Multi-system multimodal switching control methods, systems, devices, and media based on the Stateflow model

Abstract

This invention belongs to the field of ground test control technology for aero-engines, and provides a multi-system, multi-modal switching control method, system, equipment, and medium based on the Stateflow model. The method constructs a structured parameter library indexed by scenario ID and stage ID; configures multiple functional state machines, including engine, pressure setpoint, and valve opening, within the Stateflow model; establishes a mapping relationship between scenarios and stages to standardized action templates; employs a time-condition hybrid transfer mechanism for safe scheduling; designs a constraint-based smooth switching execution layer, continuously merging control commands and applying dynamic hard constraints during switching; and invokes the corresponding robust control strategy according to the current control mode. This invention deeply integrates the test process, state scheduling, safe switching, and modal control, achieving disturbance-free, highly reliable, and automated collaborative operation throughout the entire process, significantly improving system control accuracy, stability, and engineering applicability.

Description

Technical Field

[0001] This invention belongs to the field of ground test control technology for aero-engines, and relates to a multi-system multi-modal switching control method, system, equipment and medium based on the Stateflow model. It is applicable to the automated coordinated control of strongly coupled subsystems such as intake, exhaust and supply in flight environment simulation systems. Background Technology

[0002] The flight environment simulation control system is a core component of aero-engine ground test facilities. Its dynamic response performance, control accuracy, and mode switching reliability directly affect the operational safety of the tested engine and the validity of the test data. In current engineering practice, facing complex test tasks involving multiple operating conditions and stages, the system typically relies on manual operation to complete control mode switching, valve action coordination, and interaction management between subsystems. Operators must manually adjust various actuators according to operating procedures or experience and continuously monitor the system status to ensure process safety. This approach is not only inefficient and labor-intensive, but also prone to misoperation due to human negligence or response delays, posing significant safety hazards and making it difficult to support the highly complex and highly automated testing requirements of modern aero-engines.

[0003] Therefore, there is an urgent need for an intelligent collaborative control scheme that can structure the experimental process, model the control logic, and automate the switching process, so as to achieve safe, smooth, and disturbance-free switching of multiple systems under different operating modes. Summary of the Invention

[0004] To address the technical problems of existing technologies, such as reliance on manual intervention, susceptibility to disturbances during switching processes, and poor coordination among multiple systems, this invention discloses a multi-system, multi-modal switching control method and system based on the Stateflow model. This method is applied to an aero-engine flight environment simulation control system, which includes an intake subsystem, an exhaust subsystem, and an air supply subsystem. The method constructs a full-link collaborative control framework integrating parameter library-driven operation, state machine mapping, hybrid transfer scheduling, constraint-based smooth switching execution, and modal adaptive robust control. Through digital orchestration of the test process, model-defined control logic, and intelligent scheduling of switching timing, and by applying dual constraints of continuity and safety during the switching process, it achieves highly reliable, disturbance-free, and automated collaborative operation of multiple subsystems under complex test scenarios.

[0005] Specifically, the technical solution for implementing the present invention is as follows:

[0006] In a first aspect, the present invention provides a multi-system multimodal switching control method based on a Stateflow model, wherein the method is executed by a Stateflow model deployed on a control computer, and includes the following steps: S1: Construct a digital parameter library and standardized task table for test scenarios. Each test scenario is uniquely identified by a scenario ID. Each test scenario consists of multiple sequentially executed stages. Each stage is identified by a stage ID and associated with a structured parameter set. S2: Configure multiple functional state machines in the Stateflow model, configure standardized action templates for each type of state machine, establish the mapping relationship between the scenario ID and the corresponding stage ID to the action templates of each state machine, so that the state machine can trigger corresponding actions, output control commands, and load the structured parameter set corresponding to the current stage according to the current test scenario and the current stage. S3: A time-condition hybrid transfer mechanism is adopted for process safety scheduling. The time-condition hybrid transfer mechanism switches between different test scenarios or stages according to preset scheduling rules. S4: Design a constraint-based smooth switching execution layer. When a cross-modal switch or cross-scenario switch occurs, this execution layer is activated to continuously fuse the control commands before and after the switch, and apply dynamic hard constraints to the fused control commands and the actions of the actuators. If an interlock is triggered, the actuator is not in place, or a timeout occurs during the switch, a degradation strategy or a rollback strategy is executed. S5: Invoke the corresponding robust control strategy based on the dynamic characteristics of the current control mode; S6: Integrate the digital parameter library of the test scenario, the multi-functional state machine, the time-condition hybrid transfer mechanism, the constraint-type smooth switching execution layer, and the modal robust controller into the Stateflow model. The Stateflow model collects system data in real time and completes the whole process of disturbance-free collaborative switching and continuous operation based on the stage-driven state machine executed in sequence.

[0007] Furthermore, in step S1, the structured parameter set includes at least one of the following: target setting parameters, execution action parameters, logical interlock conditions, dynamic constraint parameters, exception handling strategies, and fallback paths.

[0008] Furthermore, in step S2, the multi-function state machine includes an engine state machine, a pressure setpoint state machine, and a valve opening state machine. The standardized action template includes an Entry action, a During action, and an Exit action. The Entry action is used to load stage parameters, the During action is used to output instructions and monitor interlocks, and the Exit action is used for state release, data recording, and rollback flag update.

[0009] Furthermore, in step S4, the control commands before and after the switching are continuously fused, and dynamic hard constraints are applied to the fused control commands and actuator actions, including: S41: A smoothing function is used to weight and combine the control commands before and after the switch to generate continuous transition control commands; S42: Apply dynamic constraints to the transition control command, the dynamic constraints including at least one of output limiting, rate limiting, or rate of change limiting of the actuator; S43: When an interlock is detected, the actuator fails to arrive within a preset time, or the control command exceeds the limit, a graded degradation strategy is executed or the original path is precisely reversed.

[0010] Furthermore, the switching transition time of the smoothing function is adaptively adjusted based on the current control error amplitude, the maximum allowable rate of the actuator, and the system safety margin; when the rate of change of the control command exceeds a preset rate of change threshold, the switching transition time is automatically extended or the switching is paused and the current state is maintained. The expression of the smoothing function is: ; in, These are continuous control commands during the switching process. For smoothing functions, and These are the original control commands generated for the control modes before and after the switch, respectively. To switch the trigger time, This is for switching transition times.

[0011] Furthermore, in step S5, the control modes include steady-state mode, slow transient mode and fast transient mode. The steady-state mode and the slow transient mode adopt an improved linear active disturbance rejection control with biased feedforward, and the fast transient mode adopts a variable gain high-order sliding mode observer combined with a fastest feedback control with a dead-zone sign gate function. The control algorithm expression for the improved linear active disturbance rejection controller with biased feedforward is as follows: ; in, For observation error, For the system's measurable output quantity, for The observed values, The observed value of the rate of change of the system output. The observed value of the total system disturbance. , , These represent the first-order, second-order, and third-order gains of the observer, respectively. As a reference input, the differential gain, For proportional gain, For differential gain, For the bias feedforward gain, For the desired output, This is an intermediate control variable. For the final control input, This is a predictor factor.

[0012] Furthermore, the variable-gain high-order sliding mode observer adaptively adjusts the gain according to the control error amplitude. When the error is large, the gain is increased to improve the response speed, and when the error is small, the gain is decreased to suppress chattering. The dead-zone sign gate function outputs zero when the absolute value of the error is less than the dead-zone threshold.

[0013] Secondly, the present invention also provides a multi-system multimodal switching control system based on the Stateflow model. The system is applied to the flight environment simulation control system of an aero-engine. The system includes an intake subsystem, an exhaust subsystem, and an air supply subsystem, and also includes a Stateflow control model deployed on a control computer. The Stateflow control model includes a scenario management module, a state machine execution module, a hybrid scheduling module, a smooth switching module, and a modal control module.

[0014] Specifically, the scenario management module is used to build a digital parameter library and standardized task table for test scenarios. Each test scenario is uniquely identified by a scenario ID. Each test scenario consists of multiple stages executed in sequence. Each stage is identified by a stage ID and associated with a structured parameter set. The state machine execution module is used to configure multiple types of functional state machines and standardized action templates, establish the mapping relationship between scenario ID and stage ID to each state machine action template, trigger corresponding actions, output control instructions, and load the structured parameter set corresponding to the current stage according to the current test scenario and current stage. The hybrid scheduling module is used to perform process safety scheduling using a time-condition hybrid transfer mechanism, and to safely switch between different test scenarios or stages according to preset scheduling rules; The smooth switching module is used to continuously fuse the control commands before and after the switching when a cross-modal or cross-scenario switching occurs, and to apply dynamic hard constraints to the fused control commands and actuator actions. If an interlock is triggered during the switching process, or if the actuator is not in place or timeout occurs, a degradation strategy or rollback strategy is executed. The modal control module is used to invoke the corresponding robust control strategy based on the dynamic characteristics of the current control mode; In this invention, the scenario management module, the state machine execution module, the hybrid scheduling module, the smooth switching module, and the modal control module together constitute the Stateflow control model, which is used to collect system data in real time and drive the state machine to complete the whole process of disturbance-free collaborative switching and continuous operation based on the stages executed in sequence in the current test scenario.

[0015] In a third aspect, the present invention also provides a computer device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned multi-system multi-modal switching control methods based on the Stateflow model to solve the technical problems such as dependence on manual intervention, easy disturbance during the switching process, and poor multi-system coordination in the prior art.

[0016] In a fourth aspect, the present invention also provides a computer-readable storage medium storing a computer program for executing any of the above-mentioned multi-system multi-modal switching control methods based on the Stateflow model to solve the technical problems such as dependence on manual intervention, easy disturbance during the switching process, and poor multi-system coordination in the prior art.

[0017] Aiming at the technical problems in the current flight environment simulation test of aeroengines, such as the control mode relying on manual switching, low efficiency of multi-system collaborative operation, instruction jumps during the mode switching process, and difficulty in兼顾control quality under different working conditions, the present invention proposes a multi-system multi-modal collaborative switching control method based on the Stateflow model. This method constructs an automated control architecture that deeply integrates parameter-driven, state machine scheduling, safety constraints, and mode adaptation, significantly improving the standardization, coordination, robustness, and engineering applicability of the test process.

[0018] Compared with the prior art, the present invention has at least the following advantages: 1. For the first time, a digital choreography mechanism for test tasks driven by a parameter library is constructed: By constructing a structured parameter library of "scene ID - stage ID - target / action - interlock - exception handling", the test logic originally coupled in the code is decoupled into a configurable digital task sequence. This mechanism realizes the high reusability and flexible reconstruction of the test process, solves the problem of deep binding between the test process and the control algorithm and poor adaptability in the traditional method, and greatly improves the test design efficiency and standardization level.

[0019] 2. A unified mapping execution framework for three types of core state machines is established: For strongly coupled subsystems such as intake, exhaust, and air supply, an engine state machine, a pressure set value state machine, and a valve opening state machine are constructed, and through the precise mapping of "scene / stage → Entry / During / Exit action templates", the serialized output of control instructions and the precise synchronization of multi-system actions are realized. Thus, the discrete operations that previously relied on manual intervention are transformed into a process-based and automated collaborative control, significantly improving the reliability of multi-system linkage and the test efficiency.

[0020] 3. A time-condition hybrid transfer logic and an interlock priority mechanism are proposed: This design differentiates between intra-scenario switching (timer-driven) and cross-scenario switching (event-triggered), constructs a hybrid transfer scheduling strategy, and embeds safety elements such as steady-state criteria and actuator arrival signals into the transfer criteria, assigning the highest priority to interlocking conditions. This design ensures the logical closed-loop nature, safety, and interpretability of process switching under complex operating conditions, overcoming the insufficient adaptability of a single transfer method in dynamic environments.

[0021] 4. Design a constraint-based smooth switching execution layer to ensure hardware compatibility and system security: During the transient state of mode switching, instead of relying solely on the superposition of controller formulas, an independent execution layer is introduced to perform weighted smooth fusion of control commands before and after the switch, and simultaneously apply dynamic hard constraints such as amplitude limiting, rate limiting, and rate of change constraints. Furthermore, a hierarchical degradation and precise backoff strategy are integrated to automatically respond to interlock triggers, timeouts, or execution anomalies, effectively avoiding command jumps and actuator impacts, and significantly improving the stability and safety of multi-system coupled operation.

[0022] 5. Optimize the modal robust control strategy to achieve full-condition control: Differentiated control algorithms are matched to different dynamic characteristics: In steady state and slow transient state, an improved linear active disturbance rejection control with bias feedforward is used to effectively suppress tracking lag under large deviations and improve steady-state accuracy; in fast transient state, a variable-gain high-order sliding mode observer combined with a dead-zone sign gate function is used to balance response speed and chattering suppression. This achieves a synergistic improvement in control accuracy, dynamic performance, and anti-interference capability across the entire operating range.

[0023] In summary, the method of this invention is not an improvement on a single control algorithm, but rather a collaborative control system built on the Stateflow model, integrating task orchestration, state scheduling, safe switching, and modal control. This system has achieved a fundamental shift from "manual intervention and fragmented control" to "fully autonomous and disturbance-free collaboration" in aero-engine environmental simulation tests, comprehensively improving the level of test automation, control quality, operational safety, and engineering feasibility. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a multi-system multimodal switching control method based on the Stateflow model disclosed in an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the construction of multiple functional state machines disclosed in an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the mapping relationship between typical test scenarios and state machines in embodiments of the present invention; Figure 4 This is the engine test task transfer logic based on Stateflow in this embodiment of the invention; Figure 5 This is a schematic diagram of the modal control switching method in an embodiment of the present invention; Figure 6 This is a schematic diagram of the switching signal for the i-th test point in an embodiment of the present invention; Figure 7 This is a schematic diagram of smooth switching in an embodiment of the present invention. Detailed Implementation

[0026] The embodiments of this application will now be described in detail with reference to the accompanying drawings.

[0027] The following specific examples illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. This application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this application. It should be noted that, in the absence of conflict, the following embodiments and features of the embodiments can be combined with each other. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] To address the current problems of relying on manual switching of control modes in aero-engine test control tasks, the inability of different modal control methods to achieve smooth switching, and the difficulty in achieving high-quality control in different modes such as transient / steady state, this invention discloses a multi-system multi-modal switching control method and system based on the Stateflow model. The system and method are applied to the aero-engine flight environment simulation control system, which includes an intake subsystem, an exhaust subsystem, and an air supply subsystem.

[0029] The method of this invention constructs a full-link collaborative control framework that integrates parameter library driving, state machine mapping, hybrid transfer scheduling, constraint-based smooth switching execution, and modal adaptive robust control. By digitally orchestrating the test process, defining the control logic in a model, and intelligently scheduling the switching timing, and by applying dual constraints of continuity and safety during the switching process, it achieves highly reliable, disturbance-free, and automated collaborative operation of multiple subsystems in complex test scenarios.

[0030] Specifically, such as Figure 1 As shown, the method is executed by a Stateflow model deployed on a control computer and includes the following steps: S1: Construct a digital parameter library and standardized task table for test scenarios. Each test scenario is uniquely identified by a scenario ID. Each test scenario consists of multiple stages executed in sequence. Each stage is identified by a stage ID and associated with a structured parameter set.

[0031] The structured parameter set includes at least one of the following: target setting parameters (including initial values ​​of intake pressure / exhaust pressure / valve opening), execution action parameters, logical interlock conditions, dynamic constraint parameters, anomaly handling strategies (including judgment thresholds for timeout, overpressure, and overtemperature), and fallback paths. Furthermore, it can preset graded degradation and precise fallback strategies under limit / fault conditions, and the constructed digital parameter library for test scenarios supports online editing and real-time loading.

[0032] S2: Configure multiple functional state machines in the Stateflow model, configure standardized action templates for each type of state machine, establish the mapping relationship between the scenario ID and the corresponding stage ID to the action templates of each state machine, so that the state machine can trigger corresponding actions, output control instructions, and load the structured parameter set corresponding to the current stage according to the current test scenario and the current stage.

[0033] For specific implementation, please refer to Figure 2 As shown, three types of state machines are constructed: an engine state machine, a pressure setpoint state machine, and a valve opening state machine. Each test stage in each test scenario within the test scenario parameter library is mapped to its corresponding state machine, establishing a mapping relationship between test scenario, test stage, and state machine. Specifically, the engine state machine includes states such as stop, start, idle, transient state, steady state, and stop; the pressure setpoint state machine includes states such as atmospheric pressure, pressure increase, target pressure, pressure decrease, and pressure decrease to atmospheric pressure; and the valve opening state machine includes states such as fully closed, slightly open, partially open, regulating, and fully open.

[0034] Standardized Entry / During / Exit actions are designed for each type of state machine, specifically: Entry action: loads the target setting parameters, dynamic constraint parameters, and interlock condition parameters for the current stage; During action: collects system data in real time, outputs control commands according to the mapping relationship, performs real-time interlock judgment, and records test data; Exit action: releases the current state resources, updates the test stage rollback flag, and saves the stage test data. Establish a unique mapping relationship between scene ID / stage ID and three types of state machine action templates. For example, "negative temperature gas supply test - start-up stage" is mapped to "engine state machine - start-up" + "pressure setpoint state machine - atmospheric pressure" + "valve opening state machine - slightly open" and automatically load all parameters and actions of this stage. Figure 3 This is a schematic diagram showing the mapping relationship between a typical test scenario and a state machine.

[0035] S3: A time-condition hybrid transfer mechanism is adopted for process safety scheduling. The time-condition hybrid transfer mechanism switches between different test scenarios or stages according to preset scheduling rules.

[0036] In this invention, a hybrid transfer logic is configured so that stage switching within the same scenario is triggered by a timer, while cross-scenario switching is triggered by system state conditions. During the transfer process, safety-related interlock criteria are prioritized. The system state conditions include at least one of the following: real-time output pressure or temperature signals, actuator positioning feedback signals, and steady-state criteria. The steady-state criteria are: within a preset time window, the absolute value of the pressure deviation is less than a first threshold, the absolute value of the pressure change rate is less than a second threshold, and all key actuators are in position.

[0037] In practical implementation, time-condition hybrid transition logic is set for each state machine in the Stateflow model, while interlock criterion priorities are set (safety interlocks such as overpressure, overtemperature, and engine failure are prioritized when triggered). The transition logic includes time-based transition logic and condition-based transition logic, such as... Figure 4 As shown. Sub-task phase switching within the same scenario uses time-based transfer, including timed transfer and timeout transfer. Timed transfer is triggered according to a preset test sequence, while timeout transfer is triggered when a phase task is not completed within a preset time, strictly adhering to the test sequence requirements. Cross-scenario switching uses conditional transfer, triggered based on real-time system output (such as pressure and temperature changes), steady-state criteria, actuator positioning signals, and interlocking conditions. The steady-state criteria include at least one or more of the following: pressure / temperature deviation within a preset time window is less than a threshold, rate of change is less than a threshold, and actuator positioning signals meet requirements.

[0038] S4: Design a constraint-based smooth switching execution layer. When a cross-modal switch or cross-scenario switch occurs, this execution layer is activated to continuously fuse the control commands before and after the switch, and apply dynamic hard constraints to the fused control commands and the actions of the actuators. If an interlock is triggered, the actuator is not in place, or a timeout occurs during the switch, a degradation strategy or a rollback strategy is executed.

[0039] The aforementioned multi-system refers to the air supply, intake, and exhaust systems. The multi-modal control method refers to different control methods corresponding to different operating conditions of the engine, such as steady-state tests, slow-speed transient tests, and fast-transient tests; that is, different test tasks require different modal control methods. Robust controllers can be used to achieve tracking and disturbance suppression under different modes. The controller type is not limited to active disturbance rejection / sliding mode, etc., and can be selected according to the characteristics of the object. Specifically, the control commands before and after the switching are continuously fused, and dynamic hard constraints are applied to the fused control commands and actuator actions, including: S41: A smoothing function is used to weight and combine the control commands before and after the switch to generate continuous transition control commands; S42: Apply dynamic constraints to the transition control command, the dynamic constraints including at least one of output limiting, rate limiting, or rate of change limiting of the actuator; S43: When an interlock is detected, the actuator fails to arrive within a preset time, or the control command exceeds the limit, a graded degradation strategy is executed or the original path is precisely reversed.

[0040] In practical implementation, during engine steady-state and slow-transient state tests, the controlled object needs to slowly and smoothly track changes in the target value. Therefore, the controller needs to have the ability to maintain the stability and accuracy of the controlled object as much as possible by changing the control quantity. Since the active disturbance rejection controller has two key components—a tracking differentiator and an extended state observer—it can achieve smooth changes in the control quantity and respond promptly to rapid changes in the controlled object, thus meeting the requirements of steady-state and slow-transient state tests quite well.

[0041] In fast transient state tests, the controlled object exhibits significant inertia, rapid changes, and unpredictable dynamics, thus placing stringent demands on the controller. First, the controller must possess sufficient power to overcome inertia and drive the controlled object to rapid changes during the test. Second, the controller needs strong robustness and precise compensation capabilities to cope with the rapid changes in the controlled object's state. High-order sliding mode control algorithms, through the design of the sliding surface, enable the system to enter a sliding mode after reaching the sliding surface and operate according to a preset state. This effectively combats system uncertainties and chattering problems, thus demonstrating good adaptability to fast transient state tests.

[0042] In one embodiment, an independent constraint-based smooth execution switching layer is added between the experimental procedure and the control algorithm. When a cross-modal switch occurs, the following operations are performed: continuity and safety constraints are imposed on the control input / execution instructions, and the following is constructed... Smoothing function The control outputs of the two controllers in step 4 before and after the switchover are weighted and combined to output continuous control commands without jumps. Amplitude, speed, and rate-of-change constraints are applied to the control commands and actuator actions. When thresholds are exceeded, the switchover transition time is automatically extended or a state-holding mode is entered. If interlocking conditions are triggered, actuators fail to reach their positions, or timeouts occur during the switchover, a degradation or rollback strategy is immediately executed. This ensures system safety and stability.

[0043] The principle of the multimodal control switching method is as follows: Figure 5 As shown. Now define the switching signal. For the first The switching trigger signal for each test point is used to output the system. ,design , Characterizing changes in pressure and temperature respectively, let... For the first A set of switching conditions for each test scenario. When , hour, , , , The number of test points For the first Total test time for each test scenario.

[0044] Assumption It has the following switching order: ,when hour, , , , Let be the total number of modes. In the formula, At the initial moment of the experiment, , The number of subtasks within a single scene. When At that time, the first A subtask was activated. For example, the first Test scenario switching signal ,as follows Figure 6 As shown.

[0045] The multimodal switching controller is then: ; in, Output instructions for switching layers. For corresponding switching signals The control mode output commands.

[0046] To avoid the impact of sudden changes in control input signals during controller switching on the strongly coupled intake, exhaust, and supply systems of aero-engines, which could lead to pressure / temperature overshoot and frequent, large-amplitude actuator movements, smooth transition measures are implemented during the switching process to ensure a continuous and stable transition and prevent adverse effects on system stability and test safety. This invention employs a constrained smooth switching algorithm to address these issues. The smooth switching algorithm in system control aims to ensure a smooth transition between commands and system states when switching between different control modes and strategies, thereby avoiding system instability risks and ensuring seamless and smooth switching. Figure 7 As shown.

[0047] The following is the design of the smooth switching algorithm: During the cross-modal switching phase, the switching transition time is defined. and construct a system that satisfies Smoothing function Such as sigmoid functions or polynomial interpolation functions. Smoothly switch execution layer outputs. for: ; in, These are continuous control commands during the switching process. For smoothing functions, and These are the original control commands generated for the control modes before and after the switch, respectively. To switch the trigger time, This is for switching transition times.

[0048] To meet the constraints of engineering test safety and the physical characteristics of the actuator, the following consistency constraints are applied simultaneously during the smooth switching of the execution layer: Amplitude limiting / speed limiting / rate of change constraints: Output of the smooth switching execution layer Output instruction increment And set an upper limit on the rate of change of the actuator's action commands, prohibit the output of commands exceeding the threshold, and match the physical adjustment capability of the actuator; Adaptive transition time constraints: The adjustment is adaptively determined by the error amplitude, actuator speed limit, and safety margin. The larger the error amplitude, the slower the actuator adjustment rate. The larger the value, the better the instruction transition matches the system response time. When the execution instruction triggers the above constraint threshold, the transition time is automatically extended. If the constraint still cannot be met after the extension, the execution layer is smoothly switched to state preservation mode, maintaining the control instruction unchanged until the system state meets the switching conditions. Interlock and rollback constraints: If an actuator fails to reach its position, or if system overpressure / overtemperature or timeout occurs during the switching process, the current smooth switching process will be terminated, and a preset abnormal handling state will be entered, and a degradation or rollback strategy will be executed to ensure the safety of the test system and the engine under test.

[0049] S5: Based on the dynamic characteristics of the current control mode, invoke the corresponding robust control strategy. The control modes include steady-state mode, slow transient mode, and fast transient mode. In specific implementation, differentiated robust control strategies can be configured according to the characteristics of different modes such as steady-state / slow transient and fast transient to achieve tracking and disturbance suppression. The controller form within the mode is not limited to active disturbance rejection, sliding mode, or PID. For steady-state and slow transient modes, an improved linear active disturbance rejection control with bias feedforward and an extended state observer are used. The tracking lag problem under large deviation conditions is solved by adding a bias feedforward stage. For fast transient modes, a variable-gain high-order sliding mode observer combined with a fastest feedback control with a dead-zone sign gate function is used. The variable-gain configuration and dead-zone sign gate function solve the problems of poor adaptability to changing operating conditions, low disturbance estimation accuracy, and significant system chattering of traditional observers.

[0050] In one embodiment, the essence of linear active disturbance rejection control (ADR) lies in estimating and compensating for the total disturbance through an extended state observer, characterized by small control overshoot, fast response speed, high accuracy, and strong anti-interference capability. A deviation proportional feedforward is added to the controller output of the traditional linear ADR algorithm. Item, will set the system settings Compared with actual output The deviation is introduced in real time to solve the tracking lag problem under large deviation conditions. The control algorithm expression of the improved linear active disturbance rejection controller with deviation feedforward is as follows: ; in, For observation error, For the system's measurable output quantity, for The observed values, The observed value of the rate of change of the system output. The observed value of the total system disturbance. , , These represent the first-order, second-order, and third-order gains of the observer, respectively. The differential gain is used as the reference input. For proportional gain, For differential gain, For the bias feedforward gain, For the desired output, This is an intermediate control variable. For the final control input, This is a predictor factor.

[0051] In one embodiment, the variable gain high-order sliding mode observer is based on the control error. The gain is adaptively adjusted to increase the gain when the error is large in order to improve the response speed, and decrease the gain when the error is small in order to suppress chattering.

[0052] The fastest feedback control with dead time introduces a sign gate function with dead time. Instead of the traditional sign gate function, the sign gate function with dead zone outputs zero when the absolute value of the error is less than the dead zone threshold, which is expressed as: exist The output is 0 at the specified time to avoid system chattering caused by frequent adjustments under small errors; It executes traditional symbolic gate function logic in real time to ensure the dynamic response capability of the system. This is the dead zone threshold.

[0053] More specifically, the fastest feedback control method for the variable gain improved high-order sliding mode observer is described below, and the high-order sliding mode observer is designed as follows: ; in, For the output observations of the higher-order sliding mode observer, For the sliding mode observer's observation of the system output derivative, The sliding mode observer's observations of the total disturbance. They are respectively The first derivative; Let Lipschitz be the constant. For lumped disturbance estimation, , , These represent the first-order, second-order, and third-order adaptive gains of the sliding mode observer, respectively. It is an observer auxiliary variable. For system-measurable output, For traditional symbolic functions, Input gain to the system The system's final control input is the fastest feedback control output.

[0054] By introducing a variable gain configuration for the observer gain, based on the control error magnitude Dynamically adjust the observer gain , , Increasing the gain during the large deviation phase improves the disturbance estimation speed and system dynamic response, enabling rapid tracking of the target value. Decreasing the gain during the small deviation phase reduces observer discontinuities, suppresses system chattering, and improves control accuracy.

[0055] Based on traditional fastest feedback control, a sign gate function with dead time is introduced. This replaces the traditional symbolic gate function, solving the actuator chattering problem caused by frequent adjustments under small errors. Simultaneously, a rate constraint term is added to the fastest feedback control list to avoid abrupt changes in control input.

[0056] The fastest feedback controller is designed as follows: ; in, As a speed adjustment factor, This is the filter adjustment factor. These are boundary layer parameters related to the sampling period. As a first-order auxiliary variable, The system outputs tracking error. To differentiate the tracking error, As a comprehensive error variable, It is a second-order auxiliary variable. It is a third-order auxiliary variable. For trajectory comprehensive variables, The output is a symbolic gate function with a dead zone. For the gate function input, This is the dead zone threshold, adaptively tuned according to operating conditions. fhan is the fastest feedback control law output. For rate-constrained gain, To control the differential correction amount.

[0057] The final controller for this algorithm is as follows: ; in, For the intermediate output of the controller, For system control input, For higher-order sliding mode observers to lumped disturbances The estimated value, This is the sliding mode input gain.

[0058] When no mode switching occurs, the corresponding controller is independently activated for closed-loop control under each operating condition: in steady state and slow transient state, an improved linear active disturbance rejection control method with bias feedforward is used to output the final control quantity. The fastest feedback control method using a variable-gain improved high-order sliding mode observer in fast transient state outputs the final control quantity. When a mode switch occurs (the transition between three modes: steady state, slow transition state, and fast transition state), the constraint-based smooth switching strategy in step 5 is executed. The outputs of the controller before and after the switch are weighted and combined to obtain the final control output.

[0059] S6: Integrate the digital parameter library of the test scenario, the multi-functional state machine, the time-condition hybrid transfer mechanism, the constraint-type smooth switching execution layer, and the modal robust controller into the Stateflow model. The Stateflow model collects system data in real time and completes the whole process of disturbance-free collaborative switching and continuous operation based on the stage-driven state machine executed in sequence.

[0060] Based on the same inventive concept, this invention also provides a multi-system multimodal switching control system based on the Stateflow model, as described in the following embodiments. Since the principle of the multi-system multimodal switching control system based on the Stateflow model is similar to the multi-system multimodal switching control method based on the Stateflow model disclosed in the above embodiments, the implementation of the multi-system multimodal switching control system based on the Stateflow model can refer to the implementation of the multi-system multimodal switching control method based on the Stateflow model, and repeated details will not be elaborated further. As used below, the terms "unit" or "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0061] This invention discloses a multi-system, multi-modal switching control system based on the Stateflow model. The system is applied to the flight environment simulation control system of an aero-engine. The system includes an intake subsystem, an exhaust subsystem, and an air supply subsystem, and also includes a Stateflow control model deployed on a control computer. The Stateflow control model includes a scenario management module, a state machine execution module, a hybrid scheduling module, a smooth switching module, and a modal control module. The structure is described below.

[0062] Specifically, the scenario management module is used to build a digital parameter library and standardized task table for test scenarios. Each test scenario is uniquely identified by a scenario ID. Each test scenario consists of multiple stages executed in sequence. Each stage is identified by a stage ID and associated with a structured parameter set. The state machine execution module is used to configure multiple types of functional state machines and standardized action templates, establish the mapping relationship between scenario ID and stage ID to each state machine action template, trigger corresponding actions, output control instructions, and load the structured parameter set corresponding to the current stage according to the current test scenario and current stage. The hybrid scheduling module is used to perform process safety scheduling using a time-condition hybrid transfer mechanism, and to safely switch between different test scenarios or stages according to preset scheduling rules; The smooth switching module is used to continuously fuse the control instructions before and after the cross-modal or cross-scenario switching, impose dynamic hard constraints on the fused control instructions and the actions of the actuators, and execute the degradation strategy or the fallback strategy if interlock, actuator not in place or timeout is triggered during the switching process; The modal control module is used to call the corresponding robust control strategy according to the dynamic characteristics of the current control mode; In the present invention, the scenario management module, the state machine execution module, the hybrid scheduling module, the smooth switching module and the modal control module jointly constitute the Stateflow control model, which is used to collect system data in real time and drive the state machine based on the stages executed in sequence in the current test scenario to complete the full-process disturbance-free collaborative switching and continuous operation. Aiming at the technical problems existing in the current aero-engine flight environment simulation test, such as the control mode relying on manual switching, the low efficiency of multi-system collaborative operation, the instruction jump during the modal switching process, and the difficulty in兼顾the control quality under different working conditions, the present invention proposes a multi-system multi-modal collaborative switching control method based on the Stateflow model. This method constructs an automated control architecture with deep integration of parameter-driven, state machine scheduling, safety constraints and modal adaptation, significantly improving the standardization, collaboration, robustness and engineering applicability of the test process.

[0063] Compared with the prior art, the present invention has at least the following advantages: 1. For the first time, a digital choreography mechanism for test tasks driven by a parameter library is constructed: By constructing a structured parameter library of "scene ID - stage ID - target / action - interlock - exception handling", the test logic originally coupled in the code is decoupled into a configurable digital task sequence. This mechanism realizes the high reusability and flexible reconstruction of the test process, solves the problem of deep binding between the test process and the control algorithm and poor adaptability in the traditional method, and greatly improves the test design efficiency and standardization level.

[0064] 2. Establish a unified mapping execution framework for three types of core state machines: For strongly coupled subsystems such as intake, exhaust, and air supply, an engine state machine, a pressure set value state machine and a valve opening state machine are constructed, and through the precise mapping of "scene / stage → Entry / During / Exit action template", the serialized output of control instructions and the precise synchronization of multi-system actions are realized. Thus, the discrete operations that previously relied on manual intervention are transformed into process-based and automated collaborative control, significantly improving the reliability of multi-system linkage and the test efficiency.

[0065] 3. Propose a time-condition hybrid transfer logic and an interlock priority mechanism: This design differentiates between intra-scenario switching (timer-driven) and cross-scenario switching (event-triggered), constructs a hybrid transfer scheduling strategy, and embeds safety elements such as steady-state criteria and actuator arrival signals into the transfer criteria, assigning the highest priority to interlocking conditions. This design ensures the logical closed-loop nature, safety, and interpretability of process switching under complex operating conditions, overcoming the insufficient adaptability of a single transfer method in dynamic environments.

[0066] 4. Design a constraint-based smooth switching execution layer to ensure hardware compatibility and system security: During the transient state of mode switching, instead of relying solely on the superposition of controller formulas, an independent execution layer is introduced to perform weighted smooth fusion of control commands before and after the switch, and simultaneously apply dynamic hard constraints such as amplitude limiting, rate limiting, and rate of change constraints. Furthermore, a hierarchical degradation and precise backoff strategy are integrated to automatically respond to interlock triggers, timeouts, or execution anomalies, effectively avoiding command jumps and actuator impacts, and significantly improving the stability and safety of multi-system coupled operation.

[0067] 5. Optimize the modal robust control strategy to achieve full-condition control: Differentiated control algorithms are matched to different dynamic characteristics: In steady state and slow transient state, an improved linear active disturbance rejection control with bias feedforward is used to effectively suppress tracking lag under large deviations and improve steady-state accuracy; in fast transient state, a variable-gain high-order sliding mode observer combined with a dead-zone sign gate function is used to balance response speed and chattering suppression. This achieves a synergistic improvement in control accuracy, dynamic performance, and anti-interference capability across the entire operating range.

[0068] In summary, the method of this invention is not an improvement on a single control algorithm, but rather a collaborative control system built on the Stateflow model, integrating task orchestration, state scheduling, safe switching, and modal control. This system has achieved a fundamental shift from "manual intervention and fragmented control" to "fully autonomous and disturbance-free collaboration" in aero-engine environmental simulation tests, comprehensively improving the level of test automation, control quality, operational safety, and engineering feasibility.

[0069] In this embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-mentioned multi-system multimodal switching control methods based on the Stateflow model.

[0070] Specifically, the computer device can be a computer terminal, a server, or a similar computing device.

[0071] In this embodiment, a computer-readable storage medium is provided, which stores a computer program that executes any of the above-described multi-system multimodal switching control methods based on the Stateflow model.

[0072] Specifically, computer-readable storage media, including both permanent and non-permanent, removable and non-removable media, can store information using any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer-readable storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable storage media does not include transient media, such as modulated data signals and carrier waves.

[0073] Obviously, those skilled in the art should understand that the modules or steps of the above-described embodiments of the present invention can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby storing them in a storage device for execution by a computing device. In some cases, the steps shown or described can be performed in a different order than those presented here, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the embodiments of the present invention are not limited to any particular hardware and software combination.

[0074] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, various modifications and variations can be made to the embodiments of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-system multi-modal switching control method based on the Stateflow model, characterized in that, The method is executed by a Stateflow model deployed on a control computer and includes: A digital parameter library and standardized task table for test scenarios are constructed. Each test scenario is uniquely identified by a scenario ID. Each test scenario consists of multiple stages executed in sequence. Each stage is identified by a stage ID and associated with a structured parameter set. In the Stateflow model, multiple functional state machines are configured, and standardized action templates are configured for each type of state machine. A mapping relationship is established between the scenario ID and the corresponding stage ID to the action templates of each state machine, so that the state machine can trigger corresponding actions, output control commands, and load the structured parameter set corresponding to the current stage according to the current test scenario and the current stage. A time-condition hybrid transfer mechanism is adopted for process safety scheduling, which switches between different test scenarios or stages according to preset scheduling rules; Design a constraint-based smooth switching execution layer. When a cross-modal switch or cross-scenario switch occurs, the execution layer is activated to continuously fuse the control commands before and after the switch, and apply dynamic hard constraints to the fused control commands and the actions of the actuators. If an interlock is triggered, the actuator is not in place, or a timeout occurs during the switch, a degradation strategy or a rollback strategy is executed. Based on the dynamic characteristics of the current control mode, invoke the corresponding robust control strategy; The digital parameter library of the test scenario, the multi-functional state machine, the time-condition hybrid transition mechanism, the constraint-type smooth switching execution layer, and the modal robust controller are integrated into the Stateflow model. The Stateflow model collects system data in real time and completes the whole process of non-disruptive collaborative switching and continuous operation based on the stage-driven state machine executed in sequence.

2. The multi-system multi-modal switching control method based on the Stateflow model according to claim 1, characterized in that, The structured parameter set includes at least one of the following: target setting parameters, execution action parameters, logical interlock conditions, dynamic constraint parameters, exception handling strategies, and fallback paths.

3. The multi-system multi-modal switching control method based on the Stateflow model according to claim 1, characterized in that, The multi-functional state machine includes an engine state machine, a pressure setpoint state machine, and a valve opening state machine. The standardized action template includes an Entry action, a During action, and an Exit action. The Entry action is used to load stage parameters, the During action is used to output instructions and monitor interlocks, and the Exit action is used to release states, record data, and update rollback flags.

4. The multi-system multi-modal switching control method based on the Stateflow model according to claim 1, characterized in that, The control commands before and after the switch are continuously fused, and dynamic hard constraints are applied to the fused control commands and actuator actions, including: A smoothing function is used to weight and combine the control commands before and after the switch to generate continuous transition control commands; Dynamic constraints are applied to the transition control command, and the dynamic constraints include at least one of output limiting, rate limiting, or rate of change limiting of the actuator; When an interlock is detected, the actuator fails to arrive within a preset time, or the control command exceeds the limit, a graded degradation strategy is executed or the original path is precisely reversed.

5. The multi-system multi-modal switching control method based on the Stateflow model according to claim 4, characterized in that, The switching transition time of the smoothing function is adaptively adjusted based on the current control error amplitude, the maximum allowable rate of the actuator, and the system safety margin. When the rate of change of the control command exceeds a preset rate of change threshold, the switching transition time is automatically extended or the switching is paused and the current state is maintained. The expression of the smoothing function is: ； in, These are continuous control commands during the switching process. For smoothing functions, and These are the original control commands generated for the control modes before and after the switch, respectively. To switch the trigger time, This is for switching transition times.

6. The multi-system multi-modal switching control method based on the Stateflow model according to claim 1, characterized in that, The control modes include steady-state mode, slow transient mode and fast transient mode. The steady-state mode and the slow transient mode adopt an improved linear active disturbance rejection control with bias feedforward, and the fast transient mode adopts a variable gain high-order sliding mode observer combined with a dead-zone sign gate function for the fastest feedback control. The control algorithm expression for the improved linear active disturbance rejection controller with biased feedforward is as follows: ； in, For observation error, For the system's measurable output quantity, for The observed values, The observed value of the rate of change of the system output. The observed value of the total system disturbance. , , These represent the first-order, second-order, and third-order gains of the observer, respectively. The differential gain is used as the reference input. For proportional gain, For differential gain, For the bias feedforward gain, For the desired output, This is an intermediate control variable. For the final control input, This is a predictive factor.

7. The multi-system multi-modal switching control method based on the Stateflow model according to claim 6, characterized in that, The variable-gain high-order sliding mode observer adaptively adjusts the gain according to the control error amplitude. When the error is large, the gain is increased to improve the response speed, and when the error is small, the gain is decreased to suppress chattering. The dead-zone sign gate function outputs zero when the absolute value of the error is less than the dead-zone threshold.

8. A multi-system multimodal switching control system based on the Stateflow model, characterized in that, An application is made to a flight environment simulation and control system for aero-engines. The system includes an intake subsystem, an exhaust subsystem, and an air supply subsystem, and also includes a Stateflow control model deployed on a control computer. The Stateflow control model includes: The scenario management module is used to build a digital parameter library and standardized task table for test scenarios. Each test scenario is uniquely identified by a scenario ID. Each test scenario consists of multiple stages that are executed in sequence. Each stage is identified by a stage ID and associated with a structured parameter set. The state machine execution module is used to configure various functional state machines and standardized action templates, establish the mapping relationship between scenario ID and stage ID to each state machine action template, trigger corresponding actions, output control commands, and load the structured parameter set corresponding to the current stage according to the current test scenario and current stage. The hybrid scheduling module is used to perform process safety scheduling using a time-condition hybrid transfer mechanism, and to safely switch between different test scenarios or stages according to preset scheduling rules. The smooth switching module is used to continuously fuse the control commands before and after the switching when a cross-modal or cross-scenario switching occurs, and to apply dynamic hard constraints to the fused control commands and actuator actions. If an interlock is triggered during the switching process, or if the actuator is not in place or timeout occurs, a degradation strategy or rollback strategy is executed. The modal control module is used to invoke the corresponding robust control strategy based on the dynamic characteristics of the current control mode.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the multi-system multimodal switching control method based on the Stateflow model as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that executes the multi-system multimodal switching control method based on the Stateflow model according to any one of claims 1 to 7.

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