Multivariable robust adaptive control method and system for aero-engine

Abstract

The invention discloses a multivariable robust adaptive control method for an aero-engine. According to the method, an augmented linear quadratic regulation control method and a model reference self-adaptive control method are organically combined, dynamic self-adaptive compensation is carried out on output of an augmented linear quadratic regulation control loop by utilizing output of a model reference self-adaptive control loop, and whether self-adaptive compensation is started or not is judged according to a control error. The invention further discloses a multivariable robust self-adaptive control system for the aero-engine. Compared with the prior art, the dynamic response characteristic of the control system at a non-design point can be improved on the premise that the steady-state control performance is not changed.

Description

Technical Field

[0001] The invention relates to an aero-engine multivariable robust adaptive control method and system, belonging to the technical field of aero-engine control. Background Art

[0002] Aircraft engines have very complex structures and highly matched components. The parameters vary widely within the envelope, and the working environment is changeable, with strong nonlinearity and uncertainty. In addition, the engine works in a high temperature, high pressure and strong vibration environment for a long time. Due to wear, fouling and other reasons, the insulation efficiency and flow capacity of the rotating parts will change, causing the engine characteristics to deviate from the design point and generate additional uncertainty. Therefore, the engine control system must be robust and adaptive.

[0003] At the same time, the engine has developed into a multivariable control system. Especially with the development of variable cycle engines, combined engines, short take-off and vertical landing engines, single variable control methods can no longer meet the control requirements, and multivariable control methods are needed to improve control performance. The multivariable linear quadratic regulator (LQR) has a simple structure, infinite amplitude margin and phase margin greater than 60°. It is a classic multivariable control method. The augmented LQR control method (ALQR) adds an integrator to give the system good steady-state performance robustness. However, when applied to engine control, due to its uncertainty and large parameter variation range, it is not possible to obtain good dynamic performance throughout the entire envelope and life cycle.

[0004] The adaptive control method is a nonlinear control method mainly used to deal with uncertain systems. When faced with unmodeled dynamics caused by factors such as engine degradation, the dynamic performance of the multivariable robust controller designed based on the nominal model will deteriorate, while adaptive control can reduce the impact of system uncertainty on the dynamic performance of the control system by updating the controller parameters in real time. Therefore, combining adaptive control with augmented LQR control can improve the dynamic performance of the engine while meeting the steady-state performance of the engine. In the current research on multivariable adaptive control methods, there are problems such as many adaptive adjustable parameters or complex control structures, which will bring new uncertainties to the adaptive adjustment of the loop gain. Summary of the invention

[0005] The technical problem to be solved by the present invention is to overcome the shortcomings of the prior art and provide an aircraft engine multivariable robust adaptive control method, which organically combines the ALQR control method with the model reference adaptive control method, and performs dynamic adaptive compensation on the ALQR control result according to the control error, thereby improving the dynamic response characteristics of the controller at non-design points without changing the steady-state control performance.

[0006] The present invention specifically adopts the following technical solutions to solve the above technical problems:

[0007] A multivariable robust adaptive control method for an aircraft engine uses a model reference adaptive control loop to dynamically compensate the main control output of a main control loop to generate an aircraft engine control variable:

[0008] In the main control loop, the output tracking error e between the control command r and the engine output y is y Integrate, e y After integration, the engine state quantity x including the actuator p Forming error augmented state quantity x a , x a The main control output u is obtained through the main controller main The main controller is x a As an augmented linear quadratic regulation controller of state variables;

[0009] In the model reference adaptive control loop, the control command r inputs the reference state x generated by the reference model m The error augmented state quantity x a The reference model state quantity tracking error e is obtained by subtraction and e is judged. If ∥e∥ is greater than or equal to the preset error threshold, the error augmented state quantity x is a , the reference model state tracking error e and the regression vector Φ(x) are input into the adaptive controller and the output u of the adaptive controller is used as ad For main control output u main Compensation is performed, otherwise, the main control output u is not main compensation; the reference model of the model reference adaptive control loop is a closed-loop linear nominal control system based on the augmented linear quadratic regulation controller, and the adaptive controller of the model reference adaptive control loop is an e-corrected robust adaptive controller.

[0010] Based on the same inventive concept, the following technical solutions can also be obtained:

[0011] An aircraft engine multivariable robust adaptive control system includes a main control loop and a model reference adaptive control loop, wherein the model reference adaptive control loop is used to perform the following dynamic compensation on the main control output of the main control loop to generate an aircraft engine control variable:

[0012] In the main control loop, the output tracking error e between the control command r and the engine output y is y Integrate, e y After integration, the engine state quantity x including the actuator p Forming error augmented state quantity x a , xa The main control output u is obtained through the main controller main The main controller is x a As an augmented linear quadratic regulation controller of state variables;

[0013] In the model reference adaptive control loop, the control command r inputs the reference state x generated by the reference model m The error augmented state quantity x a The reference model state quantity tracking error e is obtained by subtraction and e is judged. If ∥e∥ is greater than or equal to the preset error threshold, the error augmented state quantity x is a , the reference model state tracking error e and the regression vector Φ(x) are input into the adaptive controller and the output u of the adaptive controller is used as ad For main control output u main Compensation is performed, otherwise, the main control output u is not main compensation; the reference model of the model reference adaptive control loop is a closed-loop linear nominal control system based on the augmented linear quadratic regulation controller, and the adaptive controller of the model reference adaptive control loop is an e-corrected robust adaptive controller.

[0014] Preferably, the controller gain of the augmented linear quadratic regulation controller is optimized by using a Newton-Raphson based meta-inspiration optimization method with the minimum tracking error as the optimization objective.

[0015] Furthermore, when switching from compensation to non-compensation during the dynamic compensation process, the following method is used for disturbance-free switching: the output tracking error integral amount e at the current time k is converted to yI Set to zero and use u(k)=u0+u main (k) is the aircraft engine control variable at the current time k, u0 = u(k-1) = u main (k-1)+u ad (k-1) is the control value of the aircraft engine at the previous moment, u main (k) is the main control output at the current time k.

[0016] Compared with the prior art, the technical solution of the present invention and its further optimization and improvement solutions have the following beneficial effects:

[0017] Adaptive control strategy is adopted only in the dynamic process, and the stability is high. Conventional adaptive compensation control methods achieve adaptation to changes in engine characteristics by adjusting the parameters of the main control loop controller, and gain changes directly affect the stability of the system, which will bring new uncertainties to the system; the present invention only compensates the output of the main controller in the dynamic process, does not change the parameters of the main controller, and maintains the robustness of the original augmented LQR control;

[0018] The reference model has good steady-state and dynamic performance, and can provide an appropriate tracking target for adaptive control. The present invention adopts a novel Newton-Raphson metaheuristic algorithm to optimize the nominal model controller parameters. The closed-loop reference model obtained based on the optimized parameters has the expected dynamic and steady-state performance, and can provide a good reference model for adaptive control;

[0019] The adaptive control has a smooth transition during entry and exit. The present invention adopts a non-disturbance switching control method, which re-initializes the integrator and the initial control amount of the controller when switching into the main controller, and ensures smooth switching even when the control amount of the adaptive compensation is large. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a schematic diagram of the structure of a twin-shaft turbofan engine;

[0021] Figure 2 Performance index transformation curve when optimizing controller parameters for Newton-Raphson metaheuristic algorithm;

[0022] Figure 3 is the closed-loop step response curve of the nominal model under the augmented LQR controller. Where (a) is N l Step response curve; (b) is π epr Step response curve; (c) is π cepr Step response curve;

[0023] Figure 4 It is the control system structure diagram;

[0024] Figure 5 The response curve of the controlled variable for linear system simulation, where (a) is N l Step response curve; (b) is π epr Step response curve; (c) is π cepr Step response curve;

[0025] Figure 6 is the curve of the control quantity change of the linear system simulation, where (a) is W fm Change curve; (b) is the change curve of A8; (c) is the change curve of A rvabi Change curve;

[0026] Figure 7 The response curve of the controlled quantity for nonlinear system simulation (H = 0km, Ma = 0), where (a) is N l Step response curve; (b) is π epr Step response curve; (c) is π cepr Step response curve;

[0027] Figure 8Simulate the control variable change curve for the nonlinear system (H=0km, Ma=0);

[0028] Fig. 9 is the nonlinear simulation controlled quantity response curve (H = 1.2km, Ma = 0.8), where (a) is N l Step response curve; (b) is π epr Step response curve; (c) is π cepr Step response curve;

[0029] Fig.10 Simulation control variable change curve for nonlinear system (H=1.2km, Ma=0.8). DETAILED DESCRIPTION

[0030] In view of the shortcomings of the prior art, the solution of the present invention is to organically combine the ALQR control method with the model reference adaptive control method, use the model reference adaptive control loop output to dynamically adaptively compensate the ALQR control loop output, and determine whether to turn on the adaptive compensation based on the control error, so as to improve the dynamic response characteristics of the controller at non-design points without changing the steady-state control performance.

[0031] The technical solution proposed in the present invention is as follows:

[0032] A multivariable robust adaptive control method for an aircraft engine uses a model reference adaptive control loop to dynamically compensate the main control output of a main control loop to generate an aircraft engine control variable:

[0033] In the main control loop, the output tracking error e between the control command r and the engine output y is y Integrate, e y After integration, the engine state quantity x including the actuator p Forming error augmented state quantity x a , x a The main control output u is obtained through the main controller main The main controller is x a As an augmented linear quadratic regulation controller of state variables;

[0034] In the model reference adaptive control loop, the control command r inputs the reference state x generated by the reference model m The error augmented state quantity x a The reference model state quantity tracking error e is obtained by subtraction and e is judged. If ∥e∥ is greater than or equal to the preset error threshold, the error augmented state quantity x is a , the reference model state tracking error e and the regression vector Φ(x) are input into the adaptive controller and the output u of the adaptive controller is used as adFor main control output u main Compensation is performed, otherwise, the main control output u is not main compensation; the reference model of the model reference adaptive control loop is a closed-loop linear nominal control system based on the augmented linear quadratic regulation controller, and the adaptive controller of the model reference adaptive control loop is an e-corrected robust adaptive controller.

[0035] An aircraft engine multivariable robust adaptive control system includes a main control loop and a model reference adaptive control loop, wherein the model reference adaptive control loop is used to perform the following dynamic compensation on the main control output of the main control loop to generate an aircraft engine control variable:

[0036] In the main control loop, the output tracking error e between the control command r and the engine output y is y Integrate, e y After integration, the engine state quantity x including the actuator p Forming error augmented state quantity x a , x a The main control output u is obtained through the main controller main The main controller is x a As an augmented linear quadratic regulation controller of state variables;

[0037] In the model reference adaptive control loop, the control command r inputs the reference state x generated by the reference model m The error augmented state quantity x a The reference model state quantity tracking error e is obtained by subtraction and e is judged. If ∥e∥ is greater than or equal to the preset error threshold, the error augmented state quantity x is a , the reference model state tracking error e and the regression vector Φ(x) are input into the adaptive controller and the output u of the adaptive controller is used as ad For main control output u main Compensation is performed, otherwise, the main control output u is not main compensation; the reference model of the model reference adaptive control loop is a closed-loop linear nominal control system based on the augmented linear quadratic regulation controller, and the adaptive controller of the model reference adaptive control loop is an e-corrected robust adaptive controller.

[0038] In order to facilitate public understanding, the cruise engine of the STOVL propulsion system, that is, a dual-shaft turbofan engine, is taken as an example below, and the technical solution of the present invention is described in detail in conjunction with the accompanying drawings:

[0039] The structure of a twin-shaft turbofan engine is as follows Figure 1As shown in the figure, it includes a fan, an outer duct, a compressor, a combustion chamber, a high-pressure turbine, a low-pressure turbine, a mixing chamber, an afterburner and a tail nozzle. Compared with conventional turbofan engines, it has additional adjustable geometric structures, including a rear variable area bypass injector (RVABI) and a low-pressure turbine inlet guide.

[0040] The output variable of the selected control system is the low pressure speed N l 、Engine pressure ratio π epr 、Engine core pressure ratio π cepr ; Input variable is fuel flow W fm , tail nozzle throat area A8, RVABI opening A rvabi ; The state quantity is low pressure speed N l , high pressure speed N h , high pressure turbine after temperature T 42 .

[0041] Considering the input uncertainty and matching uncertainty of the system, we have

[0042]

[0043] Where x = [N l N h T 42 W fm A8A rvabi ] T is the state quantity after the actuator is augmented, and the input quantity is u=[W fm,r A 8,r A rvabi,r ] T , the subscript 'r' represents the command signal of the corresponding variable, and the output is y = [N l π epr π cepr ] T . Λ is an unknown positive diagonal matrix, and the diagonal elements represent the uncertainty of the effectiveness of the control quantity. Θ *T Φ(x) is the parameter matching uncertainty, Θ * is the unknown constant matrix, and Φ(x) is the known bounded regression function.

[0044] In order to make the controller have good robust tracking performance, the output tracking error is added to the state quantity, which is conducive to the controller to achieve zero static error tracking. The output tracking error can be expressed as

[0045]

[0046] Among them, r is the control instruction.

[0047] By augmenting the integral of the output error in equation (2) into the state quantity in equation (1), we can obtain the augmented state space model:

[0048]

[0049] in, C a =[C 0],D a =D.

[0050] The present invention adopts the augmented LQR method to eliminate the steady-state error. The control command is idealized as a step command, and its derivative is 0. Therefore, taking the nominal model of formula (3) and taking its derivative, we have

[0051]

[0052] in,

[0053] The state feedback controller is designed for equation (4), and the performance functional is expressed as

[0054]

[0055] Among them, Q c and R c is the dimensionally appropriate weighting matrix, and Q c =Q c T ≥0, R c =R c T >0.

[0056] According to the LQR control method, the control law u is obtained c =K c x c ,and Where P c Obtained from the Riccati equation

[0057]

[0058] Then there is a control law

[0059]

[0060] The controller gain is

[0061] The controller gain of the ALQR controller usually needs to be optimized by an optimization algorithm. The currently widely used optimization algorithms are mainly divided into gradient-based and population-based optimization algorithms. The gradient-based optimization algorithm is prone to fall into the local optimum, while the population-based optimization algorithm is conducive to finding the global optimum, but the population-based optimization algorithm has the disadvantages of slow convergence and difficult parameter adjustment. Therefore, this embodiment adopts the Newton-Raphson-based meta-heuristic optimization (NRBO) algorithm to optimize the controller gain, combining the gradient-based and population-based optimization algorithms to overcome the shortcomings of both.

[0062] The NRBO algorithm is mainly based on the Newton-Raphson search rule and the trap avoidance operator to search and avoid falling into the local optimum. The main optimization steps are as follows:

[0063] Step A. Population initialization.

[0064] Generate an initial random population within a bound of candidate solutions:

[0065]

[0066] Where n = 1, 2, ..., N p , j = 1, 2, ..., dim, x j n represents the position of the jth dimension of the nth individual, lb represents the lower bound of the candidate solution, ub represents the upper bound of the candidate solution, and rand represents a random number between (0,1). So the population matrix can be described as

[0067]

[0068] Step B. Newton-Raphson search rule.

[0069] The search rule is an important part of NRBO, which is mainly based on the Newton iteration method and is an iterative approximate solution method. In addition, NRBO uses the current position variable instead of the fitness value to update the variable, which reduces the calculation time. The update rule of the nth individual in the t+1th generation is as follows:

[0070]

[0071] in

[0072]

[0073] in, represents the Hadamard product, randn is a normally distributed random number with a mean of 0 and a variance of 1, and X w represents the current worst individual, X b represents the current optimal individual, Mean() represents the mean, n1 and n2 represent the optimal individual in (1, Np ), and n1≠n2. a and b are random numbers between (0,1), t max represents the maximum number of iterations, r1 and r2 represent random numbers between (0,1).

[0074] Step C. Trap avoidance operator.

[0075] The trap avoidance operator can help NRBO avoid falling into local optimality and improve the effectiveness of dealing with practical problems. Through the trap avoidance operator, the optimal individual X can be combined with a certain probability. b , and obtain an individual with better quality.

[0076]

[0077] Where DF is the probability of executing the trap avoidance operator, θ1 and θ2 are random numbers between (-1, 1) and (-0.5, 0.5), respectively, and the random number μ1 = 3β × rand + (1-β), μ2 = β × rand + (1-β).

[0078] Therefore, in order to obtain good dynamic tracking performance, this embodiment takes the minimum tracking error as the optimization goal and uses NRBO to optimize Q c , R c Matrix parameters. The optimization objective can be expressed as

[0079]

[0080] Among them, R is the time series of the input signal, and Y is the time series of the output signal.

[0081] The input signal N 1,r , π epr,r , π cepr,r Set the unit step response output of the inertial system with a time constant of 0.8, and set Q c , R c The matrix is ​​simplified to a diagonal matrix, and there are 12 parameters to be optimized, corresponding to Q c , R c The diagonal elements of the matrix. Before optimization, the upper bound of the parameter is set to 100 and the lower bound is set to 10. -4 After 300 generations of optimization, the performance index changes as shown in the following curve: Figure 2 shown.

[0082] Depend on Figure 2 It can be found that with the increase of iteration number, the performance index gradually decreases, the tracking error gradually decreases, and the performance of the controller is continuously optimized. c , R c Matrix parameters are used to design the controller and obtain the ALQR controller gain Kc Based on the nominal model, the unit step response of the ALQR controller closed-loop system is performed, and the response curve is obtained as follows: Figure 3 As shown. Figure 3 As shown in the figure, the system optimized by NRBO responds quickly, tracks the reference signal well, and has no obvious errors in both steady state and dynamic state.

[0083] ALQR is designed for the nominal model. When the engine parameters change due to factors such as operating point changes or performance degradation, the controller parameters no longer match the object, and the dynamic characteristics of the system deteriorate. The present invention uses the model reference adaptive control (MRAC) method to compensate for parameter changes. Considering that standard MRAC control may produce high gain when facing sufficiently large disturbances, it is impossible to determine the boundaries of adaptive parameters, which ultimately leads to system instability. Therefore, in order to enhance the robustness of MRAC, the e correction method is used to add damping terms to constrain the adaptive parameters. The damping term is reduced proportionally by the norm of the tracking error. When the tracking error approaches 0, the damping term is also reduced to 0.

[0084] The nominal model is obtained from formula (3):

[0085]

[0086] At the same time, from formula (7) we can get Let u=u main , then the reference model

[0087] It can be expressed as

[0088]

[0089] Among them, A m =A a +B a K c , B m =B r , C m =C a +D a K c .

[0090] The adaptive compensation control quantity u ad Introducing formula (3), we have

[0091]

[0092] make Then we can design an adaptive controller

[0093] u ad =-K ad x a -Θ TΦ(x) (17)

[0094] Among them, K ad and Θ are and θ * The estimated value of , then let the estimated error be

[0095] Substituting formula (17) into formula (16), we get

[0096]

[0097] The closed-loop tracking error equation is obtained as

[0098]

[0099] In order to enhance the robustness of the adaptive control system, the robust adaptive law modified by e is taken as

[0100]

[0101] in, is the adaptive rate matrix. e =P e T >0 is Lyapunov equation The only solution of e =Q e T > 0. η1>0 and η2>0 are correction coefficients.

[0102] Select the Lyapunov function as

[0103]

[0104] Differentiating both sides, we get

[0105]

[0106] Then there is

[0107]

[0108] Available

[0109]

[0110] where λ min () represents the minimum eigenvalue of the matrix.

[0111] To ensure Then there is

[0112]

[0113] or

[0114]

[0115] Based on the above analysis, the robust adaptive control method based on e correction ensures the consistent final boundedness of the error e and the boundedness of the adaptive gain. When the conditions described in equation (25) or equation (26) are met, the robust adaptive control method based on e correction can improve the dynamic performance of the control system under the condition of stability. When the lower bound of equation (25) or equation (26) is reached, the adaptive compensation control is turned off and only ALQR control is relied on. The switching logic is set according to the state tracking error e:

[0116]

[0117] In order to realize the disturbance-free switching when the adaptive loop exits the working state, the present invention further uses the following method to perform the disturbance-free switching when switching from compensation to non-compensation during the dynamic compensation process: the output tracking error integral amount e at the current time k is converted into yI Set to zero and use u(k)=u0+u main (k) is the aircraft engine control variable at the current time k, u0 = u(k-1) = u main (k-1)+u ad (k-1) is the control value of the aircraft engine at the previous moment, u main (k) is the main control output at the current time k. The switching logic of the disturbance-free switching can be expressed as:

[0118]

[0119] The final formation is Figure 4 The aircraft engine multivariable adaptive control system of the present invention includes a main control loop and a model reference adaptive control loop. Figure 4 As shown, the input control command r enters the control system; in the main control loop, the output tracking error e is obtained by the command r and the engine output y y , e y After integration, the state quantity x of the engine actuator is augmented p Forming error augmented state quantity x a , x a The main control output u is obtained through the main controller ALQR main =K c x a , plus the adaptive compensation output u ad Get the engine control input u = u main +u ad, u then acts on the engine controlled object; in the model reference adaptive control loop, the command r is input into the reference model, which is designed as a closed-loop linear nominal control system optimized by the ALQR controller. The reference state quantity x output by the reference model m is subtracted from the actual state quantity x a to obtain the reference model state quantity tracking error e. Subsequently, e is judged. If ∥e∥≥p, then the actual state quantity x a , the reference model state quantity tracking error e, and the regression vector Φ(x) are substituted into the adaptation law to obtain the adaptation parameters K ad and Θ, and then the adaptive compensation output u ad =-K ad x a -Θ T Φ(x); if ∥e∥<p, then let u ad =0, that is, no adaptive compensation is performed.

[0120] In the Figure 4 structure, the bounded tracking caused by the corrected e has little impact on the actual application of the control system. Since the error is easy to calculate, it is judged by Equation (25). When ∥e∥≥p, the ALQR controller is dynamically compensated through robust adaptive control to improve the dynamic characteristics of the ALQR controller. Otherwise, only rely on ALQR for steady-state control to maintain the good steady-state performance of ALQR.

[0121] The ALQR control, model reference adaptive control, robust adaptive control method for e correction, etc. involved in the technical solution of the present invention are all existing technologies, and those skilled in the art can design and implement them according to the targeted aeroengine.

[0122] To verify the effect of the technical solution of the present invention, an engine control system as shown in Figure 4 is built to carry out simulation verification.

[0123] First, carry out simulation verification based on the linear model. Set Q e as a 9th-order identity matrix, the adaptation rate Γ K =diag([1,145,1,1,500,1,1,485,1]), Γ Θ =diag([1,1,110]), the correction parameters η1 = η2 = 0.3. Take Θ * Φ(x)=m·[1,1,1;1,1,1;1,1,1]·[N l N h T 42 T ​, m is the uncertainty interval range, and m = 0 is the nominal model. In order to verify the adaptive control effect, m = 0.5 and m = 0.8 are taken to perform linear system unit step simulation respectively. The controlled quantity response curve of the linear system is shown in Figure 5 As shown in the figure, "ALQR" represents the simulation results of the ALQR control system without adaptive strategy, and "ALQR+RAC" represents the simulation results of the ALQR control system based on robust adaptive dynamic compensation. The specific dynamic performance indicators of the linear system simulation when m = 0.5 and m = 0.8 are shown in Tables 1 and 2.

[0124] Table 1 Linear simulation dynamic performance indicators when m = 0.5

[0125]

[0126] Table 2 Linear simulation dynamic performance indicators when m = 0.8

[0127]

[0128] From Table 1, Table 2 and Figure 5 It can be seen that when there is unmodeled dynamics, the controlled variable response of the ALQR control system has overshoot and changes in response speed. In the simulation of the nominal model, the overshoot of the three controlled variables is zero, and the adjustment time is t s =2.40s. As the unmodeled dynamics increase, the responses of the simple ALQR control loops differ significantly. The unmodeled dynamics make the low-pressure speed N l and core engine pressure ratio π cepr The response speed becomes faster, and the engine pressure ratio π epr The response speed slows down, and the circuit with faster response speed overshoots, especially when m=0.8 epr The overshoot reached 5.93% and the speed overshoot reached 2.49%, which did not meet the control indicators. epr The response speed becomes much slower, and the adjustment time in both cases is greater than 6s, which does not meet the control requirements.

[0129] However, the ALQR+RAC control based on robust adaptive dynamic compensation faces unmodeled dynamics, N l and π cepr The response adjustment time is between 2.32s and 2.44s, which is not much different from the nominal system. eprThe response becomes faster, between 1.84 and 2.26s. In both unmodeled dynamic conditions, the ALQR+RAC control method has no overshoot, which verifies that the robust adaptive compensation control effectively compensates for the impact of unmodeled dynamics on the dynamic performance of the system. When ∥e∥ reaches the lower bound of equation (25), the adaptive compensation is turned off and only ALQR is used for steady-state control, which solves the problem of bounded tracking of the robust adaptive controller based on e correction and maintains the good steady-state performance of ALQR. It is worth noting that a disturbance-free switching control strategy is adopted between the dynamic ALQR+RAC and the steady-state ALQR control to ensure a smooth transition between the control and controlled quantities of the two controllers.

[0130] The change curve of the control quantity in linear simulation is as follows: Figure 6 As shown in the figure, "ALQR" represents the ALQR control system, "ALQR+RAC(u)" represents the ALQR+RAC total output u, and "RAC(u ad )” represents the adaptive compensation output u in ALQR+RAC ad .

[0131] Depend on Figure 6 It is found that in the simulation of the ALQR control system based on robust adaptive dynamic compensation when m = 0.5 and m = 0.8, the system turns off the robust adaptive compensation control (u ad =0), relying only on ALQR for control.

[0132] The dynamic change process of the control quantity in the ALQR control system is different from that in the ALQR control system based on robust adaptive dynamic compensation, but the final steady-state value is the same, and good steady-state performance is also achieved.

[0133] Based on the F135-PW-600 engine component-level model, the ALQR controller and the ALQR+RAC controller based on robust adaptive dynamic compensation are simulated and verified. According to the flight envelope of the F35B aircraft, the operating points of H=0km, Ma=0 and H=1.2km, Ma=0.8 are selected for simulation verification, and a 1% step is made on the controlled quantity command. The response curve is as follows Figures 7 to 10 The specific performance indicators are shown in Table 3 and Table 4.

[0134] Table 3 Dynamic performance indicators when H = 0km, Ma = 0

[0135]

[0136] Depend on Figure 7As can be seen from Table 3, at the design point, the control performance of ALQR and ALQR+RAC is similar, and only π cepr A slight overshoot occurs, and the speed N under ALQR+RAC control l , π epr The response is fast, less than 2.1s, π cepr The response is slower, but also slightly faster than π under ALQR control epr The adjustment time of the three loops under ALQR control differs by 0.86s. After adaptive control, the adjustment time of the three loops differs by 0.68s, which is 20.93% smaller. This is beneficial to the stability of the system and can avoid oscillation or instability caused by the fast loop compensating for the change of the slow loop in practical applications. The dynamic response process of ALQR+RAC is more stable. When ALQR is controlled, the time difference is π before 1s. epr There is a fluctuation of about 12%, π cepr There is a fluctuation of 1.68%. The controlled quantity responds smoothly in the ALQR+RAC control system, still maintaining the good steady-state performance of ALQR and achieving zero-static-error tracking. Figure 8 It can be found that after adaptive dynamic compensation, the control amount W fm The fluctuation is reduced, achieving a relatively smooth change.

[0137] The controlled quantity response curve at the working point of H = 1.2km, Ma = 0.8 is as follows Fig. 9 As shown, the control quantity change curve is as follows Fig.10 The dynamic performance indicators are shown in Table 4.

[0138] Table 4 Dynamic performance indicators when H = 1.2 km, Ma = 0.8

[0139]

[0140] Depend on Fig. 9 As can be seen from Table 4, at non-design points, due to the existence of unmodeled dynamic responses, the dynamic response performance of the controlled quantity in the ALQR control system deteriorates, π epr , π cepr The overshoot reaches 3.75% and 2.50%. l The response slowed down to 3.48s, π epr , π cepr The response speed becomes faster, and the adjustment time of the three loops differs by 1.84s. After the adaptive control is adopted, π cepr The response speed slowed down, and the adjustment time of the three loops differed by 0.88s, which was 52.17% smaller than that of ALQR. Fig.10 It can be found that the change of the control quantity becomes more stable after adaptive compensation, which is beneficial to the stability of the system.

Claims

1. A multivariable robust adaptive control method for an aircraft engine, characterized in that: The model reference adaptive control loop is used to dynamically compensate the main control output of the main control loop to generate the aircraft engine control quantity: In the main control loop, the output tracking error e between the control command r and the engine output y is y Integrate, e y After integration, the engine state quantity x including the actuator p Forming error augmented state quantity x a , x a The main control output u is obtained through the main controller main The main controller is x a As an augmented linear quadratic regulation controller of state variables; In the model reference adaptive control loop, the control command r inputs the reference state x generated by the reference model m The error augmented state quantity x a The reference model state quantity tracking error e is obtained by subtraction and e is judged. If ∥e∥ is greater than or equal to the preset error threshold, the error augmented state quantity x is a , the reference model state tracking error e and the regression vector Φ(x) are input into the adaptive controller and the output u of the adaptive controller is used as ad For main control output u main Compensation is performed, otherwise, the main control output u is not main compensation; the reference model of the model reference adaptive control loop is a closed-loop linear nominal control system based on the augmented linear quadratic regulation controller, and the adaptive controller of the model reference adaptive control loop is an e-corrected robust adaptive controller.

2. The aircraft engine multivariable robust adaptive control method according to claim 1, characterized in that: The controller gain of the augmented linear quadratic control controller is optimized by using a Newton-Raphson based meta-inspiration optimization method with the minimum tracking error as the optimization objective.

3. The aircraft engine multivariable robust adaptive control method according to claim 1, characterized in that: When switching from compensation to non-compensation during dynamic compensation, the following method is used for disturbance-free switching: the output tracking error integral e at the current time k is yI Set to zero and use u(k)=u0+u main (k) is the aircraft engine control variable at the current time k, u0 = u(k-1) = u main (k-1)+u ad (k-1) is the control value of the aircraft engine at the previous moment, u main (k) is the main control output at the current time k.

4. An aircraft engine multivariable robust adaptive control system, characterized in that: The invention comprises a main control loop and a model reference adaptive control loop, wherein the model reference adaptive control loop is used to perform the following dynamic compensation on the main control output of the main control loop to generate the aircraft engine control quantity: In the main control loop, the output tracking error e between the control command r and the engine output y is y Integrate, e y After integration, the engine state quantity x including the actuator p Forming error augmented state quantity x a , x a The main control output u is obtained through the main controller main The main controller is x a As an augmented linear quadratic regulation controller of state variables; In the model reference adaptive control loop, the control command r inputs the reference state x generated by the reference model m The error augmented state quantity x a The reference model state quantity tracking error e is obtained by subtraction and e is judged. If ∥e∥ is greater than or equal to the preset error threshold, the error augmented state quantity x is a , the reference model state tracking error e and the regression vector Φ(x) are input into the adaptive controller and the output u of the adaptive controller is used as ad For main control output u main Compensation is performed, otherwise, the main control output u is not main compensation; the reference model of the model reference adaptive control loop is a closed-loop linear nominal control system based on the augmented linear quadratic regulation controller, and the adaptive controller of the model reference adaptive control loop is an e-corrected robust adaptive controller.

5. The aircraft engine multivariable robust adaptive control system as claimed in claim 4, characterized in that: The controller gain of the augmented linear quadratic regulation controller is optimized by using a Newton-Raphson based meta-inspiration optimization method with the minimum tracking error as the optimization objective.

6. The aircraft engine multivariable robust adaptive control system according to claim 4, characterized in that: When switching from compensation to non-compensation during dynamic compensation, the following method is used for disturbance-free switching: the output tracking error integral e at the current time k is yI Set to zero and use u(k)=u0+u main (k) is the aircraft engine control variable at the current time k, u0 = u(k-1) = u main (k-1)+u ad (k-1) is the control value of the aircraft engine at the previous moment, u main (k) is the main control output at the current time k.

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