CN110609568B - Strong self-coupling PI cooperative control method for large unmanned aerial vehicle UAV - Google Patents

Strong self-coupling PI cooperative control method for large unmanned aerial vehicle UAV Download PDF

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CN110609568B
CN110609568B CN201910904419.2A CN201910904419A CN110609568B CN 110609568 B CN110609568 B CN 110609568B CN 201910904419 A CN201910904419 A CN 201910904419A CN 110609568 B CN110609568 B CN 110609568B
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曾喆昭
贺文锋
谷帅
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Guangdong Mingyang Information Technology Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
    • G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
    • G05D1/10—Simultaneous control of position or course in three dimensions
    • G05D1/101—Simultaneous control of position or course in three dimensions specially adapted for aircraft

Abstract

The invention provides a strong self-coupling PI cooperative control method for a large Unmanned Aerial Vehicle (UAV), which is used for solving the control problem of a complex system with input limited non-affine nonlinear strong coupling Multiple Input Multiple Output (MIMO). The control method of the invention defines the UAV dynamic and internal and external uncertainties as the sum disturbance, thereby transforming the MIMO complex system of the non-affine nonlinear strong coupling into the uncertain MIMO linear system, further constructing the error dynamic system under the excitation of the sum disturbance, accordingly designing the strong self-coupling PI (EAC-PI) cooperative controller model for the core coupling factor through the speed factor and the enhancement factor which are irrelevant to the controlled object model, and the theoretical analysis and the simulation result show that the EAC-PI cooperative control system has good global robust stability, thereby effectively solving the setting problem of PID, and the invention has wide application prospect in the field of aircraft control.

Description

Strong self-coupling PI cooperative control method for large unmanned aerial vehicle UAV
Technical Field
The invention relates to the technical field of aircraft control, in particular to an Enhanced Auto-Coupling Proportment-Integral (EAC-PI) cooperative control method for a strong Auto-Coupling PI (autonomous Unmanned Aerial Vehicle) of a large UAV (UAV).
Background
For more than half a century recently, classical control (cybernetics) based on a frequency domain design method and modern control (model theory) based on a time domain design method have been developed independently to form respective methodology systems. In the actual control engineering, the error between the control target and the actual behavior of the controlled object is easy to obtain and can be properly processed, so that the original form of the control strategy of "eliminating the error based on the error", that is, the PID (Proportional-Integral-Derivative) controller is widely applied in the field of industrial control. For practical control engineering problems, because the description of an internal mechanism is generally difficult to give, a control strategy given by modern control theory based on a mathematical model is difficult to be effectively applied to the practical control engineering. This is a disjointing phenomenon that extends over half a century between control engineering practices and control theories and is not well solved. The essence of the classical control theory is that a control strategy is generated according to the deviation of an actual value and a control target, and the control target can be achieved as long as a PID gain is reasonably selected to stabilize a closed-loop system, which is the reason for wide adoption. However, the development of scientific technology puts higher demands on the accuracy, speed and robustness of the controller, and the disadvantages of PID control gradually emerge: although PID control can ensure system stability, closed loop system dynamics are sensitive to PID gain variations. This drawback leads to an irreconcilable conflict between "rapidity" and "overshoot" in the control system, and therefore, when the system operating conditions change, the controller gain also needs to change, which is the original motivation for various improved PID control methods such as adaptive PID, nonlinear PID, neuron PID, intelligent PID, fuzzy PID, expert system PID, etc. Although various improved PID controllers can improve the adaptive control capability of the system by optimizing and setting the gain parameters of the controller on line, the existing various PID controllers still have no function for the control problem of a non-affine non-linear uncertain system, and particularly have poor disturbance resistance robustness.
Because a large Unmanned Aerial Vehicle (UAV) is a typical multi-input multi-output and input-limited non-affine nonlinear strong-coupling uncertain system, for the control of such a complex system, the conventional PID and various improved PID control methods thereof are unable to be applied, and the existing control methods mainly include: the method comprises a second-order nonsingular dynamic terminal sliding mode control method, a second-order nonsingular terminal sliding mode control method based on a Cerebellum Model (CMAC) disturbance observer and a second-order nonsingular terminal sliding mode control method based on a Wavelet Cerebellum Model (WCMAC) disturbance observer. However, when the UAV has parameter perturbation and actuator failure, the second-order nonsingular dynamic tertiary sliding mode control method is out of control; the CMAC-based second-order nonsingular final sliding mode control method and the WCMAC-based second-order nonsingular final sliding mode control method both utilize CMAC or WCMAC to carry out interference estimation on internal and external uncertainties of the UAV, however, the two methods have large calculated amount and poor real-time performance.
Disclosure of Invention
The invention aims to provide a strong self-coupling PI cooperative control method of a large Unmanned Aerial Vehicle (UAV) aiming at the defects in the background technology, and the control method has the advantages of simple model structure, easy parameter setting, good dynamic quality, high control precision and strong disturbance resistance capability, and can effectively improve the dynamic quality and the steady-state performance of the UAV control system, reduce the calculated amount, improve the real-time property and enhance the stability of the control system.
In order to achieve the purpose, the invention adopts the following technical scheme:
a strong self-coupling PI cooperative control method for a large Unmanned Aerial Vehicle (UAV) comprises the following specific steps:
step A: measurement and acquisition of expected trajectory y of Unmanned Aerial Vehicle (UAV)djDifferential signal
Figure BDA0002212851500000021
And the actual output y of the UAVjAnd thereby establishing a tracking error e thereofjAnd integral e of the tracking errorj0；
Comprising establishing the tracking error and the integral of the tracking error, respectively, using the following calculation:
Figure BDA0002212851500000031
wherein e isjRepresents a tracking error; e.g. of the typej0Denotes the integral of the tracking error, j 1,2,3 is the channel number of the UAV.
Preferably, step B: obtaining UAV UA according to step AV tracking error ejAccording to the tracking error ejIntegral of tracking error ej0And a differential signal of the desired track
Figure BDA0002212851500000032
Creating EAC-PI cooperative control law ujSpecifically, the method comprises the following steps of obtaining the cooperative control law of the EAC-PI by using the following formula:
Figure BDA0002212851500000033
wherein z isjRepresenting the speed factor, z, of the UAV jth channel EAC-PI controllerj＞0；
λjDimensionless enhancement factor, λ, for UAV jth channel EAC-PI controllerj＞0；
bjDenotes the control gain of the j-th channel, and b1＝g/M,b2＝1,b3＝1；
ujAnd the output cooperative control force of the EAC-PI cooperative controller of the j channel is shown.
Preferably, step C: according to the UAV jth channel EAC-PI cooperative control law model established in the step B, and through the integral control force of the jth channel
Figure BDA0002212851500000034
And a cooperative control force ujRespectively carrying out amplitude limiting treatment to avoid integral saturation phenomenon in a dynamic process and meet the requirement of an input limited system;
controlling the unmanned aerial vehicle according to the EAC-PI controller model, specifically performing amplitude limiting processing by using the following formula:
|ujI|≤0.5ujm，|uj|≤ujm
wherein u isjmRepresents the maximum amplitude, u, of the jth channel cooperative control inputjm＞0。
Has the advantages that:
compared with the existing three main flow controllers, the strong self-coupling proportional-integral (EAC-PI) control method of the invention integrates the advantages of the three main flow controllers and eliminates the limitations of the three main flow controllers, namely: the method has the advantages of simple PID structure, good robustness and stability of SMC, and strong ADRC disturbance resistance; the problem of difficulty in PID gain setting is effectively avoided, the problem that SMC is not adjustable between high-frequency buffeting and disturbance resisting capacity is effectively solved, and the problems of excessive ADRC gain parameters and large calculated amount are effectively avoided. The invention of the EAC-PI control method enriches the control theory system for more than half a century and provides effective technical support for the technical upgrade of various PID controllers in the prior operation. The method has wide application prospect in the field of non-affine non-linear uncertain control systems and aircraft control.
Drawings
FIG. 1 is a block diagram of the UAV control system framework of the invention based on an EAC-PI controller;
FIG. 2 is a plot of airspeed tracking trajectories in the nominal UAV system tracking control results of the present invention;
FIG. 3 is a plot of the track-tilt-angle tracking trajectory in the nominal UAV system tracking control results of the present invention;
FIG. 4 is a plot of the track azimuth tracking trajectory in the nominal UAV system tracking control results of the present invention;
FIG. 5 is a diagram of UAV system thrust control inputs in the nominal UAV system tracking control results of the present invention;
FIG. 6 is a diagram of the UAV system overload factor control input in the nominal UAV system tracking control result of the present invention;
FIG. 7 is a diagram of UAV system roll angle control inputs in the nominal UAV system tracking control results of the present invention;
FIG. 8 is a plot of airspeed tracking trajectories in the result of the perturbed UAV system tracking control of the present invention;
FIG. 9 is a plot of track-tilt-angle tracking trajectories in the result of the perturbed UAV system tracking control of the present invention;
FIG. 10 is a plot of the track azimuth tracking trajectory in the result of the perturbed UAV system tracking control of the present invention;
FIG. 11 is a diagram of UAV system thrust control inputs in the interfered UAV system tracking control results of the present invention;
FIG. 12 is a diagram of UAV system overload factor control inputs in the perturbed UAV system tracking control results of the present invention;
figure 13 is a diagram of UAV system roll angle control inputs in the perturbed UAV system tracking control of the present invention.
Detailed Description
The technical scheme of the invention is further explained by the specific implementation mode in combination with the attached drawings.
1. Mapping thought from non-affine non-linear uncertain system to reflection linear uncertain system
Consider an Unmanned Aerial Vehicle (UAV) system:
Figure BDA0002212851500000051
wherein: v, γ, and χ are the airspeed, track inclination, and track azimuth, respectively, of the UAV; t, n, mu are engine thrust, overload coefficient and roll angle respectively; g is the acceleration of gravity; m is the mass of the UAV; d is the resistance and is expressed as:
Figure BDA0002212851500000052
wherein, the parameters of the formula (2) are detailed as shown in table one:
TABLE 1UAV basic parameters
Figure BDA0002212851500000053
Figure BDA0002212851500000061
At the same time, the UAV actual flight safety requirements are also considered, i.e. the roll angle μ should satisfy: mu | is less than or equal to 90 degrees; overload factor n should avoid stall risk, n<0.5ρV2SCLmax/M。
For ease of analysis, define: y is1＝V，y2＝γ，y3＝χ，u1＝T，u2＝n，u3μ. Substituting equation (2) into equation (1), there is a UAV system as follows:
Figure BDA0002212851500000062
wherein the content of the first and second substances,
Figure BDA0002212851500000066
c12＝2kMg/(ρS)。
according to the UAV system (3), the UAV is a three-input three-output non-affine nonlinear strong coupling system, and aiming at the control of the complex system, the invention firstly defines the non-affine nonlinear uncertain dynamics of each channel as three sum disturbances respectively, namely:
Figure BDA0002212851500000063
Figure BDA0002212851500000064
Figure BDA0002212851500000065
then the UAV system (3) reduces to:
Figure BDA0002212851500000071
wherein, bjNot equal to 0 is the control gain of the j-th channel of the linear uncertainty system (4), and b1＝g/M，b2＝1，b3＝1。
It is clear that the system (4) is a complete equivalent to the UAV system (3)A three-input three-output linear uncertain system, the control of the system (4) is equivalent to the control of the UAV system (3). As long as the sum perturbation is bounded: | dj|<Infinity (j ═ 1,2,3), then the MIMO non-affine non-linear uncertainty system can all be expressed in the form of MIMO linear uncertainty system (4), with general significance. Moreover, by the definition of the sum disturbance, all known or unknown uncertain factors are expressed by the sum disturbance, the thought of converting a non-affine non-linear uncertain system into a linear uncertain system is completely diluted, and the concepts of system classification such as linearity and nonlinearity, certainty and uncertainty, time variation and time invariance, affine and non-affine are completely diluted, so that various problems of how to apply an effective control method to different types of controlled systems by a control theory and model theory two control thought systems for more than half a century are effectively solved, any complex non-linear system is converted into the linear uncertain system, and the control method of any complex non-linear system can be unified.
How to exert effective control on the UAV system described by equation (3) or equation (4) is the core technology of the present invention, namely, the EAC-PI cooperative control technology.
2. EAC-PI cooperative controller design
Aiming at the control problem of the UAV system (4), the expected track of the j channel (j is 1,2 and 3) is set as ydjAnd defining the tracking control error as:
ej＝ydj-yj (5)
and is
Figure BDA0002212851500000072
Then there is
Figure BDA0002212851500000081
Wherein, b1＝g/M，b2＝1，b3＝1。
The error dynamics for the j-th channel can be established according to equations (6) and (7) as follows:
Figure BDA0002212851500000082
obviously, the Error System (8) is a second order dynamic Error System (EDS), and defines the EAC-PI cooperative control law u for stabilizing the EDSjComprises the following steps:
Figure BDA0002212851500000084
wherein z isj>0 and lambdaj>0 is the speed factor and dimensionless enhancement factor of the UAV system jth channel EAC-PI controller, bjIs the control gain of the UAV jth channel, and b1＝g/M，b2＝1，b3＝1；
Figure BDA0002212851500000085
Is the differential of the desired trajectory of the j-th channel, ujThe cooperative control force is not only output by the EAC-PI cooperative controller of the jth channel, but also input by a controlled object of the jth channel.
And a traditional PI controller model: k ═ upej+kpej0Compared with the prior art, the gain setting rule of the EAC-PI controller can be obtained:
Figure BDA0002212851500000083
according to the setting rule (10), the speed factor is not only two gains k in the EAC-PI controllerpAnd kiIs an important relationship factor between, and is also kpAnd kiEquivalent conversion factor of (c). In addition, the main function of the enhancement factor is to adjust the control force of the proportional link when 0<λj<And 1, reducing the control force action of the proportional link, or else, enhancing the control force action of the proportional link.
3. Closed loop control system stability analysis
Theorem 1: assuming the synthetic perturbation is bounded: | dj|<Infinity (j ═ 1,2,3), then if and only if zj>0、λj>At 0, a closed-loop control system consisting of the EAC-PI controller (9) is globally robust and stable, and the EAC-PI controller has good disturbance robustness.
And (3) proving that:
(1) stability analysis
Substituting the EAC-PI controller (9) into the Error Dynamics System (EDS) shown in equation (8) then has:
Figure BDA0002212851500000091
it is apparent that the error system (11) is a bounded perturbation | dj|<And (3) dynamic system under infinity excitation. Considering the initial state:
Figure BDA0002212851500000098
taking a single-sided laplace transform of the error dynamics system (11), there are:
Figure BDA0002212851500000092
the closed-loop control system obtained by arrangement is as follows:
Figure BDA0002212851500000093
it is apparent that the first term of the closed loop control system (13) is a zero input response and the second term is a zero state response. The system transfer function is:
Figure BDA0002212851500000094
since the system (14) is characterized by
Figure BDA0002212851500000095
Therefore, when z isj>0、0<λjWhen < 1, the system (14) has a pair of conjugate complex roots in the left half complex plane:
Figure BDA0002212851500000096
Figure BDA0002212851500000097
the error system (11) or (13) is thus consistently stable; when z isj>0、λjWhen 1, the system (14) has a heavy root on the real axis of the left semi-complex plane: s1,2＝-λjzjThus the error system (11) or (13) is gradually stable; z is a radical ofJ>0、λJAt > 1, the system (14) has two real roots on the real axis of the left semi-complex plane:
Figure BDA0002212851500000101
thus, the error system (11) or (13) is also gradually stable, in any case, as long as z isJ>0、λJ>0, the system error (11) or (13) is always stable, and the system stability is independent of the model of the controlled object, so the system is stable globally.
(2) Robust analysis of disturbance rejection
When z isJ>0、0<λJWhen the impulse response is less than 1, the unit impulse response of the system (14) is as follows:
Figure BDA0002212851500000102
wherein the content of the first and second substances,
Figure BDA0002212851500000103
obviously, there are: lim (small)t→∞hj(t)＝0。
(z is when z)j>0、λjWhen the impulse response is 1, the unit impulse response of the system (14) is as follows:
Figure BDA0002212851500000105
obviously, there are also: lim (small)t→∞hj(t)＝0。
(z is when z)j>0、λjWhen the impulse response is more than 1, the unit impulse response of the system (14) is as follows:
Figure BDA0002212851500000106
wherein the content of the first and second substances,
Figure BDA0002212851500000104
k1＝0.5(λj-ξj)/ξj，k2＝-0.5(λj+ξj)/ξj。
obviously, there are also: lim (small)t→∞hj(t)＝0。
In general, as long as zj>0、λj>0, then there must be: lim (small)t→∞hj(t)＝0。
Due to the fact that
Figure BDA0002212851500000109
Thus, there are:
Figure BDA0002212851500000107
due to zj>0、λj>At 0, limt→∞hj(t) is 0, so as long as the sum perturbation is bounded: | dj|<Infinity (j ═ 1,2,3), lim must be presentt→∞hj(t) is equal to 0, namely the tracking error e of j channel of the MIMO controlled systemj(t) can be taken from any initial state other than zero
Figure BDA0002212851500000108
The balance point zero point is consistently and stably approached, and accurate control can be realized.
The above theoretical analysis shows that when z isj>0、λj>At 0, the closed-loop control system consisting of the AC-PI controller isThe global robustness is stable; as long as the sum perturbation is bounded: | dj|<Infinity, then the tracking error e of the j channel of the MIMO controlled systemj(t) may be asymptotic from any non-zero initial state towards a stable equilibrium point zero. Due to ej(t) approaching a stable equilibrium zero from an arbitrary non-zero initial state with | d onlyj|<Infinity, and is related to the sum disturbance djIs irrelevant, therefore, the EAC-PI cooperative controller has good disturbance robustness.
4. Speed factor setting method
Theorem 1 shows that: when z isj>0、λj>At 0, the gain setting rule of equation (10) may ensure the global stability of the closed loop control system consisting of the EAC-PI controller (9), and thus, zjHas a large setting margin. In order to make the EAC-PI cooperative controller have fast response speed and strong disturbance resistance, z is requiredjThe larger the better. However, zjToo large an overshoot and oscillation phenomenon due to integral saturation is likely to occur, so that it is required to reasonably set the speed factor z of the EAC-PIjThe specific method comprises the following steps:
known from the conventional PI controller, the proportional gain kpAnd integral gain kiThe relationship of (1) is:
ki＝kp/Ti (19)
wherein k isp>0 is an independent proportional gain without attribute, TiIs the independent integration time constant of the PI controller.
Is due to kpIs an independent variable without physical attributes, TiIs an independent time variable, and thus, the integral gain ki＝kp/TiIs also an independent variable without physical properties, therefore, kpAnd k isiSurface relationship k betweeni＝kp/TiIn effect, a loose relationship with no binding force.
Furthermore, kpWhat physical attributes are there? k is a radical ofpAnd TiIs there an inherent inevitable relationship? k is a radical ofpAnd TiAnd the controlled objectWhat characteristics of? The three problems are three key scientific problems which are ignored by scholars at home and abroad in the last hundred years, and are also the setting problem of the PI controller. In order to effectively solve the key scientific problems, the invention is characterized in that according to the setting rule (10) of the EAC-PI controller and the gain relation of the traditional PI controller: k is a radical ofi＝kp/TiAn important theoretical result is obtained:
zj＝2λj/Ti (20)
obviously, z isjIs TiIs the reciprocal of (i.e. z)jIs/sec and is therefore referred to as the velocity factor. T isiThe smaller, zjThe larger the size, otherwise the opposite is true. If the time constant T is integratediApproximating the time scale τ, T, as the controlled objectiτ, then:
zj≈2λj/τ (21)
as can be seen from equation (21), the smaller the time scale τ, the faster the dynamic characteristics of the controlled object, and therefore, the speed factor z of the EAC-PI controller is requiredjThe larger the size, otherwise the opposite is true. Obviously, the velocity factor zjNot only the internal core coupling factor of the EAC-PI controller (9) and the equivalent conversion factor of the EAC-PI controller gain setting rule (10), but also a determined external relation (21) is established with the interval scale tau reflecting the speed of the controlled object, therefore, the speed factor z can be completely set according to the speed characteristic of the controlled objectjThe value range of (a).
5. Amplitude limiting processing method for cooperative control force
Since the actuator of an actual physical system often has a saturation phenomenon, when control saturation occurs, the tracking performance of the system is reduced, and even the system is out of control, so that the control force must be subjected to amplitude limiting processing. Considering that integral saturation is the main reason causing control force saturation, firstly, the control force of the integral link is considered to be subjected to amplitude limiting processing, and then the control force u of the EAC-PI is consideredjPerforming clipping processing, namely: in order to effectively avoid the integral saturation phenomenon in the dynamic process and consider the system with limited input, the integral link of the j channelControlling force: u. ofjI＝z2 jej0/bjAnd control force ujRespectively carrying out amplitude limiting treatment:
|ujI|≤0.5ujm，|uj|≤ujm
wherein u isjm> 0 is the maximum amplitude of the jth channel control input.
A block diagram of an UAV control system based on an EAC-PI controller is shown in figure 1.
6. Performance test and analysis of EAC-PI cooperative control method of large unmanned aerial vehicle UAV
In order to verify the effectiveness of an EAC-PI control method of the present invention, the following simulation experiments are performed for the control problem of a large-scale unmanned aerial vehicle UAV system (1) or (3), the relevant parameters of the UAV system (1) or (3) are shown in table 1, and the UAV initial conditions are as follows:
V(0)＝90m/s，γ(0)＝χ(0)＝0°，T(0)＝1kN，n(0)＝1，μ(0)＝0°
the expected trajectory is:
Figure BDA0002212851500000131
the three EAC-PI controller related simulation conditions are set as follows:
speed factor: z is a radical of1＝40，z2＝400，z3＝40；
Enhancement factor: lambda [ alpha ]1＝10，λ2＝20，λ3＝20；
And (3) controlling force amplitude limiting in an integration link: | u1I|≤0.5Tmax，|u2I|≤0.5nmax，|u3I|≤0.01π；
Controlling force amplitude limiting: | u1|≤Tmax，|u2|≤nmax，|u3|≤0.45π；
Controlling the channel gain: b1＝g/M，b2＝1，b3＝1。
The simulation verification is carried out in two cases: one is the case where the nominal UAV system, i.e., the UAV model, is precisely known; and the other is disturbed UAV system, namely the UAV model has the situations of uncertain pneumatic parameters and actuator faults.
Simulation experiment 1: nominal UAV system tracking control experiment
An instruction tracking control experiment is carried out on the MIMO controlled object UAV system shown in the system (1) or (3), the EAC-PI control method is used, and simulation results are shown in the figures 2-7. 2-7 show that the UAV control system based on the EAC-PI controller not only has fast response speed and high control precision, but also has good robust stability, thus being an effective control method.
Simulation experiment 2: disturbed UAV system tracking control experiment
When the UAV performs a flight mission, the influence of factors such as flight environment may cause problems such as uncertainty of pneumatic parameters and failure of an actuator. Wherein, if the UAV is damaged due to combat or the actuator causes failure, the expected overload coefficient n and the actual overload coefficient naBetween is provided with na＝(1-kn) n, and knSatisfies k is more than or equal to 0n<1; if the UAV is not malfunctioning, k n0. Thus, the following set of disturbed UAV system related parameters are as follows:
set during UAV flight, kn0.25, aerodynamic parameter CD0And k is present-30% indeterminate, i.e.
Figure BDA0002212851500000141
Δ k is-0.3 k and considering the case where the maximum engine thrust is also reduced by 30%, i.e., Δ Tmax＝-0.3Tmax。
Under the flight conditions, the relevant parameters of the three EAC-PI cooperative controllers are identical to those of the nominal UAV system, and the simulation results are shown in fig. 8-13. Fig. 8-13 show that, when the UAV has uncertain parameters and fails in the actuator, the EAC-PI control method of the present invention not only has a fast response speed and a high control accuracy, but also has good robust stability, further showing that the strong self-coupling PI control method of a large and small drone of the present invention is a strong disturbance rejection control method with global stability.
7. Conclusion
Although a PID controller, an SMC and an ADRC based on a control theory strategy are three main flow controllers widely used in the field of control engineering at present, the limitations of the traditional PID controller are very obvious, and firstly, the gain parameter requirement changes along with the change of a working condition state, so that the parameter setting is difficult; secondly, poor nonlinear control capability; and thirdly, the disturbance resistance is weak. Although various improved PID controllers such as an adaptive PID controller, a nonlinear PID controller, a parameter self-learning nonlinear PID controller, a fuzzy PID controller, an optimal PID controller, a neuron PID controller, an expert PID controller and the like overcome the parameter setting problem of the traditional PID controller to a great extent and have certain nonlinear control capability, the existing improved PID controller has the defects of large calculated amount, poor real-time performance and poor disturbance resistance capability; although the robust stability performance of the SMC is good, an irreconcilable contradiction exists between high-frequency buffeting and disturbance rejection capability; although the robust stability performance of the ADRC is good, excessive gain parameters exist, the calculation amount of the related nonlinear function is too large, the structure of the control system is complex, and the stability of the control system cannot be theoretically analyzed.
Compared with the existing three main flow controllers, the strong self-coupling proportional-integral (EAC-PI) control method of the invention integrates the advantages of the three main flow controllers and eliminates the limitations of the three main flow controllers, namely: the method has the advantages of simple PID structure, good robustness and stability of SMC, and strong ADRC disturbance resistance; the problem of difficulty in PID gain setting is effectively avoided, the problem that SMC is not adjustable between high-frequency buffeting and disturbance resisting capacity is effectively solved, and the problems of excessive ADRC gain parameters and large calculated amount are effectively avoided. The invention of the EAC-PI control method enriches the control theory system for more than half a century and provides effective technical support for the technical upgrade of various PID controllers in the prior operation.
The technical principle of the present invention is described above in connection with specific embodiments. The description is made for the purpose of illustrating the principles of the invention and should not be construed in any way as limiting the scope of the invention. Based on the explanations herein, those skilled in the art will be able to conceive of other embodiments of the present invention without inventive effort, which would fall within the scope of the present invention.

Claims (1)

1. A strong self-coupling PI cooperative control method of a large Unmanned Aerial Vehicle (UAV) is characterized in that: the method comprises the following specific steps:
establishing an Unmanned Aerial Vehicle (UAV) system:
Figure FDA0002768296080000011
wherein: v, γ, and χ are the airspeed, track inclination, and track azimuth, respectively, of the UAV; t, n, mu are engine thrust, overload coefficient and roll angle respectively; g is the acceleration of gravity; m is the mass of the UAV; d is the resistance and is expressed as:
Figure FDA0002768296080000012
wherein, the parameters of the formula (2) are detailed as shown in table one:
table-UAV basic parameters
Figure FDA0002768296080000013
The roll angle mu should satisfy: mu | is less than or equal to 90 degrees; the overload coefficient n is to avoid the stall risk, and n is less than 0.5 rho V2SCLmax/M；
Defining: y is1＝V，y2＝γ，y3＝χ，u1＝T，u2＝n，u3＝μ；
The conversion UAV system is as follows:
Figure FDA0002768296080000021
wherein the content of the first and second substances,
Figure FDA0002768296080000027
the non-affine non-linear uncertainty dynamics of each channel are respectively defined as three sum disturbances, namely:
Figure FDA0002768296080000022
Figure FDA0002768296080000023
Figure FDA0002768296080000024
the UAV system (3) is then:
Figure FDA0002768296080000025
wherein, bjNot equal to 0 is the control gain of the j-th channel of the linear uncertainty system (4), and b1＝g/M，b2＝1，b3＝1；
Step A: measurement and acquisition of expected trajectory y of Unmanned Aerial Vehicle (UAV)djDifferential signal
Figure FDA0002768296080000026
And the actual output y of the UAVjAnd thereby establishing a tracking error e thereofjAnd integral e of the tracking errorj0；
Comprising establishing the tracking error and the integral of the tracking error, respectively, using the following calculation:
ej＝ydj-yj，
Figure FDA0002768296080000034
wherein e isjRepresents a tracking error; e.g. of the typej0Represents the integral of the tracking error, j is 1,2,3 is the channel number of the UAV;
and B: obtaining the tracking error e of the UAV according to the step AjAccording to the tracking error ejIntegral of tracking error ej0And a differential signal of the desired track
Figure FDA0002768296080000031
Creating EAC-PI cooperative control law ujSpecifically, the method comprises the following steps of obtaining the cooperative control law of the EAC-PI by using the following formula:
Figure FDA0002768296080000032
wherein z isjRepresenting the speed factor, z, of the UAV jth channel EAC-PI controllerj＞0；
λjDimensionless enhancement factor, λ, for UAV jth channel EAC-PI controllerj＞0；
bjDenotes the control gain of the j-th channel, and b1＝g/M，b2＝1，b3＝1；
ujRepresenting the output cooperative control force of the EAC-PI cooperative controller of the j channel;
and C: according to the UAV jth channel EAC-PI cooperative control law model established in the step B, and through the integral control force of the jth channel
Figure FDA0002768296080000033
And a cooperative control force ujRespectively carrying out amplitude limiting treatment to avoid integral saturation phenomenon in a dynamic process and meet the requirement of an input limited system;
controlling the unmanned aerial vehicle according to the EAC-PI controller model, specifically performing amplitude limiting processing by using the following formula:
|ujI|≤0.5ujm，|uj|≤ujm
wherein u isjmRepresents the maximum amplitude, u, of the jth channel cooperative control inputjm＞0。
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