CN113515108A - Control method and control device of unmanned equipment - Google Patents
Control method and control device of unmanned equipment Download PDFInfo
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- CN113515108A CN113515108A CN202110394470.0A CN202110394470A CN113515108A CN 113515108 A CN113515108 A CN 113515108A CN 202110394470 A CN202110394470 A CN 202110394470A CN 113515108 A CN113515108 A CN 113515108A
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- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
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- G05D1/0088—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
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- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
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- Y02T10/10—Internal combustion engine [ICE] based vehicles
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Abstract
The specification discloses a method and a device for controlling unmanned equipment, and particularly discloses that current state data of the unmanned equipment and historical state data of the unmanned equipment are obtained, state quantity change rates generated based on the current actual environment of the unmanned equipment and the current actual equipment state of the unmanned equipment are determined according to the state data, then the current control quantity of the unmanned equipment is determined under the condition that the state quantity change rates are used as constraints, and the unmanned equipment is controlled. Therefore, when the current control quantity is solved, the solved current control quantity is corrected by utilizing the state quantity change rate which can embody the influence of the actual environment where the unmanned equipment is located and the actual equipment state on the unmanned equipment per se on the whole, and therefore the unmanned equipment can more accurately carry out self-adaptive control on the unmanned equipment per se.
Description
Technical Field
The present disclosure relates to the field of unmanned technologies, and in particular, to a control method and a control device for an unmanned device.
Background
With the continuous development of unmanned technology, unmanned equipment such as unmanned aerial vehicles, unmanned robots, unmanned vehicles and the like are applied to various fields such as logistics industry, shared travel, public transportation and the like, so that the labor cost is effectively reduced, and the service efficiency is effectively improved.
In the actual driving process of the unmanned equipment, adaptive control is usually involved, that is, the actual environment (such as temperature, gradient, friction coefficient and the like) where the unmanned equipment is located at present is automatically distinguished through a preset control algorithm, or various parameters (such as tire pressure, steering wheel zero position and the like) involved in the unmanned equipment are automatically distinguished, so that the adaptive control of the unmanned equipment is realized.
However, in order to realize adaptive control of the unmanned aerial vehicle, parameters such as gradient, friction coefficient, tire pressure and the like are generally required to be expressed in a control model for controlling the unmanned aerial vehicle, which greatly increases the complexity of the control model, resulting in low computational efficiency of the control model. In addition, during the construction process of the control model, some other unknown factors such as engine aging and transmission shaft wear may be omitted, so that the finally constructed control model may not be capable of accurately distinguishing the current actual environment of the unmanned equipment and various parameters related to the unmanned equipment, and further accurate adaptive control on the unmanned equipment cannot be realized.
Disclosure of Invention
The present specification provides a control method and a control apparatus for an unmanned aerial vehicle, which partially solve the above problems of the prior art.
The technical scheme adopted by the specification is as follows:
the present specification provides a control method of an unmanned aerial vehicle device, including:
acquiring current state data of the unmanned equipment and historical state data of the unmanned equipment;
determining a state quantity change rate generated based on the actual environment where the unmanned equipment is currently located and the current actual equipment state of the unmanned equipment according to the current state data and the historical state data;
determining the current control quantity of the unmanned equipment under the condition that the state quantity change rate is taken as a constraint condition, and taking the current control quantity as the current control quantity;
and controlling the unmanned equipment according to the current control quantity.
Optionally, determining, as the current control quantity, a current control quantity of the unmanned aerial vehicle under a constraint condition that the state quantity change rate is used as a constraint condition, specifically includes:
determining a control quantity estimation model configured by the unmanned equipment;
performing polynomial transformation on the control quantity estimation model according to the historical control quantity, the historical state data and the state quantity change rate to determine a control quantity optimization model corresponding to the control quantity estimation model under the condition that the unmanned equipment is in the current actual environment and the current actual equipment state;
and determining the current control quantity according to the control quantity optimization model.
Optionally, determining the current controlled variable according to the controlled variable optimization model specifically includes:
determining a target time period containing the current time;
according to the control quantity optimization model, under the condition that the state quantity change rate is taken as a constraint condition, integrally estimating the control quantity corresponding to each moment of the unmanned equipment in the target time period to obtain an estimated control quantity sequence corresponding to the unmanned equipment in the target time period;
the current control amount is determined from the estimated control amount sequence.
Optionally, according to the control quantity optimization model, under a constraint condition that the state quantity change rate is used as the constraint condition, performing overall estimation on the control quantity corresponding to each moment of the unmanned aerial vehicle within the target time period to obtain an estimated control quantity sequence corresponding to the unmanned aerial vehicle within the target time period, specifically including:
aiming at each moment in the target time period, determining a comprehensive deviation corresponding to the moment according to the control quantity optimization model;
and determining an estimated control quantity sequence corresponding to the unmanned equipment at each moment in the target time period according to the comprehensive deviation.
Optionally, the synthetic deviation comprises: a control amount deviation corresponding to the time and a state amount deviation corresponding to the time;
for each moment in the target time period, determining a comprehensive deviation corresponding to the moment according to the control quantity optimization model, specifically comprising:
aiming at each moment in the target time period, determining an expected control quantity and an expected state quantity corresponding to the moment;
determining a control quantity deviation between the expected control quantity corresponding to the moment and the target control quantity corresponding to the moment as a control quantity deviation corresponding to the moment and determining a state quantity deviation between the expected state quantity corresponding to the moment and the target state quantity corresponding to the moment as a state quantity deviation corresponding to the moment according to the control quantity optimization model;
wherein, the target control quantity corresponding to the moment comprises: at least one of an actual control quantity corresponding to the unmanned equipment at the moment and an estimated control quantity corresponding to the unmanned equipment at the moment, wherein the target state quantity corresponding to the moment comprises: at least one of an actual state quantity corresponding to the unmanned device at the moment and an estimated state quantity corresponding to the unmanned device at the moment.
Optionally, determining, according to the comprehensive deviation, an estimated control quantity sequence corresponding to the unmanned device in the target time period, specifically including:
and determining an estimated control quantity sequence corresponding to the unmanned equipment in the target time period by taking the minimum sum of the comprehensive deviations corresponding to each moment in the target time period as an optimization target.
Optionally, the method further comprises:
according to the control quantity optimization model, parameters contained in the control quantity estimation model are adjusted to obtain the adjusted control quantity estimation model;
and controlling the unmanned equipment according to the adjusted control quantity estimation model.
The present specification provides a control apparatus of an unmanned aerial vehicle, including:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring current state data of the unmanned equipment and historical state data of the unmanned equipment;
the change rate determining module is used for determining the change rate of the state quantity generated based on the current actual environment of the unmanned equipment and the current actual equipment state of the unmanned equipment according to the current state data and the historical state data;
the control quantity determining module is used for determining the current control quantity of the unmanned equipment as the current control quantity under the condition that the state quantity change rate is taken as a constraint condition;
and the control module is used for controlling the unmanned equipment according to the current control quantity.
The present specification provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of the above-described unmanned aerial device.
The present specification provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the above-described method of controlling an unmanned device when executing the program.
The technical scheme adopted by the specification can achieve the following beneficial effects:
in the method for controlling the unmanned aerial vehicle provided by the present specification, current state data of the unmanned aerial vehicle and historical state data of the unmanned aerial vehicle are acquired, then, according to the current state data and the historical state data, a state quantity change rate generated based on the current actual environment where the unmanned aerial vehicle is located and the current actual device state of the unmanned aerial vehicle is determined, then, under the condition that the state quantity change rate is taken as a constraint condition, a current control quantity of the unmanned aerial vehicle is determined as a current control quantity, and finally, the unmanned aerial vehicle is controlled according to the current control quantity.
According to the method, the state quantity change rate corresponding to the unmanned equipment at the current moment is determined according to the state data of the unmanned equipment, the actual environment where the unmanned equipment is located and the influence of the actual equipment state on the unmanned equipment on the whole are indirectly reflected according to the state quantity change rate, and then the current control quantity of the unmanned equipment is determined, and the current control quantity of the unmanned equipment is solved under the condition that the state quantity change rate is used as the constraint condition. Therefore, compared with a mode that a plurality of influence factors of the control quantity are reflected in the control quantity prediction model, the influence of the current actual environment of the unmanned equipment and the actual equipment state of the unmanned equipment on the whole is reflected by the state quantity change rate in the specification, so that each influence factor capable of influencing the accuracy of the control quantity prediction model is not required to be determined one by one, the condition that the constructed model is inaccurate due to omission of other unknown factors such as engine aging, transmission shaft abrasion and the like is avoided, and the unmanned equipment can realize accurate self-adaptive control on the unmanned equipment. Furthermore, since a plurality of influence factors are considered as a whole, the control quantity prediction model can be prevented from being excessively complicated, and the calculation efficiency of the control quantity prediction model is ensured.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
fig. 1 is a schematic flow chart of a control method of an unmanned aerial vehicle in the present specification;
fig. 2 is a schematic diagram illustrating a detailed flow when the control method of the unmanned aerial vehicle according to the present specification is executed;
FIG. 3 is a schematic diagram of a control device for an unmanned aerial vehicle of the present disclosure;
fig. 4 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a control method of an unmanned aerial vehicle in this specification, and specifically includes the following steps:
step S100, current state data of the unmanned equipment and historical state data of the unmanned equipment are obtained.
At present, in the driving process of the unmanned equipment, the state data of the unmanned equipment at the next moment is predicted according to the state data of the unmanned equipment at the current moment, the control quantity executed by the unmanned equipment at the previous moment and a predetermined planning track based on a trained model, and the control quantity required to be executed by the unmanned equipment at the current moment is solved according to the predicted state data of the unmanned equipment at the next moment. Correspondingly, when the unmanned equipment controls the unmanned equipment according to the determined control quantity required to be executed at the current moment and drives to the next moment, the state data corresponding to the unmanned equipment is also converted into the state data close to the predicted unmanned equipment at the next moment.
For example, the state data of the drone at time t is x (t), and the amount of control performed by the drone at time t-1 is u (t-1). When the unmanned equipment is controlled, the state data of the unmanned equipment at the moment of t +1 is predicted to be x (t +1) according to the predetermined planning track, the state data x (t) and the control quantity u (t-1). Then, based on the state data x (t +1), it is found that the amount of control that the unmanned facility needs to execute at time t when the state data x (t) is converted into the state data x (t +1) is u (t).
The state data of the unmanned device may include parameters such as speed, acceleration, and angular velocity. The control amount of the above-described unmanned aerial vehicle may be a control amount that indirectly or directly affects the state data, for example, a control amount such as an accelerator opening degree, a brake opening degree, a steering angle, and a shift change.
In this specification, the method for controlling the drone may be executed by the drone or may be executed by a server that provides service support for the drone. For convenience of description, the following description will take the unmanned aerial vehicle as an execution subject. The unmanned device to which the control method of the unmanned device provided by the specification is applied can be used for executing delivery tasks in the delivery field, such as business scenes of delivery such as express delivery, logistics and takeaway by using the unmanned device.
Step S102, determining a state quantity change rate generated based on the current actual environment of the unmanned equipment and the current actual equipment state of the unmanned equipment according to the current state data and the historical state data.
In an actual scene, due to the influence of the actual environment where the unmanned equipment is currently located and the state of the unmanned equipment, the unmanned equipment controls the actual state data obtained by driving according to the solved control quantity, and the state data expected to be reached when the control quantity is obtained through solving are deviated. At this time, the actual environment where the unmanned aerial vehicle is currently located and the actual device state of the unmanned aerial vehicle need to be determined, the influence degree on the prediction model needs to be determined, and then the solved control quantity is corrected according to the determined result, so that the unmanned aerial vehicle can accurately and adaptively control the unmanned aerial vehicle.
In this way, for any historical time, the unmanned aerial vehicle can obtain the actual state data of the unmanned aerial vehicle at the next time after driving according to the solved control amount at the time on the basis of the state data at the time. At this time, the state data difference from the state data at that time to the actual state data at the next time is affected not only by the amount of control performed by the unmanned aerial vehicle but also by the actual environment in which the unmanned aerial vehicle is currently located and the actual device state of the unmanned aerial vehicle itself. Therefore, the above state data difference can be used to indirectly reflect the influence of the actual environment where the unmanned device is currently located and the actual device state of the unmanned device on the state data of the unmanned device.
Therefore, the state quantity change rate is used in the present specification to represent the degree of change of the state data of the unmanned aerial vehicle in the unit time compared with the state data of the unmanned aerial vehicle at the previous time under the comprehensive influence of the current actual environment, the current actual device state, and the control quantity corresponding to the previous time.
During specific implementation, the unmanned equipment determines the state quantity change rate which is generated along with the state data of the unmanned equipment under the influence of the current actual environment and the current actual equipment state of the unmanned equipment according to the acquired current state data and historical state data.
Specifically, the unmanned aerial vehicle may determine a rate of change of state quantity generated by state data of the unmanned aerial vehicle itself when the unmanned aerial vehicle travels from a previous time to a current time, based on the acquired state data of the current time and the state data of the previous time.
For example, the state data at time t is denoted as x (t), and the state data at time t-1 is denoted as x (t-1). When the unmanned equipment determines the corresponding state quantity change rate from t-1 time to t time, the state data x (t) at the t time and the state data x (t-1) at the t-1 time are firstly obtained, and then the unmanned equipment determines that the unmanned equipment is in the time period from the t-1 time to the t timeThe rate of change of the state data per unit time:
and is recorded as w (t-1) as the state quantity change rate of the unmanned aerial vehicle at the time t-1. Wherein tau is the time length between the t-1 moment and the t moment.
Generally, in the self-driving process of the unmanned equipment, in order to realize smooth driving, the data acquisition time interval is smaller. In this way, the actual environment in which the drone is currently located and the current actual device state of the drone itself may be considered to be approximately the same for several adjacent data acquisition time intervals. In other words, for a plurality of adjacent data acquisition time intervals, even if the actual environment where the unmanned device is currently located and the actual device state of the unmanned device itself are slightly different, the influence of the actual environment on the final solution result can be ignored. That is, the deviation of the unmanned aerial vehicle caused by the current actual environment such as the slope and the current self actual device state such as the tire pressure at the time t is consistent with the deviation caused by the influence of the current actual environment such as the slope and the current self actual device state such as the tire pressure at the time t-1. In this way, the state quantity change rate determined by the unmanned aerial vehicle at time t-1 can be taken as the state quantity change rate of the unmanned aerial vehicle at time t.
In addition, the unmanned equipment can also select state data of a plurality of moments from the current moment in the past in time, determine the weighted average value of the state quantity change rate of the state data of the unmanned equipment in the time length range corresponding to the state data, and then take the weighted average value as the state quantity change rate generated by the unmanned equipment based on the current actual environment and the current actual equipment state.
And step S104, determining the current control quantity of the unmanned equipment under the condition that the state quantity change rate is taken as a constraint condition, and taking the current control quantity as the current control quantity.
In specific implementation, the unmanned equipment firstly determines a control quantity estimation model configured by the unmanned equipment, then carries out polynomial transformation on the control quantity estimation model according to historical control quantity, historical state data and state quantity change rate so as to determine a control quantity optimization model corresponding to the control quantity estimation model under the condition that the unmanned equipment is in the current actual environment and the current self actual equipment state, and finally solves the current control quantity according to the control quantity optimization model.
The control quantity estimation model configured for the unmanned equipment is a pre-trained control quantity estimation model, and the control quantity estimation model can be regarded as a complex function.
In order to facilitate subsequent data processing, the control quantity estimation model may be converted into a complex function composed of simple functions.
In specific implementation, when the unmanned equipment carries out polynomial transformation on the control quantity estimation model according to the historical control quantity, the historical state data and the state quantity change rate, the control quantity estimation model can be converted into a composite function consisting of a plurality of simple functions through a Taylor formula. In this way, the unmanned aerial vehicle can construct the taylor expansion corresponding to the control quantity estimation model at any moment, and further construct the control quantity optimization model at the moment according to the taylor expansion corresponding to the constructed control quantity estimation model at the moment and the determined state quantity change rate at the moment, so that the control quantity corresponding to the unmanned aerial vehicle at the moment can be solved according to the control quantity optimization model under the condition that the unmanned aerial vehicle is in the current actual environment and the current actual device state.
For example, the control amount estimation model configured by the unmanned aerial vehicle is: f (x (t), u (t)), at t ═ t0The taylor expansion corresponding to the control quantity estimation model at the time can be recorded as: Δ f (x (t), u (t)), unmanned device at t0The rate of change of the state quantity at that time is denoted as w (t)0-1)). Then, according to the taylor expansion corresponding to the controlled variable estimation model and the determined state variable change rate, the expression of the constructed controlled variable optimization model can be simply expressed as: f (x (t), u (t)) Δ F (x)(t),u(t))+w(t0-1)。
In this specification, after the unmanned aerial vehicle constructs the control quantity optimization model, the control quantity corresponding to the unmanned aerial vehicle at the current time, that is, the current control quantity, is solved by further using the control quantity optimization model.
When the unmanned equipment solves the current control quantity, a target time period containing the current time is determined, then the control quantity corresponding to each time of the unmanned equipment in the target time period is integrally estimated according to a control quantity optimization model under the condition that the state quantity change rate is taken as a constraint condition, an estimated control quantity sequence corresponding to the unmanned equipment in the target time period is obtained, and then the current control quantity is determined from the estimated control quantity sequence.
In a specific implementation, the drone first determines a target time period. In determining the target time period, the unmanned aerial device may pick up successive times forward in time from the current time, and take the time period determined by these times and the current time as the target time period. The unmanned aerial vehicle may also select a plurality of consecutive times backward from the current time, and set a time period determined by these times and the current time as the target time period. Of course, the unmanned aerial vehicle may select a plurality of consecutive times forward in time and a plurality of consecutive times backward with reference to the current time, and then set the time period determined by these times and the current time as the target time period.
The following will exemplify the determination of the target time periods by taking the sliding window as 7 and the current time as t time.
The first target time period determination mode is as follows: if the target time period is determined only from the historical time, the target time period determined for the time t may be [ t-6, t ], and correspondingly, the estimated control quantity sequence corresponding to the unmanned equipment in the target time period, which is solved subsequently, may include: { u (t-6), u (t-5), u (t-4), u (t-3), u (t-2), u (t-1), u (t) }.
A second target time period determination mode: if the target time period is determined only from the future time, the target time period determined for the time t may be [ t, t +6], and correspondingly, the estimated control quantity sequence corresponding to the unmanned equipment in the target time period, which is solved subsequently, may include: { u (t) }, u (t +1), u (t +2), u (t +3), u (t +4), u (t +5), u (t + 6).
A third target time period determination mode: when the target time period is determined from the historical time and the future time, the target time period determined for the time t may be [ t-3, t +3], and correspondingly, the subsequently solved sequence of the estimated control amount corresponding to the unmanned aerial vehicle in the target time period may include: { u (t-3), u (t-2), u (t-1), u (t +1), u (t +2), u (t +3) }.
After the target time period is determined, the unmanned equipment further determines an estimated control quantity sequence corresponding to the unmanned equipment in the target time period. At the moment, the unmanned equipment firstly determines the comprehensive deviation corresponding to each moment in the target time period according to the control quantity optimization model, and then solves the estimated control quantity sequence corresponding to each moment of the unmanned equipment in the target time period according to the comprehensive deviation.
When the unmanned device determines the comprehensive deviation, the expected control quantity and the expected state quantity corresponding to each time are determined for each time in the target time period. Then, the unmanned aerial vehicle determines, as the control amount deviation corresponding to the time, the control amount deviation between the desired control amount corresponding to the time and the target control amount corresponding to the time, and determines, as the state amount deviation corresponding to the time, the state amount deviation between the desired state amount corresponding to the time and the target state amount corresponding to the time, based on the control amount optimization model.
For each time in the target time period, the expected control amount and the expected state amount corresponding to the time are the state amount expected to be reached by the unmanned aerial vehicle and the control amount expected to be executed by the unmanned aerial vehicle when the unmanned aerial vehicle travels to the time according to the predetermined planned trajectory.
The target control amount and the target state amount are the state amount of the unmanned aerial vehicle and the state amount required to be executed when the unmanned aerial vehicle travels to the time in the actual environment where the unmanned aerial vehicle is currently located and the state of the own actual device.
The above-described integrated deviation can be reflected in the total deviation level of the predicted deviation between the target state quantity and the desired state quantity and the solved deviation between the target controlled quantity and the desired controlled quantity in the target time period. If the comprehensive deviation is smaller, the obtained actual running track is closer to the predetermined planned track when the unmanned equipment controls the unmanned equipment to run according to the target control quantity. Wherein the integrated deviation may include: a control amount deviation corresponding to the time and a state amount deviation corresponding to the time. The control amount deviation corresponding to the time represents a deviation between the target control amount and the desired control amount obtained at the time, and the state amount deviation corresponding to the time represents a deviation between the target state amount and the desired state amount predicted at the time.
The target control amount corresponding to the time point includes: at least one of an actual control amount corresponding to the unmanned aerial vehicle at the time and an estimated control amount corresponding to the unmanned aerial vehicle at the time. The target state quantity corresponding to the time includes: at least one of an actual state quantity corresponding to the unmanned device at the time and an estimated state quantity corresponding to the unmanned device at the time.
Continuing with the above example, for the first target time period determination method, the target control amount corresponding to the target time period may be an actual control amount corresponding to the time at which the unmanned aerial vehicle actually performs (i.e., a control amount actually performed in history) or an estimated control amount corresponding to the time at which the unmanned aerial vehicle actually performs (i.e., a control amount estimated in history). The target state quantity corresponding to the target time period may be an actual state quantity corresponding to the time at which the unmanned aerial vehicle is present (i.e., a state quantity actually observed in history), or may be an estimated state quantity corresponding to the time at which the unmanned aerial vehicle is present (i.e., a state quantity estimated in history).
In the second target time period determination method, the target control amount corresponding to the target time period may be an estimated control amount corresponding to the unmanned aerial vehicle at the time, and the target state amount corresponding to the time may be an estimated state amount corresponding to the unmanned aerial vehicle at the time.
In the third target time period determination method, for each time in the history of the target time period, the target control amount corresponding to the time may be an actual control amount corresponding to the time (i.e., a control amount actually executed in the history) of the unmanned aerial vehicle, or may be an estimated control amount corresponding to the time (i.e., a control amount estimated in the history) of the unmanned aerial vehicle. The target state quantity corresponding to the time may be an actual state quantity corresponding to the time at which the unmanned aerial vehicle is present (i.e., a state quantity actually observed in history), or may be an estimated state quantity corresponding to the time at which the unmanned aerial vehicle is present (i.e., a state quantity estimated in history). For each future time within the target time period, the corresponding target control quantity within the target time period may be an estimated control quantity corresponding to the time of the unmanned device, and the corresponding target state quantity may be an estimated state quantity corresponding to the time of the unmanned device.
And finally, the unmanned equipment takes the minimum sum of the comprehensive deviations corresponding to each moment in the target time period as an optimization target, and an estimated control quantity sequence corresponding to the unmanned equipment in the target time period is solved.
That is to say, the unmanned aerial vehicle takes the estimated control quantity sequence corresponding to the minimum value of the comprehensive deviation as the estimated control quantity sequence corresponding to the unmanned aerial vehicle in the target time period, takes the estimated control quantity corresponding to each moment in the estimated control quantity sequence as the control quantity corresponding to the moment, and then solves the current control quantity of the unmanned aerial vehicle according to the determination mode of the target time period.
And S106, controlling the unmanned equipment according to the current control quantity.
Specifically, after the current control quantity is solved by the unmanned equipment, the unmanned equipment is controlled to move forward according to the current control quantity, and self-adaptive control of the unmanned equipment is completed.
Compared with the mode that a plurality of influence factors of the control quantity are reflected in the control quantity prediction model, the current control quantity of the unmanned equipment is solved by the steps, the actual environment where the unmanned equipment is located currently and the influence of the actual equipment state of the unmanned equipment on the whole are reflected by using the state quantity change rate, so that the influence factors which can influence the accuracy of the control quantity prediction model are not required to be determined one by one, the condition that the constructed model is inaccurate due to omission of other unknown factors such as engine aging, transmission shaft abrasion and the like is avoided, and the unmanned equipment can realize the self-adaptive control. Furthermore, since a plurality of influence factors are taken into consideration as a whole, the control model can be prevented from being excessively complicated, and the calculation efficiency of the control model is ensured.
In addition, the unmanned equipment can also adjust parameters contained in the control quantity estimation model according to the control quantity optimization model to obtain an adjusted control quantity estimation model, and control the unmanned equipment according to the adjusted control quantity estimation model. Therefore, the adjusted control quantity estimation model can be used, the solved control quantity is more accurate, and the unmanned equipment can be controlled adaptively more accurately.
In the following, with reference to the drawings, a sliding window is 7, and the current time is t1At this time, only the target time period is selected from the future time as an example, and a detailed flow when the control method of the unmanned aerial vehicle provided in this specification is executed will be described in detail, specifically referring to fig. 2.
Step S200, acquiring current state data x (t) of the unmanned equipment1) And last time state data x (t)1-1)。
Step S202, determining the state quantity change rate w (t) according to the current state data and the historical state data1-1)。
Step S204, acquiring a control quantity estimation model f (x (t), u (t)) configured by the unmanned equipment.
In step S206, a polynomial conversion is performed on the controlled variable estimation model based on the historical controlled variable, the historical state data, and the state variable change rate to obtain a controlled variable optimization model F (x) (t), u (t) ═ Δ F (x (t), u (t) + w (t) () corresponding to the controlled variable estimation model1-1))。
Step S208, determining a target time period [ t ] containing the current time1，t1+6]。
Step S210, aiming at each time in the target time period, determining the expected control quantity corresponding to the time
And the desired state quantity
Step S212, aiming at each time in the target time period, determining the comprehensive deviation corresponding to the time according to the control quantity optimization model
Step S214, with the minimum sum of the comprehensive deviations corresponding to each moment in the target time period as an optimization target, solving an estimated control quantity sequence { u (t) corresponding to the unmanned equipment in the target time period1)、u(t1+1)、u(t1+2)、u(t1+3)、u(t1+4)、u(t1+5)、u(t1+6)}。
Step S216, determining the current control quantity u (t) of the unmanned equipment from the estimated control quantity sequence1)。
Step S218, according to the current control quantity u (t)1) And controlling the unmanned equipment.
Based on the same idea, the present specification further provides a control device of the unmanned aerial vehicle, as shown in fig. 3, for the control method of the unmanned aerial vehicle provided in one or more embodiments of the present specification.
Fig. 3 is a schematic diagram of a control device of an unmanned aerial vehicle provided in this specification, and specifically includes:
an obtaining module 300, configured to obtain current state data of an unmanned aerial vehicle and historical state data of the unmanned aerial vehicle;
a change rate determining module 301, configured to determine, according to the current state data and the historical state data, a state quantity change rate generated based on an actual environment where the unmanned aerial vehicle is currently located and a current actual device state of the unmanned aerial vehicle;
a control quantity determining module 302, configured to determine, as a current control quantity, a current control quantity of the unmanned aerial vehicle under a constraint condition that the state quantity change rate is used;
and the control module 303 is configured to control the unmanned device according to the current control amount.
Optionally, the control quantity determining module 302 is specifically configured to determine a control quantity estimation model configured by the unmanned aerial vehicle; performing polynomial transformation on the control quantity estimation model according to the historical control quantity, the historical state data and the state quantity change rate to determine a control quantity optimization model corresponding to the control quantity estimation model under the condition that the unmanned equipment is in the current actual environment and the current actual equipment state; and determining the current control quantity according to the control quantity optimization model.
Optionally, the control amount determining module 302 is specifically configured to determine a target time period including a current time; according to the control quantity optimization model, under the condition that the state quantity change rate is taken as a constraint condition, integrally estimating the control quantity corresponding to each moment of the unmanned equipment in the target time period to obtain an estimated control quantity sequence corresponding to the unmanned equipment in the target time period; the current control amount is determined from the estimated control amount sequence.
Optionally, the control quantity determining module 302 is specifically configured to determine, for each time in the target time period, a comprehensive deviation corresponding to the time according to the control quantity optimization model; and determining an estimated control quantity sequence corresponding to the unmanned equipment at each moment in the target time period according to the comprehensive deviation.
Optionally, the synthetic deviation comprises: a control amount deviation corresponding to the time and a state amount deviation corresponding to the time;
the control quantity determining module 302 is specifically configured to determine, for each time in the target time period, an expected control quantity and an expected state quantity corresponding to the time; determining a control quantity deviation between the expected control quantity corresponding to the moment and the target control quantity corresponding to the moment as a control quantity deviation corresponding to the moment and determining a state quantity deviation between the expected state quantity corresponding to the moment and the target state quantity corresponding to the moment as a state quantity deviation corresponding to the moment according to the control quantity optimization model; wherein, the target control quantity corresponding to the moment comprises: at least one of an actual control quantity corresponding to the unmanned equipment at the moment and an estimated control quantity corresponding to the unmanned equipment at the moment, wherein the target state quantity corresponding to the moment comprises: at least one of an actual state quantity corresponding to the unmanned device at the moment and an estimated state quantity corresponding to the unmanned device at the moment.
Optionally, the control quantity determining module 302 is specifically configured to determine an estimated control quantity sequence corresponding to the unmanned aerial vehicle in the target time period, with a minimum sum of the comprehensive deviations corresponding to each time in the target time period as an optimization target.
Optionally, the control device further comprises:
an adjusting module 304, configured to adjust parameters included in the controlled variable estimation model according to the controlled variable optimization model to obtain an adjusted controlled variable estimation model; and controlling the unmanned equipment according to the adjusted control quantity estimation model.
The present specification also provides a computer-readable storage medium storing a computer program, where the computer program is operable to execute the route planning method for the shared power bank service shown in fig. 1.
This specification also provides a schematic block diagram of the electronic device shown in fig. 4. As shown in fig. 4, the drone includes, at the hardware level, a processor, an internal bus, a network interface, a memory, and a non-volatile memory, although it may also include hardware required for other services. The processor reads a corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the method for controlling the unmanned aerial vehicle described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A control method of an unmanned aerial vehicle, characterized by comprising:
acquiring current state data of the unmanned equipment and historical state data of the unmanned equipment;
determining a state quantity change rate generated based on the actual environment where the unmanned equipment is currently located and the current actual equipment state of the unmanned equipment according to the current state data and the historical state data;
determining the current control quantity of the unmanned equipment under the condition that the state quantity change rate is taken as a constraint condition, and taking the current control quantity as the current control quantity;
and controlling the unmanned equipment according to the current control quantity.
2. The method according to claim 1, wherein determining, as the current control quantity, the current control quantity of the unmanned aerial vehicle under the constraint condition of the state quantity change rate specifically includes:
determining a control quantity estimation model configured by the unmanned equipment;
performing polynomial transformation on the control quantity estimation model according to the historical control quantity, the historical state data and the state quantity change rate to determine a control quantity optimization model corresponding to the control quantity estimation model under the condition that the unmanned equipment is in the current actual environment and the current actual equipment state;
and determining the current control quantity according to the control quantity optimization model.
3. The method according to claim 2, wherein determining the current control quantity according to the control quantity optimization model specifically includes:
determining a target time period containing the current time;
according to the control quantity optimization model, under the condition that the state quantity change rate is taken as a constraint condition, integrally estimating the control quantity corresponding to each moment of the unmanned equipment in the target time period to obtain an estimated control quantity sequence corresponding to the unmanned equipment in the target time period;
the current control amount is determined from the estimated control amount sequence.
4. The method according to claim 3, wherein the step of integrally estimating the control quantity corresponding to each time of the unmanned aerial vehicle within the target time period by using the state quantity change rate as a constraint condition according to the control quantity optimization model to obtain an estimated control quantity sequence corresponding to the unmanned aerial vehicle within the target time period specifically comprises:
aiming at each moment in the target time period, determining a comprehensive deviation corresponding to the moment according to the control quantity optimization model;
and determining an estimated control quantity sequence corresponding to the unmanned equipment at each moment in the target time period according to the comprehensive deviation.
5. The method of claim 4, wherein the synthetic deviation comprises: a control amount deviation corresponding to the time and a state amount deviation corresponding to the time;
for each moment in the target time period, determining a comprehensive deviation corresponding to the moment according to the control quantity optimization model, specifically comprising:
aiming at each moment in the target time period, determining an expected control quantity and an expected state quantity corresponding to the moment;
determining a control quantity deviation between the expected control quantity corresponding to the moment and the target control quantity corresponding to the moment as a control quantity deviation corresponding to the moment and determining a state quantity deviation between the expected state quantity corresponding to the moment and the target state quantity corresponding to the moment as a state quantity deviation corresponding to the moment according to the control quantity optimization model;
wherein, the target control quantity corresponding to the moment comprises: at least one of an actual control quantity corresponding to the unmanned equipment at the moment and an estimated control quantity corresponding to the unmanned equipment at the moment, wherein the target state quantity corresponding to the moment comprises: at least one of an actual state quantity corresponding to the unmanned device at the moment and an estimated state quantity corresponding to the unmanned device at the moment.
6. The method according to claim 4, wherein determining the sequence of estimated control quantities for the drone device within the target time period based on the composite deviation comprises:
and determining an estimated control quantity sequence corresponding to the unmanned equipment in the target time period by taking the minimum sum of the comprehensive deviations corresponding to each moment in the target time period as an optimization target.
7. The method of claim 2, wherein the method further comprises:
according to the control quantity optimization model, parameters contained in the control quantity estimation model are adjusted to obtain the adjusted control quantity estimation model;
and controlling the unmanned equipment according to the adjusted control quantity estimation model.
8. A control apparatus of an unmanned aerial vehicle, characterized by comprising:
the system comprises an acquisition module, a storage module and a display module, wherein the acquisition module is used for acquiring current state data of the unmanned equipment and historical state data of the unmanned equipment;
the change rate determining module is used for determining the change rate of the state quantity generated based on the current actual environment of the unmanned equipment and the current actual equipment state of the unmanned equipment according to the current state data and the historical state data;
the control quantity determining module is used for determining the current control quantity of the unmanned equipment as the current control quantity under the condition that the state quantity change rate is taken as a constraint condition;
and the control module is used for controlling the unmanned equipment according to the current control quantity.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 7.
10. An unmanned aerial vehicle comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the method of any of claims 1 to 7.
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