CN110824919A - Automatic control method and system for direct-current computer mouse - Google Patents
Automatic control method and system for direct-current computer mouse Download PDFInfo
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
Abstract
The invention provides a direct current computer mouse automatic control method and a direct current computer mouse automatic control system, which are used for solving the problems of slow response speed, step loss, oscillation and collision of a computer mouse control object. The automatic control method of the direct current computer mouse obtains the tracking speed and the tracking acceleration from the running target speed on the basis of obtaining the current running target speed and the actual speed of the direct current computer mouse, obtains the observation disturbance, the observation speed and the observation acceleration from the actual speed, obtains the tracking speed error and the tracking acceleration error after difference, and obtains the system control quantity through a nonlinear feedback link to realize the automatic control of the direct current computer mouse. The invention utilizes ADRC algorithm to automatically control the direct current computer mouse, effectively reduces speed tracking error and acceleration and deceleration switching times in the operation process of the computer mouse, realizes accurate control on the direct current computer mouse, effectively reduces the search time, sprint time and collision times of the direct current computer mouse on a destination, and improves the search success rate.
Description
Technical Field
The invention belongs to the field of miniature intelligent robot control, and particularly relates to a direct-current computer mouse automatic control method and system.
Background
The intelligent robot is bionic to human, is endowed with multiple functions such as limbs, sense organs, brains and the like, and plays an increasingly important role in science and technology and daily life. Similarly, the computer mouse is a bionic to the mouse, is derived from the mouse maze, is a basic example which can be used for explaining the comprehensive work of limbs, senses and brains, is an intelligent walking device which is formed by an embedded microcontroller, a sensor and an electromechanical motion part, and is also a micro-robot. The computer mouse can automatically memorize and select paths in different mazes, and quickly reach the set destination by adopting a corresponding algorithm. In the international competition of computer mouse walking maze competition, according to the competition rule, when the computer mouse puts in the starting point and presses the start key, it must decide the searching rule and go forward in the maze, turn, memorize the wall data of maze, calculate the shortest path, search the terminal point, etc.
In the prior art, the most common computer mouse is a direct current computer mouse, and the control algorithm for determining the performance of the direct current computer mouse is used. The conventional dc mouse is generally controlled by a proportional-integral-derivative (PID) control algorithm or a fuzzy PID (proportional-integral-derivative) control algorithm. However, the running parameters and various interferences in the running process of the direct-current computer mouse have high randomness, and a computer mouse control system based on a PID control algorithm cannot well adapt to the interferences, so that the problems of slow response speed of a control object to track a target curve, easy generation of overshoot, sensitivity to environmental changes during output and the like exist in actual control, and the performance of the computer mouse is influenced. For example, in a computer mouse maze competition, the control algorithm has the defects that the direct current computer mouse has the problems of desynchronization, oscillation, collision and the like.
Disclosure of Invention
In order to improve the search destination and the advancing capability of a computer mouse and overcome the problem of slow response speed of a computer mouse control object, the invention provides a direct current computer mouse automatic control method and a direct current computer mouse automatic control system, which are combined with a second-order Active Disturbance Rejection Control (ADRC) algorithm to correct a speed tracking error, reduce a speed response error and acceleration and deceleration switching times in the operation process of the direct current computer mouse, more accurately control the computer mouse, eliminate the problems of desynchronization, oscillation, collision and the like in the control process of the direct current computer mouse, reduce the time for the computer mouse to search the destination and improve the search success rate.
In order to achieve the purpose, the invention adopts the following technical scheme.
In a first aspect, an embodiment of the present invention provides an automatic control method for a dc computer mouse, where the automatic control method for a dc computer mouse includes the following steps:
step S1, acquiring the current running target speed and the actual speed of the direct current computer mouse;
step S2, calculating the tracking speed and the tracking acceleration according to the running target speed by using a tracking differentiator;
step S3, estimating the object state and the uncertain disturbance action according to the actual speed by using the extended state observer, and then obtaining observation disturbance, observation speed and observation acceleration;
step S4, obtaining a tracking speed error by the difference between the tracking speed and the observation speed, and obtaining a tracking acceleration error by the difference between the tracking acceleration and the observation acceleration;
step S5, subtracting the product of the observation disturbance and the reciprocal of the system gain from the output obtained after the tracking speed error and the tracking acceleration error pass through a nonlinear feedback link to obtain the system control quantity;
and step S6, converting the system control quantity into corresponding control force, and realizing automatic control of the DC computer mouse through the control force.
Preferably, in step S1, the current operation target speed is calculated according to the threshold values of the speed of the dc computer mouse, the acceleration during linear acceleration, the acceleration during linear deceleration, and the acceleration of a single wheel during turning; and simultaneously measuring the actual speed of the direct current computer mouse in real time.
Preferably, in step S2, the tracking differentiator is used to calculate the tracking speed and the tracking acceleration according to the operation target speed, and the calculation is performed by equation (1):
wherein v is1(k),v2(k) Respectively tracking speed and tracking acceleration; r is0Is the velocity coefficient of the tracking differentiator, r0The larger the tracking speed, when r is0Input of a tracking control amount that a tracking differentiator does not have a difference → ∞; h is0Setting a certain value for the filtering factor to eliminate the high-frequency oscillation; fst being the first non-lineA function of the nature.
Preferably, in step S3, the observation disturbance, the observation speed, and the observation acceleration are obtained by using an extended state observer according to the actual speed, and are calculated by equation (3):
e(k)＝z1(k)-y(k)
z1(k+1)＝z1(k)+z2(k)-β01e(k)
z2(k+1)＝z2(k)+z3(k)-β02fal(e(k),0.5,δ)+b0u(k)
z3(k+1)＝z3(k)-β03fal(e(k),0.25,δ) (3)
wherein z is1(k),z2(k),z3(k) Respectively observation disturbance, observation acceleration and observation speed, e (k) is tracking error, u (k) is system control quantity, β01,β02,β03For the three observer parameters, b is the system gain, y (k) is the object output, and fal is the second nonlinear function.
Preferably, the fal is:
of these, η and η0Is the linear interval of ESO and NLSEF; if the parameters of ESO are properly selected, z1,z2
' it can well estimate the controlled quantity y and its differential y, z3The expansion state of the system, a ═ f (x, x', w (t)), can be estimated, a being a parameter between 0 and 1; sign is a sign function used to return positive and negative values.
Preferably, the nonlinear feedback loop equation (5) in step S5 gives the input and the output:
wherein v is1(k),v2(k) Respectively tracking speed and tracking acceleration; z is a radical of1(k),z2(k),z3(k) Respectively observing disturbance, acceleration and speed; e.g. of the type1(k)，e2(k) β tracking velocity error and tracking acceleration error, respectively1，β2Is observer coefficient α1，α2Is a parameter between 0 and 1; δ represents the size of the set linear interval; u. of0(k) U (k) are respectively the output of nonlinear feedback, system control quantity; b0Is an estimate of the system gain.
Preferably, the tracking of the parameters of the differentiator, the extended state observer and the nonlinear feedback in the steps S2, S3 and S5 comprises the following steps:
the fitness function of the improved particle swarm algorithm is determined as follows:
in the formula w1,w2As weight, e (t) is tracking error, J is fitness function value; the ADRC core parameter with the minimum fitness function is used as an optimal control parameter;
taking a direct current computer mouse mathematical model as follows:
h, h with less influence on the control effect of the second-order active disturbance rejection controller according to debugging experience0,r,δ,α1,α2,b0Taking and determining basic parameters;
β with great influence on second-order active disturbance rejection controller by improved particle swarm optimization01,β02,β03,β1,β2Setting five core parameters;
the sum is taken as the final parameter of the ADRC controller.
Preferably, the tuning process for five core parameters includes the following steps:
step a, initializing a particle swarm, and giving upper and lower limits of a parameter to be set according to experience; get the beginningStarting value w1＝1,w 210, inertial weight wstart＝0.93,wendThe particle swarm Size is 70, the search space dimension D is 5, and the particle swarm breeding algebra G is 100;
b, updating the inertial weight factor and the position and the speed of each particle;
step c, calculating the fitness function value of each particle, namely selecting and obtaining the fitness function value of the optimal control effect of the ADRC controller;
d, sorting the fitness function values of the particle swarm, and enabling one third of the optimal fitness function values to enter next iteration; operating the particles by using genetic operators to generate a new batch of particles, and replacing one third of the particles in the original population by using the new batch of particles; for the last one third of the particles, performing dimensionality random initial variation;
e, comparing the current position fitness function value with the relation between the historical optimal fitness function value and the global optimal fitness function value for each particle; if the current position is better than the historical optimal fitness function value, the historical optimal position and the historical optimal fitness function value are correspondingly replaced; if the global optimal fitness function value is better than the global optimal fitness function value, the global optimal position and the global optimal fitness function value are correspondingly replaced, and the global optimal particle position is recorded;
f, judging whether the maximum iteration times is reached, if so, finishing the algorithm, otherwise, repeating the steps c, d and e, and obtaining the optimal core parameter β of the ADRC controller through 100 generations of iteration01,β02,β03,β1,β2。
In a second aspect, an embodiment of the present invention further provides an automatic control system for a dc computer mouse, where the automatic control system includes: the device comprises a standard speed calculation module, an infrared sensor, an actual direct current computer mouse model and an ADRC controller; wherein the content of the first and second substances,
the target speed calculation module is used for obtaining the running target speed and the actual speed of the direct-current computer mouse;
the infrared sensor is used for detecting the state of obstacles around the direct current computer mouse, preliminarily judging the distance of the obstacles and transmitting the detected information back to the ADRC controller;
the actual direct current computer mouse model is used for inputting a mathematical model of a controlled direct current computer mouse;
the ADRC controller is used for obtaining the control quantity output of the actual direct current computer mouse model on the basis of the target speed calculation module and the infrared sensor data.
Preferably, the ADRC controller comprises a tracking differentiator, a nonlinear feedback, an ESO, a controlled object; wherein the content of the first and second substances,
the tracking differentiator is used for arranging a transition process, extracting a differential signal of the transition process, inputting a target speed, outputting a tracking speed and tracking an acceleration;
the nonlinear feedback is used for generating a control signal by nonlinear combination of errors between a scheduled transition process and state estimation and compensation of a disturbance estimator, inputting a tracking speed error and a tracking acceleration error, and outputting;
the ESO is used for estimating the state of an object and the uncertain disturbance action, inputting the output of the object, outputting observation disturbance, observing acceleration and observing speed;
the controlled object is a controlled direct current computer mouse model, the system control quantity and disturbance are input, and the object output is output.
According to the technical scheme provided by the embodiment of the invention, the ADRC-based controller has low access condition to the mathematical model of the controlled object, the state observer based on the modern control theory is introduced, the anti-interference is integrated into the automatic control, the anti-interference system parameters are quickly set by using the improved particle swarm algorithm, and the method is easy to realize. The embodiment of the invention utilizes the ADRC algorithm to automatically control the direct current computer mouse, effectively reduces the speed tracking error and the times of acceleration and deceleration switching in the operation process of the computer mouse, eliminates the problems of desynchronization, oscillation and collision in the traditional computer mouse control process, overcomes the inherent defects of slow response speed, easy generation of overshoot and sensitivity to environmental change of the PID control algorithm and the fuzzy PID control algorithm, realizes the accurate control of the direct current computer mouse, effectively reduces the search time, the sprint time and the times of collision of the direct current computer mouse to a destination, and improves the success rate of search.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method for automatically controlling a DC computer mouse according to an embodiment of the present invention;
FIG. 2 is a block diagram of a system for tuning ADRC controller parameters using a modified particle swarm optimization in an embodiment of the present invention;
FIG. 3 shows a straight-ahead simulation response curve and a straight-ahead actual measurement response curve of a DC mouse on a track according to an embodiment of the present invention;
FIG. 4 shows a 90-degree turn simulation curve and a 90-degree turn actual measurement response curve of a DC mouse on a track according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an embodiment of an automatic control system for a DC computer mouse;
fig. 6 is a schematic diagram of an internal structure of the ADRC controller according to the embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The invention provides an automatic Control method of a direct current computer mouse, based on second-order Active Disturbance Rejection Control (ADRC), firstly obtaining the current running target speed and actual speed of the computer mouse, arranging a transition process by the running target speed through a Tracking Differentiator (TD) and extracting a differential signal thereof, and then obtaining a Tracking speed and a Tracking acceleration; then, estimating the object state and the uncertain disturbance action by the actual speed through an Extended State Observer (ESO) to obtain observation disturbance, observation speed and observation acceleration; obtaining a tracking speed error by making a difference between the tracking speed and the observation speed, and obtaining a tracking acceleration error by making a difference between the tracking acceleration and the observation acceleration; the tracking speed error and the tracking acceleration error are subjected to nonlinear feedback (NLSEF) link (nonlinear combination of errors between a scheduled transition process and state estimation and compensation of disturbance estimation), and the product of observation disturbance and the reciprocal of system gain is subtracted from the output obtained after the nonlinear combination of errors and the disturbance estimation amount is obtained, so that the system control amount is obtained, and the updated ADRC system control amount is converted into corresponding control force and is output to a direct current computer mouse to realize automatic control. The invention corrects the speed tracking error by combining the advantages of the second-order ADRC algorithm, controls the computer mouse more accurately, can eliminate the problems of step loss, oscillation and collision in the traditional computer mouse control process, overcomes the inherent defects of slow reaction speed, easy generation of overshoot and sensitivity to environmental change of the PID control algorithm and the fuzzy PID control algorithm, reduces the search time, sprint time and collision times of the direct-current computer mouse to the destination, and effectively improves the search success rate.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
First embodiment
The present embodiment provides an automatic control method for a dc computer mouse, and fig. 1 is a schematic flow chart of the automatic control method for a dc computer mouse. As shown in fig. 1, the automatic control method for the dc computer mouse comprises the following steps:
and step S1, acquiring the current running target speed and the actual speed of the direct current computer mouse.
In step S2, the tracking differentiator calculates the tracking velocity and the tracking acceleration from the operation target velocity.
And step S3, estimating the object state and the uncertain disturbance action according to the actual speed by using the extended state observer, and then obtaining the observation disturbance, the observation speed and the observation acceleration.
And step S4, obtaining a tracking speed error by the difference between the tracking speed and the observation speed, and obtaining a tracking acceleration error by the difference between the tracking acceleration and the observation acceleration.
And step S5, obtaining the system control quantity after subtracting the product of the observation disturbance and the reciprocal of the system gain from the output obtained after the tracking speed error and the tracking acceleration error pass through a nonlinear feedback link (the nonlinear combination of the errors between the arranged transition process and the state estimation and the compensation of the disturbance estimation quantity).
And step S6, converting the system control quantity into corresponding control force, and realizing automatic control of the DC computer mouse through the control force.
In the step S1, a current operation target speed is calculated based on the threshold values of the speed of the dc mouse, the acceleration during linear acceleration, the acceleration during linear deceleration, and the acceleration of the single wheel during turning; and simultaneously measuring the actual speed of the direct current computer mouse in real time.
In step S2, the tracking differentiator calculates the tracking velocity and the tracking acceleration from the operating target velocity, and the calculation is performed by equation (1):
wherein v is1(k),v2(k) Respectively, tracking speed and tracking acceleration. r is0Is the velocity coefficient of the tracking differentiator, r0The larger the tracking speed, when r is0The tracking differentiator can input no difference in the tracking control amount → ∞. h is0For the filtering factor, a certain value is set to eliminate the high frequency oscillation. fst is a first non-linear function.
Preferably, the fst is:
d＝rh0,
d0＝dh0,
y＝x1+h0x2,
wherein r represents the fast and slow factors of the tracking differentiator, h0Representing the filter factor, a representing the expansion state of the system, x representing the expansion state variable, x (t) being a measure of the quantity, x1Is its first derivative, x2Its second derivative.
In step S3, the observation disturbance, the observation speed, and the observation acceleration are obtained from the actual speed by using the extended state observer, and the calculation is performed by equation (3):
e(k)＝z1(k)-y(k)
z1(k+1)＝z1(k)+z2(k)-β01e(k)
z2(k+1)＝z2(k)+z3(k)-β02fal(e(k),0.5,δ)+b0u(k)
z3(k+1)＝z3(k)-β03fal(e(k),0.25,δ) (3)
wherein z is1(k),z2(k),z3(k) Respectively, observed disturbance, observed acceleration, and observed speed, e (k) are tracking errors, u (k) are system control quantities, β01,β02,β03For the three observer parameters, b is the system gain, y (k) is the object output, and fal is the second nonlinear function.
Preferably, the fal is:
of these, η and η0Is the linear interval of ESO and NLSEF. If the parameters of ESO are properly selected, z1,z2
'
The controlled quantity y and the differential y, z thereof can be well estimated3The expansion state of the system, a ═ f (x, x', w (t)), can be estimated, a being a parameter between 0 and 1. sign is a sign function used to return positive and negative values.
In step S5, the nonlinear feedback element gives an input and an output by equation (5):
wherein v is1(k),v2(k) Respectively tracking speed and tracking acceleration. z is a radical of1(k),z2(k),z3(k) Respectively observing disturbance, acceleration and speed. e.g. of the type1(k)，e2(k) β tracking velocity error and tracking acceleration error, respectively1，β2Is observer coefficient α1，α2Is a parameter between 0 and 1.δ represents the size of the set linear interval. u. of0(k) And u (k) are respectively the output of the nonlinear feedback and the system control quantity. b0Is an estimate of the system gain.
The parameter setting of tracking differentiators, expanding state observers and nonlinear feedback links in the steps S2, S3 and S5 comprises the following steps:
the fitness function of the improved particle swarm algorithm is determined as follows:
in the formula w1,w2As weights, e (t) is the tracking error, and J is the fitness function value. The ADRC core parameter which can minimize the fitness function is the optimal control parameter.
The mathematical model of the fixed direct current computer mouse is as follows
H, h with less influence on the control effect of the second-order active disturbance rejection controller according to debugging experience0,r,δ,α1,α2,b0And (5) determining basic parameters.
β with great influence on second-order active disturbance rejection controller by improved particle swarm optimization01,β02,β03,β1,β2And setting five core parameters.
Fig. 2 is a schematic diagram illustrating a tuning process for five core parameters. As shown in fig. 2, the tuning process for five core parameters further includes the following steps:
step a, initializing a particle swarm, and giving upper and lower limits of a parameter to be set according to experience. Taking an initial value w1＝1,w 210, inertial weight wstart＝0.93,wendThe particle swarm Size is 70, the search space dimension D is 5, and the particle swarm breeding algebra G is 100;
b, updating the inertial weight factor and the position and the speed of each particle;
step c, calculating the fitness function value of each particle, namely selecting and obtaining the fitness function value of the optimal control effect of the ADRC controller;
and d, sequencing the fitness function values of the particle swarm, and entering the next iteration for the optimal one third. And operating on them with genetic operators to generate a new batch of particles and replacing the middle third of the particles in the original population with them. For the last one third of particles, carrying out dimension random initial variation to avoid the occurrence of the local optimal condition;
and e, comparing the relation between the current position fitness function value of each particle and the historical optimal fitness function value and the global optimal fitness function value. And if the historical optimal fitness function value is better than the historical optimal fitness function value, the historical optimal position and the historical optimal fitness function value are correspondingly replaced. If the global optimal fitness function value is better than the global optimal fitness function value, the global optimal position and the global optimal fitness function value are correspondingly replaced, and the global optimal particle position is recorded;
f, judging whether the maximum iteration times is reached, if so, finishing the algorithm, otherwise, repeating the steps c, d and e, and finally obtaining the optimal core parameter β of the ADRC controller through 100 generations of iteration01,β02,β03,β1,β2。
The h, h0,r,δ,α1,α2,b0And β01,β02,β03,β1,β2As the final parameter of the ADRC controller.
The automatic control method of the direct current computer mouse is adopted to control the computer mouse on the track.
Fig. 3 shows a straight-line simulated response curve and a straight-line measured response curve of a dc computer mouse on a track for a target curve. As shown in fig. 3, the straight-going target curve of the dc mouse on the track includes five states of start, acceleration, uniform speed, deceleration, and stop. In the comparison of the direct-current response curve on the direct-current computer mouse track, in the tracking process from the acceleration state to the uniform speed state, the direct-current simulation response curve and the actual measurement curve are overlapped with the direct-current target curve to a greater extent, so that the direct-current computer mouse based on the ADRC control algorithm can effectively control the tracking error and overshoot, the problems of step loss, oscillation and collision are eliminated, and the accurate control of the direct-current computer mouse in the direct-current process is realized.
Fig. 4 is a graph showing the response curve of the dc computer mouse to the target curve of the 90-degree turn simulation and the response curve of the 90-degree turn measurement on the track. As shown in fig. 4, the 90-degree turning target curve of the dc cybercase on the track includes five states, i.e., before turning, the outer wheel accelerates and the inner wheel decelerates, the inner wheel maintains the original speed, the outer wheel decelerates and the inner wheel accelerates, and after turning. In the comparison of the 90-degree turning response curve of the direct current computer mouse on the track, the invention can see that the tracking error of the two-wheel (outer wheel and inner wheel) speed response curve of the direct current computer mouse based on the ADRC control algorithm is very small, the overshoot is basically eliminated, and the turning is relatively stable; in the case where the speed is drastically changed in a short time, the 90-degree turning target curve is tracked. The accurate control of the DC mouse when turning 90 degrees is realized.
In the automatic control method for the direct current computer mouse, the ADRC-based controller has low access condition to a mathematical model of a controlled object, a state observer based on a modern control theory is introduced, anti-interference is integrated into automatic control, anti-interference system parameters are quickly set by using an improved particle swarm algorithm, and the method is easy to realize. The embodiment of the invention utilizes the ADRC algorithm to automatically control the direct current computer mouse, effectively reduces the speed tracking error and the times of acceleration and deceleration switching in the operation process of the computer mouse, eliminates the problems of desynchronization, oscillation and collision in the traditional computer mouse control process, overcomes the inherent defects of slow response speed, easy generation of overshoot and sensitivity to environmental change of the PID control algorithm and the fuzzy PID control algorithm, realizes the accurate control of the direct current computer mouse, effectively reduces the search time, the sprint time and the times of collision of the direct current computer mouse to a destination, and improves the success rate of search.
Second embodiment
The embodiment provides an automatic control system for a dc computer mouse, and fig. 5 is a schematic structural diagram of the automatic control system for a dc computer mouse. As shown in fig. 5, the automatic control system for a dc computer mouse includes: the device comprises a target speed calculation module 1, an infrared sensor 2, an actual direct current computer mouse model module 3 and an ADRC controller 4.
The target speed calculation module 1 is used for obtaining the running target speed and the actual speed of the direct current computer mouse. The infrared sensor 2 is used for detecting the state of obstacles around the direct current computer mouse, roughly judging the distance and transmitting the detected information back to the ADRC controller. The actual DC mouse model module 3 is used for inputting the mathematical model of the controlled DC mouse. The ADRC controller 4 is used for obtaining the control quantity output of the actual direct current computer mouse model 3 on the basis of the data of the target speed calculation module 1 and the infrared sensor 2.
Fig. 6 is a schematic diagram of the internal structure of the ADRC controller 4. As shown in fig. 6, the ADRC controller 4 includes a tracking differentiator 41, a nonlinear feedback 42, an ESO43, and a controlled object 44.
Wherein the tracking differentiator 41 is used for arranging the transition process and extracting the differential signal thereof, and inputting the target speed v0Outputting the tracking velocity v1Tracking acceleration v2. The non-linear feedback 42 is used to generate a control signal from a non-linear combination of the errors between the scheduled transient and the state estimate and the compensation of the disturbance estimate, the tracking velocity error e being input1And tracking acceleration error e2Output u0。ESO43 for estimating the state of the object and the effect of the uncertain disturbance, inputting the output y of the object and outputting the observed disturbance z1Observing the acceleration z2Observation velocity z3. The controlled object 44 is a controlled dc computer mouse model, and inputs the system control quantity u and the disturbance w, and outputs y.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
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 apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments.
The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Those of ordinary skill in the art will understand that: the components in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be correspondingly changed in one or more devices different from the embodiments. The components of the above embodiments may be combined into one component, or may be further divided into a plurality of sub-components.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A direct current computer mouse automatic control method is characterized by comprising the following steps:
step S1, acquiring the current running target speed and the actual speed of the direct current computer mouse;
step S2, calculating the tracking speed and the tracking acceleration according to the running target speed by using a tracking differentiator;
step S3, estimating the object state and the uncertain disturbance action according to the actual speed by using the extended state observer, and then obtaining observation disturbance, observation speed and observation acceleration;
step S4, obtaining a tracking speed error by the difference between the tracking speed and the observation speed, and obtaining a tracking acceleration error by the difference between the tracking acceleration and the observation acceleration;
step S5, subtracting the product of the observation disturbance and the reciprocal of the system gain from the output obtained after the tracking speed error and the tracking acceleration error pass through a nonlinear feedback link to obtain the system control quantity;
and step S6, converting the system control quantity into corresponding control force, and realizing automatic control of the DC computer mouse through the control force.
2. The method according to claim 1, wherein in step S1, the current operation target speed is calculated based on thresholds of the speed of the dc mouse, the acceleration during linear acceleration, the acceleration during linear deceleration, and the acceleration during single round of turning; and simultaneously measuring the actual speed of the direct current computer mouse in real time.
3. The method according to claim 1, wherein the step S2 is performed by calculating a tracking velocity and a tracking acceleration according to the operation target velocity by using a tracking differentiator according to formula (1):
wherein v is1(k),v2(k) Respectively tracking speed and tracking acceleration; r is0Is the velocity coefficient of the tracking differentiator, r0The larger the tracking speed, when r is0Input of a tracking control amount that a tracking differentiator does not have a difference → ∞; h is0Setting a certain value for the filtering factor to eliminate the high-frequency oscillation; fst is a first non-linear function.
4. The method according to claim 1, wherein the step S3 is performed by using an extended state observer to obtain the observed disturbance, the observed speed, and the observed acceleration according to the actual speed, and calculating according to equation (3):
e(k)＝z1(k)-y(k)
z1(k+1)＝z1(k)+z2(k)-β01e(k)
z2(k+1)＝z2(k)+z3(k)-β02fal(e(k),0.5,δ)+b0u(k)
z3(k+1)＝z3(k)-β03fal(e(k),0.25,δ) (3)
wherein z is1(k),z2(k),z3(k) Respectively observation disturbance, observation acceleration and observation speed, e (k) is tracking error, u (k) is system control quantity, β01,β02,β03For the three observer parameters, b is the system gain, y (k) is the object output, and fal is the second nonlinear function.
5. The method according to claim 4, wherein the fal is:
of these, η and η0Lines being ESO and NLSEFA sexual interval; if the parameters of ESO are properly selected, z1,z2Can well estimate the controlled quantity y and the differential y' and z thereof3The expansion state of the system, a ═ f (x, x', w (t)), can be estimated, a being a parameter between 0 and 1; sign is a sign function used to return positive and negative values.
6. The method according to claim 1, wherein the nonlinear feedback loop equation (5) in step S5 gives input and output:
wherein v is1(k),v2(k) Respectively tracking speed and tracking acceleration; z is a radical of1(k),z2(k),z3(k) Respectively observing disturbance, acceleration and speed; e.g. of the type1(k)，e2(k) β tracking velocity error and tracking acceleration error, respectively1，β2Is observer coefficient α1，α2Is a parameter between 0 and 1; δ represents the size of the set linear interval; u. of0(k) U (k) are respectively the output of nonlinear feedback, system control quantity; b0Is an estimate of the system gain.
7. The automatic control method for a DC computer mouse as claimed in claim 1, wherein the parameter settings of tracking differentiator, extended state observer and nonlinear feedback in steps S2, S3 and S5 comprise the following steps:
the fitness function of the improved particle swarm algorithm is determined as follows:
in the formula w1,w2As weight, e (t) is tracking error, J is fitness function value; the ADRC core parameter with the minimum fitness function is used as an optimal control parameter;
taking a direct current computer mouse mathematical model as follows:
h, h with less influence on the control effect of the second-order active disturbance rejection controller according to debugging experience0,r,δ,α1,α2,b0Taking and determining basic parameters;
β with great influence on second-order active disturbance rejection controller by improved particle swarm optimization01,β02,β03,β1,β2Setting five core parameters;
the sum is taken as the final parameter of the ADRC controller.
8. The method according to claim 7, wherein the process of setting the five core parameters comprises the following steps:
step a, initializing a particle swarm, and giving upper and lower limits of a parameter to be set according to experience; taking an initial value w1＝1,w210, inertial weight wstart＝0.93,wendThe particle swarm Size is 70, the search space dimension D is 5, and the particle swarm breeding algebra G is 100;
b, updating the inertial weight factor and the position and the speed of each particle;
step c, calculating the fitness function value of each particle, namely selecting and obtaining the fitness function value of the optimal control effect of the ADRC controller;
d, sorting the fitness function values of the particle swarm, and enabling one third of the optimal fitness function values to enter next iteration; operating the particles by using genetic operators to generate a new batch of particles, and replacing one third of the particles in the original population by using the new batch of particles; for the last one third of the particles, performing dimensionality random initial variation;
e, comparing the current position fitness function value with the relation between the historical optimal fitness function value and the global optimal fitness function value for each particle; if the current position is better than the historical optimal fitness function value, the historical optimal position and the historical optimal fitness function value are correspondingly replaced; if the global optimal fitness function value is better than the global optimal fitness function value, the global optimal position and the global optimal fitness function value are correspondingly replaced, and the global optimal particle position is recorded;
f, judging whether the maximum iteration times is reached, if so, finishing the algorithm, otherwise, repeating the steps c, d and e, and obtaining the optimal core parameter β of the ADRC controller through 100 generations of iteration01,β02,β03,β1,β2。
9. An automatic control system for a DC computer mouse, the automatic control system comprising: the device comprises a standard speed calculation module, an infrared sensor, an actual direct current computer mouse model and an ADRC controller; wherein the content of the first and second substances,
the target speed calculation module is used for obtaining the running target speed and the actual speed of the direct-current computer mouse;
the infrared sensor is used for detecting the state of obstacles around the direct current computer mouse, preliminarily judging the distance of the obstacles and transmitting the detected information back to the ADRC controller;
the actual direct current computer mouse model is used for inputting a mathematical model of a controlled direct current computer mouse;
the ADRC controller is used for obtaining the control quantity output of the actual direct current computer mouse model on the basis of the target speed calculation module and the infrared sensor data.
10. The dc computer mouse automatic control system according to claim 8, wherein the ADRC controller comprises a tracking differentiator, a nonlinear feedback, an ESO, a controlled object; wherein the content of the first and second substances,
the tracking differentiator is used for arranging a transition process, extracting a differential signal of the transition process, inputting a target speed, outputting a tracking speed and tracking an acceleration;
the nonlinear feedback is used for generating a control signal by nonlinear combination of errors between a scheduled transition process and state estimation and compensation of a disturbance estimator, inputting a tracking speed error and a tracking acceleration error, and outputting;
the ESO is used for estimating the state of an object and the uncertain disturbance action, inputting the output of the object, outputting observation disturbance, observing acceleration and observing speed;
and the controlled object is a controlled direct-current computer mouse model, and system control quantity and disturbance are input and output.
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