CN115248601A - Trajectory planning control method and system for underwater operation robot - Google Patents
Trajectory planning control method and system for underwater operation robot Download PDFInfo
- Publication number
- CN115248601A CN115248601A CN202210451482.7A CN202210451482A CN115248601A CN 115248601 A CN115248601 A CN 115248601A CN 202210451482 A CN202210451482 A CN 202210451482A CN 115248601 A CN115248601 A CN 115248601A
- Authority
- CN
- China
- Prior art keywords
- control
- displacement
- fuzzy
- controller
- underwater
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course or altitude of land, water, air, or space vehicles, e.g. automatic pilot
- G05D1/04—Control of altitude or depth
- G05D1/06—Rate of change of altitude or depth
- G05D1/0692—Rate of change of altitude or depth specially adapted for under-water vehicles
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P.I., P.I.D.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Aviation & Aerospace Engineering (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Feedback Control In General (AREA)
Abstract
The invention discloses a trajectory planning control method of an underwater operation robot, which comprises the following steps: performing transition processing on the displacement set value through a tracking differentiator; calculating displacement deviation amount and deviation change rate according to real-time displacement data of the underwater operation robot and a displacement set value obtained through transition processing, inputting the displacement deviation amount and the deviation change rate into a fuzzy controller, and obtaining an adjusting quantity output by the fuzzy controller and used for controlling parameters of a fractional order PID controller; and performing online real-time adjustment on the control parameters of the fractional order PID controller according to the adjustment quantity of the control parameters, so that the fractional order PID controller performs control operation according to the displacement deviation quantity based on the real-time control parameters to obtain the control quantity for controlling the action of the underwater robot. According to the invention, aiming at the characteristics of hysteresis, time-varying property, strong coupling property, nonlinearity and the like of the underwater robot system, the fuzzy control technology is combined with the fractional order controller, so that the state tracking property and robustness of the control process can be improved, and the steady-state error is reduced.
Description
Technical Field
The invention relates to the technical field of robots, in particular to a trajectory planning control method and system for an underwater operation robot.
Background
In the face of unknown oceans, mankind invented underwater robots to explore and understand the underwater environment. Underwater robots are classified into Autonomous Underwater Vehicles (AUVs) and remote underwater vehicles (ROVs). The remote control underwater vehicle mainly has the functions of marine resource exploration, marine pipeline detection, underwater mineral or biological sample sampling, underwater unknown area target observation and the like. However, since the underwater environment in which the underwater robot operates is highly complex and time-varying, and the underwater robot system itself is uncertain due to environmental variations, these will affect the stability and reliability of the underwater robot system.
Several methods have been proposed for the accuracy and stability of current underwater robots performing underwater special operations. The method comprises the following steps: controlling the underwater robot based on a fuzzy PID controller; controlling the underwater robot based on the fuzzy PID and the dynamic compensation; sliding Mode Control (SMC) has also been successfully applied to the control of underwater robots due to its low accuracy of the model, its uncertainty in parameters and its insensitivity to external disturbances.
In all the above researches, the proposed controller is based on the integral calculus of the controller, and the practical results show that the problems of overlarge steady-state error, low precision and the like of position tracking exist.
Disclosure of Invention
Considering that the fractional order controller can simultaneously ensure the quick dynamic response and the robustness of the system, the fuzzy control can express the control experience and knowledge of an operator or an expert as the control rule of the language variable description, and is particularly suitable for the control of a complex nonlinear system with an unknown mathematical model.
The invention aims to provide a method and a system for controlling a track planning of an underwater operation robot, aiming at the characteristics of hysteresis, time-varying property, strong coupling property, nonlinearity and the like of an underwater robot system, a fuzzy control technology is combined with a fractional order controller, the state tracking property and robustness of a control process are improved, and a steady-state error is reduced. The technical scheme adopted by the invention is as follows.
In one aspect, the present invention provides a trajectory planning control method for an underwater operation robot, including:
acquiring real-time displacement data and a given displacement set value of the underwater operation robot;
performing transition processing on the displacement set value through a tracking differentiator;
calculating the displacement deviation amount and the deviation change rate of the underwater operation robot according to the real-time displacement data and the displacement set value obtained through transition processing;
inputting the displacement deviation amount and the deviation change rate into a fuzzy controller to obtain the adjustment amount of the control parameter of the fractional order PID controller, which is output by the fuzzy controller;
and performing online real-time adjustment on the control parameters of the fractional order PID controller according to the adjustment quantity of the control parameters, so that the fractional order PID controller performs control operation according to the displacement deviation quantity based on the real-time control parameters to obtain the control quantity for controlling the action of the underwater robot.
Optionally, the method of the present invention obtains the real-time displacements of the centroid of the underwater robot in the X, Y, Z triaxial direction, and respectively uses the real-time displacements in the X, Y, Z triaxial direction for calculating the control quantity of the underwater robot. That is, corresponding to the three-axis direction of X, Y, Z, the method of the present invention needs to obtain a control amount respectively. Of course for the case of multipoint control, the control principle of each point refers to the control principle of the centroid point.
Optionally, the formula for calculating the displacement deviation e and the deviation change rate ec of the underwater operation robot is as follows:
e k ＝x rk -x k
ec k ＝e k -e k-1
in the formula, e k And ec k Indicating the amount and rate of change of displacement at time k, x rk And x k Respectively setting values of the real-time displacement at the moment k and the displacement at the moment k after the transitional processing of the tracking differentiator.
Optionally, the fuzzy controller is in a two-input five-output form, the input variables are e and ec, and the output variables include control parameter adjustment amount Δ K corresponding to each control parameter in the fractional order controller P 、ΔK i 、ΔK d 、Δμ、Δλ；
The basic domain value of the input variable is e, ec belongs to [ -10,10], the fuzzy subset is { NB, NM, NS, ZO, PS, PM, PB }, and the corresponding basic domain value is divided into: { [ -10, -7.5], [ -7.5, -5], [ -5, -2.5], [ -2.5,0], [0,2.5], [2.5,5, 5], [5.5,7.5], [7.5,10] };
the basic discourse domain value of the control parameter regulating quantity is as follows:
△K p ∈[-1,1]、△K i ∈[-0.75,0.75]、△K d ∈[-8,8]、△μ∈[0,1.8]、△λ∈[0,1.4]；
ΔK P 、ΔK i 、ΔK d the fuzzy subset of Δ μ, Δ λ is { NB, NM, NS, ZO, PS, PM, PB }.
Optionally, the obtaining, by the fuzzy controller, an adjustment amount of a control parameter of the fractional order PID controller based on the displacement deviation amount and the deviation change rate includes:
fuzzification processing is carried out on the input displacement deviation amount and the deviation change rate, and a fuzzy subset corresponding to the displacement deviation amount and the deviation change rate is determined;
according to the displacement deviation amount and the fuzzy subset corresponding to the deviation change rate, according to a preset fuzzy control rule, reasoning and determining delta K P 、ΔK i 、ΔK d Control parameter adjustment values of ambiguity domains of Δ μ, Δ λ;
for Δ K P 、ΔK i 、ΔK d And the control parameter regulating quantity values of the fuzzy domain of delta mu and delta lambda are subjected to deblurring to obtain the control parameter regulating quantity which is output to the fractional order controller.
Optionally, the preset fuzzy control rule includes Δ K shown in table 1 P 、ΔK i 、ΔK d Control rules and Δ μ, Δ λ control rules shown in table 2:
table 1:
table 2:
optionally, the pair Δ K P 、ΔK i 、ΔK d And the control parameter regulating values of the fuzzy domains of delta mu and delta lambda are subjected to de-fuzzy, and the control parameter regulating values of the fuzzy domains and the corresponding membership values are substituted into an area barycentric method formula by adopting an area barycentric method:
in the formula, v 0 ＝{△K p ，△K i ，△K d Delta mu and delta lambda, which are the precise values of the control parameter regulating quantity after the ambiguity resolution; v. of k ＝{△K pk ，△K ik ，△K dk ，△μ k ，△λ k J, control adjustment magnitude for the fuzzy domain, μ v (v k ) Is v is k A membership value of; and m is the number of output fuzzy subsets. And the area gravity center method is used for forming a figure area gravity center point by enclosing the abscissa and the membership function curve as a final output value of defuzzification control.
Optionally, e, ec, Δ K p 、△K i 、△K d Gauss membership functions with smooth curves are respectively selected for the membership functions of delta mu and delta lambda. The stability and the sensitivity of the control system can be improved, so that the underwater robot control system has better control precision and stability.
In a second aspect, the present invention provides a trajectory planning control system for an underwater operation robot, comprising:
the displacement tracking module is configured for acquiring real-time displacement data of the underwater operation robot and a given displacement set value;
the set value transition processing module is configured for performing transition processing on the displacement set value through a tracking differentiator;
the deviation calculation module is configured for calculating the displacement deviation amount and the deviation change rate of the underwater operation robot according to the real-time displacement data and the displacement set value obtained through the transition processing;
the fuzzy control module is configured for inputting the displacement deviation amount and the deviation change rate into the fuzzy controller to obtain the adjustment quantity of the control parameters of the fractional order PID controller output by the fuzzy controller;
and the fractional order control module is configured for carrying out online real-time regulation on the control parameters of the fractional order PID controller according to the regulating quantity of the control parameters, so that the fractional order PID controller carries out control operation according to the displacement deviation quantity based on the real-time control parameters to obtain the control quantity for controlling the action of the underwater robot.
In a third aspect, the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the trajectory planning control method for an underwater work robot according to the first aspect.
Advantageous effects
Compared with the prior art, the invention has the following advantages and progresses:
the advantages of fractional calculus and fuzzy logic theory are combined, the control effect of the controller is improved, the underwater robot trajectory control system has strong speed tracking performance and robustness, and the controller has faster and more stable response;
by adopting the fractional order controller, more adjustable parameters are provided, the corresponding control range is enlarged, and the flexibility and the accuracy of control are greatly improved;
the integral order lambda and the differential order mu in the fractional order controller can be adjusted on line through a fuzzy control rule, the traditional definition mode of a fractional order operator is eliminated, and a new control parameter setting method is realized by combining the application state of an actual underwater robot and is superior to an experience setting method;
the fractional order fuzzy PID control method acts on the underwater robot, and has great significance for improving the accuracy and stability of the underwater robot during operation.
Drawings
Fig. 1 is a schematic diagram of a controller applied to an underwater robot according to the method of the present invention;
FIG. 2 is a schematic diagram illustrating a trajectory planning control method of an underwater operation robot according to the present invention;
FIG. 3 is a graph of the Gaussian membership function for e and ec;
FIG. 4 shows an X-axis displacement diagram under three controllers PID, fuzzy FOPID;
FIG. 5 shows a Y-axis displacement diagram under three controllers PID, fuzzy FOPID.
Detailed Description
The following further description is made in conjunction with the accompanying drawings and the specific embodiments.
The technical conception of the invention is as follows: considering that the fractional order controller can simultaneously ensure the quick dynamic response and the robustness of the system, the fuzzy control can express the control experience and knowledge of an operator or an expert as control rules described by language variables, and then the rules are used for controlling the robot system. And the fuzzy control is particularly suitable for the control of a complex nonlinear system with unknown mathematical model and can support various control parameters.
Therefore, the method combines the fractional order controller technology and the fuzzy control technology, is applied to the track planning control of the underwater robot, dynamically adjusts the control parameters of the fractional order control stage based on the displacement deviation by utilizing the fuzzy control, realizes the on-line setting of the fractional order controller, and improves the control effect of the controller.
Example 1
The trajectory planning control method of the underwater operation robot comprises the following steps:
acquiring real-time displacement data and a given displacement set value of the underwater operation robot;
performing transition processing on the displacement set value through a tracking differentiator;
calculating the displacement deviation amount and the deviation change rate of the underwater operation robot according to the real-time displacement data and the displacement set value obtained by the transition processing;
inputting the displacement deviation amount and the deviation change rate into a fuzzy controller to obtain the adjustment amount of the control parameter of the fractional order PID controller, which is output by the fuzzy controller;
and performing online real-time adjustment on the control parameters of the fractional order PID controller according to the adjustment quantity of the control parameters, so that the fractional order PID controller performs control operation according to the displacement deviation quantity based on the real-time control parameters to obtain the control quantity for controlling the action of the underwater robot.
The principle of the above control method is shown with reference to fig. 2. The specific implementation of the present invention is described below.
1. Underwater robot control system modeling
In order to realize the control of the underwater robot, firstly, a power system of the underwater robot needs to be modeled, and referring to fig. 1, the whole framework of the underwater robot track planning control system consists of a propelling force and torque subsystem, an environmental force fidelity subsystem, a 6-degree-of-freedom euler angle system, a sensor information fusion subsystem and a controller. Wherein, the input of the subsystem of the propelling force and the torque is the control quantity output of the control method of the application.
The control command corresponding to the control quantity obtained by the track control method is converted into a PWM wave signal provided for a motor in the subsystem, so that the displacement control of the underwater robot is realized. A motor model can be established according to data provided by the T100-200 propeller, and the PWM waves are converted into actual thrust output by the motor model.
The environmental force fidelity subsystem generates the main environmental parameters of gravity, buoyancy, resistance and the like of the real system. The design of the vivid system is ingenious, the buoyancy and resistance selection is not fixed values, and the buoyancy and resistance selection can be changed according to the actual state of the robot body, such as the inclination of the robot body, the buoyancy coefficient, the resistance coefficient and the like.
The function of the sensor information fusion subsystem is to return the position, velocity, angle and angular velocity signals of the output of the conventional sensor to the controller. It contains an internal filter consisting of six low-pass filters. Its function is to reduce high frequency noise and obtain the actual desired position signal.
2. Displacement tracking calculation
For the displacement of the underwater robot in the three-axis direction, the invention can adopt three fractional order fuzzy controllers to respectively control three-axis position signals, and the displacement control in one axis direction is taken as an example for description.
Referring to fig. 2, first, given a displacement set value, considering that a sudden rise of the set value is likely to cause overshoot, in order to solve the contradiction between the rapidity of the system and the overshoot, the present embodiment performs a transition process on the initial target value by a tracking differentiator. The principle formula of the discretized tracking differentiator TD is as follows:
where h is the integration step, h 0 For new variables independent of h, there is some filtering effect on noise, k is the kth sampling time, x 1 (k) For the transition of the set value of the displacement, x 2 (k) For the differential of the transition, fhan (x) 1 (k)-x d (k),x 2 (k) R, h 0) is a nonlinear function whose expression is:
wherein r is a speed factor, and the larger the value of r is, the faster the speed of approaching the target value is within the bearing capacity of the controlled object. a and d are defined by the following formulas:
from the above, TD has three parameters to be adjusted, r, h 0 . Wherein r determines the tracking speed of the TD tracking signal, and the larger r, the faster the system tracking speed, but the filtering effect will also be worsened. In order to suppress system overshoot, r < 1 is generally taken. Filter factor h 0 Usually, it takes 3h to 10h. The larger the integral step length h is, the stronger the filtering effect isBut will have some impact on the tracking performance of the signal. In the parameter adjusting process, r can be used for coarse adjustment of the TD tracking performance, and the parameter h can be used for fine adjustment. And the proper parameters are selected to ensure that the system obtains more gentle tracking performance and better filtering effect.
Subtracting the real-time displacement from the displacement set value after TD transition processing to obtain displacement deviation amount e and deviation change rate ec, wherein the formula is as follows:
e k ＝x rk -x k
ec k ＝e k -e k-1
in the formula, e k And ec k Indicating the amount and rate of change of displacement at time k, x rk And x k Respectively setting values of the real-time displacement at the moment k and the displacement at the moment k after the transitional processing of the tracking differentiator.
3. Fuzzy control
In this embodiment, according to the control requirement, the fuzzy controller is set to a two-input five-output form, the input variables are e and ec, and the output variables include the control parameter adjustment amount Δ K corresponding to each control parameter in the fractional order controller P 、ΔK i 、ΔK d Δ μ, Δ λ, for controller scaling factor K in fractional order controllers, respectively p The controller integral coefficient K i Differential coefficient K of controller d The integral order mu of the controller and the derivative order lambda of the controller.
3.1 blurring
Input variable fuzzy domain e, ec E [ -10,10]Output variable Δ K p ∈[-1,1]、△K i ∈[-0.75,0.75]、 △K d ∈[-8,8]、△μ∈[0,1.8]、△λ∈[0,1.4]，
Fuzzifying the two values of e and ec, determining the discourse domain corresponding to the fuzzy sets of e and ec, and dividing (-10, 10) into 8 parts of-10 to-7.5, -7.5 to-5, -5 to 2.5, -2.5 to 0,0 to 2.5, 2.5 to 5,5 to 7.5 and 7.5 to 10. We denote-7.5, -5, -2.5,0, 2.5,5,7.5 by NB, NM, NS, ZO, PS, PM, PB respectively, and similarly for Δ K P 、ΔK i 、ΔK d μ, λ are blurred, and their blur subsets are set as: NB (negative large), NM (negative median), NS (negative small), ZO (zero), PS (positive small), PM (positive median), PB (positive large).
The characteristics of membership function curves and the like can reflect the stability and sensitivity of the control system, and e, ec and delta K are used for ensuring that the underwater robot control system has better control precision and stability p 、△K i 、△K d And selecting Gaussian membership functions with smooth curves for the membership functions of delta mu and delta lambda respectively. e. The membership function for ec is chosen as shown in fig. 3.
3.2 fuzzy inference
Fuzzy reasoning is based on set fuzzy control rules, and the ideas based on the rules comprise:
in the initial stage of adjustment, in order to accelerate the dynamic response speed of the underwater robot system and enable the output quantity to track the reference input quantity quickly, a larger K should be adopted p (ii) a To prevent integrator saturation during the early stages of regulation, leading to large overshoot, K i Smaller values should be taken and even zero; to reduce or even avoid overshoot in the regulation of the initial underwater robotic system, K d Should take a larger value. In the middle stage of regulation, K is used for ensuring that the underwater robot system has small overshoot and has certain dynamic response speed p Should take a medium to small value; in order to avoid influencing the stability of the underwater robot system, the medium K is taken at the initial stage of adjustment i (ii) a In the middle stage of regulation, the underwater robot system is paired with K d Is relatively sensitive, so that in the middle of regulation K d Should remain unchanged and be taken relatively small. In the later period of regulation, in order to improve the control precision of the underwater robot, a larger K is required p (ii) a In order to eliminate steady state errors of the underwater robot system at the later stage of regulation, the integral action of the controller should be enhanced, K i Taking a larger value; in the later period of regulation, when the absolute value of ec is larger, it is necessary to take smaller K d So as to avoid oscillation and reduce the adjusting time;
small set lambda, large steady state error, and adjustable timeLong. The set lambda is large, the control precision is low, and the underwater robot system is unstable. In order to reduce the influence of lambda on the stability of the underwater robot system, lambda should be set to be finely adjusted within a small interval, the overall trend and K i And keeping consistent. The set mu is small, the overshoot is large, and the adjusting time is long. The set mu is large, the overshoot is reduced, and the adjusting time is shortened. However, if the value is too large, the underwater robot system adjustment time will increase, the oscillations will be very strong, and if severe the underwater robot system will be unstable, therefore the overall trend of the μ parameter tuning should be related to K d And the consistency is maintained.
From the above rule, the fuzzy control rule table of the present embodiment is as follows.
TABLE 1. DELTA.K P 、ΔK i 、ΔK d Control rule table
TABLE 2 lambda, mu control rule Table
From the above tables 1 and 2, according to the real-time e and ec fuzzy theory domain values, the Mamdani reasoning method is adopted to obtain the Delta K p 、△K i 、△K d Ambiguity domain values of Δ μ and Δ λ.
3.3 deblurring
In this embodiment, the area barycenter method is used to solve the ambiguity, and the control quantity value and the membership value of the ambiguity domain are substituted into the area barycenter method formula to obtain Δ K P 、ΔK i 、ΔK d Adjustment values for Δ μ, Δ λ:
in the formula, v 0 ＝{△K p ，△K i ，△K d Δ μ, Δ λ } after deblurringThe accurate value of the control parameter regulating quantity of (2); v. of k ＝{△K pk ，△K ik ，△K dk ，△μ k ，△λ k J, control adjustment magnitude for the fuzzy domain, μ v (v k ) Is v is k A membership value of; and m is the number of output fuzzy subsets.
4. Fractional order PID control
In a strict sense, fractional calculus should be called non-integer calculus, and integer calculus is a special case thereof, so that modeling using fractional calculus is more consistent with real-world system characteristics. Definition of
An operator for fractional calculus has the formula:
in the formula, alpha and tau are respectively an upper limit and a lower limit, alpha is a calculus order, and tau is an integral variable.
There are currently three types of fractional calculus definitions prevailing in the control area, namely the Riemann-Liouville (RL) definition, the Grunnold-Letnikow (GL) definition, and the Caputo definition. The three definition forms are shown in the following three formulas, wherein Γ (·) is a Gamma function.
The fractional order PID controller is generally expressed in the form of PI λ D μ It is proportional toThe order PID is increased by two adjustable parameters λ and μ, an integral order and a differential order, respectively.
The expression of the fractional order controller in the embodiment is the last one, and the control quantity output of the controller is expressed as:
wherein S λ As integral operator, S μ For differential operators, controlling the parameter K p 、K i 、K d And lambda and mu are obtained by respectively counting the adjustment quantity obtained by fuzzy control on the original basis.
And (3) outputting the u (t) after the parameters are adjusted to act on a propelling force and torque subsystem of the underwater robot, so that the track control of the underwater robot with higher robustness and stability can be realized.
When the method of the present embodiment is applied to practice, the control effect is shown in fig. 4-5, and the displacement response index is shown in table 3. It can be seen that the fuzzy fractional order PID algorithm is fast in response and convergence, and obviously small in overshoot, so that a very effective improvement effect is achieved.
Table 3: the displacement response indexes of the three controllers to position changes in different directions.
Example 2
The present embodiment introduces a trajectory planning control system for an underwater operation robot based on the same inventive concept as embodiment 1, and as shown in fig. 2, the trajectory planning control system includes:
the displacement tracking module is configured for acquiring real-time displacement data and a given displacement set value of the underwater operation robot;
the set value transition processing module is configured for performing transition processing on the displacement set value through a tracking differentiator;
the deviation calculation module is configured for calculating the displacement deviation amount and the deviation change rate of the underwater operation robot according to the real-time displacement data and the displacement set value obtained through the transition processing;
the fuzzy control module is configured for inputting the displacement deviation amount and the deviation change rate into the fuzzy controller to obtain the adjustment quantity of the control parameters of the fractional order PID controller output by the fuzzy controller;
and the fractional order control module is configured for carrying out online real-time regulation on the control parameters of the fractional order PID controller according to the regulating quantity of the control parameters, so that the fractional order PID controller carries out control operation according to the displacement deviation quantity based on the real-time control parameters to obtain the control quantity for controlling the action of the underwater robot.
The specific function implementation of each functional module refers to the relevant content in the method in embodiment 1.
Example 3
This embodiment describes a computer-readable storage medium on which a computer program is stored which, when executed by a processor, implements the steps of the trajectory planning control method for an underwater work robot as described in embodiment 1.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 so forth) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (10)
1. A trajectory planning control method of an underwater operation robot is characterized by comprising the following steps:
acquiring real-time displacement data and a given displacement set value of the underwater operation robot;
performing transition processing on the displacement set value through a tracking differentiator;
calculating the displacement deviation amount and the deviation change rate of the underwater operation robot according to the real-time displacement data and the displacement set value obtained by the transition processing;
inputting the displacement deviation amount and the deviation change rate into a fuzzy controller to obtain the adjustment amount of the control parameter of the fractional order PID controller, which is output by the fuzzy controller;
and performing online real-time adjustment on the control parameter of the fractional order PID controller according to the adjustment component of the control parameter, so that the fractional order PID controller performs control operation according to the displacement deviation value based on the real-time control parameter to obtain the control quantity for controlling the action of the underwater robot.
2. The method as claimed in claim 1, wherein the real-time displacements of the center of mass of the underwater robot in X, Y, Z triaxial directions are respectively obtained and are respectively used for calculating the control quantity of the underwater robot in X, Y, Z triaxial directions.
3. The method according to claim 1, wherein the formula for calculating the displacement deviation e and the deviation change rate ec of the underwater operation robot is as follows:
e k ＝x rk -x k
ec k ＝e k -e k-1
in the formula, e k And ec k Indicating the amount and rate of change of displacement at time k, x rk And x k Respectively setting values of the real-time displacement at the moment k and the displacement at the moment k after the transitional processing of the tracking differentiator.
4. The method of claim 1, wherein the fuzzy controller is in a two-input five-output form, the input variables are a displacement deviation amount e and a deviation change rate ec, and the output variables include a control parameter adjustment amount Δ K corresponding to each control parameter in the fractional order controller P 、ΔK i 、ΔK d 、Δμ、Δλ；
The basic universe of discourse of the input variable is taken as e, ec is [ -10,10], the fuzzy subset is { NB, NM, NS, ZO, PS, PM, PB }, and the corresponding basic universe of discourse is divided into: { [ -10, -7.5], [ -7.5, -5], [ -5, -2.5], [ -2.5,0], [0,2.5], [2.5,5, 5], [5.5,7.5], [7.5,10] };
the basic discourse domain values of the control parameter regulating quantity are as follows:
△K p ∈[-1,1]、△K i ∈[-0.75,0.75]、△K d ∈[-8,8]、△μ∈[0,1.8]、△λ∈[0,1.4]；
ΔK P 、ΔK i 、ΔK d the fuzzy subset of Δ μ, Δ λ is { NB, NM, NS, ZO, PS, PM, PB }.
5. The method of claim 1 or 4, wherein the fuzzy controller derives an adjustment to a control parameter of the fractional order PID controller based on the displacement deviation amount and the deviation change rate, comprising:
fuzzification processing is carried out on the input displacement deviation amount and the deviation change rate, and a fuzzy subset corresponding to the displacement deviation amount and the deviation change rate is determined;
according to the displacement deviation amount and the fuzzy subset corresponding to the deviation change rate, according to the preset fuzzy control rule, reasoning and determining delta K P 、ΔK i 、ΔK d Adjusting values of control parameters of fuzzy domains of delta mu and delta lambda;
for Δ K P 、ΔK i 、ΔK d And the control parameter regulating quantity values of the fuzzy domain of delta mu and delta lambda are subjected to deblurring to obtain the control parameter regulating quantity which is output to the fractional order controller.
6. The method of claim 5, wherein the predetermined fuzzy control rule comprises Δ K as shown in Table 1 P 、ΔK i 、ΔK d Control rule and Δ μ, Δ λ control rules shown in table 2:
table 1:
table 2:
7. the method of claim 5, wherein said pair Δ K P 、ΔK i 、ΔK d And the control parameter regulating values of the fuzzy domains of delta mu and delta lambda are subjected to ambiguity resolution, and the control parameter regulating values of the fuzzy domains and the corresponding membership values are substituted into an area barycentric method formula by adopting an area barycentric method:
in the formula, v 0 ＝{△K p ，△K i ，△K d Delta mu and delta lambda are accurate values of the control parameter regulating quantity after the ambiguity is resolved; v. of k ＝{△K pk ，△K ik ，△K dk ，△μ k ，△λ k J, control adjustment magnitude for the fuzzy domain, μ v (v k ) Is v is k A membership value of; and m is the number of output fuzzy subsets.
8. The method of claim 5, wherein e, ec, Δ K p 、△K i 、△K d Gauss membership functions with smooth curves are respectively selected for the membership functions of delta mu and delta lambda.
9. A trajectory planning control system of an underwater operation robot is characterized by comprising:
the displacement tracking module is configured for acquiring real-time displacement data of the underwater operation robot and a given displacement set value;
the set value transition processing module is configured for performing transition processing on the displacement set value through a tracking differentiator;
the deviation calculation module is configured for calculating the displacement deviation amount and the deviation change rate of the underwater operation robot according to the real-time displacement data and the displacement set value obtained through the transition processing;
the fuzzy control module is configured for inputting the displacement deviation amount and the deviation change rate into the fuzzy controller to obtain the adjustment amount of the control parameters of the fractional order PID controller, which is output by the fuzzy controller;
and the fractional order control module is configured for performing online real-time adjustment on the control parameter of the fractional order PID controller according to the adjustment quantity of the control parameter, so that the fractional order PID controller performs control operation according to the displacement deviation quantity based on the real-time control parameter to obtain the control quantity for controlling the action of the underwater robot.
10. A computer-readable storage medium on which a computer program is stored, wherein the computer program, when executed by a processor, implements the trajectory planning control method for an underwater work robot according to the first aspect.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210451482.7A CN115248601A (en) | 2022-04-27 | 2022-04-27 | Trajectory planning control method and system for underwater operation robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210451482.7A CN115248601A (en) | 2022-04-27 | 2022-04-27 | Trajectory planning control method and system for underwater operation robot |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115248601A true CN115248601A (en) | 2022-10-28 |
Family
ID=83698454
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210451482.7A Pending CN115248601A (en) | 2022-04-27 | 2022-04-27 | Trajectory planning control method and system for underwater operation robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115248601A (en) |
-
2022
- 2022-04-27 CN CN202210451482.7A patent/CN115248601A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112817231B (en) | High-precision tracking control method for mechanical arm with high robustness | |
| CN108614426B (en) | Multi-mobile-robot formation robust control method based on disturbance observer | |
| CN105785999B (en) | Unmanned boat course motion control method | |
| CN107024863B (en) | UUV trajectory tracking control method for avoiding differential explosion | |
| CN110221541B (en) | Feedforward coefficient acquisition device and method of feedforward controller in servo system | |
| CN108415249A (en) | A kind of electrohydraulic servo system adaptive robust control method based on low frequency study | |
| JPH03118618A (en) | Control system applying sliding mode control having damping effect | |
| CN109857100B (en) | Composite track tracking control algorithm based on inversion method and fast terminal sliding mode | |
| CN111716360B (en) | Fuzzy logic-based flexible joint mechanical arm sampling control method and device | |
| CN111546346B (en) | Flexible joint disturbance observation method, moment control method and equipment | |
| CN109901511B (en) | Control algorithm for servo system contour error | |
| CN108267952B (en) | Self-adaptive finite time control method for underwater robot | |
| CN113110511B (en) | Intelligent ship course control method based on generalized fuzzy hyperbolic model | |
| CN112782981B (en) | Fuzzy self-adaptive output feedback designated performance control method and system for intelligent ship autopilot system | |
| Xia et al. | Adaptive energy-efficient tracking control of a X rudder AUV with actuator dynamics and rolling restriction | |
| Pan et al. | Online data‐driven composite adaptive backstepping control with exact differentiators | |
| Ding et al. | Output feedback sliding mode control for path-tracking of autonomous agricultural vehicles | |
| CN107490958A (en) | A kind of Fuzzy Adaptive Control Scheme of series parallel robot in five degrees of freedom | |
| Sun et al. | Visual servoing of flying robot based on fuzzy adaptive linear active disturbance rejection control | |
| CN110389528A (en) | Data-driven MEMS gyroscope drive control method based on disturbance observation | |
| Miao et al. | DOPH∞-based path-following control for underactuated marine vehicles with multiple disturbances and constraints | |
| CN115248601A (en) | Trajectory planning control method and system for underwater operation robot | |
| Li et al. | Adaptive optimal trajectory tracking control of AUVs based on reinforcement learning | |
| CN113110021B (en) | Method for identifying servo system and designing controller | |
| CN111152213B (en) | Mechanical arm vibration compensation method and device based on hybrid control |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |