CN104102133A - Improved artificial bee colony algorithm based quadrotor proportional integral derivative (PID) parameter optimization method - Google Patents

Abstract

An improved artificial bee colony algorithm based quadrotor proportional integral derivative (PID) parameter optimization method includes the following steps of (1) constructing a quadrotor aircraft mathematic model, (2) designing a PID controller of a quadrotor aircraft, (3) optimizing a PID parameter process by means of a basic artificial bee colony algorithm, (4) optimizing PID control parameters by means of the basic artificial bee colony algorithm with immunity compression operations, and (5) outputting the PID property control parameters, i.e., transfer functions G<1(S)>, G<2(S)>, and G<3(S)>. The improved artificial bee colony algorithm based quadrotor PID parameter optimization method is high in accuracy, short in time consumption, and good in adaptive ability and applicability.

Description

A kind of four rotor pid parameter optimization methods based on improved artificial bee colony algorithm

Technical field

The present invention relates to aircraft optimization control field, especially a kind of pid parameter optimization method, is specially adapted to the problem of quadrotor control aspect.

Background technology

In recent years, quadrotor becomes forward position new in aviation academic research and focus gradually.Quadrotor is a kind of non co axial formula multi-rotor aerocraft that can realize vertical takeoff and landing, and the rotating speed of four rotors that can only distribute by adjusting butterfly, realizes the control to quadrotor flight attitude.Owing to not needing empennage, quadrotor structure is compacter, and the lifting force of four rotors is more even than single rotor, thereby flight attitude is more stable.In addition, quadrotor also has to take off and requires low, the feature such as can hover.

It is the gordian technique in quadrotor control that flight is controlled.The Mckerrow of University of Wollongong of Australia has carried out Accurate Model to quadrotor.The people such as the Gabe Hoffman of Stanford Univ USA have developed the flight controller based on Nonlinear control law, the raw flight control method having designed based on FSMC that waits of the Wang Jun of the National University of Defense technology.Research at present focuses mostly in nonlinear Control field, and because nonlinear Control has stronger dependence to model accuracy, under the condition existing at model error, PID controls more practical.K _p, K _i, K _dthese 3 parameters determine the quality of PID control performances, and they affect reliability and the robustness of control system.In inner-outer loop control, between these 3 parameters, not simple isolated relation, but influence each other, change one of them parameter, can affect the control action of other two parameters.More traditional PID controller parameter adjustment adopts and tries the mode of gathering, and this not only needs skilled operation skill and experience, and often consuming time longer.The more important thing is, when controlled device characteristic changes need to control parameter respective change time, conventional PID controllers does not have adaptive ability, can only rely on manually and readjust.

In recent years, along with the development of artificial intelligence, a lot of intelligent algorithm Optimize Multivariable PID Controller problems have been there are.

Summary of the invention

For overcome in existing four rotor pid parameter optimization methods degree of accuracy not high, consuming time long, without adaptive ability, the poor deficiency of applicability, the invention provides higher, consuming time shorter, adaptive ability is better, applicability the is good four rotor pid parameter optimization methods based on improved artificial bee colony algorithm of a kind of degree of accuracy.

The technical solution adopted for the present invention to solve the technical problems is:

Based on four rotor pid parameter optimization methods of improved artificial bee colony algorithm, described optimization method comprises the following steps:

1) set up quadrotor mathematical model: setting quadrotor physical construction full symmetric, under low-angle, is simple integral relation between the angular velocity of setting Eulerian angle and body angular velocity, has:

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{\mathrm{\&phi;}}={\mathrm{\&omega;}}_x\\ \stackrel{\&CenterDot;}{\mathrm{\&theta;}}={\mathrm{\&omega;}}_y\\ \stackrel{\&CenterDot;}{\mathrm{\&psi;}}={\mathrm{\&omega;}}_z\end{array}\right.---\left(1\right)$

In formula, φ, θ, be respectively the roll angle of quadrotor, the angle of pitch and crab angle; ω _x, ω _y, ω _zbe respectively x, y, z axis angular rate;

Quadrotor Non-linear coupling is resolved into 4 independent control channels, and define system control inputs amount is:

$\left\{\begin{array}{c}{U}_1=k_t({\mathrm{\&omega;}}_3^2-{\mathrm{\&omega;}}_1^2)\\ U_2=k_t({\mathrm{\&omega;}}_4^2-{\mathrm{\&omega;}}_2^2)\\ U_3=k_d({\mathrm{\&omega;}}_1^2-{\mathrm{\&omega;}}_2^2+{\mathrm{\&omega;}}_3^2-{\mathrm{\&omega;}}_4^2)\\ U_4=k_t{\mathrm{\&Sigma;}}_{i=1}^4{\mathrm{\&omega;}}_i^2\end{array}\right.---\left(2\right)$

U in formula ₁for pitch control subsystem amount; U ₂for rolling controlled quentity controlled variable; U ₃for driftage controlled quentity controlled variable; U ₄for vertical controlled quentity controlled variable; ω _i(i=1,2,3,4) are respectively the angular velocity of each rotor; k _t, k _dbe respectively rotor lift coefficient and rotor resistance coefficient;

2) design quadrotor PID controller:

In conjunction with quadrotor housing construction data, provide the transport function G of pitch channel _{1 (S)}as follows:

$G_{1\left(S\right)}=\frac{\mathrm{\&theta;}\left(s\right)}{U_1\left(s\right)}=\frac{57.95s+4411}{s^3+107.5s^2+892.5s+4435}---\left(3\right)$

Wherein, the output function that θ (s) is pitch channel, U ₁(s) be the input function of pitch channel, s represents the pull-type conversion of transport function;

The transport function G of roll channel _{2 (S)}for:

$G_{2\left(S\right)}=\frac{\mathrm{\&phi;}\left(s\right)}{U_2\left(s\right)}=\frac{63s+4563.873}{s^3+109.347s^2+1023.292s+2930.432}---\left(4\right)$

Wherein, the output function that φ (s) is roll channel, U ₂(s) be the input function of roll channel, s represents the pull-type conversion of transport function;

The transport function G of jaw channel _{3 (S)}for:

Wherein, for the output function of jaw channel, U ₃(s) be the input function of jaw channel, s represents the pull-type conversion of transport function;

3) basic artificial bee colony algorithm optimization pid parameter process:

3.1) when initialization, generate at random SN feasible solution and calculate fitness function value, the quantity of feasible solution equals to employ the quantity of honeybee, and the formula that produces at random feasible solution is as follows:

$x_i^j=x_{\min }^j+\mathrm{rand}\left(\mathrm{0,1}\right)(x_{\max }^j-x_{\min }^j)---\left(\text{6}\right)$

In formula, x _ifor D dimensional vector, i=1,2 ..., SN, the number that D is Optimal Parameters, j ∈ 1,2 ..., D}, represent x _icomponent in j dimension; Rand (0,1) is for getting the random number between interval [0,1], for x ^jthe vector maximization of direction, for x ^jthe vectorial minimum value of direction;

3.2) employ honeybee to record oneself optimal value up to the present, and launch neighborhood search near current foodstuff source, the formula that produces the alternative previous nectar source of a New food source is:

In formula, i represents that in group, certain is specifically individual, j ∈ 1,2 ..., D}, k ∈ 1,2 ..., SN}, k is random generation and k ≠ i, representative exists neighborhood in the position in a more excellent nectar source finding, the random position that generates another nectar source of coming of representative; for the random number between [1,1];

3.3) basic artificial bee colony algorithm, observation honeybee selects to employ the probability P of honeybee _ifor:

$P_i=\frac{\mathrm{fit}\left(x_i\right)}{\underset{n=1}{\overset{\mathrm{SN}}{\mathrm{\&Sigma;}}}\mathrm{fit}\left(x_n\right)}---\left(8\right)$

In formula, fit (x _i) be the degree of enriching of i the corresponding food source of the adaptive value of separating;

4) the artificial bee colony algorithm optimization pid control parameter with immune squeeze operation:

4.1) select threshold epsilon and maximum iteration time N _max, initial honeybee position z _ijfor i individual value on j dimension component in colony, j ∈ 1,2 ..., D}, according to f ⁽⁰⁾the position in the optimum nectar source of finding for original state, for the initial position of the 1st honeybee individuality in colony, for the initial position of the 2nd honeybee individuality in colony, for the initial position of SD honeybee individuality in colony, artificial bee colony algorithm forms a colony by SD honeybee, and each honeybee individuality is all searched for, and finds out global optimum imputation method iterations k=0, the span of k is since 0 until N _max;

4.2) k ← k+1, upgrades position, each nectar source according to formula (6), and adopts the negative mechanism of selecting, and nectar source affinity degree is less than and suppresses threshold value σ s and the highest individuality of fitness is retained, and suppress other all nectar sources;

4.3) calculate fitness function value it is expressed as f ⁽ⁿ⁾for the position in the optimum nectar source found in the n time iterative process, be according to each individual nectar source positional value in its neighborhood, whether exist local renewal compared with excellent solution to find optimum position, nectar source, the position in the new nectar source of finding after n the iteration for the 1st honeybee individuality in colony, the position in the new nectar source of finding after n the iteration for the 2nd honeybee individuality in colony, the position in the new nectar source of finding after n the iteration for SD honeybee individuality in colony, if meet (F ^(n-1)-F ⁽ⁿ⁾)/F ⁽ⁿ⁾> ε and n < N _max, jump to step 4.2), otherwise finishing iteration;

5) output PID Properties Control parameter, i.e. transport function G _{1 (S)}, G _{2 (S)}, G _{3 (S)}.

Further, described step 3) in, in the time that certain food source iteration is not improved for limit time, just abandon this food source, and this food source is recorded in taboo list, and what this nectar source was corresponding simultaneously employ, and honeybee changes into that search bee produces a new position at random by formula (6) replaces former nectar source.

Technical conceive of the present invention is: in order to solve four rotor pid control parameter optimization problems, propose the pid parameter optimisation strategy of the artificial bee colony algorithm (ABC) based on immune compressibility factor.Can quick and precisely find the feature of optimized parameter solution and PID control to combine the artificial bee colony algorithm with immune compressibility factor, in control procedure using pid parameter the nectar source in artificial bee colony, nectar source is compressed with immune algorithm, adjust pid parameter with artificial bee colony algorithm, draw the PID controller parameter of optimum position, nectar source as quadrotor.The method has stronger dirigibility, adaptability and robustness, and can improve the precision of control system, has good engineering using value.

Based on this, the present invention controls as research object taking quadrotor PID, introduces artificial intelligence technology, takes into full account the feature of PID control system, proposes a kind of based on improved artificial bee colony algorithm.Realize the optimization of four rotor pid parameters by the method.

Introduce artificial intelligence technology, take into full account the feature of PID control system, the mathematical model that model quadrotor is simplified; Secondly design quadrotor PID controller; Again, by basic artificial bee colony algorithm optimization pid parameter process; Finally, in basic artificial bee colony algorithm, incorporate immune compressibility factor, quick and precisely find optimized parameter solution, realize the optimal control of PID.

Beneficial effect of the present invention is: the present invention effectively overcomes the deficiency that in existing four rotor pid parameter optimization methods, degree of accuracy is not high, applicability is poor, realize controlling from steady of PID, degree of accuracy is higher, consuming time shorter, adaptive ability is better, applicability is good, has good using value.

Brief description of the drawings

Fig. 1 is the theory diagram of PID controller.

Fig. 2 is basic artificial bee colony algorithm flow chart.

Fig. 3 is the artificial bee colony algorithm optimization pid control parameter figure with immune compressibility factor.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.

With reference to Fig. 1～Fig. 3, a kind of four rotor pid parameter optimization methods based on improved artificial bee colony algorithm, described optimization method comprises the following steps:

1) set up quadrotor mathematical model: the mathematical model of quadrotor is the basis of Design of Flight Control.For simplifying the structure of mathematical model, suppose aircraft physical construction full symmetric.And under low-angle, can further suppose between the angular velocity of Eulerian angle and body angular velocity to be simple integral relation, have:

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{\mathrm{\&phi;}}={\mathrm{\&omega;}}_x\\ \stackrel{\&CenterDot;}{\mathrm{\&theta;}}={\mathrm{\&omega;}}_y\\ \stackrel{\&CenterDot;}{\mathrm{\&psi;}}={\mathrm{\&omega;}}_z\end{array}\right.---\left(1\right)$

In formula, φ, θ, be respectively roll angle, the angle of pitch and crab angle; ω _x, ω _y, ω _zbe respectively x, y, z axis angular rate.

For quadrotor Non-linear coupling being resolved into 4 independent control channels, define system control inputs amount is:

$\left\{\begin{array}{c}{U}_1=k_t({\mathrm{\&omega;}}_3^2-{\mathrm{\&omega;}}_1^2)\\ U_2=k_t({\mathrm{\&omega;}}_4^2-{\mathrm{\&omega;}}_2^2)\\ U_3=k_d({\mathrm{\&omega;}}_1^2-{\mathrm{\&omega;}}_2^2+{\mathrm{\&omega;}}_3^2-{\mathrm{\&omega;}}_4^2)\\ U_4=k_t{\mathrm{\&Sigma;}}_{i=1}^4{\mathrm{\&omega;}}_i^2\end{array}\right.---\left(2\right)$

U in formula ₁for pitch control subsystem amount; U ₂for rolling controlled quentity controlled variable; U ₃for driftage controlled quentity controlled variable; U ₄for vertical controlled quentity controlled variable; ω _i(i=1,2,3,4) are respectively the angular velocity of each rotor; k _t, k _dbe respectively rotor lift coefficient and rotor resistance coefficient;

2) design quadrotor PID controller: four rotor control strategies adopt conventional PID to control conventionally, and its structure as shown in Figure 1.

In conjunction with quadrotor housing construction data, provide the transport function G of pitch channel _{1 (S)}as follows:

$G_{1\left(S\right)}=\frac{\mathrm{\&theta;}\left(s\right)}{U_1\left(s\right)}=\frac{57.95s+4411}{s^3+107.5s^2+892.5s+4435}---\left(3\right)$

Wherein, the output function that θ (s) is pitch channel, U ₁(s) be the input function of pitch channel, s represents the pull-type conversion of transport function;

The transport function G of roll channel _{2 (S)}for:

$G_{2\left(S\right)}=\frac{\mathrm{\&phi;}\left(s\right)}{U_2\left(s\right)}=\frac{63s+4563.873}{s^3+109.347s^2+1023.292s+2930.432}---\left(4\right)$

Wherein, the output function that φ (s) is roll channel, U ₂(s) be the input function of roll channel, s represents the pull-type conversion of transport function;

The transport function G of jaw channel _{3 (S)}for:

Wherein, for the output function of jaw channel, U ₃(s) be the input function of jaw channel, s represents the pull-type conversion of transport function;

3) basic artificial bee colony algorithm optimization pid parameter process: its ultimate principle figure as shown in Figure 2.

3.1) when initialization, generate at random SN feasible solution and calculate fitness function value, the quantity of feasible solution equals to employ the quantity of honeybee, and the formula that produces at random feasible solution is as follows:

$x_i^j=x_{\min }^j+\mathrm{rand}\left(\mathrm{0,1}\right)(x_{\max }^j-x_{\min }^j)---\left(\text{6}\right)$

In formula, x _ifor D dimensional vector, i=1,2 ..., SN, the number that D is Optimal Parameters, j ∈ 1,2 ..., D}, represent x _icomponent in j dimension; Rand (0,1) is for getting the random number between interval [0,1], for x ^jthe vector maximization of direction, for x ^jthe vectorial minimum value of direction;

3.2) employ honeybee to record oneself optimal value up to the present, and launch neighborhood search near current foodstuff source, the formula that produces the alternative previous nectar source of a New food source is:

In formula, i represents that in group, certain is specifically individual, j ∈ 1,2 ..., D}, k ∈ 1,2 ..., SN}, k is random generation and k ≠ i, representative exists neighborhood in the position in a more excellent nectar source finding, the random position that generates another nectar source of coming of representative; for the random number between [1,1];

3.3) basic artificial bee colony algorithm, observation honeybee selects to employ the probability P of honeybee _ifor:

$P_i=\frac{\mathrm{fit}\left(x_i\right)}{\underset{n=1}{\overset{\mathrm{SN}}{\mathrm{\&Sigma;}}}\mathrm{fit}\left(x_n\right)}---\left(8\right)$

In formula, fit (x _i) be the degree of enriching of i the corresponding food source of the adaptive value of separating; Nectar source is abundanter, and the probability of being followed honeybee selection is larger.

For preventing that algorithm is absorbed in local optimum, in the time that certain food source iteration is not improved for limit time, just abandon this food source, and this food source is recorded in taboo list, and what this nectar source was corresponding simultaneously employ, and honeybee changes into that search bee produces a new position at random by formula (6) replaces former nectar source.

4) the artificial bee colony algorithm optimization pid control parameter with immune squeeze operation: its ultimate principle as shown in Figure 3.

4.1) select threshold epsilon and maximum iteration time N _max, initial honeybee position z _ijfor i individual value on j dimension component in colony, j ∈ 1,2 ..., D}, according to f ⁽⁰⁾the position in the optimum nectar source of finding for original state, for the initial position of the 1st honeybee individuality in colony, for the initial position of the 2nd honeybee individuality in colony, for the initial position of SD honeybee individuality in colony, artificial bee colony algorithm forms a colony by SD honeybee, and each honeybee individuality is all searched for, and finds out global optimum imputation method iterations k=0, the span of k is since 0 until N _max;

4.2) k ← k+1, upgrades position, each nectar source according to formula (6), and adopts the negative mechanism of selecting, and nectar source affinity degree is less than and suppresses threshold value σ _sand the individuality that fitness is the highest is retained, and suppress other all nectar sources;

4.3) calculate fitness function value it is expressed as f ⁽ⁿ⁾for the position in the optimum nectar source found in the n time iterative process, be according to each individual nectar source positional value in its neighborhood, whether exist local renewal compared with excellent solution to find optimum position, nectar source, the position in the new nectar source of finding after n the iteration for the 1st honeybee individuality in colony, the position in the new nectar source of finding after n the iteration for the 2nd honeybee individuality in colony, the position in the new nectar source of finding after n the iteration for SD honeybee individuality in colony, if meet (F ^(n-1)-F ⁽ⁿ⁾)/F ⁽ⁿ⁾> ε and n < N _max, jump to step 4.2), otherwise finishing iteration;

5) output PID Properties Control parameter, i.e. transport function G _{1 (S)}, G _{2 (S)}, G _{3 (S)}.

Claims (2)

1. four rotor pid parameter optimization methods based on improved artificial bee colony algorithm, is characterized in that: described optimization method comprises the following steps:

1) set up quadrotor mathematical model: setting quadrotor physical construction full symmetric, under low-angle, is simple integral relation between the angular velocity of setting Eulerian angle and body angular velocity, has:

$\left\{\begin{array}{c}\stackrel{\&CenterDot;}{\mathrm{\&phi;}}={\mathrm{\&omega;}}_x\\ \stackrel{\&CenterDot;}{\mathrm{\&theta;}}={\mathrm{\&omega;}}_y\\ \stackrel{\&CenterDot;}{\mathrm{\&psi;}}={\mathrm{\&omega;}}_z\end{array}\right.---\left(1\right)$

In formula, φ, θ, be respectively the roll angle of quadrotor, the angle of pitch and crab angle; ω _x, ω _y, ω _zbe respectively x, y, z axis angular rate;

Quadrotor Non-linear coupling is resolved into 4 independent control channels, and define system control inputs amount is:

$\left\{\begin{array}{c}{U}_1=k_t({\mathrm{\&omega;}}_3^2-{\mathrm{\&omega;}}_1^2)\\ U_2=k_t({\mathrm{\&omega;}}_4^2-{\mathrm{\&omega;}}_2^2)\\ U_3=k_d({\mathrm{\&omega;}}_1^2-{\mathrm{\&omega;}}_2^2+{\mathrm{\&omega;}}_3^2-{\mathrm{\&omega;}}_4^2)\\ U_4=k_t{\mathrm{\&Sigma;}}_{i=1}^4{\mathrm{\&omega;}}_i^2\end{array}\right.---\left(2\right)$

U in formula ₁for pitch control subsystem amount; U ₂for rolling controlled quentity controlled variable; U ₃for driftage controlled quentity controlled variable; U ₄for vertical controlled quentity controlled variable; ω _i(i=1,2,3,4) are respectively the angular velocity of each rotor; k _t, k _dbe respectively rotor lift coefficient and rotor resistance coefficient;

2) design quadrotor PID controller:

In conjunction with quadrotor housing construction data, provide the transport function G of pitch channel _{1 (S)}as follows:

$G_{1\left(S\right)}=\frac{\mathrm{\&theta;}\left(s\right)}{U_1\left(s\right)}=\frac{57.95s+4411}{s^3+107.5s^2+892.5s+4435}---\left(3\right)$

Wherein, the output function that θ (s) is pitch channel, U ₁(s) be the input function of pitch channel, s represents the pull-type conversion of transport function;

The transport function G of roll channel _{2 (S)}for:

$G_{2\left(S\right)}=\frac{\mathrm{\&phi;}\left(s\right)}{U_2\left(s\right)}=\frac{63s+4563.873}{s^3+109.347s^2+1023.292s+2930.432}---\left(4\right)$

Wherein, the output function that φ (s) is roll channel, U ₂(s) be the input function of roll channel, s represents the pull-type conversion of transport function;

The transport function G of jaw channel _{3 (S)}for:

Wherein, for the output function of jaw channel, U ₃(s) be the input function of jaw channel, s represents the pull-type conversion of transport function;

3) basic artificial bee colony algorithm optimization pid parameter process:

3.1) when initialization, generate at random SN feasible solution and calculate fitness function value, the quantity of feasible solution equals to employ the quantity of honeybee, and the formula that produces at random feasible solution is as follows:

$x_i^j=x_{\min }^j+\mathrm{rand}\left(\mathrm{0,1}\right)(x_{\max }^j-x_{\min }^j)---\left(\text{6}\right)$

In formula, x _ifor D dimensional vector, i=1,2 ..., SN, the number that D is Optimal Parameters, j ∈ 1,2 ..., D}, represent x _icomponent in j dimension; Rand (0,1) is for getting the random number between interval [0,1], for x ^jthe vector maximization of direction, for x ^jthe vectorial minimum value of direction;

3.2) employ honeybee to record oneself optimal value up to the present, and launch neighborhood search near current foodstuff source, the formula that produces the alternative previous nectar source of a New food source is:

In formula, i represents that in group, certain is specifically individual, j ∈ 1,2 ..., D}, k ∈ 1,2 ..., SN}, k is random generation and k ≠ i, representative exists neighborhood in the position in a more excellent nectar source finding, the random position that generates another nectar source of coming of representative; for the random number between [1,1];

3.3) basic artificial bee colony algorithm, observation honeybee selects to employ the probability P of honeybee _ifor:

$P_i=\frac{\mathrm{fit}\left(x_i\right)}{\underset{n=1}{\overset{\mathrm{SN}}{\mathrm{\&Sigma;}}}\mathrm{fit}\left(x_n\right)}---\left(8\right)$

In formula, fit (x _i) be the degree of enriching of i the corresponding food source of the adaptive value of separating;

4) the artificial bee colony algorithm optimization pid control parameter with immune squeeze operation:

4.1) select threshold epsilon and maximum iteration time N _max, initial honeybee position z _ijfor i individual value on j dimension component in colony, j ∈ 1,2 ..., D}, according to f ⁽⁰⁾the position in the optimum nectar source of finding for original state, for the initial position of the 1st honeybee individuality in colony, for the initial position of the 2nd honeybee individuality in colony, for the initial position of SD honeybee individuality in colony, artificial bee colony algorithm forms a colony by SD honeybee, and each honeybee individuality is all searched for, and finds out global optimum imputation method iterations k=0, the span of k is since 0 until N _max;

4.2) k ← k+1, upgrades position, each nectar source according to formula (6), and adopts the negative mechanism of selecting, and nectar source affinity degree is less than and suppresses threshold value σ _sand the individuality that fitness is the highest is retained, and suppress other all nectar sources;

4.3) calculate fitness function value it is expressed as f ⁽ⁿ⁾for the position in the optimum nectar source found in the n time iterative process, be according to each individual nectar source positional value in its neighborhood, whether exist local renewal compared with excellent solution to find optimum position, nectar source, the position in the new nectar source of finding after n the iteration for the 1st honeybee individuality in colony, the position in the new nectar source of finding after n the iteration for the 2nd honeybee individuality in colony, the position in the new nectar source of finding after n the iteration for SD honeybee individuality in colony, if meet (F ^(n-1)-F ⁽ⁿ⁾)/F ⁽ⁿ⁾> ε and n < N _max, jump to step 4.2), otherwise finishing iteration;

5) output PID Properties Control parameter, i.e. transport function G _{1 (S)}, G _{2 (S)}, G _{3 (S)}.

2. a kind of four rotor pid parameter optimization methods based on improved artificial bee colony algorithm as claimed in claim 1, it is characterized in that: described step 3) in, in the time that certain food source iteration is not improved for limit time, just abandon this food source, and this food source is recorded in taboo list, and what this nectar source was corresponding simultaneously employ, and honeybee changes into that search bee produces a new position at random by formula (6) replaces former nectar source.