Prediction-based constrained pilot model and modeling method

Abstract

The invention provides a predication-based constrained pilot model and a modeling method, the predication-based constrained pilot model comprises a ship-aircraft ring module, a fuzzy sensing module, a pilot decision module and a pilot execution module which are connected in sequence, and the pilot execution module is connected with the ship-aircraft ring module; the fuzzy sensing module comprises a pilot height deviation sensing sub-module, a pilot transverse deviation sensing sub-module and a pilot flight state sensing sub-module which work cooperatively; the fuzzy sensing module is responsible for receiving and processing optical guiding information, centering deviation information, the distance from a ship to the ship and LSO voice instruction information from the ship-aircraft ring module, and simulating the process that a pilot estimates deviation and an expected control target according to the information; an expected operation instruction is output by integrating flight trend prediction and considering a control constraint decision of a pilot, a final actual operation instruction is generated after the expected operation instruction passes through a pilot execution module, and the flight safety and stability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of flight mechanics and flight control technology, and in particular to a prediction-based constrained pilot model and a modeling method. Background Art

[0002] The essence of the carrier-based aircraft pilot model is a practical model that meets the needs of describing human control under environmental constraints in aircraft dynamics modeling. As flight missions become more complex, in order to meet research needs, many variants of the pilot model have emerged from the modeling perspective and algorithm application, but its core of matching mission requirements has never changed. An effective pilot model always needs to describe control characteristics while matching aircraft characteristics, environmental constraints and mission requirements.

[0003] During the approach and landing process, carrier-based aircraft pilots need to control three variables. Under the disturbance of the ship's wake and atmospheric turbulence, the aircraft is landed in a basketball court-sized and constantly moving arresting area at a certain speed and attitude. The task is difficult and the workload is huge. The behavior of rigorously trained pilots is regular when completing specific tasks, making it possible to establish a pilot control behavior model. The perception, decision-making and execution behavior of pilots have always been the focus of research in the fields of flight mechanics, flight control and flight safety. In complex environments, establishing a model that can simulate and predict pilot behavior is of great significance to flight safety analysis and flight strategy formulation. At present, there are many pilot models available in the field of carrier-based aircraft. Generally speaking, they can be divided into three categories according to the modeling ideas: quasi-linear models based on classical control theory, optimal theoretical models based on modern control theory, and fuzzy control models based on fuzzy theory.

[0004] Quasi-linear models based on classical control theory include the McRuer model and the structural pilot model. The McRuer model is a parameter adjustment model that studies the control behavior characteristics of pilots based on the analysis of a large number of pilot-in-the-loop simulation experiment results. Hess first proposed the structural pilot model in the compensation tracking task in 1979, which reflects more realistic signal processing than the McRuer model. The model consists of two parts: the central nervous system and the neuromuscular system, in order to highlight the two functions of the pilot's signal processing and control decision-making. The core parameter of the structural pilot model is the internal feedback signal. By changing its form, the pilot model can be adapted to controlled objects with different characteristics. Hess himself analyzed the human-in-the-loop pitch angle control characteristics of different aircraft models during carrier-based aircraft approach and landing based on this model. However, the problem with this model is that it only models the inner loop control, lacks an outer loop representation, and cannot be directly used in a complete carrier-based aircraft man-machine system. And due to the inherent characteristics of the model, it is impossible to express the coupling operation. Existing technicians have solved the application problem of the structural pilot model in the carrier-based aircraft approach and landing mission to a certain extent by describing the pilot's outer loop control behavior as a bang-bang control similar to a relay, but still use decoupling processing. And because the outer loop is in the form of bang-bang control, the convergence of the model in complex environments is poor. In addition, in addition to McRuer and structural pilot models, there are other variants of quasi-linear models in pilot control behavior modeling, but none of them are as representative as these two models. Although quasi-linear models have very good applications in the field of PIO, they are difficult to apply when facing multi-loop control tasks. As NASA pointed out in the report, under simple tasks, the linear part of the model will account for most of the controller output power. However, as the complexity of the task increases, the quasi-linear model will appear insufficient. When it comes to multi-channel control, even if the construction method of the single-channel model can be extended to multi-channel, it is difficult to solve the problem of judging the model structure, and each channel is required to be decoupled from each other. These reasons limit the application of quasi-linear models under complex working conditions. Therefore, quasi-linear models are not suitable for describing the control behavior of carrier-based aircraft pilots in approach and landing under complex environments.

[0005] The optimal pilot model based on modern control theory has great advantages in dealing with multi-loop control problems. Since the model describes the pilot's control behavior from the perspective of overall performance based on the optimal assumption, strict decoupling is not required. The biggest difference between the optimal pilot model and the structural model is that its modeling is not based on the frequency domain identification criterion, but a natural intuition assumption: the control behavior of human pilots is optimal to a certain extent. The rationality of this assumption has been studied for a long time. According to experience, pilots always expect to keep the human-machine system with a phase margin of at least 60°, which is consistent with some theories in optimal control theory. Based on this assumption, it becomes feasible to discuss the pilot's control behavior from the perspective of overall performance optimization. Only by designing a reasonable model structure, the optimal model can be gradually extended to a wider range of flight missions, such as the LQR pilot model, the MOCM-AE pilot model, etc., proving the rationality of the extrapolated model based on the optimal assumption. Existing technicians have applied the MOCM-AE model to the carrier-based aircraft landing mission, and considered the influence of low-altitude turbulence and ship wake, proving that the pilot model based on modern control theory is suitable for carrier-based aircraft landing missions. However, the model still has certain shortcomings. First, it does not consider the pilot's control input constraint problem. When approaching and landing, in order to prevent overshoot of the carrier-based aircraft, the LSO usually constrains the pilot's control rate. Due to the characteristics of optimal control, the control constraint will significantly affect the performance of the model. Secondly, the model only considers the pilot's adaptive estimation of noise, and does not incorporate the pilot's trend prediction behavior into the model. Therefore, it is impossible to describe the control behavior of the carrier-based aircraft pilot based on the overall trend.

[0006] Due to human uncertainty and ambiguity, some studies have also established pilot models based on fuzzy control theory. For example, the variable strategy pilot model designed by existing technicians has an outer loop structure that approximates a fuzzy mapping containing two sets. There are also existing technicians who have designed a discrete fuzzy pilot model suitable for carrier-based aircraft landing control, which decomposes the pilot's control behavior into a series of discrete square waves and designs the pilot's response mode based on fuzzy rules. Due to the mathematical characteristics of fuzzy sets, the model has a clear rule mapping when responding to LSO instructions, so it can explain the working principle of the pilot-LSO loop. However, in describing the pilot's control behavior of the carrier-based aircraft, since the model output is a pulse square wave signal and the control performance depends on the fuzzy rule design, the convergence is poor in complex environments. Therefore, it is not suitable for explaining pilot models in complex environments, but it can be used to describe the control behavior of LSO. Summary of the invention

[0007] The present invention aims at the problems that the various pilot models in the field of carrier-based aircraft do not consider the control input constraints of the pilots, cannot describe the control behavior of the pilots of carrier-based aircraft based on the overall trend, and have poor convergence in complex environments. A constrained pilot model based on prediction is provided. Based on optical guidance information, centering deviation information, LSO voice command information, and parameters such as distance from the ship, a prediction equation group for trend prediction is established by adopting a specific calculation method and equation, and an objective function is constructed under the established specific constraints to calculate the actual operation instructions of the pilots, which can help pilots better predict future trends, reduce the risks caused by excessive operation of pilots, and improve the safety and stability of flight. The present invention also relates to a modeling method of a constrained pilot model based on prediction.

[0008] The technical solution of the present invention is as follows:

[0009] A prediction-based constrained pilot model, characterized by comprising a ship-machine ring module, a fuzzy perception module, a pilot decision module and a pilot execution module connected in sequence, wherein the pilot execution module is connected to the ship-machine ring module; the fuzzy perception module comprises a pilot altitude deviation perception submodule, a pilot lateral deviation perception submodule and a pilot flight status perception submodule,

[0010] The aircraft ring module is used to provide optical guidance information, centering deviation information between the carrier aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the carrier aircraft and the ideal landing point on the aircraft carrier, and the navigation parameters of the carrier aircraft;

[0011] The pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected according to the optical guidance information and the LSO voice command information and adopts the fuzzy optical perception model, and calculates the corrected altitude deviation estimation value according to the glide path altitude deviation angle, the distance from the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path altitude deviation angle and adopts the altitude deviation estimator;

[0012] The pilot lateral deviation perception submodule calculates the lateral deviation angle of the centerline of the aircraft carrier according to the centering deviation information and the LSO voice command information and adopts the fuzzy lateral perception model, and calculates the lateral deviation observation value according to the lateral deviation angle of the centerline of the aircraft carrier and the observation noise; and then calculates the track deviation angle according to the speed and heading in the navigation parameters;

[0013] The pilot flight state perception submodule calculates the actual observation amount of the pilot according to the observation noise, and calculates the flight state estimation value according to the actual observation amount and using the flight state estimator;

[0014] The pilot decision module constructs an expected trajectory sequence based on the corrected altitude deviation estimate, lateral deviation observation value and track deviation angle; obtains a state space model of the aircraft based on the carrier-based aircraft's landing reference motion state and a coefficient freezing method, discretizes the state space model to obtain a discrete state space equation, and obtains a prediction equation group according to the discrete state space equation; then constructs a prediction observation sequence according to the flight state estimate at each moment in the prediction equation group, and constructs a control sequence according to the pilot's expected instructions at each moment in the prediction equation group; and establishes an objective function according to the expected trajectory sequence, the prediction observation sequence and the control sequence under the constraint conditions established based on the value range of the expected instructions at each moment; then uses quadratic programming to process the objective function to obtain a new objective function, and calculates the pilot's optimal expected instruction according to the new objective function and an interior point method;

[0015] The pilot execution module calculates the pilot's actual operation instruction according to the optimal expected instruction, and sends the actual operation instruction to the carrier-based aircraft ring module so that the carrier-based aircraft executes the actual operation instruction.

[0016] Preferably, in the pilot altitude deviation perception submodule, the corrected altitude deviation estimate value calculation process specifically includes:

[0017] The height deviation value is calculated according to the glide path height deviation angle and the distance to the ship, and the state vector is constructed based on the distance to the ship and the height deviation value. The state transfer equation is established according to the state vector at the current moment, the state vector at the next moment of the current moment, the system noise and the observation noise. The expression of the height deviation estimate is obtained according to the state transfer equation, and then the corrected height deviation estimate is calculated.

[0018] Preferably, in the pilot altitude deviation perception submodule, the fuzzy optical perception model includes a fuzzifier, a membership function, fuzzy reasoning, a fuzzy rule base and a defuzzifier.

[0019] Preferably, in the pilot altitude deviation perception submodule, the glide slope altitude deviation angle calculation process to be corrected specifically includes:

[0020] The optical guidance information and LSO voice command information are converted into a fuzzy set defined by a membership function through the fuzzifier and input into the fuzzy reasoning. The fuzzy reasoning matches the input fuzzy set with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set. The defuzzifier defuzzifies the output fuzzy set obtained by the fuzzy reasoning to obtain the glide slope height deviation angle to be corrected.

[0021] Preferably, in the pilot decision module, the constraint conditions established based on the value range of the pilot's expected instructions at each moment specifically include: obtaining the physical structure constraints of the aircraft based on the aircraft structure, and obtaining the maximum and minimum values ​​of the expected instructions based on the physical structure constraints of the aircraft, and establishing the constraint conditions based on the expected instructions at each moment, the maximum value of the expected instructions, and the minimum value of the expected instructions.

[0022] A prediction-based constrained pilot model modeling method, characterized in that the method is performed by a model as described in any one of claims 1 to 5, and the method comprises:

[0023] The ship-aircraft ring module provides optical guidance information, centering deviation information between the carrier-based aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the carrier-based aircraft and the ideal landing point on the aircraft carrier, and the navigation parameters of the carrier-based aircraft; the pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected based on the optical guidance information and the LSO voice command information and adopts the fuzzy optical perception model, and calculates the corrected altitude deviation estimate value based on the glide path altitude deviation angle, the distance to the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path altitude deviation angle and adopts the altitude deviation estimator; the pilot lateral deviation perception submodule calculates the lateral deviation angle of the centerline of the aircraft carrier based on the centering deviation information and the LSO voice command information and adopts the fuzzy lateral perception model, and calculates the lateral deviation observation value based on the lateral deviation angle of the centerline of the aircraft carrier and the observation noise; and then calculates the track deviation angle based on the speed and heading in the navigation parameters; the pilot flight state perception submodule calculates the actual observation amount of the pilot based on the observation noise, and calculates the flight state estimation value based on the actual observation amount and adopts the flight state estimator;

[0024] The pilot decision module constructs the expected trajectory sequence based on the corrected altitude deviation estimate, lateral deviation observation value and track deviation angle; the state space model of the aircraft is obtained based on the carrier-based aircraft landing reference motion and the coefficient freezing method, and the state space model is discretized to obtain the discrete state space equation, and the prediction equation group is obtained according to the discrete state space equation, and then the prediction observation sequence is constructed according to the flight state estimate at each moment in the prediction equation group, and the control sequence is constructed according to the pilot's expected instructions at each moment in the prediction equation group; and under the constraint conditions established based on the value range of the expected instructions at each moment, the objective function is established according to the expected trajectory sequence, the prediction observation sequence and the control sequence; and the objective function is processed by quadratic programming to obtain a new objective function, and the optimal expected instruction of the pilot is calculated according to the new objective function and the interior point method;

[0025] The pilot execution module calculates the pilot's actual operation instructions based on the optimal expected instructions, and sends the actual operation instructions to the ship-aircraft ring module so that the carrier-based aircraft executes the actual operation instructions.

[0026] Preferably, calculating the corrected altitude deviation estimate using an altitude deviation estimator according to the altitude deviation angle of the glide path, the distance from the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the altitude deviation angle of the glide path specifically includes:

[0027] The height deviation value is calculated according to the glide path height deviation angle and the distance to the ship, and the state vector is constructed based on the distance to the ship and the height deviation value. The state transfer equation is established according to the state vector at the current moment, the state vector at the next moment of the current moment, the system noise and the observation noise. The expression of the height deviation estimate is obtained according to the state transfer equation, and then the corrected height deviation estimate is calculated.

[0028] Preferably, the fuzzy optical perception model and the fuzzy lateral perception model both include a fuzzifier, a membership function, fuzzy reasoning, a fuzzy rule base and a defuzzifier.

[0029] Preferably, calculating the glide slope height deviation angle to be corrected according to the optical guidance information and the LSO voice command information and using the fuzzy optical perception model specifically includes:

[0030] The optical guidance information and LSO voice command information are converted into a fuzzy set defined by a membership function through the fuzzifier and input into the fuzzy reasoning. The fuzzy reasoning matches the input fuzzy set with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set. The defuzzifier defuzzifies the output fuzzy set obtained by the fuzzy reasoning to obtain the glide slope height deviation angle to be corrected.

[0031] Preferably, the constraint conditions established based on the range of values ​​of the pilot's expected instructions at each moment specifically include: obtaining the physical structure constraints of the aircraft based on the aircraft structure, and obtaining the maximum and minimum values ​​of the expected instructions based on the physical structure constraints of the aircraft, and establishing the constraint conditions based on the expected instructions at each moment, the maximum value of the expected instructions, and the minimum value of the expected instructions.

[0032] The beneficial effects of the present invention are:

[0033] The present invention provides a prediction-based constrained pilot model, which is used to solve the problems of various pilot models in the field of carrier-based aircraft, such as poor convergence in complex environments, failure to consider the pilot's control input constraints, and inability to describe the control behavior of the carrier-based aircraft pilot based on the overall trend. The model comprises a carrier-based aircraft ring module, a fuzzy perception module, a pilot decision module and a pilot execution module connected in sequence, wherein the pilot execution module is connected to the carrier-based aircraft ring module; the fuzzy perception module comprises a pilot altitude deviation perception submodule, a pilot lateral deviation perception submodule and a pilot flight status perception submodule, and each module works in coordination with each other. The carrier-based aircraft ring module provides optical guidance information, centering deviation information between the carrier-based aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the carrier-based aircraft and the ideal landing point on the aircraft carrier, and The navigation parameters of the carrier-based aircraft, the pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected according to the optical guidance information and LSO voice command information and adopts the fuzzy optical perception model to convert the unstructured voice (LSO voice command information) and perception data (optical guidance information) into structured deviation data, so that the pilot can better understand the current state and deviation, improve the pilot's perception of the flight state, enhance the efficiency and accuracy of information processing, and thus improve the safety and reliability of the entire landing process; then a specially designed altitude deviation estimator is used to calculate the corrected altitude deviation estimate, which effectively solves the deviation problem caused by the observation noise when the pilot observes the glide path altitude deviation angle, and improves the accuracy of the pilot's information acquisition. The pilot lateral deviation perception submodule adopts the fuzzy lateral perception model to calculate the lateral deviation angle of the aircraft carrier centerline, and adopts a specific calculation method to calculate the lateral deviation observation value and track deviation angle, which effectively reduces the pilot's operating burden, relieves work pressure, and improves overall efficiency; the pilot flight status perception submodule calculates the pilot's actual observation value based on the observation noise, and calculates the flight status estimation value based on the actual observation value and adopts a specifically designed flight status estimator. By converting complex state variables into intuitive estimation behaviors, the pilot does not need to pay attention to the changes of all variables, but only needs to pay attention to key indicators, which effectively reduces the workload and improves work efficiency; at the same time, by estimating future trend changes, the pilot can predict possible situations in advance and avoid potential risks.The pilot decision module constructs the expected trajectory sequence, and discretizes the state space model of the aircraft to obtain the discrete state space equation. The prediction equation group is obtained based on the discrete state space equation, which can help the pilot better predict future trends and make better decisions, effectively improving the safety and efficiency of the flight. Then, the objective function is established under the constraints established based on the value range of the expected instruction at each moment. The objective function is then processed by quadratic programming to obtain a new objective function. The pilot's optimal expected instruction is calculated based on the new objective function and the interior point method. By considering the pilot's control input constraints, the risk caused by excessive operation of the pilot can be reduced, thereby improving the safety and stability of the flight. The execution module calculates the pilot's actual operation instruction based on the optimal expected instruction, and sends the actual operation instruction to the ship-aircraft ring module so that the carrier-based aircraft executes the actual operation instruction. By considering the delay and noise during the execution of the pilot's physiological mechanism, the pilot's behavior can be more accurately simulated and the pilot's control signal can be estimated, thereby improving the accuracy of the decision.

[0034] The present invention also relates to a prediction-based constrained pilot model modeling method, which corresponds to the above-mentioned prediction-based constrained pilot model, and can be understood as an implementation method of the above-mentioned prediction-based constrained pilot model. Based on optical guidance information, centering deviation information, LSO voice command information, and parameters such as distance from the ship, a prediction equation group for trend prediction is established by adopting specific calculation methods and equations, and an objective function is constructed under the established specific constraints to calculate the pilot's actual operation instructions, which can help pilots better predict future trends, reduce the risks caused by excessive operation of pilots, and improve flight safety and stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 It is a schematic diagram of the structure of the constrained pilot model based on prediction of the present invention.

[0036] Figure 2 It is a schematic diagram of the preferred structure of the constrained pilot model based on prediction of the present invention.

[0037] Figure 3 It is a working principle diagram of the FLOLS system of the present invention.

[0038] Figure 4 It is a structural schematic diagram of the pilot altitude deviation perception submodule of the present invention.

[0039] Figure 5 It is a structural schematic diagram of the pilot lateral deviation perception submodule of the present invention.

[0040] Figure 6 It is a structural schematic diagram of the flight status perception submodule of the present invention. DETAILED DESCRIPTION

[0041] The present invention will be described below in conjunction with the accompanying drawings.

[0042] The present invention relates to a prediction-based constrained pilot model, the structural schematic diagram of the model is as follows: Figure 1 As shown, it includes a ship-aircraft ring module, a fuzzy perception module, a pilot decision module and a pilot execution module connected in sequence. The fuzzy perception module is responsible for receiving and processing the optical guidance information from the ship-aircraft ring module, the centering deviation information of the aircraft carrier centerline, the distance between the aircraft tail hook and the ideal landing point on the aircraft carrier, and the LSO voice command information on the ship surface, etc., to simulate the process of the pilot estimating the deviation and the expected control target based on the optical guidance information. The pilot decision module is based on the information solved by the fuzzy perception module, comprehensively predicts the flight trend and considers the pilot's control constraints to output the expected operation instructions. After the pilot execution module, the final actual operation instructions are generated to solve the problems of poor convergence in complex environments in various pilot models in the field of carrier-based aircraft, failure to consider the pilot's control input constraints, and inability to describe the control behavior of carrier-based aircraft pilots based on the overall trend. Among them, in order to make the model fit the actual landing process as much as possible, the following assumptions are designed to make the pilot model as consistent as possible with the human ability limit. The specific assumptions are as follows: 1) In the whole process, the noise generated by the pilot's own physiological characteristics is the most common zero-mean white noise in natural organisms, and the noise in each time interval is independent, and the noise intensity is linearly related to the task load. 2) The pilot will try to make the best operation, but the pilot's decision will only be in line with the Bellman equation within a limited time interval. 3) The pilot has sufficient prior knowledge to interpret voice, body sensation and other information, as well as the dynamic characteristics of the aircraft. The pilot will not make any pre-operations for unknown disturbances. This model is suitable for pilots who are proficient in landing missions.

[0043] Specifically, Figure 2 As shown in the schematic diagram of the preferred structure, the model includes a ship-aircraft ring module, a fuzzy perception module, a pilot decision module and a pilot execution module which are sequentially connected into a closed loop, and the pilot execution module is connected to the ship-aircraft ring module; the fuzzy perception module includes a pilot altitude deviation perception submodule, a pilot lateral deviation perception submodule and a pilot flight status perception submodule.

[0044] Among them, the aircraft ring module is used to provide optical guidance information in FOLS (shipborne optical landing system), centering deviation information between the carrier-based aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the tail hook of the carrier-based aircraft and the ideal landing point on the aircraft carrier, and the navigation parameters of the carrier-based aircraft, including speed and heading. In the landing mission, optical guidance information is the main way to obtain altitude deviation. The optical guidance information seen by the driver is a fixed reference light and a movable "ball", which is formed by the light of the reference light group and the aiming light group. The FLOL optical scene (i.e., the optical guidance information in FOLLS) indicates to the driver the altitude deviation information between the carrier-based aircraft and the ideal glide path, such as Figure 3 When the FLOLS optical scene seen by the pilot is that the ball is flush with the reference line (i.e., the reference light group), the carrier-based aircraft is located on the ideal glide path; when the ball is above or below the reference line, the carrier-based aircraft is above or below the ideal glide path; when the distance between the ball and the reference line increases or decreases, the carrier-based aircraft is away from or close to the ideal glide path; pilots usually use "ball" to describe the current position of the carrier and aircraft.

[0045] The pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected based on the optical guidance information and LSO voice command information and adopts the fuzzy optical perception model. The altitude deviation estimator is used to calculate the corrected altitude deviation estimate based on the glide path altitude deviation angle, the distance to the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path altitude deviation angle.

[0046] Specifically, Figure 4 As shown in the figure, the optical guidance information and LSO voice command information are first converted into a fuzzy set defined by a membership function through the fuzzifier in the fuzzy optical perception model and input into the fuzzy reasoning, wherein the fuzzy set is defined by the membership function, and each fuzzy set has a clear boundary and shape, which is used to describe the fuzziness of the input. Fuzzy reasoning matches the input fuzzy set with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set; the defuzzifier defuzzifies the output fuzzy set obtained by the fuzzy reasoning to obtain the glide path height deviation angle Δe to be corrected, thereby converting the pilot's observation behavior of the glide path height deviation angle Δe to be corrected into a fuzzy reasoning process. Among them, the defuzzification process designs the maximum membership principle, that is, the fuzzy set with the highest membership (the membership is defined by the membership function) is selected as the final output.

[0047] At different landing distances, the pilot's gain of correcting the glide path height deviation angle Δe is also different. In order to express this difference, it is necessary to convert the glide path height deviation angle Δe into a height deviation value ΔH. And due to human physiological limitations, when the pilot observes the glide path height deviation angle Δe, there must be observation noise v, which causes observation deviation. It is necessary to perform state estimation to obtain the corrected height deviation estimate ΔH K This process is realized by the altitude deviation estimator. The relationship between the altitude deviation angle Δe, the altitude deviation value ΔH and the distance Rx from the ship is:

[0048]

[0049] Then, the state vector X is constructed based on the distance to the ship and the height deviation value, and the According to the state vector X(k) at the current moment, the state vector X(k+1) at the next moment after the current moment, and the introduction of system noise w1 and observation noise v, the state transfer equation of the FLOLS system can be established:

[0050] X(k+1)=A FLOLS X(k)+w1

[0051] Z=C FLOLS X(k)+v (2)

[0052] Where X is the state vector of the FLOLS system state transfer equation, A FLOLS is the state transfer matrix, C FLOLS is the observation matrix, k represents the current moment, and T is the transpose.

[0053] The expression of the altitude deviation estimate is obtained according to the state transfer equation. Then the pilot's estimate of the altitude deviation (i.e., the corrected altitude deviation estimate) can be expressed as follows:

[0054]

[0055] In the above formula, F is the Kalman gain coefficient. According to the above formula (3), the corrected height deviation estimate ΔH can be calculated K .

[0056] The pilot lateral deviation perception submodule calculates the lateral deviation angle of the carrier centerline based on the centering deviation information and the LSO voice command information and adopts the fuzzy lateral perception model, and calculates the lateral deviation observation value based on the lateral deviation angle of the carrier centerline and the observation noise; and then calculates the track deviation angle based on the speed and heading.

[0057] Specifically, in a landing mission, the lateral deviation is mainly measured by the deviation angle between the carrier-based aircraft and the aircraft carrier deck. The pilot can refer to the deck centerline and the landing officer (LSO) instructions to obtain lateral centering deviation information. During the day, some pilots will also align with the centerline by the carrier's wake. The centering deviation margin is higher than the height deviation. This is because the lateral safety envelope is wider than the longitudinal one. Existing technicians require the LSO to perform centering commands only when the glide path is qualified or slightly higher, and to be vigilant about settlement caused by instructions. This shows that in landing missions, the priority of centerline alignment is second only to altitude. For example Figure 5 As shown in the figure, the fuzzifier in the fuzzy lateral perception model first converts the lateral centering deviation information and the LSO voice command information into a fuzzy set defined by a membership function and inputs it into the fuzzy reasoning, where the fuzzy set is defined by a membership function, and each fuzzy set has a clear boundary and shape, which is used to describe the fuzziness of the input. Fuzzy reasoning matches the input fuzzy set with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set; the defuzzifier defuzzifies the output fuzzy set obtained by the fuzzy reasoning to obtain the lateral deviation angle of the centerline of the aircraft carrier, thereby converting the pilot's observation behavior of the lateral deviation angle of the centerline of the aircraft carrier into a fuzzy reasoning process. Among them, the defuzzification process designs the maximum membership principle, that is, selects the fuzzy set with the highest membership (the membership is defined by the membership function) as the final output.

[0058] In addition, since it is particularly difficult to estimate errors through vision in the centering task, pilots are more inclined to reduce the burden of operation through some pre-calculations, such as correcting the track deviation of the carrier-based aircraft in advance through the speed and heading of the ship, and the track speed and heading of the carrier-based aircraft. This makes the pilot's lateral deviation correction more dependent on the LSO voice command and the track deviation based on prior knowledge. The corrected track deviation The calculation formula is as follows:

[0059]

[0060] In the above formula, V ship Indicates the ship speed, V K represents the track speed of the carrier-based aircraft, Install corners for the deck.

[0061] The pilot flight state perception submodule calculates the pilot's actual observations based on the observation noise, and obtains the pilot's estimate of the flight state based on the actual observations and the flight state estimator.

[0062] Specifically, Figure 6As shown in the figure, the judgment of trend is the key point of pilot control technology, which usually predicts and controls the trend changes in the next 2s-3s. This requires the pilot to have an overall control over the flight state of the aircraft, but not all state quantities are observable or will be noticed by the pilot. Generally speaking, in addition to the position and angle of attack (speed) required by the flight mission, only the changes in pitch angle and roll angle can be better observed with the help of the horizon. Other state quantities will not occupy the pilot's energy and are generally described as a "stable" or "changing" state. Therefore, these quantities are described in the pilot perception model as an estimation of prior knowledge through observable quantities combined with flight dynamics. In addition, the landing officer (LSO) will also give speed instructions. The adjustment of some instructions is intuitive, such as the "power" instruction directly corresponds to the throttle operation, but there are also instructions such as adjusting the descent rate and drift speed that require the coordination of the rudder and throttle. In order to facilitate modeling, the necessary state observables are also converted in the fuzzy perception module.

[0063] The pilot's overall control of the flight trend can be regarded as an estimation behavior. Assume that the state space model of the aircraft is:

[0064]

[0065] y=Cx+Du p (5)

[0066] Wherein, x is the state vector of the aircraft, and the initial state vector x0 of the aircraft is obtained by the carrier-based aircraft landing reference motion state (the reference motion state includes the attitude angle, speed and glide path of the carrier-based aircraft); u p is the actual operation instruction of the pilot, the initial actual operation instruction u p0 It is obtained from the carrier-based aircraft landing reference motion; A is the state matrix, B is the input matrix, w2 is the noise excitation function (including all noises including system noise, disturbance noise, etc.), E is the disturbance matrix, C is the observation matrix, and D is the direct transfer matrix.

[0067] Then the actual observation of the pilot is calculated based on the observation noise, and the actual observation of the pilot is:

[0068] y p =C Pilot x+v y (6)

[0069] Among them, C Pilot is the pilot observation matrix, v y is the observation noise, and x is the state vector of the aircraft.

[0070] y pIt is the state quantity that the pilot can actually observe (the pilot's actual observation quantity), which changes with the working conditions. The pilot's perception of the flight state can be regarded as an estimation behavior. This process is realized by the flight state estimator. Therefore, the pilot's estimation of the flight state can be calculated based on the actual observation quantity and the flight state estimator. The input required for estimating the flight state includes the observable quantity y p (actual observations of the pilot) and the pilot's own desired instructions u c , assuming that the noise in the process is zero-mean white noise, we have:

[0071]

[0072] in, represents the state observation value,

[0073] Σ is the solution of the following Ricatti equation:

[0074]

[0075] Where W is the noise matrix. Based on formula (6), the pilot’s estimate of the flight state can be obtained: (i.e., flight status estimate).

[0076] The pilot decision module constructs the expected trajectory sequence based on the corrected altitude deviation estimate, lateral deviation observation value and track deviation angle; the state space model of the aircraft is obtained based on the carrier-based aircraft's landing reference motion and the coefficient freezing method, and the state space model is discretized to obtain the discrete state space equation, and the prediction equation group is obtained according to the discrete state space equation, and then the prediction observation sequence is constructed according to the flight state estimate at each moment in the prediction equation group, and the control sequence is constructed according to the pilot's expected instructions at each moment in the prediction equation group; and under the constraints established based on the value range of the expected instructions at each moment, the objective function is established according to the expected trajectory sequence, the prediction observation sequence and the control sequence; and the objective function is processed by the quadratic programming method to obtain a new objective function, and the optimal expected instruction of the pilot is calculated according to the new objective function and the interior point method.

[0077] The pilot decision module describes the pilot making decisions based on the information processed by the fuzzy perception module and outputting the expected operation instruction u cprocess. In addition, in order to establish a more realistic pilot decision model, this application refers to the opinions of real pilots. According to the pilots' descriptions, the judgment of trends is the focus of pilot control technology. The pilots emphasized the importance of anticipation (i.e., trend prediction). The flight captain described the anticipation capability as: the pilot's ability to understand the aerodynamics currently occurring and to make accurate predictions about future trends. Usually, pilots predict trend changes in the next 2-3s and complete operations. Therefore, this application establishes a pilot decision module based on predictive features. Specifically, first, the expected trajectory sequence Y is constructed based on the corrected altitude deviation estimate, lateral deviation observation, and track deviation angle. tracjtory ; Then, based on the carrier-based aircraft's landing reference motion and using the coefficient freezing method, the aircraft's state space model is obtained and expressed as follows:

[0078]

[0079] y=Cx+Du p (9)

[0080] Wherein, x is the state vector of the aircraft, and the initial state vector x0 of the aircraft is obtained by the carrier-based aircraft landing reference motion state (the reference motion state includes the attitude angle, speed and glide path of the carrier-based aircraft); u p is the actual operation instruction of the pilot, the initial actual operation instruction u p0 It is obtained from the carrier-based aircraft landing reference motion; w is the noise excitation function (including all noises including system noise, disturbance noise, etc.), A is the state matrix, B is the input matrix, E is the disturbance matrix, C is the observation matrix, and D is the direct transfer matrix.

[0081] The state space model is discretized to obtain the discrete state space equation, which is expressed as follows:

[0082] x(k+1)=Ax(k)+Bu c (k)+Ew(k)

[0083] y=Cx(k)+Du c (k) (10)

[0084] The prediction equations can be obtained by recursively deducing the discrete state space equations:

[0085] x(k+2)=A 2 x(k)+ABu c (k)+Bu c (k+1)+AEw

[0086]

[0087] …

[0088]

[0089] In the above formula, the prediction equation group obtained by recursion based on the discrete state space equation represents the pilot's prediction behavior on the flight state trend, P is the pilot's prediction time domain, and k represents the current moment.

[0090] After obtaining the prediction equations, the flight state estimation value at each moment in the prediction equations is That is, x(k) in the prediction equation group (10) constructs the prediction observation sequence Y p , and according to the expected command u of the pilot at each moment in the prediction equation group c Construct the control sequence ΔU(k).

[0091] In addition, constraints are very common in the pilot's working environment. Some studies describe the pilot's control behavior as a highly constrained optimal linear controller. In carrier-based aircraft landing, military thrust is usually maintained at around 85%, and in order to maintain the stability of the flight state, the change in control volume should be as small as possible. According to pilot experience, 1% to 2% of command input is usually considered appropriate, and more than 5% is considered to be an inappropriate rough adjustment, which limits the pilot's control rate. In addition, at each sampling time, the control resources that the pilot can allocate are different, which will also significantly affect the pilot's decision-making behavior. This not only restricts the pilot's control performance, but also introduces the intelligent human characteristics of the deployment based on the control margin. The pilot model established in this paper has the advantage of being able to explicitly handle constraint problems. After the control volume constraint is introduced as a condition into the performance index, the pilot's control behavior is described as a planning problem. The throttle control resource margin retained by the small disturbance model established under the conventional working point is usually only 10%-15%. If the form of hard constraints is used in modeling, it may cause the control volume to be always at the maximum value. Therefore, it is necessary to consider the constraints of the control input. First, the physical constraints of the aircraft are obtained based on the aircraft structure, and the maximum and minimum values ​​of the expected instructions are obtained based on the physical constraints of the aircraft. Constraints are established based on the expected instructions, the maximum value of the expected instructions, and the minimum value of the expected instructions at each moment. The constraints of the control input are expressed as follows:

[0092] u min (k+i)≤u(k+i)≤u max (k+i) (12)

[0093] Under the constraints established above, the objective function is established according to the expected trajectory sequence, the predicted observation sequence and the control sequence. The objective function J is shown as follows:

[0094] J=||Q*(Y p (k+1|k)-Ytracjtory (k+1|k))|| 2 +||R*ΔU(k)||2 (13)

[0095] In the above formula, Y p To predict the observation sequence, Y tracjtory is the desired trajectory sequence, ΔU(k) ​​is the control sequence, Q is the state weight matrix, and R is the control weight matrix.

[0096] The objective function includes the degree to which the flight trend deviates from the mission and the degree of consumption of control resources. Based on the above function, the closed-loop pilot optimal control can be derived as:

[0097]

[0098] in:

[0099] E(k+1)=R(k+1)-S x Δx(k)-Iy c (k)-S d Δd(k) (15)

[0100]

[0101] Among them, I represents the unit matrix and m is the control time domain.

[0102] It should be noted that after adding constraints, the objective function J contains inequalities, and the optimal pilot gain can no longer be obtained by solving the Riccati equation. This is a typical QP problem, so it needs to be considered from the perspective of optimization. Therefore, based on the above assumption 2), it is believed that the pilot will optimize the performance function at each sampling moment. In a physical sense, the pilot gain obtained by the optimization method is essentially a control strategy that minimizes the overall deviation of the flight state by combining the pilot's experience with his own estimation of the flight trend. Selecting appropriate Q and R will not result in inhuman control behavior. The quadratic programming (QP) problem is a classic type of optimization problem. The relevant numerical optimization methods are relatively mature and have been solved. Therefore, the pilot's decision-making problem can be solved by converting it into the standard form of the QP problem. The standard form of the QP problem is:

[0103]

[0104] z represents the solution to be sought, which is the pilot's decision problem ΔU(k). The prediction equation is transformed into:

[0105]

[0106] Then the objective function J is transformed into:

[0107]

[0108] At this point, the pilot's decision problem is transformed into a standard form of a QP problem, that is, the objective function J is processed using the constrained quadratic programming standard form to obtain a new objective function The optimization numerical calculation of the QP problem uses the interior point method to solve the pilot's optimal expected instruction u c (Also called the optimal expected operation instruction u c ).

[0109] The pilot execution module calculates the pilot's actual operation instructions based on the optimal expected instructions, and sends the actual operation instructions to the ship-aircraft ring module so that the carrier-based aircraft executes the actual operation instructions.

[0110] Specifically, Figure 6 As shown, the desired instruction u in the pilot's mind c The actual operation instruction u with the actual output p There are always errors, which are caused by ambiguity and non-repeatability due to human physiological limitations. The role of the pilot execution module is to establish an error model caused by physiological noise and describe the expected command u c To actual operation instructions p In order to describe the pilot's output of the final action, the method of superimposing the action error on the basis of the pilot's expected action is adopted. This is consistent with the output principle of the actual pilot's final action and is easy to implement from a mathematical perspective. Therefore, the pilot's actual operation instruction u p It can be expressed as follows:

[0111] u p =u c +B d *w (20)

[0112] In the formula, B d is the covariance matrix of zero-mean white noise disturbance, and w is the zero-mean white noise excitation function. According to scientific research, normal distribution is the most common form of biological and physiological noise in nature. Therefore, according to assumption 1), the pilot's physiological noise also satisfies the normal distribution relationship, so w is the zero-mean white noise excitation function.

[0113] The present invention also relates to a prediction-based constrained pilot model modeling method, which corresponds to the above-mentioned prediction-based constrained pilot model and can be understood as an implementation method of the prediction-based constrained pilot model, and the method comprises:

[0114] The ship-aircraft ring module provides optical guidance information, centering deviation information between the carrier-based aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the carrier-based aircraft and the ideal landing point on the aircraft carrier, and the navigation parameters of the carrier-based aircraft; the pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected based on the optical guidance information and the LSO voice command information and adopts the fuzzy optical perception model, and calculates the corrected altitude deviation estimate value based on the glide path altitude deviation angle, the distance to the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path altitude deviation angle and adopts the altitude deviation estimator; the pilot lateral deviation perception submodule calculates the lateral deviation angle of the centerline of the aircraft carrier based on the centering deviation information and the LSO voice command information and adopts the fuzzy lateral perception model, and calculates the lateral deviation observation value based on the lateral deviation angle of the centerline of the aircraft carrier and the observation noise; and then calculates the track deviation angle based on the speed and heading in the navigation parameters; the pilot flight state perception submodule calculates the actual observation amount of the pilot based on the observation noise, and calculates the flight state estimation value based on the actual observation amount and adopts the flight state estimator;

[0115] The pilot decision module constructs the expected trajectory sequence based on the corrected altitude deviation estimate, lateral deviation observation value and track deviation angle; the state space model of the aircraft is obtained based on the carrier-based aircraft landing reference motion and the coefficient freezing method, and the state space model is discretized to obtain the discrete state space equation, and the prediction equation group is obtained according to the discrete state space equation, and then the prediction observation sequence is constructed according to the flight state estimate at each moment in the prediction equation group, and the control sequence is constructed according to the pilot's expected instructions at each moment in the prediction equation group; and under the constraint conditions established based on the value range of the expected instructions at each moment, the objective function is established according to the expected trajectory sequence, the prediction observation sequence and the control sequence; and the objective function is processed by quadratic programming to obtain a new objective function, and the optimal expected instruction of the pilot is calculated according to the new objective function and the interior point method;

[0116] The pilot execution module calculates the pilot's actual operation instructions based on the optimal expected instructions, and sends the actual operation instructions to the ship-aircraft ring module so that the carrier-based aircraft executes the actual operation instructions.

[0117] Preferably, calculating the corrected altitude deviation estimate using an altitude deviation estimator according to the altitude deviation angle of the glide path, the distance from the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the altitude deviation angle of the glide path specifically includes:

[0118] The height deviation value is calculated according to the glide path height deviation angle and the distance to the ship, and the state vector is constructed based on the distance to the ship and the height deviation value. The state transfer equation is established according to the state vector at the current moment, the state vector at the next moment of the current moment, the system noise and the observation noise. The expression of the height deviation estimate is obtained according to the state transfer equation, and then the corrected height deviation estimate is calculated.

[0119] Preferably, the fuzzy optical perception model and the fuzzy lateral perception model both include a fuzzifier, a membership function, fuzzy reasoning, a fuzzy rule base and a defuzzifier.

[0120] Preferably, calculating the glide slope height deviation angle to be corrected according to the optical guidance information and the LSO voice command information and using the fuzzy optical perception model specifically includes:

[0121] The optical guidance information and LSO voice command information are converted into a fuzzy set defined by a membership function through the fuzzifier and input into the fuzzy reasoning. The fuzzy reasoning matches the input fuzzy set with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set. The defuzzifier defuzzifies the output fuzzy set obtained by the fuzzy reasoning to obtain the glide slope height deviation angle to be corrected.

[0122] Preferably, the constraint conditions established based on the range of values ​​of the pilot's expected instructions at each moment specifically include: obtaining the physical structure constraints of the aircraft based on the aircraft structure, and obtaining the maximum and minimum values ​​of the expected instructions based on the physical structure constraints of the aircraft, and establishing the constraint conditions based on the expected instructions at each moment, the maximum value of the expected instructions, and the minimum value of the expected instructions.

[0123] The present invention provides an objective and scientific prediction-based constrained pilot model and modeling method. Based on optical guidance information, centering deviation information, LSO voice command information, and parameters such as distance from the ship, a prediction equation group for trend prediction is established by adopting a specific calculation method and equation, and an objective function is constructed under the established specific constraint conditions to calculate the pilot's actual operation instructions. This can help pilots better predict future trends, reduce the risks caused by excessive operation of pilots, and improve the safety and stability of flight.

[0124] It should be noted that the above-described specific implementations can enable those skilled in the art to more fully understand the invention, but do not limit the invention in any way. Therefore, although this specification has described the invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the invention can still be modified or replaced by equivalents. In short, all technical solutions and improvements that do not deviate from the spirit and scope of the invention should be included in the protection scope of the patent for the invention.

Claims

1. A prediction-based constrained pilot model, characterized in that: It includes a ship-machine ring module, a fuzzy perception module, a pilot decision module and a pilot execution module connected in sequence, wherein the pilot execution module is connected to the ship-machine ring module; the fuzzy perception module includes a pilot altitude deviation perception submodule, a pilot lateral deviation perception submodule and a pilot flight status perception submodule, The aircraft ring module is used to provide optical guidance information, centering deviation information between the carrier aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the carrier aircraft and the ideal landing point on the aircraft carrier, and the navigation parameters of the carrier aircraft; The pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected according to the optical guidance information and the LSO voice command information and adopts the fuzzy optical perception model, and calculates the corrected altitude deviation estimation value according to the glide path altitude deviation angle, the distance from the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path altitude deviation angle and adopts the altitude deviation estimator; The pilot lateral deviation perception submodule calculates the lateral deviation angle of the centerline of the aircraft carrier according to the centering deviation information and the LSO voice command information and adopts the fuzzy lateral perception model, and calculates the lateral deviation observation value according to the lateral deviation angle of the centerline of the aircraft carrier and the observation noise; and then calculates the track deviation angle according to the speed and heading in the navigation parameters; The pilot flight state perception submodule calculates the actual observation amount of the pilot according to the observation noise, and calculates the flight state estimation value according to the actual observation amount and using the flight state estimator; The pilot decision module constructs an expected trajectory sequence based on the corrected altitude deviation estimate, lateral deviation observation value and track deviation angle; obtains a state space model of the aircraft based on the carrier-based aircraft's landing reference motion state and a coefficient freezing method, discretizes the state space model to obtain a discrete state space equation, and obtains a prediction equation group according to the discrete state space equation; then constructs a prediction observation sequence according to the flight state estimate at each moment in the prediction equation group, and constructs a control sequence according to the pilot's expected instructions at each moment in the prediction equation group; and establishes an objective function according to the expected trajectory sequence, the prediction observation sequence and the control sequence under the constraint conditions established based on the value range of the expected instructions at each moment; then uses quadratic programming to process the objective function to obtain a new objective function, and calculates the pilot's optimal expected instruction according to the new objective function and an interior point method; The pilot execution module calculates the pilot's actual operation instruction according to the optimal expected instruction, and sends the actual operation instruction to the carrier-based aircraft ring module so that the carrier-based aircraft executes the actual operation instruction.

2. The prediction-based constrained pilot model of claim 1, wherein: In the pilot altitude deviation perception submodule, the corrected altitude deviation estimate value calculation process specifically includes: The height deviation value is calculated according to the glide path height deviation angle and the distance to the ship, and the state vector is constructed based on the distance to the ship and the height deviation value. The state transfer equation is established according to the state vector at the current moment, the state vector at the next moment of the current moment, the system noise and the observation noise. The expression of the height deviation estimate is obtained according to the state transfer equation, and then the corrected height deviation estimate is calculated.

3. The prediction-based constrained pilot model of claim 1, wherein: In the pilot altitude deviation perception submodule, the fuzzy optical perception model includes a fuzzifier, a membership function, fuzzy reasoning, a fuzzy rule base and a defuzzifier.

4. The prediction-based constrained pilot model of claim 3, wherein: In the pilot altitude deviation perception submodule, the glide slope altitude deviation angle calculation process to be corrected specifically includes: The optical guidance information and LSO voice command information are converted into a fuzzy set defined by a membership function through the fuzzifier and input into the fuzzy reasoning. The fuzzy reasoning matches the input fuzzy set with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set. The defuzzifier defuzzifies the output fuzzy set obtained by the fuzzy reasoning to obtain the glide slope height deviation angle to be corrected.

5. The prediction-based constrained pilot model of claim 1, wherein: In the pilot decision module, the constraint conditions established based on the value range of the pilot's expected instruction at each moment specifically include: obtaining the physical structure constraints of the aircraft based on the aircraft structure, and obtaining the maximum and minimum values ​​of the expected instructions based on the physical structure constraints of the aircraft, and establishing the constraint conditions based on the expected instruction at each moment, the maximum value of the expected instruction and the minimum value of the expected instruction.

6. A method for modeling a constrained pilot model based on prediction, characterized in that: The method is performed by a model according to any one of claims 1 to 5, and the method comprises: The ship-aircraft ring module provides optical guidance information, centering deviation information between the carrier-based aircraft and the centerline of the aircraft carrier, LSO voice command information, the distance between the carrier-based aircraft and the ideal landing point on the aircraft carrier, and the navigation parameters of the carrier-based aircraft; the pilot altitude deviation perception submodule calculates the glide path altitude deviation angle to be corrected based on the optical guidance information and the LSO voice command information and adopts the fuzzy optical perception model, and calculates the corrected altitude deviation estimate value based on the glide path altitude deviation angle, the distance to the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path altitude deviation angle and adopts the altitude deviation estimator; the pilot lateral deviation perception submodule calculates the lateral deviation angle of the centerline of the aircraft carrier based on the centering deviation information and the LSO voice command information and adopts the fuzzy lateral perception model, and calculates the lateral deviation observation value based on the lateral deviation angle of the centerline of the aircraft carrier and the observation noise; and then calculates the track deviation angle based on the speed and heading in the navigation parameters; the pilot flight state perception submodule calculates the actual observation amount of the pilot based on the observation noise, and calculates the flight state estimation value based on the actual observation amount and adopts the flight state estimator; The pilot decision module constructs the expected trajectory sequence based on the corrected altitude deviation estimate, lateral deviation observation value and track deviation angle; the state space model of the aircraft is obtained based on the carrier-based aircraft landing reference motion and the coefficient freezing method, and the state space model is discretized to obtain the discrete state space equation, and the prediction equation group is obtained according to the discrete state space equation, and then the prediction observation sequence is constructed according to the flight state estimate at each moment in the prediction equation group, and the control sequence is constructed according to the pilot's expected instructions at each moment in the prediction equation group; and under the constraint conditions established based on the value range of the expected instructions at each moment, the objective function is established according to the expected trajectory sequence, the prediction observation sequence and the control sequence; and the objective function is processed by quadratic programming to obtain a new objective function, and the optimal expected instruction of the pilot is calculated according to the new objective function and the interior point method; The pilot execution module calculates the pilot's actual operation instructions based on the optimal expected instructions, and sends the actual operation instructions to the ship-aircraft ring module so that the carrier-based aircraft executes the actual operation instructions.

7. The prediction-based constrained pilot model building method according to claim 6, characterized in that: According to the glide path height deviation angle, the distance from the ship, the system noise in the optical guidance information, and the observation noise when the pilot observes the glide path height deviation angle, the height deviation estimator is used to calculate the corrected height deviation estimate, which specifically includes: The height deviation value is calculated according to the glide path height deviation angle and the distance to the ship, and the state vector is constructed based on the distance to the ship and the height deviation value. The state transfer equation is established according to the state vector at the current moment, the state vector at the next moment of the current moment, the system noise and the observation noise. The expression of the height deviation estimate is obtained according to the state transfer equation, and then the corrected height deviation estimate is calculated.

8. The prediction-based constrained pilot model building method according to claim 6, characterized in that: The fuzzy optical perception model and the fuzzy lateral perception model both include a fuzzifier, a membership function, fuzzy reasoning, a fuzzy rule base and a defuzzifier.

9. The prediction-based constrained pilot model building method according to claim 8, characterized in that: The glide slope height deviation angle to be corrected is calculated based on the optical guidance information and the LSO voice command information and by using the fuzzy optical perception model, specifically including: The optical guidance information and the LSO voice command information are converted into fuzzy sets defined by membership functions by the fuzzifier and input into fuzzy reasoning, and the fuzzy reasoning matches the input fuzzy sets with the rules in the fuzzy rule base to generate an output fuzzy set corresponding to the input fuzzy set; The defuzzifier performs defuzzification processing on the output fuzzy set obtained by fuzzy reasoning to obtain the glide slope height deviation angle to be corrected.

10. The prediction-based constrained pilot model building method according to claim 6, characterized in that: The constraints established based on the range of values ​​of the pilot's expected instructions at each moment specifically include: obtaining the physical structure constraints of the aircraft based on the aircraft structure, and obtaining the maximum and minimum values ​​of the expected instructions based on the physical structure constraints of the aircraft, and establishing constraints based on the expected instructions at each moment, the maximum value of the expected instructions, and the minimum value of the expected instructions.

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