CN113093769A - Active disturbance rejection control method for perching and falling of fixed-wing unmanned aerial vehicle - Google Patents
Active disturbance rejection control method for perching and falling of fixed-wing unmanned aerial vehicleDownload PDFInfo
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Abstract
Description
Technical Field
The invention relates to a method for controlling perching and landing maneuvers of a fixed-wing unmanned aerial vehicle, belongs to the field of unmanned aerial vehicle control, and particularly relates to fixed-wing unmanned aerial vehicle control with special application.
Background
As common operation unmanned aerial vehicle, fixed wing unmanned aerial vehicle has obvious advantage in the aspect of the ability of continuing a journey, flight range and cruise speed etc, but fixed wing unmanned aerial vehicle adopts to slide to slow down, hit complicated descending modes such as net and parachute, it is big to have a place space demand, the ground is assisted the shortcoming such as the landing device is complicated and the landing point is uncertain, if fixed wing unmanned aerial vehicle can realize similar birds and freely descend in positions such as ground, branches, will compensate to a great extent that present fixed wing unmanned aerial vehicle relies on the not enough of runway take-off and landing. In addition, the fixed-wing unmanned aerial vehicle can live in air all day long after staying behind, and the fixed-wing unmanned aerial vehicle can live in air all day long, and has important significance for improving survival and investigation capability in battlefield environment.
The fixed wing unmanned aerial vehicle perches and moves and needs to pass through the stall stage, when the unmanned aerial vehicle surpasses the stall angle of attack, gets into the stall forbidden zone after, relates to a great deal of flight problem. In the over-stall region, the aerodynamic characteristics of the airplane become very complex, and the control strategy, the design method, the system structure and the like of the airplane are greatly different from those of the conventional system. Perching and falling on the fixed-wing unmanned aerial vehicle, patent publication no: CN 111232197 a proposes a method for enhancing habitat control ability from the structural design point of view, but does not clarify the control method. An article 'Control Synthesis and Verification for a Perching UAV using LQR-Trees' is designed for a class of unpowered glider perches, but the requirement on model precision is high, and model uncertainty and external disturbance cannot be well processed. Aiming at the problems of large attack angle maneuver, fast model parameter time change, rapid actuator efficiency reduction and the like in the perching and falling process of the unmanned aerial vehicle, the conventional flight control strategy and method are difficult to process.
Disclosure of Invention
Technical problem to be solved
In order to solve the problems of fast time variation of model parameters and insufficient manipulation capability in the perching and falling process of the unmanned aerial vehicle, the invention designs an active disturbance rejection control method for perching and falling of the fixed-wing unmanned aerial vehicle. Firstly, designing an optimal reference track for a longitudinal perching model of the fixed-wing unmanned aerial vehicle, then further decomposing a speed control subsystem and a pitching attitude control subsystem according to a dynamic relation between control input and system state variables, and finally respectively designing an active disturbance rejection track tracking controller for the two subsystems.
Technical scheme
An active disturbance rejection control method for perching of a fixed-wing unmanned aerial vehicle is characterized by comprising the following steps:
step 1: considering a fixed-wing drone perching maneuver longitudinal kinematics model:
the longitudinal habitat model comprises seven state variables
And two control inputs
Wherein V represents velocity, γ represents track angle, α represents angle of attack, q represents pitch angle rate, x represents horizontal flight distance, h represents vertical flight altitude, s represents flight path length, T represents propeller drag, δeRepresenting the amount of elevator deflection; m represents the mass of the drone, g represents the acceleration of gravity, IyRepresenting the moment of inertia of the pitch axis; d, L and M respectively represent lift force, resistance force and pitching moment;
step 2: generating optimal perch reference trajectories
Introducing augmented state vectors, denoted
The new control input signal is defined as the derivative of the original control signal, i.e. the rate signal, noted
The initial condition for designing the optimal perch trajectory is the trim state during horizontal flight, and is recorded as
In the formula, V0,γ0,α0,q0,x0,h0,s0Variable value, T, of system described by equations (1) to (7) in horizontal flight0,δe0Inputting variable values for the respective control;
designing a quadratic optimization target loss function J, and enabling the J to obtain a minimum value in the perching process;
in the formula, R, QfAre all gain diagonal square matrices with appropriate dimensions, tfRepresenting the time of the perch terminal; the constraint function comprises an integral index related to input and an index related to a final value of a state variable, and different optimization targets can be realized by adjusting parameters of the gain diagonal matrix;
defining the constraint range of the expansion state and the control input variable by describing an upper bound condition and a lower bound condition;
the upper bound of the constraint for the augmented state variable is expressed as:
the lower bound of the constraint for the augmented state variable is expressed as:
the upper bound constraint for the control input is expressed as:
the lower bound constraint for the control input is expressed as:
the upper bound and the lower bound of the expansion state and the control input variable are reasonably set according to the actual flight condition; further, an optimal reference track can be obtained by means of a solving method of nonlinear optimal control;
the optimal reference trajectory is recorded
Optimal reference control input is noted
And step 3: designing closed-loop control channel
Taking into account the design desired flying speed VdAngle gamma to the desired trackdAnd introducing the current flight position x, y and the reference position xr,yrThe deviation of the unmanned aerial vehicle track tracking process caused by the integral accumulated error is avoided; designing the expectation-tracking instruction as
Where V, gamma denotes the current flight speed and track angle, Vr,γrFor reference flight speed and track angle, k1,k2Is an adjustable proportionality coefficient;
controlling the velocity component v of an aircraft on the longitudinal axis of the fuselage by means of the tension TbxUsing elevators deltaeControlling aircraft pitch angle θ, vbxThe formula for calculating the sum theta is
θ＝α+γ (18)
vbx＝Vcos(α) (19)
Corresponding desired speed vbxdAnd a desired pitch angle θdIs calculated by the formula
θd＝α+γd (20)
vbxd＝Vd cos(α) (21)
And 4, step 4: design pitch control subsystem
According to equation (18), the dynamic equation of the pitch control channel is derived and linearized at the reference flight trajectory
In the formula (I), the compound is shown in the specification,
representing unknown disturbances, ω representing time-dependent disturbances, bθRepresenting parameters associated with the model; delta theta and DeltaeIs defined as
Δθ＝θ-θd (23)
Δδe＝δe-δer (24)
For the system described by equation (27), the extended observer is designed as
Where ε represents the observer estimation error, z1,z2,z3Respectively system state and disturbance
Estimated value of yθ＝Δθ；β01,β02,β03,a1,a2,δ1Are all observer adjustable parameters; fal (. circle.) is a non-linear function defined as
In the formula, a and delta represent input parameters of the function;
the design error feedback controller is
Δδe0＝β1fal(-z1,a3,δ2)+β2fal(-z2,a4,δ2) (27)
In the formula, beta1,β2,a3,a4,δ2Are all controller adjustable parameters;
and 5: design speed control subsystem
The dynamic model of the velocity control channel derived from equation (19) and linearized at the reference flight trajectory is
In the formula (I), the compound is shown in the specification,
in order to be an unknown perturbation of the model,
is a constant;
because the speed channel control subsystem and the pitching channel control subsystem have very similar design processes, in order to simplify the use of symbols, part of the symbols are reused when the observer and the controller are designed;
design the extended observer as
Where ε is the observer estimation error, z1Is Δ vbxEstimate of z2To be disturbed
Estimated value of uT＝ΔT，yvbx＝Δvbx；β11,β12,a5,δ3Expanding observer adjustable parameters;
designing the ADRC to be
ΔT0＝β3fal(-z1,a6,δ4) (31)
In the formula,. DELTA.T0For control input signals when disturbances are not compensated, beta3,a6,δ4Is an adjustable controller parameter.
Preferably: observer adjustable parameter beta in step 401＝80,β02＝700,β03＝1000,a1＝0.55,a2＝0.25,δ1＝0.07。
Preferably: step 4 controller adjustable parameter beta1＝30.5,β2＝10.0,a30.75,a4＝1.5,δ2＝0.03。
Preferably: step 5, expanding the adjustable parameter beta of the observer11＝80,β12＝200,a5＝0.85,δ3＝0.03。
Preferably: step 5 controller parameter beta3＝10,a6＝0.75,δ4＝0.02。
Advantageous effects
According to the active disturbance rejection control method for perching of the fixed-wing unmanned aerial vehicle, aiming at the problems of large attack angle, fast time-varying aerodynamic parameters, low control rudder efficiency of an actuator and the like in the longitudinal perching process of the fixed-wing unmanned aerial vehicle, firstly, an optimal reference track is designed, secondly, a reference instruction is designed based on the optimal reference track, and the current flight position is introduced into the calculation of the reference instruction, so that the unmanned aerial vehicle is prevented from deviating from the optimal track due to the cumulative effect of tracking errors of a controller. And finally, decomposing the unmanned aerial vehicle track tracking control system into a speed control subsystem and a pitch angle control subsystem, and respectively designing an active disturbance rejection controller. Through verification, the control method can effectively solve the main problems existing in the longitudinal perching of the fixed-wing unmanned aerial vehicle.
The beneficial effects are that: 1) the optimal reference track is designed, the limited control moment at the perching tail end is fully utilized, and the feasibility of the perching track is ensured; 2) aiming at the track tracking control under a large attack angle, the invention redesigns a track tracking instruction, eliminates an integral error in track tracking, further decouples a speed control subsystem (taking the speed of a longitudinal axis of a machine body as a feedback signal) and a pitch angle control subsystem, and avoids the coupling of the track angle control and the speed control under the large attack angle; 3) the method is based on the active disturbance rejection control technology, realizes the tracking of the optimal perch track, can effectively inhibit unknown modeling dynamics and other disturbance existing in the model, and improves the robustness of perch control.
Drawings
FIG. 1 is a flow chart of an active disturbance rejection control method for perching of a fixed wing drone in accordance with the present invention
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
referring to fig. 1, the invention designs an active disturbance rejection control method for perching of a fixed-wing unmanned aerial vehicle, which is applied to longitudinal perching control of a class of fixed-wing unmanned aerial vehicles and is specifically realized through the following steps.
Step 1: considering a fixed-wing drone perching maneuver longitudinal kinematics model:
the longitudinal habitat model comprises seven state variables
And two control inputs
Wherein V represents velocity, γ represents track angle, α represents angle of attack, q represents pitch angle rate, x represents horizontal flight distance, h represents vertical flight altitude, s represents flight path length, T represents propeller drag, δeRepresenting the amount of elevator deflection; m represents the mass of the drone, g represents the acceleration of gravity, IyThe rotational inertia of the pitch axis; d, L and M respectively represent lift force, resistance force and pitching moment. The relevant force and moment parameters are defined as follows:
CL(α)＝2sinαcosα，CD(α)＝2sin2α，
wherein q represents dynamic pressure, ρ is air density, S is aerodynamic reference area of the airfoil, and S istailFor aerodynamic reference area of the tail, CL(α),CDAnd (alpha) is a lift coefficient and a drag coefficient respectively.
Step 2: generating optimal perch reference trajectories
Introducing augmented state vectors, denoted
The new control input signal is defined as the derivative of the original control signal, i.e. the rate signal, noted
Calculating the initial value of the optimal perching track in the trim state as
In the formula, V0＝10,γ0＝0,α0＝0.12,q0＝0,x0＝0,h0＝0,s00 is the variable value of the system described by equations (1) to (7) in the horizontal flight state (trim state), T0＝0,δe0And 0 is the corresponding control input variable value.
And designing a quadratic optimization target loss function J, and enabling J to obtain the minimum value in the perching process.
Wherein R is diag ([1,2 ]])，Q＝diag([0,0,0,0,1,5,0,0,0]) Diag (·) denotes setting a diagonal element, tfRepresenting the time of the terminal of the habitat. The constraint function comprises an integral index related to input and an index related to a state variable final value, and different optimization targets can be realized by adjusting parameters of the gain diagonal matrix.
The constraint ranges of the expansion state and the control input variable are defined by upper and lower bound conditions.
The upper bound of the constraint for the augmented state variable is expressed as:
the lower bound of the constraint for the augmented state variable is expressed as:
the upper bound constraint for the control input is expressed as:
the lower bound constraint for the control input is expressed as:
the upper bound and the lower bound of the expansion state and the control input variable are set reasonably according to the actual flight condition. And further, the optimal reference track can be obtained by means of a solving method of nonlinear optimal control.
The optimal reference trajectory assumed is recorded as
Optimal reference control input is noted
And step 3: designing closed-loop control channel
Taking into account the design desired flying speed VdAngle gamma to the desired trackdAnd introducing the current flight position x, y and the reference position xr,yrAnd the deviation of the unmanned aerial vehicle track tracking process caused by the integral accumulated error is avoided. Designing the expectation-tracking instruction as
Where V, gamma denotes the current flight speed and track angle, Vr,γrFor reference to flight speed and track angle, the proportionality coefficient k can be adjusted1＝5,k2＝5
Controlling the velocity component v of an aircraft on the longitudinal axis of the fuselage by means of the tension TbxUsing elevators deltaeControlling aircraft pitch angle θ, vbxThe formula for calculating the sum theta is
θ＝α+γ (18)
vbx＝V cos(α) (19)
Corresponding desired speed vbxdAnd a desired pitch angle θdIs calculated by the formula
θd＝α+γd (20)
vbxd＝Vd cos(α) (21)
And 4, step 4: design pitch control subsystem
According to equation (18), the dynamic equation of the pitch control channel is derived and linearized at the reference flight trajectory
In the formula (I), the compound is shown in the specification,
representing unknown disturbances, ω representing time-dependent disturbances, bθ-0.0667 represents a parameter associated with the model. Delta theta and DeltaeIs defined as
Δθ＝θ-θd (23)
Δδe＝δe-δer (24)
For the system described by equation (27), the extended observer is designed as
Where ε represents the observer estimation error, z1,z2,z3Respectively system state and disturbance
Estimated value of yθΔ θ; observer adjustable parameter beta01＝80,β02＝700,β03＝1000,a1＝0.55,a2＝0.25,δ10.07; fal (. circle.) is a non-linear function defined as
In the formula, a and δ represent input parameters of the function.
The design error feedback controller is
Δδe0＝β1fal(-z1,a3,δ2)+β2fal(-z2,a4,δ2) (27)
In the formula, the controller can adjust the parameter beta1＝30.5,β2＝10.0,a30.75,a4＝1.5,δ2＝0.03。
And 5: design speed control subsystem
The dynamic model of the velocity control channel derived from equation (19) and linearized at the reference flight trajectory is
In the formula (I), the compound is shown in the specification,
in order to be an unknown perturbation of the model,
is a constant.
The speed channel control subsystem and the pitching channel control subsystem have quite similar design processes, and in order to simplify the use of symbols, partial symbols are reused when an observer and a controller are designed.
Design the extended observer as
Where ε is the observer estimation error, z1Is Δ vbxEstimate of z2To be disturbed
Estimated value of uT＝ΔT，yvbx＝Δvbx(ii) a Adjustable parameter beta of extended observer11＝80,β12＝200,a5＝0.85,δ3＝0.03。
Designing the ADRC to be
ΔT0＝β3fal(-z1,a6,δ4) (31)
In the formula,. DELTA.T0For control input signals when disturbances are not compensated, controller parameter beta3＝10,a6＝0.75,δ4＝0.02。
Claims (5)
1. An active disturbance rejection control method for perching of a fixed-wing unmanned aerial vehicle is characterized by comprising the following steps:
step 1: considering a fixed-wing drone perching maneuver longitudinal kinematics model:
the longitudinal habitat model comprises seven state variables
And two control inputs
Wherein V represents velocity, γ represents track angle, α represents angle of attack, q represents pitch angle rate, x represents horizontal flight distance, h represents vertical flight altitude, s represents flight path length, T represents propeller drag, δeRepresenting the amount of elevator deflection; m represents the mass of the drone, g represents the acceleration of gravity, IyRepresenting the moment of inertia of the pitch axis; d, L and M respectively represent lift force, resistance force and pitching moment;
step 2: generating optimal perch reference trajectories
Introducing augmented state vectors, denoted
The new control input signal is defined as the derivative of the original control signal, i.e. the rate signal, noted
The initial condition for designing the optimal perch trajectory is the trim state during horizontal flight, and is recorded as
In the formula, V0,γ0,α0,q0,x0,h0,s0Variable value, T, of system described by equations (1) to (7) in horizontal flight0,δe0Inputting variable values for the respective control;
designing a quadratic optimization target loss function J, and enabling the J to obtain a minimum value in the perching process;
in the formula, R, QfAre all gain diagonal square matrices with appropriate dimensions, tfRepresenting the time of the perch terminal; the constraint function comprises an integral index related to input and an index related to a final value of a state variable, and different optimization targets can be realized by adjusting parameters of the gain diagonal matrix;
defining the constraint range of the expansion state and the control input variable by describing an upper bound condition and a lower bound condition;
the upper bound of the constraint for the augmented state variable is expressed as:
the lower bound of the constraint for the augmented state variable is expressed as:
the upper bound constraint for the control input is expressed as:
the lower bound constraint for the control input is expressed as:
the upper bound and the lower bound of the expansion state and the control input variable are reasonably set according to the actual flight condition; further, an optimal reference track can be obtained by means of a solving method of nonlinear optimal control;
the optimal reference trajectory is recorded
Optimal reference control input is noted
And step 3: designing closed-loop control channel
Taking into account the design desired flying speed VdAngle gamma to the desired trackdAnd introducing the current flight position x, y and the reference position xr,yrThe deviation of the unmanned aerial vehicle track tracking process caused by the integral accumulated error is avoided; designing the expectation-tracking instruction as
Where V, gamma denotes the current flight speed and track angle, Vr,γrFor reference flight speed and track angle, k1,k2Is an adjustable proportionality coefficient;
controlling the velocity component v of an aircraft on the longitudinal axis of the fuselage by means of the tension TbxUsing elevators deltaeControlling aircraft pitch angle θ, vbxThe formula for calculating the sum theta is
θ＝α+γ (18)
vbx＝V cos(α) (19)
Corresponding desired speed vbxdAnd a desired pitch angle θdIs calculated by the formula
θd＝α+γd (20)
vbxd＝Vdcos(α) (21)
And 4, step 4: design pitch control subsystem
According to equation (18), the dynamic equation of the pitch control channel is derived and linearized at the reference flight trajectory
In the formula (I), the compound is shown in the specification,
representing unknown disturbances, ω representing time-dependent disturbances, bθRepresenting parameters associated with the model; delta theta and DeltaeIs defined as
Δθ＝θ-θd (23)
Δδe＝δe-δer (24)
For the system described by equation (27), the extended observer is designed as
Where ε represents the observer estimation error, z1,z2,z3Respectively system state and disturbance
Estimated value of yθ＝Δθ；β01,β02,β03,a1,a2,δ1Are all observer adjustable parameters; fal (. circle.) is a non-linear function defined as
In the formula, a and delta represent input parameters of the function;
the design error feedback controller is
Δδe0＝β1fal(-z1,a3,δ2)+β2fal(-z2,a4,δ2) (27)
In the formula, beta1,β2,a3,a4,δ2Are all controller adjustable parameters;
and 5: design speed control subsystem
The dynamic model of the velocity control channel derived from equation (19) and linearized at the reference flight trajectory is
In the formula (I), the compound is shown in the specification,
in order to be an unknown perturbation of the model,
is a constant;
because the speed channel control subsystem and the pitching channel control subsystem have very similar design processes, in order to simplify the use of symbols, part of the symbols are reused when the observer and the controller are designed;
design the extended observer as
Where ε is the observer estimation error, z1Is Δ vbxEstimate of z2To be disturbed
Estimated value of uT＝ΔT，yvbx＝Δvbx；β11,β12,a5,δ3Expanding observer adjustable parameters;
designing the ADRC to be
ΔT0＝β3fal(-z1,a6,δ4) (31)
In the formula,. DELTA.T0For control input signals when disturbances are not compensated, beta3,a6,δ4Is an adjustable controller parameter.
2. The active disturbance rejection control method for fixed wing drone perches according to claim 1, wherein in step 4, observer adjustable parameter β is01＝80,β02＝700,β03＝1000,a1＝0.55,a2＝0.25,δ1＝0.07。
3. The active disturbance rejection control method for fixed wing drone perches according to claim 1, wherein the controller can adjust parameter β in step 41＝30.5,β2＝10.0,a30.75,a4＝1.5,δ2＝0.03。
4. The active disturbance rejection control method for stationary wing drone perches according to claim 1, wherein in step 5, the observer adjustable parameter β is extended11＝80,β12＝200,a5＝0.85,δ3＝0.03。
5. The active disturbance rejection control method for fixed wing drone perches according to claim 1, wherein the controller parameter β in step 53＝10,a6＝0.75,δ4＝0.02。
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