Aero-engine Limitation Protection Control Method Based on a Multi-dimensional Instruction Regulator

Abstract

The present invention discloses a method for limiting protection control of an aircraft engine based on a multi-dimensional command regulator, including establishing a state space model of an inner-loop control system including a multi-variable main controller and an aircraft engine; designing a multi-dimensional command regulator with input and output constraints; and digitally simulating the limiting protection control method of the multi-dimensional command regulator. The present invention establishes a state space model of an inner-loop control system of an aircraft engine as a prediction model, and uses a multi-dimensional command regulator to online rolling optimize the limiting protection control problem with input and output constraints, thereby realizing rapid tracking of multiple parameters and limiting protection of multiple parameters of the aircraft engine. The pre-command regulator technology in the method can be applied to various main controllers, and has universal applicability to power mechanical systems with multiple adjustable variables.

Description

Technical Field

[0001] The present invention belongs to the technical field of aero-engine control, and particularly relates to an aero-engine limit protection control method based on a multi-dimensional command regulator. Background Technique

[0002] An aero-engine is one of the most difficult modern industrial products developed and manufactured by humans at present, aiming to pursue extreme performance of long-term stability in an extremely limited space and under extremely harsh conditions. To improve the working safety and reliability of the engine, the aero-engine control system maintains key engine variables within the allowable limit range through an effective limit protection control method to prevent the engine from entering abnormal operating conditions.

[0003] Currently, the aero-engine limit protection control often adopts a low-select - high-select switching control method to realize the switching between the main controller and the limiter, ensuring that during the process of the engine realizing control tasks, various constrained output quantities (such as temperature, pressure, surge margin, speed, etc.) do not exceed the limit. This method has been successfully applied in multi-loop single-variable control systems. With the development of aero-engine technology, the number of its control variables has increased sharply. When the limit protection control method based on low-select - high-select switching is applied to multi-variable control, the problems such as the switching between the multi-variable main controller and the single-variable limiter making the conservativeness and affecting the performance of the main controller more prominent, greatly reducing the thrust response speed of the aero-engine. The aero-engine limit protection control method based on a command regulator connects the command regulator in series with the main controller. While retaining the excellent performance of the main controller, it solves the virtual command in real time through an optimization algorithm to ensure that the system has a faster dynamic response speed and a stronger limit protection ability, and is more suitable for the aero-engine multi-variable control system. Summary of the Invention

[0004] Object of the Invention: In order to reduce the conservativeness in the aero-engine multi-variable limit protection control and the influence between the main controller and the limiter, the present invention proposes an aero-engine limit protection control method based on a multi-dimensional command regulator. Based on a main controller with good stability, robustness, and tracking control response speed, by designing a multi-dimensional command regulator and online rolling optimization of the virtual command, rapid tracking of multiple parameters of the aero-engine and limit protection of multiple parameters are realized.

[0005] To achieve the above object, an aero-engine limit protection control method based on a multi-dimensional command regulator includes the following steps:

[0006] Step 1: Establish a state-space model of the inner-loop control system including a multi-variable main controller and an aero-engine;

[0007] Step 2: Design a multi-dimensional command regulator with input and output constraints;

[0008] Step 3: Digital simulation of the multi-dimensional instruction regulator limit protection control method.

[0009] Further, the specific steps in Step 1 are as follows:

[0010] Step 1-1: At the current flight altitude, Mach number, and working state, establish a discrete state-space model of the aero-engine using small perturbation and fitting method, and normalize the model, as shown in Equation (1)

[0011]

[0012] where A, B, C, and D are dimensionally appropriate matrices, x(k), u(k), and y(k) are the state variables, input variables, and output variables of the aero-engine, and the state variables of the aero-engine include the fan speed n L , the compressor speed n H , the input variables include the fuel flow rate W f , the area of the nozzle throat A 8 , and the output variables include the fan speed, compressor speed, engine pressure ratio EPR, the temperature at the outlet of the low-pressure turbine T 6 , the pressure at the outlet of the compressor P 3 ;

[0013] Step 1-2: Design an augmented LQ tracking controller as the main controller of the inner-loop system based on the discrete state-space model of the aero-engine. The form of the controller is shown in Equation (2)

[0014] Δu(k) = K 1 Δx(k) + K 2 (y m (k) - r(k)) (2)

[0015] where K 1 is a dimensionally appropriate proportional coefficient matrix, K 2 is a dimensionally appropriate integral coefficient matrix, Δu(k) is the first-order difference of the input variable of the aero-engine, Δx(k) is the first-order difference of the state variable of the aero-engine, y m (k) is the controlled output of the aero-engine, including the compressor speed and the engine pressure ratio, and r(k) is the tracking command of the aero-engine;

[0016] Step 1-3: At the same flight altitude, Mach number, and working state, establish a discrete state-space model of the inner-loop control system of the aero-engine using decoupled step response and fitting method, as shown in Equation (3)

[0017]

[0018] where A cl , B cl , C cl , Dcl is the adaptability matrix, x cl , v(k) and y cl are the state variables, input variables and output variables of the inner loop control system of the aeroengine. The state variables of the inner loop control system of the aeroengine include the fan and compressor speeds and their first-order differences, the fuel flow rate, and the nozzle throat area. The input variables include the virtual command v nL of the compressor speed and the virtual command v EPR of the engine pressure ratio. The output variables include the compressor speed, the low-pressure turbine outlet temperature, the compressor outlet pressure, the fuel flow rate, the nozzle throat area, the first-order difference ΔW f of the fuel flow rate, and the first-order difference ΔA 8 of the nozzle throat area.

[0019] Furthermore, the specific steps in step 2 are as follows:

[0020] Step 2-1: Consider the input-output constraints of the aeroengine

[0021]

[0022] The subscripts max and min represent the maximum limit and the minimum limit respectively. According to the discrete state space model of the inner loop control system of the aeroengine, the maximum output allowable set is solved by an incremental optimization algorithm

[0023] O j ={(x cl (0), v(0)) | y cl (k; (x cl (0), v(0))) ∈ Y, k = 0, 1, …, j} (5) where Y = {y cl (k) | Sy cl (k) ≤ s}, Finally, the prediction time domain length j is determined;

[0024] Step 2-2: Take the quadratic function of the difference between the compressor speed and engine pressure ratio commands and their virtual commands as the optimization objective According to the maximum output allowable set and the discrete state space model of the inner loop control system of the aeroengine, the static and dynamic constraint matrices are initially set to

[0025]

[0026] The static and dynamic constraint matrices are established by a recursive algorithm as the constraint conditions of the optimization problem, as shown in equation (7).

[0027]

[0028] Step 2-3: Under the current state of the aero-engine inner-loop control system, use the quadratic programming algorithm to solve the optimization problem established by the optimization objective and constraint conditions in Step 2-2

[0029]

[0030] Obtain the compressor speed and the virtual command v(k) of the engine pressure ratio at the current moment. If the solution of the optimization problem (8) fails, use the virtual command at the previous moment as the virtual command at the current moment.

[0031] Furthermore, the specific steps in Step 3 are as follows:

[0032] Step 3-1: Given the maximum, minimum values of the aero-engine input and output limits and the simulation duration k, and initialize the simulation time t = 0;

[0033] Step 3-2: Obtain the flight altitude, Mach number and working state at the current moment, and give a suitable multi-dimensional step tracking command;

[0034] Step 3-3: Obtain the discrete state space model of the aero-engine inner-loop control system and the corresponding prediction time domain length at the current moment, and establish the optimization objective, static and dynamic constraint matrices;

[0035] Step 3-4: Obtain the current state variables of the aero-engine inner-loop control system, and solve the multi-dimensional command optimization problem with input and output constraints using the quadratic programming algorithm to obtain the virtual command at the current moment.

[0036] Step 3-5: Use the virtual command as the tracking command to calculate the fuel flow rate and the nozzle throat area at the next moment by the main controller, and calculate the state variables and output variables of the aero-engine at the next moment;

[0037] Step 3-6: t = t + 1, repeat Steps 3-2 to 3-5 until t = k.

[0038] Beneficial effects: A method for aero-engine limit protection control based on a multi-dimensional command regulator provided by the present invention has the following technical effects compared with the prior art by adopting the above technical solutions:

[0039] (1) The present invention adopts a single-loop control architecture to replace the multi-loop switching control architecture, with a simple control system architecture and simpler design;

[0040] (2) The present invention uses the technical means of a pre-set multi-dimensional command regulator to achieve multi-variable limit protection control of the aero-engine. Compared with the technical means of traditional switching control, the entire control process is realized by a multi-variable controller, making full use of the advantages of multiple control variables to ensure fast tracking of multiple parameters and limit protection of multiple parameters;

[0041] (3) The present invention adopts the technical means of online rolling optimization of multi-dimensional instructions, and the obtained virtual instructions are the optimal ones for the inner loop control system of the aero-engine under the current state. While ensuring that multiple parameters do not exceed the limits, the parameters are restricted to be as close to the limit values as possible, enabling the inner loop control system to have a faster dynamic response speed. Brief Description of the Drawings

[0042] Figure 1 It is a structural diagram of the aero-engine limit protection control system based on a multi-dimensional instruction regulator.

[0043] Figure 2 It is a digital simulation flow chart of the aero-engine limit protection control based on a multi-dimensional instruction regulator.

[0044] Figure 3 It is a compressor speed response diagram comparing the switching method with the multi-dimensional instruction adjustment method.

[0045] Figure 4 It is an engine pressure ratio response diagram comparing the switching method with the multi-dimensional instruction adjustment method.

[0046] Figure 5 It is a low-pressure turbine outlet temperature response diagram comparing the switching method with the multi-dimensional instruction adjustment method.

[0047] Figure 6 It is a compressor outlet pressure response diagram comparing the switching method with the multi-dimensional instruction adjustment method.

[0048] Figure 7 It is a fuel flow diagram comparing the switching method with the multi-dimensional instruction adjustment method.

[0049] Figure 8 It is a nozzle throat area diagram comparing the switching method with the multi-dimensional instruction adjustment method.

[0050] Figure 9 It is a first-order difference diagram of fuel flow comparing the switching method with the multi-dimensional instruction adjustment method.

[0051] Figure 10 It is a first-order difference diagram of nozzle throat area comparing the switching method with the multi-dimensional instruction adjustment method. Detailed Embodiment

[0052] The technical solution of the present invention will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0053] In order to reduce the conservatism in the multi-variable limit protection control of aero-engines and the influence between the main controller and the limiter, the present invention proposes an aero-engine limit protection control method based on a multi-dimensional command regulator. Based on a main controller with good stability, robustness, and tracking control response speed, by designing a multi-dimensional command regulator, the virtual command is optimized online and rolled, realizing the fast tracking of multiple parameters of the aero-engine and the limit protection of multiple parameters.

[0054] Figure 1 The structure diagram of the aero-engine limit protection control system based on a multi-dimensional command regulator applied by the method of the present invention is shown. Taking the design of the aero-engine limit protection control method based on a multi-dimensional command regulator for a certain type of turbofan engine as an example, the design of the aero-engine limit protection control method based on a multi-dimensional command regulator includes the following steps:

[0055] Step 1: Establish the state space model of the inner-loop control system including the multi-variable main controller and the aero-engine;

[0056] Step 2: Design a multi-dimensional command regulator with input and output constraints;

[0057] Step 3: Digital simulation of the multi-dimensional command regulator limit protection control method.

[0058] Taking the operating state of 100% compressor speed and 100% engine pressure ratio at ground point of a certain type of turbofan engine as an example, further, the specific steps in Step 1 are as follows:

[0059] Step 1-1: Under the conditions of a flight altitude of 0 km, a Mach number of 0, a compressor speed of 100%, and an engine pressure ratio of 100%, use small perturbation and fitting method to establish the discrete state space model of the aero-engine, and normalize the model, as shown in Equation (1)

[0060]

[0061] where A, B, C, and D are dimensionally appropriate matrices, x(k), u(k), and y(k) are the state quantity, input quantity, and output quantity of the aero-engine, the state quantity of the aero-engine includes the fan speed n L , the compressor speed n H , the input quantity includes the fuel flow rate W f , the area of the nozzle throat A 8 , the output quantity includes the fan speed, the compressor speed, the engine pressure ratio EPR, the low-pressure turbine outlet temperature T 6 , the compressor outlet pressure P 3 , and each coefficient matrix is as shown in Equation (2)

[0062]

[0063]

[0064] Step 1-2: Design an augmented LQ tracking controller as the main controller of the inner-loop system according to the discrete state space model of the aero-engine. The form of the controller is shown in Equation (3).

[0065] Δu(k) = K 1 Δx(k) + K 2 (y m (k) - r(k)) (3)

[0066] where K 1 is a dimension-appropriate proportional coefficient matrix, K 2 is a dimension-appropriate integral coefficient matrix, Δu(k) is the first-order difference of the aero-engine input quantity, Δx(k) is the first-order difference of the aero-engine state quantity, y m (k) is the controlled output of the aero-engine, including the compressor speed and the engine pressure ratio, r(k) is the aero-engine tracking command, and the controller parameters are shown in Equation (4).

[0067]

[0068] Step 1-3: At a flight altitude of 0 km, a Mach number of 0, a compressor speed of 100%, and an engine pressure ratio of 100%, establish a discrete state space model of the aero-engine inner-loop control system using the decoupled step response and fitting method, as shown in Equation (5).

[0069]

[0070] where A cl , B cl , C cl , D cl are dimension-appropriate matrices, x cl , v(k) and y cl are the state quantity, input quantity, and output quantity of the aero-engine inner-loop control system. The state quantity of the aero-engine inner-loop control system includes the fan and compressor speeds and their first-order differences, the fuel flow rate, and the nozzle throat area. The input quantity includes the compressor speed virtual command v nL and the engine pressure ratio virtual command v EPR , and the output quantity includes the compressor speed, the low-pressure turbine outlet temperature, the compressor outlet pressure, the fuel flow rate, the nozzle throat area, the first-order difference of the fuel flow rate ΔW f , and the first-order difference of the nozzle throat area ΔA 8 , and each coefficient matrix is shown in Equation (6).

[0071]

[0072]

[0073]

[0074] Furthermore, the specific steps in Step 2 are as follows:

[0075] Step 2-1: Consider the input-output constraints of the aero-engine

[0076]

[0077] The subscripts max and min represent the maximum limit and the minimum limit respectively. According to the discrete state space model of the aero-engine inner loop control system, the maximum output allowable set is solved by the incremental optimization algorithm

[0078] O j ={(x cl (0), v(0)) | y cl (k; (x cl (0), v(0))) ∈ Y, k = 0, 1, …, j} (8)

[0079] where Y = {y cl (k) | Sy cl (k) ≤ s}, Finally, the prediction time domain length j = 64 is determined;

[0080] Step 2-2: Take the quadratic function of the difference between the compressor speed and the engine pressure ratio command and its virtual command as the optimization objective According to the maximum output allowable set and the discrete state space model of the aero-engine inner loop control system, the static and dynamic constraint matrices are initially set as

[0081]

[0082] The static and dynamic constraint matrices are established by the recursive algorithm as the constraint conditions of the optimization problem, as shown in Equation (10).

[0083]

[0084] Step 2-3: In the current state of the aero-engine inner loop control system, use the quadratic programming algorithm to solve the optimization problem established by the optimization objective and constraint conditions in Step 2-2

[0085]

[0086] Obtain the virtual commands v(k) of the compressor speed and the engine pressure ratio at the current moment. If the solution of the optimization problem (11) fails, use the virtual command at the previous moment as the virtual command at the current moment.

[0087] Figure 2 Shown is the digital simulation flowchart of the aero-engine limit protection control based on a multi-dimensional instruction regulator. Further, the specific steps in step 3 are as follows:

[0088] Step 3-1: Given the maximum, minimum values of the aero-engine input and output limits and the simulation duration k, and initialize the simulation time t = 0;

[0089] Step 3-2: Obtain the flight altitude, Mach number, and working state at the current moment, and give a suitable multi-dimensional step tracking instruction

[0090] Step 3-3: Obtain the discrete state space model of the aero-engine inner loop control system and the corresponding prediction time domain length at the current moment, and establish the optimization objective, static and dynamic constraint matrices;

[0091] Step 3-4: Obtain the current state variables of the aero-engine inner loop control system, and solve the multi-dimensional instruction optimization problem with input and output constraints using the quadratic programming algorithm to obtain the virtual instruction at the current moment.

[0092] Step 3-5: Use the virtual instruction as the tracking instruction to calculate the fuel flow rate and the area of the nozzle throat of the tail nozzle at the next moment by the main controller, and calculate the state variables and output variables of the aero-engine at the next moment;

[0093] Step 3-6: t = t + 1, repeat steps 3-2 to 3-5 until t = k.

[0094] The digital simulation of the aero-engine limit protection control based on the multi-dimensional instruction regulator is as Figures 3 to 10 shown. It can be seen from the figure that the aero-engine limit protection control method based on the multi-dimensional instruction regulator can achieve multi-parameter fast tracking and multi-parameter limit protection under the multi-variable control of the aero-engine. Compared with the traditional low-select - high-select switching method, on the one hand, since the instruction regulation does not affect the performance of the main controller, in the multi-variable control system, the regulation of the fuel flow rate and the area of the tail nozzle is more active. On the other hand, the multi-dimensional instruction regulator has an online rolling optimization effect, which enables the aero-engine limit parameters to work closer to their limit values, and obtains a faster response speed of the compressor speed and the engine pressure ratio.

[0095] The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and what is described in the above embodiments and the specification is only to illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. An aeroengine limit protection control method based on a multi-dimensional instruction regulator, characterized in that: It includes the following steps: Step 1: Establish a state space model of the inner loop control system including a multivariable main controller and an aeroengine; Step 2: Design a multi-dimensional instruction regulator with input and output constraints; Step 3: Digital simulation of the multi-dimensional instruction regulator limit protection control method; The specific steps for establishing the state space model of the inner loop control system in Step 1 are as follows: Step 1-1: At the current flight altitude, Mach number and working state, establish a discrete state space model of the aeroengine using small perturbation and fitting method, and normalize the model, as shown in Equation (1) where A, B, C, and D are matrices of appropriate dimensions, x(k), u(k), and y(k) are the state variables, input variables, and output variables of the aeroengine, and the state variables of the aeroengine include the fan speed n L , the compressor speed n H , the input variables include the fuel flow rate W f , the area of the throat of the exhaust nozzle A 8 , and the output variables include the fan speed, the compressor speed, the engine pressure ratio EPR, the temperature at the outlet of the low-pressure turbine T 6 , the pressure at the outlet of the compressor P 3 ; Step 1-2: Design an augmented LQ tracking controller as the main controller of the inner loop system based on the discrete state space model of the aeroengine, and the controller form is as shown in Equation (2) Δu(k) = K 1 Δx(k) + K 2 (y m (k) - r(k)) (2) where K 1 is the dimension-adaptive proportional coefficient matrix, K 2 is the dimension-adaptive integral coefficient matrix, Δu(k) is the first-order difference of the input quantity of the aero-engine, Δx(k) is the first-order difference of the state quantity of the aero-engine, y m (k) is the controlled output of the aero-engine, including the compressor speed and the engine pressure ratio, and r(k) is the tracking command of the aero-engine; Step 1-3: At the same flight altitude, Mach number and working state, establish a discrete state space model of the aeroengine inner loop control system using decoupled step response and fitting method, as shown in Equation (3) where A cl , B cl , C cl , D cl are dimension-adaptive matrices, x cl , v(k) and y cl are the state variables, input variables and output variables of the inner-loop control system of an aero-engine. The state variables of the inner-loop control system of an aero-engine include the fan and compressor speeds and their first-order differences, the fuel flow rate, and the nozzle throat area. The input variables include the virtual command v nL of the compressor speed and the virtual command v EPR of the engine pressure ratio. The output variables include the compressor speed, the low-pressure turbine outlet temperature, the compressor outlet pressure, the fuel flow rate, the nozzle throat area, the first-order difference ΔW f of the fuel flow rate, and the first-order difference ΔA 8 of the nozzle throat area; The specific steps for designing the multi-dimensional instruction regulator with input and output constraints in Step 2 are as follows: Step 2-1: Consider the input and output constraints of the aeroengine The subscripts max and min represent the limit maximum value and limit minimum value respectively. Based on the discrete state space model of the aeroengine inner loop control system, solve the maximum output allowable set using an incremental optimization algorithm O j = {(x cl (0), v(0))|y cl (k; (x cl (0), v(0))) ∈ Y, k = 0,1,…,j} (5) where Y = {y cl (k)|Sy cl (k) ≤ s}, finally determine the prediction time domain length j; Step 2-2: Taking the quadratic function of the differences between the compressor speed and the engine pressure ratio command and their virtual commands as the optimization objective Q is a positive definite matrix. According to the maximum output allowable set and the discrete state space model of the aero-engine inner loop control system, the static and dynamic constraint matrices are initially set as Establish static and dynamic constraint matrices as the constraint conditions of the optimization problem using a recursive algorithm, as shown in Equation (7) When t = j, the recursive algorithm ends; Step 2-3: In the current state of the aeroengine inner loop control system, solve the optimization problem established by the optimization objective and constraint conditions in Step 2-2 using a quadratic programming algorithm Obtain the virtual commands v(k) of the compressor speed and engine pressure ratio at the current moment. If the solution of the optimization problem (8) fails, use the virtual command of the previous moment as the virtual command at the current moment; The specific steps for the digital simulation of the multi-dimensional instruction regulator limit protection control method in Step 3 are as follows: Step 3-1: Given the maximum and minimum values of the aeroengine input and output and the simulation duration k, and initialize the simulation time t = 0; Step 3-2: Obtain the current flight altitude, Mach number and working state, and give a suitable multi-dimensional step tracking command; Step 3-3: Obtain the discrete state space model of the aeroengine inner loop control system and the corresponding prediction time domain length at the current moment, and establish an optimization objective, static and dynamic constraint matrices; Step 3-4: Obtain the current state variables of the aeroengine inner loop control system, and solve the multi-dimensional instruction optimization problem with input and output constraints using a quadratic programming algorithm to obtain the virtual command at the current moment; Step 3-5: Use the virtual command as the tracking command to calculate the fuel flow rate and the area of the nozzle throat of the afterburner at the next moment by the main controller, and calculate the state variables and output variables of the aeroengine at the next moment; Step 3-6: t = t + 1, repeat Steps 3-2 to 3-5 until t = k.

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