A preset index control method based on intelligent adaptive boundary adjustment

Abstract

The application discloses a preset index control method based on intelligent adaptive boundary adjustment, and the method is as follows: a preset performance boundary function basic form is designed, and a control equation under a constrained state is obtained according to a dynamic equation; a state variable capable of affecting control performance is selected, and a full feedback adaptive recurrent neural network model is combined to exert weight distribution on parameter values in the preset performance boundary function and subsequent control gain; an equivalent unconstrained conversion is performed on the constrained control model based on a barrier function, so that an unconstrained equivalent control model based on an auxiliary variable is obtained; a controller is designed by using a preset time stability theory and a backstepping method, so that a preset index controller design which can only adjust a control response speed by using one parameter and can constrain control performance is completed, and control gain can be set in advance according to a flight task and corresponding performance requirements. The method can fully exert the capability of the aircraft, and improve the control quality in any flight stage under a full flight profile.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of aircraft preset index control method, specifically to a kind of preset index control method based on intelligent adaptive boundary adjustment. BACKGROUND

[0002] Variable aircraft in flight, due to flight span, task variety is miscellaneous, multi-task target demand is different, resulting in its controller design often compromise processing.However, compromise design controller cannot fully exert the ability of aircraft, and the advantage of variable flight is reduced.In addition, due to the fixed control parameters during flight, the control performance demand change problem caused by sudden task change cannot be solved.Therefore, an intelligent adaptive control performance boundary adjustment control method according to the implementation flight state is needed, and a control method capable of presetting performance index is needed. SUMMARY

[0003] In order to solve the problems that the traditional aircraft control system cannot accurately design the control performance index in advance, and cannot change the control parameters according to the real-time flight state and task demand, optimize the control performance, and fully exert the flight ability of aircraft, the present application provides a kind of preset index control method based on intelligent adaptive boundary adjustment, which can not only set the control performance index in advance, but also can adjust the control performance boundary and control gain according to the real-time flight state and task demand of aircraft, so as to fully exert the ability of aircraft and improve the control quality in any flight phase of whole flight profile.

[0004] The purpose of the present application is achieved by the following technical solutions:

[0005] A kind of preset index control method based on intelligent adaptive boundary adjustment, comprising the following steps:

[0006] Step 1: according to the task scene and corresponding performance demand, design the basic form of preset performance boundary function, and according to the dynamic equation, obtain the control equation under the constraint state;

[0007] Step 2: select the state quantity in real-time flight state, deformation state and multi-task demand that can affect control performance, combine the full feedback adaptive recurrent neural network model to exert weight distribution on the parameter value in preset performance boundary function and subsequent control gain, to achieve the purpose of adaptive adjustment of control performance;

[0008] Step 3: based on barrier function, the constrained control model is converted into equivalent unconstrained, to obtain the unconstrained equivalent control model based on auxiliary variable, so as to facilitate the design of controller;

[0009] Step 4: preset time stabilization theory and backstepping method are used to design the controller, so that the preset index controller design which can only adjust the control response speed by one parameter and can constrain the control performance is completed, and the control gain can be set in advance according to the flight task and the corresponding performance demand.

[0010] Compared with the prior art, the present application has the following advantages:

[0011] (1) The intelligent adaptive boundary adjustment technology is adopted, the preset performance boundary of the controller can be adjusted according to the real-time flight state and task demand of the aircraft, the real-time flight capability is fully utilized, and the control quality at any moment of the whole flight profile is improved.

[0012] (2) A controller which can adjust the response speed only by one control parameter is designed based on the double limit weighting theory, and the control system capability can be quickly and conveniently predefined by combining the preset performance function, and the controller has good engineering practicability. BRIEF DESCRIPTION OF DRAWINGS

[0013] Figure 1 It is a flowchart of the intelligent adaptive boundary adjustment preset index control method. DETAILED DESCRIPTION

[0014] The technical solutions of the present application will be further described below in combination with the drawings, but are not limited thereto, and any modification or equivalent replacement to the technical solutions of the present application without departing from the spirit and scope of the present application shall be covered in the protection scope of the present application.

[0015] The present application provides an intelligent adaptive boundary adjustment preset index control method, as shown in Figure 1 Fig. 1, taking a morphing aircraft as an example, the method comprises the following steps:

[0016] Step 1: design the basic form of the preset performance boundary function according to the task scene and the corresponding performance demand, and obtain the control equation under the constrained state according to the dynamic equation, and the specific steps are as follows:

[0017] Step 1.1: according to the control accuracy ρ ∞ , overshoot σ a and response time T a required by the control system according to the task scene, design the basic form of the preset performance boundary function ρ

[0018]

[0019] Where ρ0 is the initial value of the performance function, ρ ∞ is the final value of the performance function, t is time, and τ is any small constant.

[0020] Step 1.2: According to the aircraft dynamics equation (2), the state variables are integrated to give the aircraft state space equation (3) for the surface type control system design:

[0021]

[0022]

[0023]

[0024] where Ω = [σ, β, α] T represents the Euler angle vector, σ, β, α are the roll angle, side slip angle and attack angle, respectively; ω = [ω x , ω y , ω z ] T represents the attitude angular velocity vector, ω x , ω y , ω z are the roll angular velocity, yaw angular velocity and pitch angular velocity, respectively; ρ1 = [ρ 11 , ρ 12 , ρ 13 ] represents the constraint on the system state x1; ρ2 = [ρ 21 , ρ 22 , ρ 23 ] represents the constraint on the system state x2; J represents the moment of inertia matrix; R represents the attitude conversion matrix; d is the unknown disturbance; M is the moment vector; the expressions of R, J, ω × are as follows:

[0025]

[0026] where x1 = Ω, x2 = ω; M δc represents the control command vector, M Δδ represents the moment caused by aerodynamic rudder deviation, M0 represents the moment generated by the projectile body; f = -J -1 ω × Jω + J -1 M0, D = J -1 d + J -1 M Λδ represents the total disturbance;

[0027] Thus, the state error equation is obtained as follows:

[0028]

[0029] where e1 = x1 - x d , e2 = x2 - x 2d , x d represents the desired command of x1, x 2dAn expected instruction representing x2.

[0030] Step 2: Select the state variables that can affect the control performance from the real-time flight state, deformation state and multi-task demand, and combine the full feedback adaptive recurrent neural network model to assign weights to the parameter values in the preset performance boundary function and the subsequent control gain, so as to achieve the purpose of adaptive adjustment of control performance. The specific steps are as follows:

[0031] Step 2.1: Select real-time flight state Ω = [σ, β, α] T , deformation state sweepback angle Λ, span L and response time demand, control accuracy demand variables as neural network training state variables;

[0032] Step 2.2: Use radial basis function to construct neural network, and adjust weight vector to obtain adaptive boundary state value;

[0033]

[0034] Wherein, is the weight coefficient matrix, φ(x) represents the activation function, and ρ a (x) is the adaptive adjustment boundary item, so as to obtain the adaptive boundary adjustment function:

[0035]

[0036] Step 3: Based on the barrier function, the constrained control model is converted into an equivalent unconstrained model based on auxiliary variables, so as to facilitate the design of the controller. The specific steps are as follows:

[0037] The barrier function is selected as:

[0038]

[0039]

[0040] Wherein, ξ is the auxiliary error variable, and the state error equation under the constraint of formula (6) is:

[0041]

[0042] Step 4: Design the controller based on the preset time stability theory and backstepping method, so as to complete the design of the preset index controller which only adjusts the control response speed by one parameter and can constrain the control performance. The control gain can be set in advance according to the flight task and its corresponding performance demand. The specific steps are as follows:

[0043] Step 4.1: According to the first formula of formula (11) and the preset time stability theory, the virtual control quantity of the following form is designed:

[0044]

[0045] where 0 < κ < 1, T(x) is a convergence time adjustment term, which can be adaptively adjusted according to the flight state, x 2d0 is an equivalent virtual control term, which acts to eliminate the disturbance term outside the reaching law;

[0046] Step 4.2: According to the second formula of formula (11), the control quantity is designed as follows:

[0047]

[0048] where M δ0 is an equivalent control term, which acts to eliminate the disturbance term outside the reaching law, thereby completing the controller design.

[0049] According to the control flow Figure 1 It can be seen that when calculating the control command, the controller first calculates the state error according to the guidance command and the flight state, then obtains the boundary function according to the designed control constraint index and the adaptive control index boundary adjustment law, and uses the obstacle transformation to convert the constrained state error variable to an unconstrained auxiliary variable; further, the control command is obtained by using the adaptive gain pre-set time controller and the variable gain adjustment law which can control the parameters according to the flight performance and flight state information, and is output to the aircraft model, thereby completing the closed-loop control of the system.

Claims

1. A method for controlling preset indices based on intelligent adaptive boundary adjustment, characterized in that... The method includes the following steps: Step 1: Design the basic form of the preset performance boundary function according to the task scenario and corresponding performance requirements, and obtain the control equation under constrained state according to the dynamic equation; Step 2: Select the state variables that can affect the control performance from the real-time flight state, deformation state and multi-task requirements, and combine them with the full feedback adaptive recurrent neural network model to apply weights to the parameter values ​​in the preset performance boundary function and the subsequent control gain, so as to achieve the purpose of adaptive adjustment of control performance. Step 3: Perform an equivalent unconstrained transformation on the constrained control model based on the obstacle function to obtain an unconstrained equivalent control model based on auxiliary variables, which facilitates controller design. The specific steps are as follows: The obstacle function is chosen to be in the following form: （9） （10） in, As an auxiliary error variable; , express Expected instructions express Expected instructions ; Represents Euler angle vectors; This represents the attitude angular velocity vector; Represents the system state variables Constraints; Represents the system state variables Constraints; Combining the state error equation, the unconstrained state error equation is obtained as follows: （11） in, Represents the attitude transformation matrix; Represents the moment of inertia matrix; , This represents the total disturbance. For unknown disturbances; Step 4: Design a controller based on the preset time stability theory and backstepping method. This completes the design of a controller with preset performance indicators where the control response speed is adjusted by only one parameter and the control performance is constrained. The control gain can be preset according to the flight mission and its corresponding performance requirements. The specific steps are as follows: Step 4.1: Based on the first equation (11) and the preset time stability theory, design the virtual control quantity in the following form: （12） in, , This is the convergence time adjustment term. This is an equivalent virtual control term; Step 4.2: Design the control quantity in the following form according to the second equation (11): （13） in, The equivalent control term is used to complete the controller design.

2. The method for controlling preset indices based on intelligent adaptive boundary adjustment according to claim 1, characterized in that... The specific steps of step 1 are as follows: Step 1.1: Analyze the required control accuracy and overshoot of the control system based on the task scenario. and response time Design a preset performance boundary function Basic form: （1） in The initial value of the performance function, Let t be the final value of the performance function, and t be time. It is a constant; Step 1.2: Based on the aircraft dynamics equation (2), integrate the state variables and give the aircraft state-space equation (3) for the surface control system design: （2） （3） （4） In the formula, Represents Euler angle vectors; This represents the attitude angular velocity vector; Represents the system state variables Constraints; Represents the system state variables Constraints; Represents the moment of inertia matrix; Represents the attitude transformation matrix; For unknown disturbances; It is a torque vector; The expression is: （5） In the formula, ; Represents the control command vector. This indicates the torque caused by aerodynamic rudder deviation. This indicates the torque generated by the projectile. , Indicates the total disturbance; These are the roll angle, sideslip angle, and angle of attack, respectively. These are roll rate, yaw rate, and pitch rate, respectively. Therefore, the state error equation is obtained as follows: （6） In the formula, , express Expected instructions express The expected instructions.

3. The method for controlling preset indices based on intelligent adaptive boundary adjustment according to claim 2, characterized in that... The specific steps of step 2 are as follows: Step 2.1: Select real-time flight status , sweep angle in deformed state Exhibition length Variables such as response time requirements and control accuracy requirements are used as state variables for neural network training. Step 2.2: Construct a neural network using radial basis functions and adjust the weight vector to obtain the values ​​that influence the adaptive boundary state; （7） in, This is the weight coefficient matrix. This represents the activation function. To adaptively adjust the boundary terms, we obtain the adaptive boundary adjustment function: （8）。

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