CN103246203B - A kind of microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodology based on GPS - Google Patents
A kind of microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodology based on GPS Download PDFInfo
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Abstract
The invention discloses the microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodology based on GPS, comprise the steps: data analysis, obtain T time delay of gps data
d; The ident instrument matching of MATLAB is used to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicle; Sliding-model control is carried out to rate pattern, obtains the discretization model of speed; Utilize the gps data of t to t-T
dthe speed state in moment carries out combined filter, obtains t-T
dthe speed state estimated value in moment; Kalman's multistep forecasting method is utilized to obtain the speed state predicted value of t; Speed control uses the speed state predicted value of the t obtained to carry out the calculating of controlled quentity controlled variable.Invention increases the quality of gps data, predict that the data obtained are accurate, to the lifting of microminiature four rotor wing unmanned aerial vehicle flight control performance, there is positive effect, there is very important practical value.
Description
Technical field
The present invention relates to the technical field such as microminiature four rotor wing unmanned aerial vehicle modeling and flight control, data processing, observation time lag and information prediction.
Background technology
Small-sized four rotor wing unmanned aerial vehicles are a kind of aircraft that can realize the action such as vertical takeoff and landing, hovering.Quadrotor, by adjustment four gyroplane rotate speeds, realizes the change of lift, thus controls attitude and the position of aircraft.The application prospect that miniature four rotor wing unmanned aerial vehicles have had: the task such as scouting, supervision that can perform low latitude in military field; Civil area can in earthquake, have in the environment of radiation and perform search mission etc.Under the driving of huge application prospect, along with the maturation of micro-machining, the enhancing of microcontroller processing power and the development of other correlation techniques, microminiature four rotor wing unmanned aerial vehicle develops into a study hotspot both domestic and external gradually.As can a member of vertical takeoff and landing rotor wing unmanned aerial vehicle, microminiature four rotor wing unmanned aerial vehicle has the feature of rotary wind type unmanned plane, can complete high flexibility flight with flying colors.In addition, it also has the advantage not available for single rotor wing unmanned aerial vehicle:
(1) because its rotorshaft is fixed on body, so avoid the physical construction of single rotor wing unmanned aerial vehicle complexity;
(2) microminiature four rotor wing unmanned aerial vehicle has more driver.Compared to single rotor wing unmanned aerial vehicle of comparable size, can produce larger lift, thus flying speed is faster;
(3) microminiature four rotor wing unmanned aerial vehicle is due to himself housing construction, and compared with microminiature list rotor wing unmanned aerial vehicle, its maneuverability is stronger, be more suitable in interior of building flight, and its rotor size is less, safer.
GPS measures the positional information of microminiature four rotor wing unmanned aerial vehicle and the sensor of velocity information, and the measurement data of this sensor exists the delay of about 1 second.Experimental technique identification can be adopted this time delay to obtain.
Inevitably there is time delay in control system, the collection of sensor signal and transmission, the calculating of controller, the start process etc. of actuator, all can cause the generation of time lag.People were in order to the convenience on theoretical analysis and control design case in the past, always ignored time lag, even but less time lag amount also can cause decline or the control system unstability of control efficiency.As everyone knows, the purely retarded in an automatic control system can make quality of system control obviously decline, and even causes system instability or system to work.
Time lag system can be divided into two classes based on state estimation: the first kind is the situation with time lag in state equation; Equations of The Second Kind is the situation with time lag in observed result.The key distinction of this two classes situation is, the time delay of first kind situation may be caused by the intrinsic time lag characteristic of system, and the time lag of Equations of The Second Kind situation is then generally because the characteristic of observation sensor itself or the time delay of signal in transmitting procedure cause.
In the Design of Flight Control of microminiature four rotor wing unmanned aerial vehicle, the delay of gps data is a kind of observation time lag.Because the data delay time of GPS is far longer than control cycle, therefore this data delay has more serious impact to Design of Flight Control.
Summary of the invention
Goal of the invention: postponing (observation time lag) harmful effect to microminiature four rotor wing unmanned aerial vehicle flight control performance to overcome gps data, the invention provides a kind of Kalman Prediction method to improve this harmful effect.
Technical scheme: a kind of microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodology based on GPS, comprises the following steps connected in turn:
1) microminiature four rotor wing unmanned aerial vehicle is flown the data analysis obtained, obtain Td time delay of gps data;
2) the ident instrument matching of MATLAB is used to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicle;
3) according to the sampling period, sliding-model control is carried out to the rate pattern obtained, obtains the discretization model of speed;
4) actual time of speed state that the gps data of t reflects is the t – Td moment, by unified for the information fusion moment to t – T
din the moment, namely utilize the gps data of t to t – T
dthe speed state in moment carries out combined filter, obtains t – T
dthe speed state estimated value in moment;
5) at t – T
don the basis of the speed state estimated value in moment, Kalman's multistep forecasting method is utilized to obtain the speed state predicted value of t; Speed control uses the speed state predicted value of the t obtained to carry out the calculating of controlled quentity controlled variable.
For convenience of calculation, step 1) is compare to the fly data of the airborne accelerometer obtained and Airborne GPS data of microminiature four rotor wing unmanned aerial vehicle, obtains T time delay of gps data
d.
In order to improve the accuracy of prediction, in step 2) in use the ident instrument matching of MATLAB to obtain rate pattern according to ash bin principle.
In order to the simplification predicted, guarantee the accuracy predicted, in step 2 simultaneously) linearization process, the kinetics equation obtaining speed is along the y-axis direction:
In formula, v represents microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction, and Φ represents the roll angle of microminiature four rotor wing unmanned aerial vehicle, U
1represent microminiature four rotor wing unmanned aerial vehicle four rotors lift and, K is overall drag coefficient;
Laplace transform is carried out to above formula and can obtain the transport function of attitude angle to aircraft speed:
Due to gps data exist one time delay T
d, so the transport function of the speed obtained measured by attitude angle to GPS is:
The speed data that the attitude angle data using the ident instrument of MATLAB to obtain according to microminiature four rotor wing unmanned aerial vehicle flight experiment and GPS measure carries out matching to the parameter of rate pattern, and the rate pattern that matching obtains is:
In step 3), following agreement is done to sample mode and hold mode: the equal interval sampling that the sample mode of sampling thief is is the cycle with constant T; Sampling period T chooses the condition meeting Shannon's sampling theorem; Discrete-time signal adopts zeroth order hold mode to the conversion of continuous time signal; Based on above agreement, in conjunction with microcontroller processing power, choosing controlled frequency is 50Hz, by the 2nd) matching obtains in step rate pattern discretize, 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 is microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction, and control inputs u is that the roll angle Φ of microminiature four rotor wing unmanned aerial vehicle, y represent that GPS measures the microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction obtained.
In step 4), GPS measures the time delay of the speed data obtained is T
d, the speeds control frequency of microminiature four rotor wing unmanned aerial vehicle is 50Hz, using the observed reading of y (k) as state x (k – 50), based on state x (k – 50-T
d) estimated value carry out state one-step prediction, obtained the estimated value of state x (k – 50) by Kalman filtering.
Speeds control frequency in step 4) also can get other values according to actual needs.
In step 5) based on the 4th) estimated value of state x (k – 50) that obtains of step, the discretization model recursion of Negotiation speed obtains predicted value x ~ (k) of state x (k), and controller uses status predication value x ~ (k) of the t obtained to carry out the calculating of controlled quentity controlled variable.
By step 4) described in the information in t – Td moment merge, ensure that by the time objectivity of fused data.
Utilize step 5) described in the speed state estimated value in t – Td moment 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, avoids and delay link is introduced rate pattern.
The not specified technology of the present invention is prior art.
Beneficial effect: the quality that invention increases gps data, effectively dope the speed state of microminiature four rotor wing unmanned aerial vehicle current time, predict that the data obtained are accurate, to the lifting of microminiature four rotor wing unmanned aerial vehicle flight control performance, there is positive effect, there is very important practical value.
Accompanying drawing explanation
Fig. 1 is the inventive method workflow block diagram;
Fig. 2 is that the rate pattern using the ident instrument matching of MATLAB to obtain in the inventive method exports and the actual comparison diagram exported;
Fig. 3 is the algorithm flow schematic diagram speed state in t – Td moment being carried out to Kalman filtering;
Fig. 4 is the speed state predicted value that obtains of the inventive method and gps data is shifted to an earlier date T
dthe comparison diagram of the data obtained after time.
Embodiment
Postponing to process the gps data existed in microminiature four rotor wing unmanned aerial vehicle speeds control below, introducing technical scheme of the present invention by describing its concrete embodiment.It should be noted that, embodiment only deepens the understanding to technical scheme, and to can not inventing any restriction effect.
As shown in Figure 1, this enforcement comprises the steps:
1) to fly the every data analysis obtained according to microminiature four rotor wing unmanned aerial vehicle, obtain T time delay of gps data
d.
Make microminiature four rotor wing unmanned aerial vehicle complete the motion process being changed to acceleration steady state from stationary state, obtain acceleration measurement that in this motion process, airborne accelerometer obtains and the velocity measurement that Airborne GPS obtains.Because airborne accelerometer data is without delay, degree of will speed up measured value and velocity measurement are compared, deduct from the moment that null value becomes nonzero value acceleration measurement to become nonzero value moment from null value by velocity measurement, T time delay of gps data can be obtained
dit is 1 second.
2) on gps data time delay known basis, the ident instrument matching of MATLAB is used to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicle.
First, the principle of work according to microminiature four rotor wing unmanned aerial vehicle sets up rate pattern.For the object simplifying problem, carry out linearization process, the kinetics equation that can obtain speed is along the y-axis direction:
In formula, v represents microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction, and Φ represents the roll angle of microminiature four rotor wing unmanned aerial vehicle, U
1represent microminiature four rotor wing unmanned aerial vehicle four rotors lift and, K is overall drag coefficient.Laplace transform is carried out to above formula and can obtain the transport function of attitude angle to aircraft speed:
Due to gps data exist one time delay T
d, so the transport function of the speed obtained measured by attitude angle to GPS is:
Secondly, the speed data that the attitude angle data using the ident instrument of MATLAB to obtain according to microminiature four rotor wing unmanned aerial vehicle flight experiment and GPS measure carries out matching to the parameter of rate pattern.The rate pattern that matching obtains is:
The rate pattern that matching obtains exports sees Fig. 2 with the contrast using GPS to measure the speed data obtained.The degree of fitting of the rate pattern obtained reaches 65.62%.The rate pattern that matching obtains can reach higher degree of fitting and also illustrate that gps data T time delay that the first step obtains
daccuracy.
3) sliding-model control is carried out to the microminiature four rotor wing unmanned aerial vehicle rate pattern obtained, obtain the discretization model of speed.
Following agreement is made to sample mode and hold mode: the equal interval sampling that the sample mode of sampling thief is is the cycle with constant T; Sampling period T chooses the condition meeting Shannon's sampling theorem; Discrete-time signal adopts zeroth order hold mode to the conversion of continuous time signal.Based on above agreement, in conjunction with microcontroller processing power, choosing controlled frequency is 50Hz, by the 2nd) matching obtains in step rate pattern discretize, obtain discrete system:
x(k+1)=0.9932·x(k)+0.0028·u(k)
y(k)=x(k-50)
Wherein, quantity of state x is microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction, and control inputs u is that the roll angle Φ of microminiature four rotor wing unmanned aerial vehicle, y represent that GPS measures the microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction obtained.
4) to t – T
dthe speed state in moment is estimated by Kalman filtering algorithm.
The actual time of the speed state reflected due to the gps data of t is t – T
din the moment, therefore arrive t – T by unified for the information fusion moment
din the moment, namely utilize the gps data of t to t – T
dthe speed state in moment carries out combined filter, on the objectivity basis ensureing fused data, obtains t – T
dthe speed state estimated value in moment.
The time delay that GPS measures the speed data obtained is 1 second, the speeds control frequency of microminiature four rotor wing unmanned aerial vehicle is 50Hz, therefore using the observed reading of y (k) as state x (k – 50), estimated value based on state x (k – 51) carries out state one-step prediction, obtained the estimated value of state x (k – 50) by Kalman filtering, specific algorithm process is shown in Fig. 3.
5) utilize Kalman's multistep forecasting method by t – T
dthe speed state estimated value recursion in moment, to current time t, obtains the speed state predicted value of t.
Based on the 4th) estimated value of state x (k – 50) that step obtains, the discretization model recursion of Negotiation speed obtains predicted value x ~ (k) of state x (k).
Controller uses speed state predicted value x ~ (k) of the t obtained to carry out the calculating of controlled quentity controlled variable.
By finding out (see accompanying drawing 4) speed state predicted value x ~ (k) and the contrast through the gps measurement data of translation process, the present invention has good estimated performance, and the speed state predicted value of the microminiature four rotor wing unmanned aerial vehicle current time obtained is accurate.Solve control performance decline problem GPS metric data delay introducing controlled device brought, to the lifting of microminiature four rotor wing unmanned aerial vehicle flight control performance, there is positive effect, there is very important practical value.
Claims (6)
1., based on a microminiature four rotor wing unmanned aerial vehicle speed state Forecasting Methodology of GPS, it is characterized in that, comprise the following steps connected in turn:
1) accelerometer data of microminiature four rotor wing unmanned aerial vehicle and gps data are analyzed, obtain T time delay of gps data
d;
2) the ident instrument matching of MATLAB is used to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicle;
3) in conjunction with the sampling period, sliding-model control is carried out to the rate pattern obtained, obtain the discretization model of speed;
4) actual time of speed state that the gps data of t reflects is t-T
dmoment, by unified for the information fusion moment to t-T
din the moment, namely utilize the GPS speed data of t to t-T
dthe microminiature four rotor wing unmanned aerial vehicle speed state in moment carries out Kalman filtering, extrapolates t-T
dthe speed state estimated value in moment;
5) at t-T
don the basis of the speed state estimated value in moment, Kalman's multistep forecasting method is utilized to obtain the speed state predicted value of t; Controller uses the speed state predicted value of the t obtained to carry out the calculating of controlled quentity controlled variable.
2. method according to claim 1, it is characterized in that: step 1) in make microminiature four rotor wing unmanned aerial vehicle complete the motion process being changed to acceleration steady state from stationary state, obtain acceleration measurement that in this motion process, airborne accelerometer obtains and the velocity measurement that Airborne GPS obtains, because airborne accelerometer data is without delay, degree of will speed up measured value and velocity measurement are compared, deduct from the moment that null value becomes nonzero value acceleration measurement to become nonzero value moment from null value by velocity measurement, 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 matching of MATLAB to obtain the rate pattern of microminiature four rotor wing unmanned aerial vehicle according to ash bin principle, the kinetics equation of microminiature four rotor wing unmanned aerial vehicle speed is along the y-axis direction:
In formula, v represents microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction,
represent the roll angle of microminiature four rotor wing unmanned aerial vehicle, U
1represent microminiature four rotor wing unmanned aerial vehicle four rotors lift and, K is overall drag coefficient;
Laplace transform is carried out to above formula and can obtain the transport function of attitude angle to aircraft speed:
Due to gps data exist one time delay T
d, so the transport function of the speed obtained measured by attitude angle to GPS is:
The speed data that the attitude angle data using the ident instrument of MATLAB to obtain according to microminiature four rotor wing unmanned aerial vehicle flight experiment and GPS measure carries out matching to the parameter of rate pattern, obtains rate pattern:
4. method according to claim 3, is characterized in that: in step 3) following agreement is done to sample mode and hold mode: the equal interval sampling that the sample mode of sampling thief is is the cycle with constant T; Sampling period T chooses the condition meeting Shannon's sampling theorem; Discrete-time signal adopts zeroth order hold mode to the conversion of continuous time signal; Based on above agreement, in conjunction with microcontroller processing power, choosing controlled frequency is 50Hz, by the 2nd) matching obtains in step rate pattern discretize, 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 is microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction, and control inputs u is the roll angle of microminiature four rotor wing unmanned aerial vehicle
y represents that GPS measures the microminiature four rotor wing unmanned aerial vehicle velocity amplitude along the y-axis direction obtained.
5. method according to claim 4, is characterized in that: in step 4) in GPS time delay of measuring the speed data obtained be T
dthe speeds control frequency of microminiature four rotor wing unmanned aerial vehicle is 50Hz, using the observed reading of y (k) as state x (k – 50), estimated value based on state x (k – 51) carries out state one-step prediction, is obtained 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 state x (k – 50) that obtains of step, the discretization model recursion of Negotiation speed obtains predicted value x ~ (k) of state x (k), and controller uses status predication value x ~ (k) of the t obtained to carry out the calculating of controlled quentity controlled variable.
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| 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 |
| 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 |
| CN106871892B (en) * | 2017-02-17 | 2020-08-11 | 张梦 | Aircraft combined navigation method and device |
| CN107703741B (en) * | 2017-08-31 | 2020-09-01 | 上海电力学院 | Robot motion system identification method based on quasi-model calibration Kalman filtering |
| 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 |
| CN108230371B (en) * | 2017-12-29 | 2020-08-18 | 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 |
| CN111275015A (en) * | 2020-02-28 | 2020-06-12 | 广东电网有限责任公司 | Unmanned aerial vehicle-based power line inspection electric tower detection and identification method and system |
| CN115309178A (en) * | 2021-05-06 | 2022-11-08 | 北京理工大学 | Method for designing and identifying model of small-sized four-rotor unmanned aerial vehicle forward flight experiment system |
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