CN116880213B - Unmanned aerial vehicle anti-interference safety control method and related products - Google Patents
Unmanned aerial vehicle anti-interference safety control method and related products Download PDFInfo
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Abstract
The invention discloses an unmanned aerial vehicle anti-interference safety control method and related products. The method comprises the following steps: estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value; and performing secondary planning according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, the control Lyapunov function and the control obstacle function to obtain a control input signal, wherein the safety set is divided into a working set and an early warning set, the control obstacle function is continuously micro, the value in the working set is greater than or equal to 0, the value in the boundary of the working set is equal to 0, the value in the early warning set is negative, and the position of a region which is not adjacent to the working set in the boundary of the early warning set is approaching to negative infinity. The robustness of the method is further improved.
Description
Technical Field
The invention belongs to the technical field of unmanned aerial vehicle control, and particularly relates to an unmanned aerial vehicle anti-interference safety control method and related products.
Background
This section is intended to provide a background or context for the embodiments recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Unmanned aerial vehicles are powered aircraft that do not carry a human operator, use aerodynamic forces to provide lift for the aircraft, and may fly autonomously or remotely pilot and carry a payload. The unmanned aerial vehicle field relates to sensor technology, communication technology, intelligent decision and control technology, flight dynamics technology and the like, and is a product with high technical content in the information age. In recent years, unmanned aerial vehicles are widely applied to military and civil fields such as investigation striking, damage evaluation, agricultural condition inspection, express delivery transportation, meteorological observation and the like by virtue of the advantages of low cost, high performance, compact body structure and the like.
Researchers at home and abroad can roughly categorize the research developed by unmanned aerial vehicle control into two categories, namely unmanned aerial vehicle task execution targets and meeting safety constraints. With respect to the study of task-performing targets, the unmanned aerial vehicle is designed to perform a specific task, such as flying at a specific speed along a preset ideal trajectory, by designing a control input signal. With respect to studies meeting safety constraints, control input signals are designed to ensure flight safety, such as to avoid the unmanned aerial vehicle approaching a hazardous area or colliding with dangerous objects. However, currently, there are few studies in which the task goal and the safety constraint of the unmanned aerial vehicle are considered simultaneously, and the difficulty is that only one controller is designed to process the task goal and the safety constraint simultaneously.
Some researchers turn the task objective and the safety constraint into a convergence problem (control error approaches zero) and a limitation problem (system state is limited in a preset set) of the same set of system states respectively by a problem transformation method aiming at specific task requirements, and then adopt a barrier lyapunov function (barrier Lyapunov function, BLF) or a preset performance control (prescribed performance control, PPC) method to limit the relevant states in a safety range and meet certain convergence characteristics so as to meet the task objective and the safety constraint of the unmanned aerial vehicle at the same time. However, the method for processing the task target and the security constraint based on the problem transformation is not universal, on one hand, different problem transformation steps are needed to be adopted for different tasks, and on the other hand, the problem transformation method is only suitable for the situation that the task target and the security constraint have no potential conflict.
To deal with the situation that there is a potential conflict between the mission objective and the safety constraint of the unmanned aerial vehicle, the related study firstly converts the mission objective and the safety constraint into a control lyapunov function (control Lyapunov functions, CLFs) and a control barrier function (control barrierfunctions, CBFs), respectively, then sets different priorities for the CLFs and the CBFs, and designs the controller based on quadratic programming (Quadratic Program, QP). However, related studies have reduced the robustness of CBFs during design in order to better mitigate potential conflicts between mission objectives and safety constraints, meaning that external disturbances that are not known in practical applications may violate the safety constraints of the system. In order to improve the anti-interference capability of the system, a part of researchers adopt an interference observer to estimate and compensate the influence of external interference on the system. The interference estimation method has two disadvantages when being applied to the CLF-CBF-QP controller, on one hand, in order to compensate the influence of the interference estimation error on the system, the upper bound of the interference or the interference change rate needs to be known in advance, and the condition is too severe in part of practical application; on the other hand, such methods require that a certain safety margin is left for the potential impact of disturbance estimation errors in the design of CBF and controller, which may lead to control performance becoming conservative to some extent, so-called "conservative" meaning that system safety is too focused to increase the difficulty of achieving the task objective.
Disclosure of Invention
The invention provides an unmanned aerial vehicle anti-interference safety control method and related products.
The invention provides the following technical scheme: an unmanned aerial vehicle anti-interference safety control method comprises the following steps: estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value;
and performing secondary planning according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, the control Lyapunov function and the control obstacle function to obtain a control input signal, wherein the safety set is divided into a working set and an early warning set, the control obstacle function is continuously micro, the value in the working set is greater than or equal to 0, the value in the boundary of the working set is equal to 0, the value in the early warning set is negative, and the position of a region which is not adjacent to the working set in the boundary of the early warning set is approaching to negative infinity.
Optionally, the control barrier function is as follows:
wherein M is a control obstacle function, x is an unmanned plane state vector, and k is 1 Is a constant value of a positive value,h (x) is continuously differentiable and satisfies: h (x) is greater than or equal to 0 when x belongs to the safety range, less than 0 when x does not belong to the safety range, and equal to 0 when x is at the boundary of the safety range.
Optionally, the control barrier function is as follows:
wherein M is a control obstacle function, x is an unmanned plane state vector, and k is 2 Is a constant value of a positive value,h (x) is continuously differentiable and satisfies: h (x) is greater than or equal to 0 when x belongs to the safety range, less than 0 when x does not belong to the safety range, and equal to 0 when x is at the boundary of the safety range.
Optionally, the interference vector estimate is determined according to the following formula:
wherein x is unmanned plane state vector, t is time, u is control input signal, l 1 And l 2 Are all positive values and constant values,for state vector estimation, +.>For interference vector estimation, +.>For the derivative of the state vector estimate with respect to time, -, etc.>The derivative of the interference vector estimate with respect to time.
Optionally, the unmanned aerial vehicle model is:
control constraints for safety in quadratic programming include:
wherein x is unmanned plane state vector, u is control input signal; t is time, f, g 1 And g 2 D (t) is an unknown external interference vector for a preset function satisfying the local Lipschitz characteristic,alpha is the estimated value of unknown interference vector 4 Expansion for setting->Class function, p > 1 is a constant, M is a control barrier function, xi= { x ε Int (S): M (x)<x is a subset of the pre-alarm set, < }>Is a constant value, which is set to be a constant value, I p The sign is calculated for the p-order norms and S is the safe set.
Optionally, the quadratic programming algorithm is:
wherein, is a relaxation term for reducing the formulaTo ensure the existence of a formula solution,is a positive definite matrix,/->Is a preset matrix, u is control input signal of m order, alpha 3 For setting +.>Class functions.
The invention provides the following technical scheme: an unmanned aerial vehicle anti-interference safety control device, comprising: the estimating module is used for estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value; the planning module is used for carrying out secondary planning according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, the control Lyapunov function and the control obstacle function to obtain a control input signal, wherein the safety set is divided into a working set and an early warning set, the control obstacle function is continuous and tiny, the value in the working set is greater than or equal to 0, the value in the boundary of the working set is equal to 0, the value in the early warning set is negative, and the position of a region which is not adjacent to the working set in the boundary of the early warning set is approaching to minus infinity.
The invention provides the following technical scheme: an unmanned aerial vehicle anti-interference safety control device, comprising: a memory storing instructions that are executed by the processor to perform the aforementioned method.
The invention provides the following technical scheme: an unmanned aerial vehicle, includes the aforesaid unmanned aerial vehicle anti-interference safety control device.
The invention provides the following technical scheme: a program product which when run on a processor performs the unmanned aerial vehicle tamper resistant safety control method described above.
According to the technical scheme provided by the invention, the unmanned aerial vehicle can finish task targets preferentially in a relatively safe area, and the system safety is ensured preferentially when the unmanned aerial vehicle is close to a dangerous area but still in a safe area. The technical scheme provided by the invention controls the adverse effect of the interference estimation error in a safety range, and improves the robustness of the control barrier function.
Drawings
Fig. 1 is a schematic flow chart of an anti-interference safety control method of an unmanned aerial vehicle.
Fig. 2 is a motion trajectory diagram of one example of the unmanned aerial vehicle tamper-resistant safety control method of the present invention.
FIG. 3 is a graph of the external interference versus the interference estimate of the example of FIG. 2 over time, where d x Representing the component of the external disturbance experienced by the drone in the x-axis,representation pair d x D y Representing the component of the external disturbance to which the unmanned aerial vehicle is subjected on the y-axis, +.>Representation pair d y Is used for the estimation of the estimated value of (a).
FIG. 4 is a graph of the ideal motion profile versus the actual motion profile of the example of FIG. 2, where x d Representing the component of the ideal motion trail of the unmanned aerial vehicle on the x-axis, x represents the component of the position of the unmanned aerial vehicle on the x-axis, y d Representing the component of the ideal motion trajectory of the drone on the y-axis, y representing the component of the position of the drone on the y-axis.
FIG. 5 is a graph of the value of h (p) over time for the example of FIG. 2, where h (p). Gtoreq.0 indicates that the operating conditions of the drone are safe.
Fig. 6 is a graph of the value of M (p) of the example of fig. 2 over time, where M (p) is a control obstacle function proposed by the present invention.
Fig. 7 is a block diagram of an anti-interference security control device for an unmanned aerial vehicle according to an embodiment of the present invention.
Fig. 8 is a block diagram of an anti-interference security control device for a drone according to another embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to specific examples, but the scope of the present invention is not limited thereto.
The invention has the following overall conception: the unmanned aerial vehicle system which is subject to unknown external disturbance and has potential conflict between a task target and safety constraint is considered, interference is estimated in real time first, and on the basis of an existing CLF-CBF-QP controller framework, a control barrier function design scheme is improved to remarkably enhance the robustness of the unmanned aerial vehicle system, so that any bounded unknown external disturbance is resisted. The method combines active disturbance rejection technology with passive disturbance rejection technology, wherein real-time estimation disturbance belongs to active disturbance rejection technology, and improvement of robustness of a control barrier function belongs to passive disturbance rejection technology. Meanwhile, the anti-interference safety control method provided by the invention can determine the priority of the task target and the safety constraint according to the safety degree of the unmanned aerial vehicle operation, so that the unmanned aerial vehicle can finish the task target preferentially in a relative safety area, and the safety of the system is ensured preferentially in a safety area close to a dangerous area.
The symbols are as follows:for real number set->Is a positive real number set, < >>Is a negative real set. The point above the variable in the formula represents the derivative with time.
The specialized vocabulary is described as follows:class function α (x), monotonically increasing in definition field [0, a ], α (0) =0; />Class function α (x), satisfying monotonically increasing, α (0) =0, in definition field [0, ] α (x) →infinity when x→infinity; expansion->The class function α (x) satisfies monotonically increasing in the definition field (- +_infinity, +_0), α (0) =0, α (x) - +_infinity, and α (x) - +_infinity.
The drone was modeled as follows:
wherein,and->The unmanned plane state vector and the control input signal are respectively f:>g 1 :and g 2 :/>Is a known function satisfying the local Lipschitz property,/and>represents the unknown external disturbance to which the unmanned aerial vehicle is subjected, which satisfies the local Lipschitz characteristic and is bounded, n, m and q being the dimensions of the vector space in which the system state, input and disturbance are located, respectively.
The unknown external disturbance causes include, for example, gusts.
Fig. 1 is a schematic flow chart of an anti-interference safety control method of an unmanned aerial vehicle. From a program point of view, the execution subject of the tamper resistant safety control method may be a computer program. From the viewpoint of the apparatus, the execution subject of the anti-interference security control method may be a processor on which these computer programs are loaded, or an application specific integrated circuit or a circuit board assembly or the like that runs the anti-interference security control method. The method specifically comprises the following steps.
And step 101, estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value.
The method for calculating the interference estimation value comprises the following steps:
wherein x is unmanned plane state vector, t is time, u is control input signal, l 1 And l 2 Are all positive values and constant values,for state vector estimation, +.>For interference vector estimation, +.>For the derivative of the state vector estimate with respect to time, -, etc.>The derivative of the interference vector estimate with respect to time.
In the controller design, interference estimation valuesIs used to complete the controller design instead of the disturbance.
That is, the derivative of the estimated value of the unmanned aerial vehicle state is obtained according to the unmanned aerial vehicle state x and the control input uAnd the derivative of the estimated value of the unknown disturbance +.>
Other methods of estimating the interference may be used by those skilled in the art to estimate the unknown external interference experienced by the drone.
Step 101 is an active immunity feedforward anti-interference security control method, eliminating the influence of a part of interference. Step 102 only needs to resist the adverse effects of interference estimation errors (which may be much smaller than the interference), thus making the control better.
Step 102, performing quadratic programming according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, the control Lyapunov function and the control obstacle function to obtain a control input signal, wherein the safety set is divided into a working set and an early warning set, the control obstacle function is continuous and tiny, the value in the working set is greater than or equal to 0, the value in the boundary of the working set is equal to 0, the value in the early warning set is negative, and the position of a region which is not adjacent to the working set in the boundary of the early warning set approaches to minus infinity.
The Lyapunov function, control Lyapunov function, abbreviated CLF, is controlled by Sontag, E.D., wang, Y.,1995.On characterizations of the input-to-state stability properties systems & Control Letters,24 (5): 351-359.
Selecting a continuous and slightly controllable Lyapunov function V:and V (x) →0 means that the mission objective of the unmanned aerial vehicle is achieved. V (x) is positively defined and radially unbounded, i.e. there is +.>Class function alpha 1 And alpha 2 The method meets the following conditions:
α 1 (||e||)≤V(x)≤α 2 (||e||)；
where e represents the error of the task objective, e=0 also means that the task objective of the unmanned aerial vehicle is achieved, and i represents the second order norm.
Selecting oneClass function alpha 3 Then a control input u (t) satisfying the following formula will cause the drone to be α 3 Is characterized by the fact that the task objective is achieved,
L f V(x)+L g1 V(x)u(t)+L g2 V(x)d(t)≤-α 3 (||e||),
wherein the method comprises the steps ofAnd->V (x) is V (x) to f (x), g respectively 1 (x) And g 2 (x) Li Daoshu (Lie derivative).
The left side of the above equation is equivalent toThen this formula will ensure/>Let V (x) follow->Class function alpha 3 I.e. the control error e approaches zero.
But given that d (t) (i.e., the disturbance) is unknown, the control input signal u (t) cannot be selected by the above formula. Using the interference estimate produced in step 101Instead of the disturbance d (t), one can obtain:
from Lyapunov theory, it is known that when the parameter l in the extended state observer 1 And l 2 When selected large enough, the interference estimation errorA control input u (t) satisfying the above formula will quickly converge into a tight enough set to enable the aircraft to better accomplish mission objectives.
The inequality that the control input signal that realizes the task goal needs to satisfy is introduced above, and the physical problem of unmanned aerial vehicle is converted into mathematical problem.
The following describes how to translate the safety constraints of the drone into an improved control obstacle function.
Assume that: the drone is secure at the initial moment.
Judging whether the unmanned aerial vehicle is safe or not according to the state of the unmanned aerial vehicle, and ensuring that all the states of the unmanned aerial vehicle form a safe setThat is to say if and only if the state vector of the unmanned aerial vehicle +.>At this time, the drone is secure. It is then obvious that the security constraints of the drone are equivalent to security sets +.>Is not denatured in the forward direction. Forward invariance refers to any ofSatisfy the following requirements
Constructing a continuously differentiable function h:security set->The following relationship is satisfied:
wherein,and->The sub-table indicates->Is defined between the boundary and the interior of the container.
The physical meaning of the function h (x) is the safety degree of the unmanned aerial vehicle, wherein h (x) is equal to or greater than 0, and the unmanned aerial vehicle is safe. In the first equationRepresenting a set of states that can ensure safe operation of the unmanned aerial vehicle +.>Representation set->Boundary of (2), third equation>Representation set->Is provided.
The controller is designed to ensure the safety of the unmanned aerial vehicle when h (x) is more than 0. The literature Ames, a.d., xu, x., grizzle, j.w., taboada, p.,2017.Control barrier function basedquadratic programs for safety critical systems.IEEE Transactions onAutomatic Control,62 (8): 3861-3876, states that when the unmanned aerial vehicle is not disturbed by the outside world, the following is trueIt is ensured that h (x) > 0. However, when the unmanned aerial vehicle is subject to unknown external interference, the +.>If the controller is designed to ignore external interference, the controller cannot ensure +.>Control barrierAnother technical advantage of the barrier function CBF over the barrier lyapunov function BLF and the preset performance control PPC is that the state x (t) is allowed to approach the security set boundary at a certain rate, thereby alleviating the contradiction between task goals and security constraints, but such a design would inherently conflict with the immunity capability of the system.
The invention integrates the safety according to the safety degree of the unmanned aerial vehicleThe unmanned aerial vehicle is divided into two subsets, and then the controller is designed to enable the unmanned aerial vehicle to display different control effects in the two subsets. Specifically, will->The relatively safe area in (1) is set as the working set +.>The main task of the unmanned aerial vehicle in the set is to alleviate the potential conflict between the task target and the safety constraint, namely, the unmanned aerial vehicle is allowed to move towards an unsafe direction at a preset speed (but always in the safety set S) to realize the task target; will collect->Relative to the collection->The complement of (2) is defined as the early warning set +.>The main task of the drone in this set C is to combat any bounded external disturbances and to ensure a safe set +.>Is not denatured in the forward direction.
At the time of selecting working setAnd early warning set->In order to make the task objective better completed, or in order to make it more likely to be completed, we would want the working set +.>The regions are selected to be larger, preferably to be the working set except for the regions near the security set boundary. But->Is selected to be too close to->Another problem arises in that increasing the stiffness of the differential equation as the drone state x approaches the system boundary leads to a sudden increase in the demand for the drone to control frequency and sensor accuracy, resulting in greater resource loss. Thus, in selecting working set +.>And early warning set->Both factors need to be considered simultaneously.
According to working setAnd early warning set->Is constructed as a continuous microcompact function M:>and satisfies the following:
where x is the state vector of the drone. The function M is a way of characterizing the degree of security of the drone,representation->Boundary of collection->Representing early warning set->And work set->Non-adjacent boundaries.
M (x) can be constructed by means of an existing h (x) function. A specific construction method is given below, but it should be noted that only an exemplary method is provided.
First, a small enough positive real number is selectedTo ensure area +.>Connect and get close enough to the boundary of the security set +.>And then M (x) is selected as follows, so that the requirement of a formula can be met:
the control barrier function is as follows:
wherein M is a control obstacle function, x is an unmanned plane state vector, and k is 1 Is a constant value of a positive value,and is constant, h (x) is continuously differentiable and satisfies: h (x) is greater than or equal to 0 when x belongs to the safety range, less than 0 when x does not belong to the safety range, and equal to 0 when x is at the boundary of the safety range.
M (x) is in working setInternal non-negative, 0 at the boundary of working set W, 0 at early warning set +.>The inner is negative and when +.>(i.e. in the early warning set C and the working set +.>Non-adjacent boundaries), M (x) → - ≡infinity.
Then a control input u (t) that satisfies the following will cause the drone to satisfy the security constraints and be in the collectionAnd->The inner exhibits the desired properties:
wherein alpha is 4 Is an extension ofClass functions. /> Andm (x) is respectively equal to f (x) and g 1 (x) And g 2 (x) Li Daoshu of (2).
The left side of the formula is virtually equal to the derivative of M (x) with respect to time, and the right side of the formula is the characteristic that M (x) is expected to satisfy. In particular, for Meaning that the value of M (x) is allowed to be +.>Class function alpha 4 The prescribed characteristics decrease, which will help mitigate potential conflicts between the unmanned mission objectives and the security constraints; for-> Meaning that the value of M (x) will not decrease, thereby ensuring that x does not exceed +.>For-> Meaning that the value of M (x) will increase, making the drone trend towards a safer state; for->And->When, -alpha 4 (M) → ++ infinity the process comprises, at this timeThe value of M (x) will be increased drastically, so that the drone will quickly go to a safer state.
Perhaps it is believed that x does not exceedTherefore->Is not useful. It is worth noting, however, that when an interference estimate is used instead of interference, the above formula is no longer accurate to the left +.>There is therefore a need for an improved control of the obstacle function M at +.>Is robust against interference estimation errors.
However, considering that d (t) in the above formula is unknown, the control input signal u (t) cannot be selected by the formula. Interference estimation using the extended state observer of the first stepInstead of the disturbance d (t), it can be derived from the above formula:
in order to make the constructed improved control obstacle function M (x) (which is still the control obstacle function in nature) robust enough to resist any unknown external disturbances that are bounded and meet the local Lipschitz characteristics, the expansion is madeClass function a 4 The following conditions should also be satisfied:
where p > 1 is a constant and,is an early warning set->Is selected from the group consisting of,is a constant value, which is set to be a constant value, I p The sign is calculated for the p-order norm.
In other embodiments, the control barrier function is as follows:
wherein M is a control obstacle function, x is an unmanned plane state vector, and k is 2 Is a constant value of a positive value,and is constant, h (x) is continuously differentiable and satisfies: h (x) is greater than or equal to 0 when x belongs to the safety range, less than 0 when x does not belong to the safety range, and equal to 0 when x is at the boundary of the safety range. The control obstacle function can achieve the same technical effect.
From the above analysis it can be determined that: satisfy the formulaAndthe control signals of the unmanned aerial vehicle are respectively used for realizing the task target and the safety constraint of the unmanned aerial vehicle. This means that the control signals of the above two formulas are satisfied simultaneously, and the task objective and the safety constraint can be achieved simultaneously. If there is no control input signal that satisfies both formulas, then the safety constraint is preferably ensured. For this purpose, the two formulas above are combined to obtain the final controller using the following quadratic programming method.
Wherein x is unmanned plane state vector, u is control input signal; t is time, f, g 1 And g 2 D (t) is an unknown external interference vector for a preset function satisfying the local Lipschitz characteristic,alpha is the estimated value of unknown interference vector 4 Expansion for setting->Class function-> Is a relaxation term for reducing the formulaTo ensure the existence of a formula solution,is a positive definite matrix,/->Is a preset matrix, V is a control Lyapunov function, M is a control barrier function, < ->Is a subset of the pre-alarm set, +.>Is a constant value, which is set to be a constant value, I p The sign is calculated for the p-order norms and S is the safe set.
Compared with the existing related achievements, the invention has stronger anti-interference capability by improving the control obstacle function. Specifically, the method can cope with any unknown external interference which is bounded and meets the local Lipschitz characteristic, and ensure the safe operation of the unmanned aerial vehicle.
The unmanned aerial vehicle anti-interference safety control method provided by the invention adopts the active anti-interference technology and the passive anti-interference technology (improved control barrier function), so that the anti-interference capability of the system is stronger.
The unmanned aerial vehicle anti-interference safety control method provided by the invention adopts an improved control obstacle function, so that the conflict between a task target and safety constraint can be better processed. In particular, task targets are preferentially executed in a relatively safe area, and safety constraints are preferentially ensured near a safety range boundary.
The following provides an implementation case of unmanned aerial vehicle safety tracking control, and introduces the design and operation process of an unmanned aerial vehicle anti-interference safety control method.
These unmanned aerial vehicles were modeled as:
wherein p (t) = [ x (t), y (t)] T Sum u (t) = [ u ] x (t),u y (t)] T Respectively representing the flying position and the flying speed of the unmanned aerial vehicle under the global coordinate system, and d (t) = [ d ] x (t),d y (t)] T And the external interference suffered by the unmanned aerial vehicle is indicated.
The task target of the unmanned aerial vehicle is to track an ideal track p d (t)＝[x d (t),y d (t)] T The safety constraint is to fly within a safety range, which is a circular area with (0, 0) as the center and R as the radius.
The first step: the following extended state observer is designed for estimating unknown external interference suffered by the unmanned aerial vehicle.
And a second step of: selecting and controlling the Lyapunov function as follows:
wherein e (t) =p (t) -p d And (t) represents the tracking error of the unmanned aerial vehicle on the ideal track. The control input signal u (t) should satisfy:
wherein the method comprises the steps ofIs a constant.
And a third step of: the safety set and the working set are respectively selected as:
wherein the method comprises the steps ofIs a constant, satisfying R < R.
Selecting a function h (p) for measuring the safety degree of the unmanned aerial vehicle as:
h(p)＝R 2 -p(t) T p(t)。
based on the h (x) function, constructing the improved control barrier function M (x) as
Wherein the method comprises the steps ofIs a constant. The control input signal u (t) should satisfy:
wherein the method comprises the steps ofIs a constant.
Fourth step: controller construction using quadratic programming method
In order to verify the effectiveness of the unmanned aerial vehicle safety anti-interference safety control method provided by the invention, simulation experiments are carried out in Matlab/Simulink aiming at the implementation cases. The main simulation procedure is as follows.
(1) Parameter setting
Ideal flight trajectory of unmanned aerial vehicle:
/>
external interference suffered by unmanned aerial vehicle:
environmental parameters and initial state of unmanned aerial vehicle
| Parameters (parameters) | Parameter value | Parameters (parameters) | Parameter value |
| Radius of safety range R | 3 (Rice) | Radius of working range r | 2.5 (Rice) |
| Initial position of x-axis | 1 (Rice) | Initial position of y-axis | 2.5 (Rice) |
| Initial x-axis velocity | 0 (m/s) | Initial velocity of y-axis | 0 (m/s) |
Parameters of controller and extended state observer
Analysis of results
The simulation results are shown in fig. 2 to 6.
The analysis result shows that in the proposed unmanned aerial vehicle safety tracking control framework, the unmanned aerial vehicle safety anti-interference safety control method based on the extended state observer and the improved control obstacle function can well resist unknown external interference and coordinate the conflict between the tracking task and the safety constraint. Fig. 2 shows a corresponding flight trajectory diagram, wherein the hatched area represents the working set. Fig. 3 compares the estimated value of the unknown external interference and the interference from the extended state observer to the unmanned plane. Fig. 4 compares the ideal flight trajectory of the drone with the actual flight trajectory, wherein the trajectory tracking task of the drone within the shadow area (after 30 seconds) conflicts with the safety constraints. Fig. 5 shows the trend of the function h (p) over time, which is used to represent the degree of safety of the drone, where h (p) > 0 represents safety. Fig. 6 shows the trend of the improved control barrier function M (p) over time.
According to fig. 2 and 3, it can be seen that the unmanned aerial vehicle can better complete the track tracking task when the ideal track is in the working set; when the ideal track is outside the working set, the unmanned aerial vehicle can be ensured to fly in the safety set all the time. As can be seen from fig. 2, the extended state observer can well estimate the unknown external interference suffered by the unmanned aerial vehicle. As can be seen from fig. 5 and 6, the drone is always safe during operation.
Based on the same inventive concept, referring to fig. 7, an embodiment of the present invention further provides an anti-interference security control device for an unmanned aerial vehicle, including: the estimating module 1 is used for estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value; the planning module 2 is configured to perform quadratic programming according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, the control lyapunov function, and the control obstacle function, so as to obtain a control input signal, where the safety set is divided into a working set and an early warning set, the control obstacle function is continuously and slightly available, the value in the working set is greater than or equal to 0, the value in the boundary of the working set is equal to 0, the value in the early warning set is negative, and the area, which is not adjacent to the working set, in the boundary of the early warning set approaches to minus infinity.
The above modules may be implemented by software, by hardware, or by a combination of software and hardware.
Based on the same inventive concept, referring to fig. 8, an embodiment of the present invention further provides an anti-interference security control device for an unmanned aerial vehicle, including: a memory storing instructions that are executed by the processor to perform the aforementioned method.
Based on the same inventive concept, the embodiment of the invention also provides an unmanned aerial vehicle, which comprises the unmanned aerial vehicle anti-interference safety control device.
Based on the same inventive concept, embodiments of the present invention also provide a program product that, when run on a processor, performs the aforementioned unmanned aerial vehicle tamper-resistant safety control method.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts of the embodiments are all referred to each other, and each embodiment is mainly described in the differences from the other embodiments.
The scope of the present invention is not limited to the above-described embodiments, and it is apparent that various modifications and variations can be made to the present invention by those skilled in the art without departing from the scope and spirit of the invention. It is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The unmanned aerial vehicle anti-interference safety control method is characterized by comprising the following steps of:
estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value;
performing secondary planning according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, a control Lyapunov function and a control barrier function to obtain a control input signal, wherein a safety set is divided into a working set and an early warning set, the control barrier function is continuously and slightly controllable, the value of the control barrier function in the working set is greater than or equal to 0, the value of the control barrier function in the boundary of the working set is equal to 0, the value of the control barrier function in the early warning set is negative, and the value of the control barrier function in the region which is not adjacent to the working set in the boundary of the early warning set approaches to minus infinity;
wherein the control obstacle function can be expressed as a function of a function h (x), the physical meaning of h (x) being the degree of safety of the unmanned aerial vehicle, wherein h (x) 0 represents that the unmanned aerial vehicle is safe;
h (x) is continuously differentiable and satisfies: h (x) is greater than or equal to 0 when x belongs to the safety range, less than 0 when x does not belong to the safety range, and equal to 0 when x is at the boundary of the safety range.
2. The method of claim 1, wherein the control barrier function is as follows:
wherein M is a control obstacle function, x is an unmanned plane state vector, and k is 1 Is a constant value of a positive value,is constant.
3. The method of claim 1, wherein the control barrier function is as follows:
wherein M is a control obstacle function, x is an unmanned plane state vector, and k is 2 Is a constant value of a positive value,is constant.
4. The method of claim 1, wherein the interference vector estimate is determined according to the formula:
wherein x is unmanned plane state vector, t is time, u is control input signal, l 1 And l 2 Are all positive values and constant values,for state vector estimation, +.>For interference vector estimation, +.>For the derivative of the state vector estimate with respect to time, -, etc.>For the derivative of the interference vector estimate with respect to time, f:>g 1 :/>and g 2 :/>Is a known function that satisfies the local Lipschitz property.
5. The method of claim 1, wherein the unmanned aerial vehicle model is:
control constraints for safety in quadratic programming include:
wherein x is the unmanned plane stateVector u is the control input signal; t is time, f, g 1 And g 2 D (t) is an unknown external interference vector for a preset function satisfying the local Lipschitz characteristic,alpha is the estimated value of unknown interference vector 4 Expansion for setting->Class function, p > 1 is a constant, M is a control barrier function, xi= { x ε Int (S): M (x)<χ is a subset of the alert set, +.>Is a constant value, which is set to be a constant value, I p Calculating the sign for the p-order norms, S being the safe set,and->M (x) is respectively equal to f (x) and g 1 (x) And g 2 (x) Li Daoshu of (2).
6. The method of claim 5, wherein the step of determining the position of the probe is performed,
the quadratic programming algorithm is as follows:
wherein, is a relaxation term for reducing the formulaTo ensure the existence of a formula solution,is a positive definite matrix,/->Is a preset matrix, u is control input signal of m order, alpha 3 For setting +.>Class function-> Andv (x) is V (x) to f (x), g respectively 1 (x) And g 2 (x) Is a metal alloy of Li Daoshu of (c),and->M (x) is respectively equal to f (x) and g 1 (x) And g 2 (x) Li Daoshu of (2).
7. An unmanned aerial vehicle anti-interference safety control device, characterized by comprising:
the estimating module is used for estimating unknown external interference suffered by the unmanned aerial vehicle to obtain an interference vector estimated value;
the planning module is used for carrying out secondary planning according to the current flight state of the unmanned aerial vehicle, the interference vector estimated value, a control Lyapunov function and a control obstacle function to obtain a control input signal, wherein a safety set is divided into a working set and an early warning set, the control obstacle function is continuous and tiny, the value of the control obstacle function in the working set is greater than or equal to 0, the value of the control obstacle function in the boundary of the working set is equal to 0, the value of the control obstacle function in the early warning set is negative, and the value of the control obstacle function in the region which is not adjacent to the working set in the boundary of the early warning set approaches to minus infinity;
wherein the control obstacle function can be expressed as a function of a function h (x), the physical meaning of h (x) being the degree of safety of the unmanned aerial vehicle, wherein h (x) 0 represents that the unmanned aerial vehicle is safe;
h (x) is continuously differentiable and satisfies: h (x) is greater than or equal to 0 when x belongs to the safety range, less than 0 when x does not belong to the safety range, and equal to 0 when x is at the boundary of the safety range.
8. An unmanned aerial vehicle tamper resistant safety control device comprising a memory and a processor, the memory storing instructions, the processor executing the instructions to perform the method of any one of claims 1 to 6.
9. A drone comprising the drone anti-jamming safety control device of claim 7 or 8.
10. A program product, characterized in that it when run on a processor performs the unmanned aerial vehicle tamper-resistant safety control method of any of claims 1 to 6.
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