CN103246203A - GPS (Global Position System) based prediction method for speed state of micro quad-rotor unmanned aerial vehicle - Google Patents

GPS (Global Position System) based prediction method for speed state of micro quad-rotor unmanned aerial vehicle Download PDF

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CN103246203A
CN103246203A CN2013101444166A CN201310144416A CN103246203A CN 103246203 A CN103246203 A CN 103246203A CN 2013101444166 A CN2013101444166 A CN 2013101444166A CN 201310144416 A CN201310144416 A CN 201310144416A CN 103246203 A CN103246203 A CN 103246203A
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unmanned aerial
speed
microminiature
wing unmanned
rotor wing
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CN103246203B (en
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孙长银
王伟
贺俊旺
董大著
沈才云
徐洪菊
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东南大学
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Abstract

The invention discloses a GPS (Global Position System) based prediction method for the speed state of a micro quad-rotor unmanned aerial vehicle. The prediction method comprises the following steps: acquiring the delay time Td of GPS data through data analysis; using the ident tool of MATLAB (Matrix Laboratory) for fitting to obtain the speed model of the micro quad-rotor unmanned aerial vehicle; performing discretization to the speed model to obtain the discretized speed model; performing combined filtering to the speed state at t-Td by virtue of the GPS data at t to obtain the estimated value of the speed state at t-Td; using the Kalman multi-step prediction method to obtain the predicted value of the speed state; and using a speed controller to compute a controlled quantity by virtue of the obtained predicted value of the speed state. The prediction method improves the quality of the GPS data, enables the predicted data to be more accurate, plays a positive role in promoting the flight control performance of the micro quad-rotor unmanned aerial vehicle, and has very important practical value.

Description

A kind of microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodologies based on GPS

Technical field

The present invention relates to technical fields such as microminiature four rotor wing unmanned aerial vehicle modelings and flight control, data processing, observation time lag and information prediction.

Background technology

Small-sized four rotor wing unmanned aerial vehicles are aircraft of action such as a kind ofly can realize vertical takeoff and landing, hover.Four rotor crafts are realized the variation of lift by regulating four gyroplane rotate speeds, thus attitude and the position of control aircraft.Miniature four rotor wing unmanned aerial vehicles have good application prospect: the tasks such as scouting, supervision that can carry out the low latitude in military field; Civil area can be in earthquake, have and carry out search mission etc. in the environment of radiation.Under the driving of great application prospect, along with the maturation of micromechanics manufacturing technology, enhancing and other development of technologies of microcontroller processing power, microminiature four rotor wing unmanned aerial vehicles develop into a domestic and international research focus gradually.But as a member of vertical takeoff and landing rotor wing unmanned aerial vehicle, microminiature four rotor wing unmanned aerial vehicles have the characteristics of rotary wind type unmanned plane, can finish high flexibility flight with flying colors.In addition, it also has the not available advantage of single rotor wing unmanned aerial vehicle:

(1) because its rotorshaft is fixed in body, so avoided single rotor wing unmanned aerial vehicle complex mechanical construction;

(2) microminiature four rotor wing unmanned aerial vehicles have more driver.Than single rotor wing unmanned aerial vehicle of comparable size, can produce bigger lift, thereby flying speed is faster;

(3) microminiature four rotor wing unmanned aerial vehicles are because himself housing construction is compared with the single rotor wing unmanned aerial vehicle of microminiature, and its maneuverability is stronger, be more suitable in interior of building flight, and its rotor size are littler, safer.

GPS measures the positional information of microminiature four rotor wing unmanned aerial vehicles and the sensor of velocity information, and there is a delay about 1 second in the measurement data of this sensor.Can adopt this time delay the experimental technique identification to obtain.

Exist the time lag phenomenon in the control system inevitably, the start process of the calculating of the collection of sensor signal and transmission, controller, actuator etc. all can cause the generation of time lag.People always ignored time lag, even but less time lag amount also can cause decline or the control system unstability of control efficiency for the convenience in theoretical analysis and the control design in the past.As everyone knows, the pure hysteresis in automatic control system can make system's control quality obviously descend, even causes system's instability or system to work.

Time lag system can be divided into two classes based on state estimation: the first kind is the situation of band time lag in the state equation; Second class is the situation that has time lag in the observed result.The key distinction of this two classes situation is that the time lag phenomenon of first kind situation may be caused that the time lag of the second class situation generally then is because characteristic or the time delay of signal in transmission course of observation sensor itself cause by the intrinsic time lag characteristic of system.

In the Design of Flight Control of microminiature four rotor wing unmanned aerial vehicles, the delay of gps data is a kind of observation time lag.Because data delay time of GPS is far longer than control cycle, so this data delay has relatively Design of Flight Control and seriously influences.

Summary of the invention

Goal of the invention: postpone (observation time lag) to the harmful effect of microminiature four rotor wing unmanned aerial vehicles flight control performance in order to overcome gps data, the invention provides a kind of Kalman's Forecasting Methodology to improve this harmful effect.

Technical scheme: a kind of microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodologies based on GPS comprise the following steps of joining in turn:

1) data that flight obtains to microminiature four rotor wing unmanned aerial vehicles are analyzed, and obtain Td time delay of gps data;

2) the ident instrument match of use MATLAB obtains the rate pattern of microminiature four rotor wing unmanned aerial vehicles;

3) according to the sampling period, the rate pattern that obtains is carried out discretize handle, obtain the discretization model of speed;

4) actual time of the speed state that reflects of t gps data constantly be t – Td constantly, information fusion is unified to t – T constantly dConstantly, namely utilize t gps data constantly to t – T dSpeed state constantly carries out combined filter, obtains t – T dSpeed state estimated value constantly;

5) at t – T dOn the basis of speed state estimated value constantly, utilize Kalman's multistep forecasting method to obtain t speed state predicted value constantly; Speed control uses resulting t speed state predicted value constantly to carry out the calculating of controlled quentity controlled variable.

Be convenience of calculation, step 1) is compared for data and the Airborne GPS data of the airborne accelerometer that the flight to microminiature four rotor wing unmanned aerial vehicles obtains, and obtains T time delay of gps data d

In order to improve prediction accuracy, in step 2) in use the ident instrument match of MATLAB to obtain rate pattern according to the ash bin principle.

For the simplification of predicting, guarantee prediction accuracy simultaneously, in step 2) linearization process, obtain along the kinetics equation of y direction of principal axis speed be:

m v · = φU 1 - Kv

In the formula, v represents microminiature four rotor wing unmanned aerial vehicles along the axial velocity amplitude of y, and Φ represents the roll angle of microminiature four rotor wing unmanned aerial vehicles, U 1The lift of expression microminiature four rotor wing unmanned aerial vehicles four rotors and, K is the overall drag coefficient;

Following formula is carried out Laplace transform can obtain attitude angle to the transport function of aircraft speed:

G φ v = U 1 ms + K

A because gps data existence T time delay dSo,, attitude angle to the transport function of the measured speed that obtains of GPS is:

G φ vm = U 1 ms + K e - T d s

The speed data that the attitude angle data that the ident instrument that uses MATLAB obtains according to microminiature four rotor wing unmanned aerial vehicle flight experiments and GPS measure is carried out match to the parameter of rate pattern, and the rate pattern that match obtains is:

G φ vm = 0.4132 1 + 2.9117 s e - s

In step 3) sample mode and hold mode are made following agreement: the sample mode of sampling thief is for being the equal interval sampling in cycle with constant T; Sampling period T chooses the condition that satisfies Shannon's sampling theorem; Discrete-time signal adopts the zeroth order hold mode to the conversion of continuous time signal; Based on above agreement, in conjunction with the microcontroller processing power, choosing controlled frequency is 50Hz, with the 2nd) the rate pattern discretize that obtains of match in the step, obtain the discretization model of speed:

x(k+1)=0.9932·x(k)+0.0028·u(k)

y(k)=x(k-50)

Wherein, quantity of state x be microminiature four rotor wing unmanned aerial vehicles along the axial velocity amplitude of y, control input u is the roll angle Φ of microminiature four rotor wing unmanned aerial vehicles, y represents that GPS measures microminiature four rotor wing unmanned aerial vehicles that obtain along the axial velocity amplitude of y.

Be T the time delay of the speed data that the GPS measurement obtains in step 4) d, the speed controlled frequency of microminiature four rotor wing unmanned aerial vehicles is 50Hz, with the observed reading of y (k) as state x (k – 50), based on state x (k – 50-T d) estimated value carry out the state one-step prediction, obtain the estimated value of state x (k – 50) by Kalman filtering.

Speed controlled frequency in the step 4) also can be got other values according to actual needs.

In step 5) based on the 4th) estimated value of the state x (k – 50) that obtains of step, obtain the predicted value x of state x (k)~(k) by the discretization model recursion of speed, controller uses constantly status predication value x of resulting t~(k) to carry out the calculating of controlled quentity controlled variable.

The described t – of step 4) Td information is constantly merged, guaranteed by the time objectivity of fused data.

Utilize the described t – of step 5) Td speed state estimated value constantly to carry out Kalman's multi-step prediction, obtain the predicted value of current time speed state; Speed control uses this value to calculate controlled quentity controlled variable, has avoided delay link is introduced rate pattern.

The technology that the present invention does not specify is prior art.

Beneficial effect: the present invention has improved the quality of gps data, doped the speed state of microminiature four rotor wing unmanned aerial vehicle current times effectively, the data that prediction obtains are accurate, lifting to microminiature four rotor wing unmanned aerial vehicles flight control performance has positive effect, has very important practical value.

Description of drawings

Fig. 1 is the inventive method workflow block diagram;

The rate pattern that Fig. 2 obtains for the ident instrument match of using MATLAB in the inventive method is exported the comparison diagram with actual output;

Fig. 3 is for carrying out the algorithm flow synoptic diagram of Kalman filtering to t – Td speed state constantly;

The speed state predicted value that Fig. 4 obtains for the inventive method with gps data is shifted to an earlier date T dThe comparison diagram of gained data after time.

Embodiment

Be example to handle the gps data delay that exists in the control of microminiature four rotor wing unmanned aerial vehicle speed below, introduce technical scheme of the present invention by describing its concrete embodiment.Need to prove that embodiment is only deepened the understanding to technical scheme, and to can not inventing any restriction effect.

As shown in Figure 1, this enforcement comprises the steps:

1) every data that flight obtains according to microminiature four rotor wing unmanned aerial vehicles are analyzed, and obtain T time delay of gps data d

Make microminiature four rotor wing unmanned aerial vehicles finish the motion process that is changed to the acceleration steady state from stationary state, obtain the velocity measurement that airborne accelerometer obtains in this motion process acceleration measurement and Airborne GPS obtain.Because airborne accelerometer data does not have delay, degree of will speed up measured value and velocity measurement are compared, be about to velocity measurement and deduct acceleration measurement from the moment that null value becomes nonzero value and become moment of nonzero value from null value, can obtain T time delay of gps data dIt is 1 second.

2) on gps data time delay known basis, use the ident instrument match of MATLAB to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicles.

At first, set up rate pattern according to the principle of work of microminiature four rotor wing unmanned aerial vehicles.For the purpose of simplifying problem, carry out linearization process, can obtain along the kinetics equation of y direction of principal axis speed be:

m v · = φU 1 - Kv

In the formula, v represents microminiature four rotor wing unmanned aerial vehicles along the axial velocity amplitude of y, and Φ represents the roll angle of microminiature four rotor wing unmanned aerial vehicles, U 1The lift of expression microminiature four rotor wing unmanned aerial vehicles four rotors and, K is the overall drag coefficient.Following formula is carried out Laplace transform can obtain attitude angle to the transport function of aircraft speed:

G φ v = U 1 ms + K

A because gps data existence T time delay dSo,, attitude angle to the transport function of the measured speed that obtains of GPS is:

G φ vm = U 1 ms + K e - T d s

Secondly, the speed data measured of the attitude angle data that obtain according to microminiature four rotor wing unmanned aerial vehicle flight experiments of the ident instrument that uses MATLAB and GPS is carried out match to the parameter of rate pattern.The rate pattern that match obtains is:

G φ vm = 0.4132 1 + 2.9117 s e - s

Fig. 2 is seen in the rate pattern output that match obtains and the contrast of using GPS to measure the speed data that obtains.The degree of fitting of resulting rate pattern reaches 65.62%.The rate pattern that match obtains can reach gps data T time delay that higher degree of fitting has illustrated that also the first step obtains dAccuracy.

3) the microminiature four rotor wing unmanned aerial vehicle rate patterns that obtain are carried out discretize and handle, obtain the discretization model of speed.

Sample mode and hold mode are made following agreement: the sample mode of sampling thief is for being the equal interval sampling in cycle with constant T; Sampling period T chooses the condition that satisfies Shannon's sampling theorem; Discrete-time signal adopts the zeroth order hold mode to the conversion of continuous time signal.Based on above agreement, in conjunction with the microcontroller processing power, choosing controlled frequency is 50Hz, with the 2nd) the rate pattern discretize that obtains of match in the step, obtain discrete system:

x(k+1)=0.9932·x(k)+0.0028·u(k)

y(k)=x(k-50)

Wherein, quantity of state x be microminiature four rotor wing unmanned aerial vehicles along the axial velocity amplitude of y, control input u is the roll angle Φ of microminiature four rotor wing unmanned aerial vehicles, y represents that GPS measures microminiature four rotor wing unmanned aerial vehicles that obtain along the axial velocity amplitude of y.

4) to t – T dSpeed state is constantly estimated by Kalman filtering algorithm.

Because be t – T the actual time of the speed state that t gps data constantly reflects dConstantly, therefore that information fusion is unified to t – T constantly dConstantly, namely utilize t gps data constantly to t – T dSpeed state constantly carries out combined filter, on the objectivity basis that guarantees fused data, obtains t – T dSpeed state estimated value constantly.

Be 1 second the time delay that GPS measures the speed data that obtains, the speed controlled frequency of microminiature four rotor wing unmanned aerial vehicles is 50Hz, therefore with the observed reading of y (k) as state x (k – 50), estimated value based on state x (k – 51) is carried out the state one-step prediction, obtain the estimated value of state x (k – 50) by Kalman filtering, the specific algorithm process is seen Fig. 3.

5) utilize Kalman's multistep forecasting method with t – T dSpeed state estimated value recursion constantly obtains t speed state predicted value constantly to current time t.

Based on the 4th) estimated value of the state x (k – 50) that obtains of step, obtain the predicted value x of state x (k)~(k) by the discretization model recursion of speed.

Controller uses constantly speed state predicted value x of resulting t~(k) to carry out the calculating of controlled quentity controlled variable.

By speed state predicted value x~(k) (is seen accompanying drawing 4) as can be seen with the contrast of the gps measurement data of handling through translation, the present invention has good estimated performance, and the speed state predicted value of resulting microminiature four rotor wing unmanned aerial vehicle current times is accurate.Solved the GPS metric data postpone has been introduced the control performance decline problem that controlled device is brought, the lifting of microminiature four rotor wing unmanned aerial vehicles flight control performance has been had positive effect, had very important practical value.

Claims (6)

1. the microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodologies based on GPS is characterized in that, comprise the following steps of joining in turn:
1) accelerometer data and the gps data of microminiature four rotor wing unmanned aerial vehicles are analyzed, obtained T time delay of gps data d
2) the ident instrument match of use MATLAB obtains the rate pattern of microminiature four rotor wing unmanned aerial vehicles;
3) in conjunction with the sampling period, the rate pattern that obtains is carried out discretize handle, obtain the discretization model of speed;
4) be t-T the actual time of the speed state that reflects of t gps data constantly dConstantly, information fusion is unified to t-T constantly dConstantly, namely utilize t GPS speed data constantly to t-T dMicrominiature four rotor wing unmanned aerial vehicle speed states constantly carry out Kalman filtering, extrapolate t-T dSpeed state estimated value constantly;
5) at t-T dOn the basis of speed state estimated value constantly, utilize Kalman's multistep forecasting method to obtain t speed state predicted value constantly; Controller uses resulting t speed state predicted value constantly to carry out the calculating of controlled quentity controlled variable.
2. method according to claim 1, it is characterized in that: make microminiature four rotor wing unmanned aerial vehicles finish the motion process that is changed to the acceleration steady state from stationary state in the step 1), obtain the velocity measurement that airborne accelerometer obtains in this motion process acceleration measurement and Airborne GPS obtain, because airborne accelerometer data does not have delay, degree of will speed up measured value and velocity measurement are compared, be about to velocity measurement and deduct acceleration measurement from the moment that null value becomes nonzero value and become moment of nonzero value from null value, obtain T time delay of gps data d
3. method according to claim 1, it is characterized in that: in step 2) in use the ident instrument match of MATLAB to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicles according to the ash bin principle, microminiature four rotor wing unmanned aerial vehicles along the kinetics equation of y direction of principal axis speed are:
In the formula, v represents microminiature four rotor wing unmanned aerial vehicles along the axial velocity amplitude of y, and represents the roll angle of microminiature four rotor wing unmanned aerial vehicles, U 1The lift of expression microminiature four rotor wing unmanned aerial vehicles four rotors and, K is the overall drag coefficient;
Following formula is carried out Laplace transform can obtain attitude angle to the transport function of aircraft speed:
A because gps data existence T time delay dSo,, attitude angle to the transport function of the measured speed that obtains of GPS is:
The speed data that the attitude angle data that the ident instrument that uses MATLAB obtains according to microminiature four rotor wing unmanned aerial vehicle flight experiments and GPS measure is carried out match to the parameter of rate pattern, obtains rate pattern:
4. method according to claim 3 is characterized in that: in step 3) sample mode and hold mode are made following agreement: the sample mode of sampling thief is for being the equal interval sampling in cycle with constant T; Sampling period T chooses the condition that satisfies Shannon's sampling theorem; Discrete-time signal adopts the zeroth order hold mode to the conversion of continuous time signal; Based on above agreement, in conjunction with the microcontroller processing power, choosing controlled frequency is 50Hz, with the 2nd) the rate pattern discretize that obtains of match in the step, obtain the discretization model of speed:
x(k+1)=0.9932·x(k)+0.0028·u(k)
y(k)=x(k-50)
Wherein, quantity of state x be microminiature four rotor wing unmanned aerial vehicles along the axial velocity amplitude of y, control input u is the roll angle of microminiature four rotor wing unmanned aerial vehicles, y represents that GPS measures microminiature four rotor wing unmanned aerial vehicles that obtain along the axial velocity amplitude of y.
5. method according to claim 4 is characterized in that: to measure the time delay of the speed data that obtains be T to GPS in step 4) d, the speed controlled frequency of microminiature four rotor wing unmanned aerial vehicles is 50Hz, with the observed reading of y (k) as state x (k – 50), carries out the state one-step prediction based on the estimated value of state x (k – 51), obtains the estimated value of state x (k – 50) by Kalman filtering.
6. method according to claim 5, it is characterized in that: in step 5) based on the 4th) estimated value of the state x (k – 50) that obtains of step, obtain the predicted value x of state x (k)~(k) by the discretization model recursion of speed, controller uses constantly status predication value x of resulting t~(k) to carry out the calculating of controlled quentity controlled variable.
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Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105136145A (en) * 2015-08-11 2015-12-09 哈尔滨工业大学 Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion method
CN106094840A (en) * 2016-07-20 2016-11-09 深圳洲际通航投资控股有限公司 Flight control system and method
CN106871892A (en) * 2017-02-17 2017-06-20 张梦 A kind of airborne vehicle Combinated navigation method and device
CN107703741A (en) * 2017-08-31 2018-02-16 上海电力学院 Robot motion's system identifying method based on quasi-mode type calibration Kalman filtering
CN108230371A (en) * 2017-12-29 2018-06-29 厦门市美亚柏科信息股份有限公司 Tracking target velocity Forecasting Methodology and storage medium based on holder
CN108230370A (en) * 2017-12-29 2018-06-29 厦门市美亚柏科信息股份有限公司 Tracking target velocity Forecasting Methodology and storage medium based on holder

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090118875A1 (en) * 2007-11-01 2009-05-07 Stroud Ken A Systems and methods for coordination of entities and/or communicating location information
CN102629847A (en) * 2012-03-29 2012-08-08 西安理工大学 Asynchronous motor pure electronic speed feedback method
CN102928858A (en) * 2012-10-25 2013-02-13 北京理工大学 GNSS (Global Navigation Satellite System) single-point dynamic positioning method based on improved expanded Kalman filtering

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20090118875A1 (en) * 2007-11-01 2009-05-07 Stroud Ken A Systems and methods for coordination of entities and/or communicating location information
CN102629847A (en) * 2012-03-29 2012-08-08 西安理工大学 Asynchronous motor pure electronic speed feedback method
CN102928858A (en) * 2012-10-25 2013-02-13 北京理工大学 GNSS (Global Navigation Satellite System) single-point dynamic positioning method based on improved expanded Kalman filtering

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
王帅,魏国: "卡尔曼滤波在四旋翼飞行器姿态测量中的应用", 《兵工自动化》 *
郭伟,高晓光,陈军: "超视距空战中数据链系统通信延迟与补偿算法", 《火力与指挥控制》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105136145A (en) * 2015-08-11 2015-12-09 哈尔滨工业大学 Kalman filtering based quadrotor unmanned aerial vehicle attitude data fusion method
CN106094840A (en) * 2016-07-20 2016-11-09 深圳洲际通航投资控股有限公司 Flight control system and method
CN106094840B (en) * 2016-07-20 2019-03-01 深圳洲际通航投资控股有限公司 Flight control system and method
CN106871892A (en) * 2017-02-17 2017-06-20 张梦 A kind of airborne vehicle Combinated navigation method and device
CN107703741A (en) * 2017-08-31 2018-02-16 上海电力学院 Robot motion's system identifying method based on quasi-mode type calibration Kalman filtering
CN108230371A (en) * 2017-12-29 2018-06-29 厦门市美亚柏科信息股份有限公司 Tracking target velocity Forecasting Methodology and storage medium based on holder
CN108230370A (en) * 2017-12-29 2018-06-29 厦门市美亚柏科信息股份有限公司 Tracking target velocity Forecasting Methodology and storage medium based on holder
CN108230370B (en) * 2017-12-29 2020-08-04 厦门市美亚柏科信息股份有限公司 Tracking target speed prediction method based on holder and storage medium

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