of the target levitation system based on the optimized solution result of the mathematical model.

[0007] Optionally, in one embodiment of this application, the step of filtering at least one set of existing model predictive control calculation results based on the state observation information of the target suspension system and the mathematical model of the target suspension system includes: obtaining online trajectory calculation results and / or offline calculation results of the mathematical model based on the state observation information; and filtering at least one set of existing model predictive control calculation results from the online trajectory calculation results and / or the offline calculation results.

[0008] Optionally, in one embodiment of this application, the step of calculating the simplified constraint set of the model predictive control of the mathematical model using the at least one set of existing model predictive control calculation results and the state observation information includes: when calculating the model predictive control calculation results of the mathematical model online, deleting the target constraints using the offline calculation results to calculate the simplified constraint set of the model predictive control of the mathematical model.

[0009] Optionally, in one embodiment of this application, the at least one set of existing model predictive control calculation results includes the state, active set, and optimal solution corresponding to the at least one set of existing model predictive control calculation results.

[0010] Optionally, in one embodiment of this application, the expression for the simplified constraint set is:

[0011]

[0012] in, It is a simplified set of constraints. It is the state and active set of the predicted control calculation results of the selected model. It is a set obtained by using the selected model to predict and control the calculation results.

[0013] Optionally, in one embodiment of this application, the simplified quadratic programming problem is expressed as:

[0014]

[0015] stGz≤Sx+W

[0016] in, G, S, and W represent the simplified constraint sets, respectively. The submatrices H, F, G, S, and W represent the parameters corresponding to the transformation of the Model Predictive Control Problem into a standard quadratic programming problem, z is the optimization variable input to the Model Predictive Control Problem, and x is the initial state of the Model Predictive Control Problem. T x T These are the transposes of z and x, respectively.

[0017] A second aspect of this application provides a model predictive control (MRC) calculation device for a levitation system, comprising: a selection module, configured to select at least one set of existing MRC calculation results based on state observation information of the target levitation system and a mathematical model of the target levitation system; a calculation module, configured to calculate a simplified constraint set of the model predictive control of the mathematical model using the at least one set of existing MRC calculation results and the state observation information, and to construct a simplified quadratic programming problem based on the simplified constraint set and the mathematical model; and a solution module, configured to solve the simplified quadratic programming problem to obtain an optimized solution result of the mathematical model, and to determine the actual control quantity of the target levitation system based on the optimized solution result of the mathematical model.

[0018] Optionally, in one embodiment of this application, the selection module includes: an acquisition unit, configured to acquire online trajectory calculation results and / or offline calculation results of the mathematical model based on the state observation information; and a selection unit, configured to filter at least one set of existing model prediction control calculation results from the online trajectory calculation results and / or the offline calculation results.

[0019] Optionally, in one embodiment of this application, the calculation module includes: a deletion unit, used to delete target constraints using the offline calculation results when the model predictive control calculation results of the mathematical model are calculated online, so as to calculate a simplified constraint set for the model predictive control of the mathematical model.

[0020] Optionally, in one embodiment of this application, the at least one set of existing model predictive control calculation results includes the state, active set, and optimal solution corresponding to the at least one set of existing model predictive control calculation results.

[0021] Optionally, in one embodiment of this application, the expression for the simplified constraint set is:

[0022]

[0023] in, It is a simplified set of constraints. It is the state and active set of the predicted control calculation results of the selected model. It is a set obtained by using the selected model to predict and control the calculation results.

[0024] Optionally, in one embodiment of this application, the simplified quadratic programming problem is expressed as:

[0025]

[0026] stGz≤Sx+W

[0027] in, G, S, and W represent the simplified constraint sets, respectively. The submatrices H, F, G, S, and W represent the parameters corresponding to the transformation of the Model Predictive Control Problem into a standard quadratic programming problem, z is the optimization variable input to the Model Predictive Control Problem, and x is the initial state of the Model Predictive Control Problem. T x T These are the transposes of z and x, respectively.

[0028] A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the model predictive control calculation method for a suspension system as described in the above embodiments.

[0029] A fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described model predictive control calculation method for a suspension system.

[0030] A fifth aspect of this application provides a computer program product, including a computer program that, when executed, is used to implement the model predictive control calculation method for the suspension system described above.

[0031] This application embodiment can calculate a simplified constraint set based on the existing model predictive control (MPC) calculation results of the target levitation system, construct a simplified quadratic programming problem, and thus obtain the optimal solution result of the mathematical model of the target levitation system, determining the actual control quantity of the target levitation system. Therefore, it achieves adaptive removal of redundant constraints based on the calculation results of the previous time step, or removal of constraints during online calculation using partial solutions of offline MPC calculations. This effectively improves the calculation speed of MPC without changing the optimal solution. Moreover, the storage and retrieval time of this offline solution can be adjusted according to the model of the control system itself, facilitating practical application in embedded systems. This solves the problems of related technologies that focus on improving the solution speed of MPC by utilizing the special structure of MPC, neglecting the role of historical solution data, making it difficult to further improve the calculation speed of MPC, and the difficulty in solving MPC and applying it to embedded systems.

[0032] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0033] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0034] Figure 1 This is a flowchart of a model predictive control calculation method for a suspension system according to an embodiment of this application;

[0035] Figure 2 This is a schematic diagram illustrating the framework of another model predictive control method according to an embodiment of this application;

[0036] Figure 3 This is a schematic diagram illustrating the framework of a model predictive control method for a suspension system based on online data deletion constraints according to an embodiment of this application.

[0037] Figure 4 This is a schematic diagram illustrating the definition of redundancy constraints in one embodiment of this application;

[0038] Figure 5 This is a schematic diagram illustrating the principle of removing redundant constraints according to an embodiment of this application;

[0039] Figure 6 This is a schematic diagram illustrating the effect of adaptive redundant constraint removal according to an embodiment of this application;

[0040] Figure 7 This is a schematic diagram illustrating the impact of offline data in one embodiment of this application;

[0041] Figure 8 This is a schematic diagram of the structure of the model predictive control computing device for a suspension system provided according to an embodiment of this application;

[0042] Figure 9 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0043] Figure label:

[0044] 10-Model predictive control computing device for suspension system: 100-Selection module, 200-Calculation module and 300-Solution module; 901-Memory, 902-Processor and 903-Communication interface. Detailed Implementation

[0045] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0046] The following description, with reference to the accompanying drawings, outlines a model predictive control (MDC) calculation method and apparatus for a suspension system according to embodiments of this application. Addressing the issues raised in the background section regarding methods that focus on improving the solution speed of model predictive control by utilizing the special structure of the MPC (Model Predictive Control Programming) system, neglecting the role of historical solution data, and thus failing to further enhance the calculation speed of the MPC, model predictive control solutions remain difficult to solve and are hard to apply to embedded systems, this application provides a model predictive control calculation method for a suspension system. In this method, a simplified constraint set can be calculated based on existing model predictive control calculation results of the target suspension system, constructing a simplified quadratic programming problem, thereby solving for the optimal solution of the mathematical model of the target suspension system and determining the actual control quantity of the target suspension system. This achieves adaptive removal of redundant constraints based on the calculation results of the previous time step, or removal of constraints during online calculation using partial solutions of the offline model predictive control, effectively improving the calculation speed of model predictive control without changing the optimal solution. Furthermore, the storage and retrieval time of this offline solution can be adjusted according to the model of the control system itself, facilitating practical application in embedded systems. This addresses the problems that related technologies focus on improving the solution speed of model predictive control by utilizing the special structure of MPC, neglecting the role of historical solution data, making it difficult to further improve the computational speed of MPC, and leaving the solution of model predictive control still difficult and hard to apply to embedded systems.

[0047] Specifically, Figure 1 This is a flowchart illustrating a model predictive control calculation method for a suspension system provided in an embodiment of this application.

[0048] like Figure 1 As shown, the model predictive control calculation method for this suspension system includes the following steps:

[0049] In step S101, at least one set of existing model predictive control calculation results is selected based on the state observation information of the target levitation system and the mathematical model of the target levitation system. The at least one set of existing model predictive control calculation results includes the state, active set, and optimal solution corresponding to at least one set of existing model predictive control calculation results.

[0050] It is understandable that the target levitation system here refers to a levitation system that applies model predictive control, such as the various control systems of maglev trains. Maglev trains are a new type of transportation that uses contactless electromagnetic levitation, guidance, and drive systems, and come in both high-speed and medium-low speed versions. High-speed maglev trains can reach speeds exceeding 500 kilometers per hour, making them the world's fastest ground passenger transport. Medium-low speed maglev trains, with speeds between 100 and 120 kilometers per hour, offer advantages such as energy saving, environmental friendliness, low noise, and a small turning radius, making them particularly suitable for operation in steep areas such as tourist attractions and suburbs, demonstrating enormous application potential.

[0051] Although maglev trains have achieved initial engineering applications, they still face several challenges in long-term operation, including issues such as suspension point derailment and track slippage. These problems can lead to train speed reductions, affecting operational efficiency and passenger comfort. Suspension point derailment refers to the failure of some suspension points in the suspension frame, requiring the train to slow down; while it can cause track slippage, where the skids contact the ground, causing the carriages to slide along the track, impacting passenger experience. Track slippage occurs when some suspension points in the suspension frame become unstable, causing the train to adhere to the track, necessitating speed reduction for safety. Therefore, developing control technology for maglev trains is one of the keys to solving current challenges and overcoming engineering bottlenecks. Applying model predictive control methods to suspension control has achieved good control results.

[0052] In some embodiments, in order to improve the solution speed of model predictive control methods, most studies have focused on the structure of MPC, which can easily limit the solution speed of model predictive control. Based on this, this application can accelerate the solution speed of model predictive control by starting from historical solution data of model predictive control, i.e., existing model predictive control calculation results.

[0053] The mechanism of model predictive control is to solve an open-loop optimization problem in the finite time domain online at each sampling time based on the current state observation information of the controlled object and the mathematical model of the controlled system, and apply the first element of the obtained optimal control sequence to the controlled object. The above process is repeated at the next sampling time.

[0054] Therefore, this application can also combine the state observation information and mathematical model of the target levitation system to select at least one set of existing model predictive control calculation results to accelerate the solution speed of model predictive control, thereby improving the accuracy of the solution. The at least one set of existing model predictive control calculation results may include, but is not limited to, the state, active set, and optimal solution of the model predictive control calculation results.

[0055] When selecting at least one set of existing predictive model control calculation results, this application may, but is not limited to, following certain standards or principles. For example, this application may select a set of model predictive control calculation results based on the criterion of minimizing the difference between the current state and the observed state. It should be noted that the specific selection requirements can be selected or adjusted by those skilled in the art according to the actual situation; this is only an illustrative example and no specific requirements are made.

[0056] Here, "state" can be understood as the system state corresponding to the model predictive control calculation result at that time; "positive solution" can be understood as the set of constraints that satisfy equality constraints or inequality constraints on the boundary under a specific solution (equality constraints usually come from the dynamic model of the system, i.e., the state equation, while inequality constraints may come from the physical limitations, safety requirements, or other constraints of the system); "optimal solution" refers to the control strategy that makes the objective function optimal (usually minimized) under the premise of satisfying all constraints (including equality constraints and inequality constraints).

[0057] Optionally, in one embodiment of this application, at least one set of existing model predictive control calculation results is selected based on the state observation information of the target suspension system and the mathematical model of the target suspension system, including: obtaining online trajectory calculation results and / or offline calculation results of the mathematical model based on the state observation information; and selecting at least one set of existing model predictive control calculation results from the online trajectory calculation results and / or offline calculation results.

[0058] As can be understood from the descriptions of other embodiments, this application can select at least one set of existing model predictive control calculation results based on the state observation information and mathematical model of the target levitation system. In this process, this application can obtain the online trajectory calculation results and offline calculation results of the mathematical model corresponding to the target levitation system based on the state observation information, and then select at least one set of existing model predictive control calculation results from the online trajectory calculation results and offline calculation results.

[0059] For example, this application may consider a linear model of the levitation system as: x +=Ax + Bu, where A and B are coefficients, x is the state corresponding to the selected model predictive control calculation result, and u is the control constraint corresponding to the selected model predictive control calculation result. The state constraint set can be set as... The control constraint set can be set as follows: In the time domain of the sampling at the k-th step, the observation state can be represented as x. k .

[0060] Next, embodiments of this application can construct an optimization problem for the model predictive control problem in the Nth step of the prediction time domain, the expression of which may be, but is not limited to, the following:

[0061]

[0062] stx t+1|k =Ax t|k +Bu t|k ,

[0063]

[0064] x 0|k =x k ,

[0065] k = 0, 1, ..., N-1

[0066] in, Given a pre-designed set of terminal constraints, Q and R are the weight matrices of the stage loss in the optimization objective of the model predictive control problem, and P is the weight matrix of the terminal loss of the optimization objective. It should be noted that Q, R, and P can all be manually adjusted and designed by those skilled in the art to balance stabilization and energy saving in the control objective. The embodiments in this application are merely illustrative and do not constitute specific limitations.

[0067] Furthermore, the original optimization problem can be rewritten as a standard quadratic programming problem using the condense MPC construction method, and the expression can be, but is not limited to, the following:

[0068]

[0069] stGz≤Sx+W

[0070] Then, the Lipsitz constant is calculated offline, and the formula can be, but is not limited to, expressed as follows:

[0071]

[0072] Where H and F are the parameters corresponding to the transformation of the model predictive control problem into a standard quadratic programming problem, respectively, and G... j It is the row vector of the j-th row of matrix G.

[0073] Finally, select a set of calculation results from the existing online trajectories or offline calculation results.

[0074] For example, we can assume that the existing online trajectory calculation result is x. i For i∈[1,k-1], the state of the offline calculation result is: From x i i∈[1,k-1] and Find a state, denoted as This state is similar to the current observed state x. k Minimum distance, that is Then, the optimal solution and active set corresponding to the predictive control calculation results of this model are denoted as follows:

[0075] This allows for the selection of model predictive control (MMC) calculation results and the acquisition of the corresponding state, active set, and optimal solution. Furthermore, the embodiments of this application can derive the relationship between two optimal solutions by analyzing the Lipsitz continuity of the quadratic programming, identify redundant constraints based on this relationship, and remove these redundant constraints without changing the optimal solution.

[0076] Step S102: Calculate the simplified constraint set of the mathematical model's model predictive control using at least one set of existing model predictive control calculation results and state observation information, and construct a simplified quadratic programming problem based on the simplified constraint set and the mathematical model.

[0077] The simplified expression for the constraint set can be, but is not limited to, the following:

[0078]

[0079] in, It is a simplified set of constraints. It is the state and active set of the predicted control calculation results of the selected model. It is a set obtained by using the selected model to predict and control the calculation results.

[0080] Furthermore, the simplified expression for the quadratic programming problem can be, but is not limited to, as:

[0081]

[0082] stGz≤Sx+W

[0083] in, G, S, and W represent the simplified constraint sets, respectively. The submatrices H, F, G, S, and W represent the parameters corresponding to the transformation of the Model Predictive Control Problem into a standard quadratic programming problem, z is the optimization variable input to the Model Predictive Control Problem, and x is the initial state of the Model Predictive Control Problem. T x T These are the transposes of z and x, respectively.

[0084] In other embodiments, after obtaining at least one set of existing model predictive control calculation results, this application can, based on these calculation results and the previously obtained state observation information, calculate a simplified constraint set for the model predictive control of the mathematical model of the target levitation system, i.e., a simplified constraint set. Furthermore, based on this simplified constraint set and the mathematical model of the target levitation system, a simplified quadratic programming problem can be constructed.

[0085] For example, the mathematical model of the target levitation system is still a linear model x. + =Ax + Bu; The existing online trajectory calculation result is x i For i∈[1,k-1], the state of the offline calculation result is: Status is The optimal solution and the active set are respectively

[0086] Next, the simplified constraint set is calculated. First, the embodiments of this application can calculate:

[0087]

[0088] in, It is an active collection The complement of G j w j S j Let G, w, and S be the row vectors (row values) of the j-th row of matrices (vectors) G, w, and S, respectively.

[0089] Then, the simplified constraint set is obtained.

[0090]

[0091] Using the simplified constraint set of computation This allows us to construct a simplified quadratic programming problem. This problem can be expressed, but is not limited to, as follows:

[0092]

[0093] stGz≤Sx+W

[0094] in, G, S, and W represent the simplified constraint sets, respectively. The submatrices H, F, G, S, and W represent the parameters corresponding to the transformation of the Model Predictive Control Problem into a standard quadratic programming problem, z is the optimization variable input to the Model Predictive Control Problem, and x is the initial state of the Model Predictive Control Problem. T x T These are the transposes of z and x, respectively.

[0095] Figure 2 This is a schematic diagram illustrating the framework of another model predictive control method according to an embodiment of this application; Figure 3 This is a schematic diagram illustrating the framework of a model predictive control method for a suspension system based on online data deletion constraints, according to one embodiment of this application. Figure 2 and Figure 3 As shown, other model predictive control methods mainly solve the optimization problem directly through explicit MPC, and then input the obtained control quantity into the controlled system to control the related system. This application's embodiment, however, incorporates the utilization of existing model predictive control calculation results to construct a simplified constraint set, thereby obtaining a simplified optimization problem, and then solves for the corresponding control quantity, which is then input into the controlled system to control the related system.

[0096] Optionally, in one embodiment of this application, the simplified constraint set of the model predictive control of the mathematical model is calculated using at least one set of existing model predictive control calculation results and state observation information, including: when the model predictive control calculation results of the mathematical model are calculated online, the target constraints are deleted using the offline calculation results to calculate the simplified constraint set of the model predictive control of the mathematical model.

[0097] Based on the descriptions of other embodiments, it is understood that when selecting at least one set of existing model prediction and control calculation results, this application mainly selects them from existing online trajectory calculation results and offline calculation results, but not limited to that.

[0098] In actual implementation, when using the model predictive control calculation results and state observation information to calculate the simplified constraint set of the mathematical model predictive control of the target suspension system, this application mainly uses the selected online trajectory calculation results or offline calculation results to delete the target constraints, thereby constructing the simplified constraint set.

[0099] Furthermore, considering that the offline computation method of model predictive control in related technologies mainly involves storing explicit MPC, that is, calculating the explicit expression of the model predictive control law offline, and obtaining the optimal solution by looking up the segment table of the expression during online control, the storage capacity and lookup time of this method increase exponentially with the size of the problem.

[0100] Therefore, the embodiments of the present application can calculate some solutions of model predictive control offline, and when calculating the model predictive control calculation results of the mathematical model of the target suspension system online, use these offline solutions to delete the target constraints and calculate a simplified constraint set, thereby improving the calculation speed. Moreover, both the storage and search time of such offline solutions can be adjusted according to the model of the control system itself.

[0101] Figure 4 Schematic diagram of the definition of redundant constraints in an embodiment of the present application; Figure 5 Schematic diagram of the principle of deleting redundant constraints in an embodiment of the present application. As Figure 4 and Figure 5 shown, the embodiments of the present application can use the existing calculation results to delete redundant constraints, thereby accelerating the model predictive control calculation. Among them, the existing calculation results include but are not limited to the calculation results of the online trajectory and the offline calculation results; the redundant constraints here refer to the constraints that will not change the optimal value after being deleted in the considered optimization problem. Thus, the present application can adaptively delete constraints according to the calculation results of the previous moment, and can also use the offline calculation results to delete redundant constraints. Moreover, the redundant constraints deleted by the embodiments of the present application will not change the optimal value of the original optimization problem, but reduce the number of constraints, thereby reducing the complexity of the optimization problem, further reducing the solution time of the solver, and accelerating the online solution process of the model predictive control.

[0102] Figure 6 Schematic diagram of the effect of adaptively deleting redundant constraints in an embodiment of the present application, Figure 7 Schematic diagram of the influence of offline data in an embodiment of the present application. As Figure 6 and Figure 7 shown, Figure 6 shows the comparison results of the ratio of the calculation time of the model predictive control calculation method of the suspension system in the embodiments of the present application and other solution methods, as well as the comparison results of the proportion of the remaining constraints in the model predictive control. It can be seen that the model predictive control calculation method of the suspension system in the embodiments of the present application has a better solution speed; as Figure 7 shown, D1, D2, and D3 are offline data sets corresponding to different scales respectively, where the scale size relationship is D1 < D2 < D3. Combining Figure 6 and Figure 7 it can be seen that for the model predictive control calculation method of the suspension system in the embodiments of the present application, the larger the scale of the offline data set, the larger the number of deleted constraints, and the smaller the total calculation time, indicating that the effect of the process of deleting constraints in the embodiments of the present application will gradually improve as the system runs. That is, by designing the model predictive controller, the system state can converge to a stable point. As the system gradually approaches the stable point, the number of deleted constraints gradually increases, and finally the original optimization problem can be simplified to an unconstrained quadratic programming problem.

[0103] Step S103: Solve the simplified quadratic programming problem to obtain the optimized solution of the mathematical model, and determine the actual control quantity of the target suspension system based on the optimized solution of the mathematical model.

[0104] As one possible approach, after constructing a simplified quadratic programming problem, the embodiments of this application can obtain the optimal solution of the model predictive control of the mathematical model of the target suspension system by solving the quadratic programming problem, and select the first element as the control variable to input into the controlled object.

[0105] Furthermore, after applying the control quantity to the controlled system, i.e. the target suspension system, the embodiments of this application can also observe the state at the next sampling time and repeat the process to achieve real-time solution of model predictive control of the target suspension system.

[0106] The model predictive control (MRC) calculation method for a suspension system proposed in this application can calculate a simplified constraint set based on the existing MRC calculation results of the target suspension system, construct a simplified quadratic programming problem, and thus obtain the optimal solution of the mathematical model of the target suspension system, determining the actual control quantity of the target suspension system. This achieves the adaptive removal of redundant constraints based on the calculation results of the previous time step, or the removal of constraints during online calculation using partial solutions of offline MRC calculations. This effectively improves the calculation speed of MRC without changing the optimal solution. Furthermore, the storage and retrieval time of this offline solution can be adjusted according to the model of the control system itself, facilitating practical application in embedded systems. This solves the problems of related technologies that focus on improving the solution speed of MRC by utilizing the special structure of MPC, neglecting the role of historical solution data, making it difficult to further improve the calculation speed of MPC, and the difficulty in solving MRC and applying it to embedded systems.

[0107] Next, the model predictive control computing device for a suspension system proposed according to an embodiment of this application is described with reference to the accompanying drawings.

[0108] Figure 8 This is a schematic diagram of the structure of the model predictive control computing device for the suspension system according to an embodiment of this application.

[0109] like Figure 8 As shown, the model predictive control computing device 10 of the suspension system includes: a selection module 100, a calculation module 200, and a solution module 300.

[0110] Among them, the selection module 100 is used to select at least one set of existing model predictive control calculation results based on the state observation information of the target suspension system and the mathematical model of the target suspension system.

[0111] The calculation module 200 is used to calculate the simplified constraint set of the model predictive control of the mathematical model using at least one set of existing model predictive control calculation results and state observation information, and to construct a simplified quadratic programming problem based on the simplified constraint set and the mathematical model.

[0112] The solver module 300 is used to solve a simplified quadratic programming problem to obtain the optimized solution of the mathematical model, and to determine the actual control quantity of the target suspension system based on the optimized solution of the mathematical model.

[0113] Optionally, in one embodiment of this application, the selection module 100 includes: an acquisition unit and a selection unit.

[0114] The acquisition unit is used to acquire online trajectory calculation results and / or offline calculation results of the mathematical model based on state observation information.

[0115] The selection unit is used to filter at least one set of existing model predictive control calculation results from online trajectory calculation results and / or offline calculation results.

[0116] Optionally, in one embodiment of this application, the calculation module 200 includes a deletion unit.

[0117] The deletion unit is used to delete the target constraints using the offline calculation results when the model predictive control calculation results of the mathematical model are calculated online, so as to calculate the simplified constraint set of the model predictive control of the mathematical model.

[0118] Optionally, in one embodiment of this application, at least one set of existing model predictive control calculation results includes the state, active set, and optimal solution corresponding to at least one set of existing model predictive control calculation results.

[0119] Optionally, in one embodiment of this application, the expression for the simplified constraint set may be, but is not limited to, as:

[0120]

[0121] in, It is a simplified set of constraints. It is the state and active set of the predicted control calculation results of the selected model. It is a set obtained by using the selected model to predict and control the calculation results.

[0122] Optionally, in one embodiment of this application, the simplified quadratic programming problem can be, but is not limited to, the expression for:

[0123]

[0124] stGz≤St+W

[0125] in, G, S, and W represent the simplified constraint sets, respectively. The submatrices H, F, G, S, and W represent the parameters corresponding to the transformation of the Model Predictive Control Problem into a standard quadratic programming problem, z is the optimization variable input to the Model Predictive Control Problem, and x is the initial state of the Model Predictive Control Problem. T x T These are the transposes of z and x, respectively.

[0126] It should be noted that the foregoing explanation of the model predictive control calculation method embodiment for the suspension system also applies to the model predictive control calculation device for the suspension system in this embodiment, and will not be repeated here.

[0127] The model predictive control (MPC) computing device for a suspension system proposed in this application can calculate a simplified constraint set based on the existing MPC calculation results of the target suspension system, construct a simplified quadratic programming problem, and thus obtain the optimal solution of the mathematical model of the target suspension system, determining the actual control quantity of the target suspension system. This achieves adaptive removal of redundant constraints based on the calculation results of the previous time step, or removal of constraints during online calculation using partial solutions of offline MPC calculations. This effectively improves the calculation speed of MPC without changing the optimal solution. Furthermore, the storage and retrieval time of this offline solution can be adjusted according to the model of the control system itself, facilitating practical application in embedded systems. This solves the problems of related technologies that focus on improving the solution speed of MPC by utilizing the special structure of MPC, neglecting the role of historical solution data, making it difficult to further improve the calculation speed of MPC, and the difficulty in solving MPC and applying it to embedded systems.

[0128] Figure 9 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include:

[0129] The memory 901, the processor 902, and the computer program stored on the memory 901 and capable of running on the processor 902.

[0130] When the processor 902 executes the program, it implements the model predictive control calculation method for the suspension system provided in the above embodiments.

[0131] Furthermore, electronic devices also include:

[0132] Communication interface 903 is used for communication between memory 901 and processor 902.

[0133] The memory 901 is used to store computer programs that can run on the processor 902.

[0134] The memory 901 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0135] If the memory 901, processor 902, and communication interface 903 are implemented independently, then the communication interface 903, memory 901, and processor 902 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 9 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0136] Optionally, in a specific implementation, if the memory 901, processor 902, and communication interface 903 are integrated on a single chip, then the memory 901, processor 902, and communication interface 903 can communicate with each other through an internal interface.

[0137] The processor 902 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0138] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described model predictive control calculation method for a suspension system.

[0139] This application also provides a computer program product, including a computer program that can run computer instructions. When the computer instructions are executed by a processor, they implement the model predictive control calculation method for the suspension system provided in this application.

[0140] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0141] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0142] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0143] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0144] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0145] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0146] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0147] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A model predictive control calculation method for a suspension system, characterized in that, Includes the following steps: Based on the state observation information of the target suspension system and the mathematical model of the target suspension system, at least one set of existing model predictive control calculation results are selected; Using the at least one set of existing model predictive control calculation results and the state observation information, a simplified constraint set for the model predictive control of the mathematical model is calculated, and based on the simplified constraint set and the mathematical model, a simplified quadratic programming problem is constructed. Solve the simplified quadratic programming problem to obtain the optimized solution of the mathematical model, and determine the actual control quantity of the target suspension system based on the optimized solution of the mathematical model; The step of selecting at least one set of existing model predictive control calculation results based on the state observation information of the target suspension system and the mathematical model of the target suspension system includes: obtaining online trajectory calculation results and / or offline calculation results of the mathematical model based on the state observation information; and selecting at least one set of existing model predictive control calculation results from the online trajectory calculation results and / or the offline calculation results.

2. The method according to claim 1, characterized in that, The calculation of the simplified constraint set for the model predictive control of the mathematical model using the at least one set of existing model predictive control calculation results and the state observation information includes: When calculating the model predictive control calculation results of the mathematical model online, the target constraints are removed using the offline calculation results to calculate the simplified constraint set of the model predictive control of the mathematical model.

3. The method according to claim 1, characterized in that, The at least one set of existing model predictive control calculation results includes the state, active set, and optimal solution corresponding to the at least one set of existing model predictive control calculation results.

4. The method according to claim 1, characterized in that, The expression for the simplified constraint set is: ， in, It is a simplified set of constraints. 、 It is the state and active set of the predicted control calculation results of the selected model. It is a set obtained by using the selected model to predict and control the calculation results.

5. The method according to claim 1, characterized in that, The simplified quadratic programming problem is expressed as follows: in, , , They are , , Corresponding to the simplified constraint set submatrix, , , , , These are the parameters corresponding to the transformation of the model predictive control problem into a standard quadratic programming problem. These are the optimization variables input to the model predictive control problem. This is the initial state of the model predictive control problem. , They are and The transpose of .

6. A model predictive control computing device for a suspension system, characterized in that, include: The selection module is used to filter at least one set of existing model predictive control calculation results based on the state observation information of the target suspension system and the mathematical model of the target suspension system. The calculation module is used to calculate the simplified constraint set of the model predictive control of the mathematical model using the at least one set of existing model predictive control calculation results and the state observation information, and to construct a simplified quadratic programming problem based on the simplified constraint set and the mathematical model. The solution module is used to solve the simplified quadratic programming problem to obtain the optimized solution result of the mathematical model, and to determine the actual control quantity of the target suspension system based on the optimized solution result of the mathematical model. The selection module includes: an acquisition unit, used to acquire online trajectory calculation results and / or offline calculation results of the mathematical model based on the state observation information; and a selection unit, used to filter at least one set of existing model prediction and control calculation results from the online trajectory calculation results and / or the offline calculation results.

7. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the model predictive control calculation method for a suspension system as described in any one of claims 1-5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the model predictive control calculation method for the suspension system as described in any one of claims 1-5.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed, it is used to implement the model predictive control calculation method for the suspension system as described in any one of claims 1-5.

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