Power sensing method of electric variable pitch propeller

Abstract

The invention discloses a power sensing method of an electric variable-pitch propeller, which comprises the following steps of: firstly, isolating a torque estimation problem by using a nonlinear flux observer, estimating an electromagnetic torque and a load torque, and ensuring the robustness of parameter selection at the same time; secondly, a cascade neural network is adopted as a variable pitch propeller model, and the air speed and the thrust are estimated at the same time; and finally, the air density is estimated by using an air density iterative correction algorithm, and the estimation capability under different air densities is improved. The method can indirectly estimate the electromagnetic torque, the load torque, the airspeed, the thrust and the air density, and has robustness for system parameters.

Description

Technical Field

[0001] The invention belongs to the field of electric aircraft propulsion system control, and in particular relates to a power sensing method for an electric pitch-changing system. Background Art

[0002] Electric aircraft is a major innovation in the field of aviation. It has received extensive attention and research due to its environmental friendliness, high efficiency, and flexibility. The electric propulsion system is the core component of the aircraft, providing the necessary power for flight. The motor-propeller system is a common electric propulsion system, which is the basis for the operation of electric aircraft. Compared with traditional fuel engines, the electric propulsion system has a simple structure and lower carbon emissions and noise during flight.

[0003] Electric vertical take-off and landing aircraft have very different requirements for electric propulsion systems during the vertical take-off phase and the cruise phase. During the vertical take-off phase, the electric propulsion system needs to operate at low airspeed conditions while providing high thrust and torque. During the cruise phase, the electric propulsion system needs to operate at high airspeed conditions while providing low thrust and high torque. The electric variable pitch propeller system has proven to be superior and has great development potential, and can well meet the needs of these two phases. It provides two degrees of control: blade angle and rotation speed, which improves the overall maneuverability of the aircraft. Due to the installation of force and torque sensors, the structural stiffness is reduced, which has a great negative impact on aircraft with multiple propulsion systems. It is not feasible to directly measure the torque and thrust generated by the variable pitch propeller on electric vertical take-off and landing aircraft.

[0004] At present, there are few studies on the power sensing methods for electric variable pitch propellers. Accurate power sensing results are helpful for closed-loop control and status monitoring of the propulsion system. In traditional atmospheric data measurement methods, airspeed and air density are usually measured by multiple atmospheric sensors and then calculated by atmospheric data computers. High-precision airspeed measurement systems are expensive. The installation of force and torque sensors will lead to a decrease in structural stiffness, which has a great negative impact on aircraft with multiple propulsion systems. It is not feasible to directly measure the torque and thrust generated by variable pitch propellers on vertical take-off and landing aircraft.

[0005] The determination of the existing technical solutions is summarized as follows:

[0006] 1. The cost of measuring airspeed and air density is expensive. The existing measurement method uses multiple atmospheric sensors to measure, and then the atmospheric data computer calculates the accurate results. The measurement data is complex and the calculation cost is high. The installation of multiple sensors increases the operation and maintenance costs.

[0007] 2. Force and torque cannot be measured directly. After installing force and torque sensors, the structural stiffness will be reduced, the original mechanical structure will be changed, and the complexity of the system will be increased, which will have a great negative impact on aircraft with multiple propulsion systems. Summary of the invention

[0008] An object of the present invention is to provide a power sensing method for an electric variable pitch propeller and to provide at least the advantages to be described below.

[0009] The present invention proposes a power sensing method for an electric variable pitch propeller, which utilizes high-frequency information of a permanent magnet synchronous motor during operation and pitot tube data to obtain the electromagnetic torque, load torque, airspeed, thrust and air density of the propulsion system. The model has strong nonlinearity, low estimation cost and is robust to parameter selection.

[0010] The technical solution of the present invention is as follows:

[0011] The dynamic sensing method of the electric variable pitch propeller, first, uses the nonlinear flux observer to isolate the torque estimation problem, estimates the electromagnetic torque and load torque, and ensures the robustness of parameter selection; secondly, uses a cascade neural network as the variable pitch propeller model to estimate the airspeed and thrust at the same time; finally, uses the air density iterative correction algorithm to estimate the air density, thereby improving the estimation ability under different air densities.

[0012] Preferably, in the electric variable pitch propeller power sensing method, the principle of the nonlinear flux observer is as follows:

[0013] The permanent magnet synchronous motor circuit model is established as

[0014]

[0015] U,I and are the voltage, current and flux of each axis respectively, R s is the phase resistance. By adding the correction term, the principle of the nonlinear flux observer is

[0016]

[0017] in are the parameters of the nonlinear flux observer, corresponding to the internal inductance, resistance and flux of the permanent magnet synchronous motor. i αβ =[i α ,i β ] T ,U αβ =[U α ,U β ] T Even if the nonlinear flux observer parameters are inconsistent with the actual motor internal parameters, accurate flux results can be obtained.

[0018] Preferably, in the electric variable pitch propeller power sensing method, the principle of estimating the electromagnetic torque and load torque by using a nonlinear flux observer is as follows:

[0019]

[0020]

[0021] Where P n , B, ω are the number of pole pairs of the permanent magnet synchronous motor, the system damping coefficient and the speed. Due to the robustness of the nonlinear flux observer, a robust electromagnetic torque estimate can be obtained based on the electromagnetic torque formula of the permanent magnet synchronous motor. Based on the kinematic equation of the motor, the load torque estimate of the motor can be obtained, which is also the torque of the variable pitch propeller.

[0022] Preferably, in the electric variable pitch propeller power sensing method, a cascade neural network is used as a variable pitch propeller model, and the principle is as follows:

[0023] First, a neural network with speed, load torque and blade angle as input and airspeed output is established to solve the airspeed estimation problem of variable pitch propeller, which is equivalent to the torque model of variable pitch propeller. Secondly, using the estimated airspeed, a neural network with airspeed, speed and blade angle as input and thrust output is established to solve the thrust estimation problem of variable pitch propeller, which is equivalent to the thrust model of variable pitch propeller. The two networks have some of the same inputs, and the estimation results of the previous network are used as the input of the next network.

[0024] Preferably, in the electric variable pitch propeller power sensing method, the air density iterative correction algorithm estimates the air density based on the following principle:

[0025]

[0026] Where k is the sequence number, is the estimated airspeed, η is the correction factor, P d is the dynamic pressure measured by the Pitot tube, p is the air density corresponding to the model establishment, δ is the blade angle, ω is the rotation speed, and f T (·) is the torque model of the variable pitch propeller, f F (·) is the thrust model of the variable pitch propeller. The dynamic pressure measured by the pitot tube is used as the correction term, and the calculation results are gradually converged to the robust value by using the digital iteration method.

[0027] Preferably, in the electric variable pitch propeller power sensing method, the convergence principle of the air density iterative correction algorithm is as follows:

[0028] The objective function is established as

[0029]

[0030] When f(ρ) satisfies the Lipschitz condition, we can obtain the existence of a positive airspeed V L and positive thrust F max Make V>V L hour At the same time, it satisfies the quadratic upper bound lemma

[0031]

[0032] Since the actual airspeed is not available, there must be a bounded error when using the estimated airspeed. When there is a bounded error in the airspeed calculation, the objective function becomes

[0033]

[0034] Therefore, the updated air density formula is Substituting it into the quadratic upper bound lemma formula, we can get

[0035]

[0036] exist When, define the function g(ρ k )for

[0037]

[0038] According to the properties of quadratic functions, it can be proved that no matter No matter how it changes, there is always an error that satisfies e1<e k <e2 makes g(ρ k )≤0, that is

[0039] f(ρ k+1 )≤f(ρ k )+g(ρ k )

[0040] ≤f(ρ k )

[0041] According to the convergence property of the series, we can get

[0042]

[0043] So the convergence result of air density is

[0044]

[0045] The above proof gives the relationship between the air density convergence result and the relative error of airspeed estimation.

[0046] The present invention has the following beneficial effects:

[0047] 1. The present invention provides a power sensing method for an electric variable pitch propeller, which utilizes the high-frequency information of a permanent magnet synchronous motor during operation and pitot tube data to indirectly and reliably estimate torque, thrust, wind speed and atmospheric density, thereby reducing the measurement cost of the propulsion system, realizing power sensing at the propulsion system level, and facilitating the propulsion system to achieve real-time thrust closed-loop control.

[0048] 2. The present invention provides an electromagnetic torque estimation method, which uses a nonlinear flux observer to estimate the flux and estimates the electromagnetic torque and load torque. The observer parameter selection is robust, which improves the reliability of the torque estimation result.

[0049] 3. The present invention provides a modeling method for a variable pitch propeller, which adopts a cascade neural network as the model of the variable pitch propeller, and simultaneously obtains the estimated thrust and wind speed, thereby improving the fitting ability of the nonlinear model.

[0050] 4. The present invention provides an iterative calculation method for air density, and proves the relationship between the relative error of establishing the airspeed model and the convergence result. It takes into account the specific implementation method of the digital system, does not use an atmospheric data computer to obtain air density, and reduces the cost and complexity of obtaining air density.

[0051] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of the flow chart of the power sensing method of an electric variable pitch propeller provided by the present invention;

[0053] Figure 2 Schematic diagram of a cascade neural network model applicable to the present invention. DETAILED DESCRIPTION

[0054] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0055] The present invention is designed for a power sensing method of an electric variable pitch propeller, and the power sensing includes estimation of electromagnetic torque, load torque, airspeed, thrust and atmospheric density.

[0056] Specifically,

[0057] like Figure 1As shown, the present invention proposes a power sensing method for an electric variable pitch propeller, which uses the high-frequency information of the permanent magnet synchronous motor during operation and the pitot tube data to obtain the electromagnetic torque, load torque, airspeed, thrust and air density of the propulsion system. Firstly, a nonlinear flux observer is used to isolate the torque estimation problem, and the electromagnetic torque and load torque are estimated, while ensuring the robustness of parameter selection; secondly, a cascade neural network is used as a variable pitch propeller model to estimate the airspeed and thrust at the same time; finally, an air density iterative correction algorithm is used to estimate the air density, so as to improve the estimation ability under different air densities.

[0058] In the electric variable pitch propeller power sensing method, the principle of the nonlinear flux observer is as follows:

[0059] The permanent magnet synchronous motor circuit model is established as

[0060]

[0061] U,I and are the voltage, current and flux of each axis respectively, R s is the phase resistance. By adding the correction term, the principle of the nonlinear flux observer is

[0062]

[0063] in are the parameters of the nonlinear flux observer, corresponding to the internal inductance, resistance and flux of the permanent magnet synchronous motor. i αβ =[i α ,i β ] T ,U αβ =[U α ,U β ] T Even if the nonlinear flux observer parameters are inconsistent with the actual motor internal parameters, accurate flux results can be obtained. The nonlinear flux observer is Euler discretized and updated at a certain frequency. When the voltage and current of each axis are collected, the real-time flux is obtained.

[0064] In the electric variable pitch propeller power sensing method, the principle of estimating electromagnetic torque and load torque using a nonlinear flux observer is as follows:

[0065]

[0066] Where P n, B, ω are the number of pole pairs of the permanent magnet synchronous motor, the system damping coefficient and the measured speed. Due to the robustness of the nonlinear flux observer, a robust electromagnetic torque estimate can be obtained based on the electromagnetic torque formula of the permanent magnet synchronous motor. Based on the kinematic equation of the motor, the load torque estimate of the motor can be obtained, which is also the torque of the variable pitch propeller.

[0067] In the electric variable pitch propeller power sensing method, if Figure 2 As shown, the cascade neural network is used as the variable pitch propeller model, and the BP neural network is selected as the basic network for cascade operation. The principle is as follows:

[0068] Firstly, a BP neural network with speed, load torque and blade angle as input and airspeed output is established to solve the airspeed estimation problem of variable pitch propeller, which is equivalent to the torque model of variable pitch propeller. Secondly, using the estimated airspeed, a BP neural network with airspeed, speed and blade angle as input and thrust output is established to solve the thrust estimation problem of variable pitch propeller, which is equivalent to the thrust model of variable pitch propeller. The two networks have some of the same inputs, and the estimation results of the previous network are used as the input of the next network.

[0069] In the electric variable pitch propeller power sensing method, the air density iterative correction algorithm estimates the air density as follows:

[0070]

[0071] Where k is the sequence number, is the estimated airspeed, η is the correction factor, P d is the dynamic pressure measured by the Pitot tube, p is the air density corresponding to the model establishment, δ is the measured blade angle, and ω is the measured rotation speed. T (·) is calculated by BP neural network to get f F (·) is calculated by cascade neural network, Obtained indirectly by the nonlinear flux observer. The air density iterative correction algorithm is executed at a certain frequency, the dynamic pressure measured by the pitot tube is used as the correction term, and the calculation results are gradually converged to the robust value by the digital iteration method.

[0072] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes. They can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to specific details.

Claims

1. A method for sensing power of an electric variable pitch propeller, characterized in that: Firstly, the nonlinear flux observer is used to isolate the torque estimation problem and estimate the electromagnetic torque and load torque while ensuring the robustness of parameter selection. Secondly, a cascade neural network is adopted as the variable pitch propeller model to estimate the airspeed and thrust simultaneously. Finally, the air density is estimated using the air density iterative correction algorithm to improve the estimation ability under different air densities.

2. The power sensing method of the electric variable pitch propeller according to claim 1, characterized in that: The principle of the nonlinear flux observer is as follows: The permanent magnet synchronous motor circuit model is established as U,I and are the voltage, current and flux of each axis respectively, R s is the phase resistance. By adding the correction term, the principle of the nonlinear flux observer is: in are the parameters of the nonlinear flux observer, corresponding to the internal inductance, resistance and flux of the permanent magnet synchronous motor; and αβ =[and α ,and β ] T ,IN αβ =[U α ,IN β ] T 。 3. The power sensing method of the electric variable pitch propeller according to claim 2, characterized in that: The principle of estimating electromagnetic torque and load torque using nonlinear flux observer is as follows: Where P n , B, ω are the pole pairs, system damping coefficient and speed of the permanent magnet synchronous motor respectively.

4. The power sensing method of the electric variable pitch propeller according to claim 2, characterized in that: The principle of using the cascade neural network as a variable pitch propeller model is as follows: Firstly, a neural network with rotational speed, load torque and blade angle as input and airspeed output is established to solve the airspeed estimation problem of variable pitch propeller, which is equivalent to the torque model of variable pitch propeller; secondly, using the estimated airspeed, a neural network with airspeed, rotational speed and blade angle as input and thrust output is established to solve the thrust estimation problem of variable pitch propeller, which is equivalent to the thrust model of variable pitch propeller; the two networks have some of the same inputs, and the estimation result of the previous network is used as the input of the next network.

5. The power sensing method of the electric variable pitch propeller according to claim 2, characterized in that: The air density iterative correction algorithm estimates the air density based on the following principle: Where k is the sequence number, is the estimated airspeed, η is the correction factor, P d is the dynamic pressure measured by the Pitot tube, p is the air density corresponding to the model establishment, δ is the blade angle, ω is the rotation speed, and f T (·) is the torque model of the variable pitch propeller, f F (·) is the thrust model of the variable pitch propeller.

6. The power sensing method of the electric variable pitch propeller according to claim 2, characterized in that: The convergence principle of the air density iterative correction algorithm is as follows: The objective function is established as When f(ρ) satisfies the Lipschitz condition, we can obtain the existence of a positive airspeed V L and positive thrust F max Make V>V L hour At the same time, it satisfies the quadratic upper bound lemma Since the actual airspeed is not available, there must be a bounded error when using the estimated airspeed; when there is a bounded error in the airspeed calculation, the objective function becomes Therefore, the updated air density formula is Substituting it into the quadratic upper bound lemma formula, we can get exist When we define the function g(ρ k )for According to the properties of quadratic functions, it can be proved that no matter No matter how it changes, there is always an error that satisfies e1<e k <e2 makes g(ρ k )≤0, that is f(r k+1 )≤f(ρ k )+g(ρ k ) ≤f(ρ k ) According to the convergence property of the series, we can get So the convergence result of air density is The above proof gives the relationship between the air density convergence result and the relative error of airspeed estimation.

Images