METHOD FOR CONTROLLING THE PRESSURE AND A MIXTURE RATIO OF A ROCKET ENGINE, AND CORRESPONDING DEVICE

Abstract

A method of controlling the pressure (PGC) and a mixture ratio of a rocket engine from a pressure setpoint (PGCc) and from a mixture ratio setpoint (RMc), the method comprising regulation delivering control signals for two control valves (VR1, VR2) of said engine, the regulation using a pressure feedback loop. The method further comprises determining an estimated value for the mixture ratio (RMe) used by said regulation, the estimated value for the mixture ratio being obtained by a model that delivers mixture ratio values as estimated from at least one of the two control valve control signals and/or from the measured pressure.

The invention also provides a control device.

Claims

1. A method of controlling the pressure and a mixture ratio of a rocket engine from a pressure setpoint and from a mixture ratio setpoint, the method comprising regulation delivering control signals for two control valves of said engine, the regulation using a pressure feedback loop, wherein the method further comprises determining an estimated value for the mixture ratio used by said regulation, the estimated value for the mixture ratio being obtained by a model that delivers mixture ratio values as estimated from at least one of the two control valve control signals and/or from the measured pressure.

2. The method according to claim 1, wherein said at least one of the control valve control signals is a control signal having greater operating sensitivity on the mixture ratio than the other control valve signal.

3. The method according to claim 1, including prior dynamic processing of at least one of the two control valve control signals and of the treasured pressure before delivering them to the model, and dynamic processing of the estimated mixture ratio value as obtained by the model.

4. The method according to claim 1, wherein said model comprises an artificial neural network.

5. The method according to claim 1, wherein an offset is applied to said at least one of the two control valve control signals and to said measured pressure prior to supplying them to said model.

6. The method according to claim 4, comprising prior training of said artificial neural network, testing said rocket engine, and resetting said artificial neural network in order to deduce said offsets therefrom.

7. A device for controlling the pressure and a mixture ratio of a rocket engine, the device having an input for receiving a pressure setpoint, an input for receiving a mixture ratio setpoint, and a pressure regulator module delivering control signals for two control valves of said engine, the regulator module using a pressure feedback loop, wherein the device includes an estimator module for estimating the mixture ratio and including a model delivering to said regulator module values for the mixture ratio that are estimated from at least one of the two control valve control signals and/or from the measured pressure.

8. A system including a rocket engine and a device according to claim 7.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Other characteristics and advantages of the present invention appear from the following description given with reference to the accompanying drawings, which show an embodiment having no limiting character.

[0034] In the figures:

[0035] FIG. 1 is a diagram showing an engine and a control device of the invention;

[0036] FIG. 2 shows more precisely a control device of the invention;

[0037] FIG. 3 is a diagram of an estimator module of the invention;

[0038] FIG. 4 shows an example of a surface that can be used in an estimator module model;

[0039] FIG. 5 shows a variant of a control device of the invention; and

[0040] FIG. 6 is a diagram showing certain prior steps of a method of the invention.

DETAILED DESCRIPTION OF AN EMBODIMENT

[0041] There follows a description of a method and a device for controlling the pressure and the mixture ratio within a rocket engine.

[0042] FIG. 1 shows a system comprising a control device DC and a rocket engine. Although the rocket engine in this figure is included in a system of the invention, its internal structure has no hearing on the invention proper.

[0043] By way indication, the structure of the engine in FIG. 1 corresponds to the structure described in greater detail in Document EP 2 187 031.

[0044] In this example, the engine has two propellant tanks 131 and 132. Downstream from each tank 131 and 132, the engine has a respective pump 111 or 112.

[0045] Downstream from the pumps, the engine has two control valves VR1 and VR2 referred to as bypass valves and located on the two turbine lines. It may be observed that in the present description the references VR1 and VR2 are used both to designate the control valves and also their control signals, which serve to determine their positions.

[0046] The engine also has a combustion chamber, and it is in the combustion chamber that it is desired to regulate the pressure (referenced PGC below) and the mixture ratio.

[0047] It may be observed that in the configuration shown, the person skilled in the art knows that the control valve VR2 is the valve that has the greater functional sensitivity on the mixture ratio.

[0048] The control device DC for controlling the pressure and the mixture ratio has an input for receiving a pressure setpoint referenced PGCc and an input for receiving a mixture ratio setpoint RMc. In order to regulate pressure in a closed loop, the real pressure PGC in the combustion chamber is measured by a sensor connected to another input of the control device DC.

[0049] The control device DC delivers control signals to both control valves VR1 and VR2.

[0050] FIG. 2 shows in greater detail the control device DC described with reference to FIG. 1.

[0051] As mentioned above, the device DC has inputs for the pressure setpoint PGCc, the mixture ratio setpoint RMc, and the measured pressure PGC, and the device outputs control signals VR1 and VR2 (which signals are for minimizing the errors input to the corrector).

[0052] In order to deliver the control signals, the device DC has a corrector COR and an estimator module M. The module M delivers estimated values for the mixture ratio, written RMe, that are estimated on the basis of the pressure PGC and of the control signal VR2, using a model that associates these parameters.

[0053] The corrector handles errors firstly between the pressure PGC and the pressure setpoint PGCc (by the feedback loop), and secondly between the estimated mixture ratio RMe and the mixture ratio setpoint RMc. The corrector can thus deliver the control signals VR1 and VR2.

[0054] The structure of the corrector COR is analogous to that of a prior art corrector in which it is possible to measure the mixture ratio.

[0055] FIG. 3 shows an embodiment of an estimator module M that includes a model SF, e.g. a surface defined by pressure values, values for the control signal VR2, and estimated values for the mixture ratio.

[0056] The model SF may comprise an artificial neural network.

[0057] This figure also shows the dynamic portion of the estimator module M. The estimator module M includes modules for prior processing of each input to the module M, dynamic processor module FTI processing the control signal VR2 and a processor module FT2 processing the pressure PGC. The model SF delivers values that are processed by a dynamic processor module FT3 subsequently to deliver the estimated value RMe for the mixture ratio.

[0058] The dynamic processor modules FT1, FT2, and FT3 may present transfer functions having zeros and poles, and the person skilled in the art knows how to select the form of these functions as a function of the application.

[0059] FIG. 4 shows an example of a surface SF defined by pressure values PGC, by values for the control signal VR2, and by estimated values RMe for the mixture ratio. Such a surface can be modeled by an artificial neural network.

[0060] In this figure, a bold line represents an example path followed when performing the control method of the invention.

[0061] FIG. 5 shows a variant of a control device DC. Elements having the same references as those given in FIG. 2 are identical.

[0062] In this figure, there is shown the application of an offset offset1 to the valve control signal VR2 prior to the signal being supplied to the estimator module M.

[0063] In analogous manner, there can be seen the application of an offset offset2 to the pressure PGC prior to the pressure being supplied to the module M.

[0064] FIG. 6 shows a set of steps P performed prior to controlling a rocket engine in an implementation and an embodiment in which the model includes an artificial neural network.

[0065] Prior to performing control, an artificial neural network is subjected to training (step E1). This may be done using a database of rocket engine test data.

[0066] The artificial neural network obtained after step E1 could be used for controlling a rocket engine. That said, in order to take account of characteristics that are specific to one particular engine, it is possible to perform tests on that engine (step E2) in order to observe how the pressure, the control signal VR2, and the mixture ratio are associated in that engine.

[0067] It is thus possible to reset (step E3) the artificial neural network (or the corresponding surface), and for example to determine the offsets offset1 and offset2 described with reference to FIG. 5.