CN102269995B - Variable structure control method of wheeled mobile robot - Google Patents

Abstract

The invention discloses a variable structure control method of a wheeled mobile robot. according the method, the linear motion of a robot is controlled by using a multimodal PID (proportion integration differentiation) control method; and a forward direction of a trolley is corrected by using the combination of a control method and a PID control and a rule control; the two control modes are switched through the changes of a directional angle and a centre offset; the robot is controlled to turn in an in-situ right angle turning mode according to turning direction information and position information; by using the multimode PID control and the combination of the rule control and the PID control, the algorithm can use different control algorithms and corresponding control parameters according to different states of the robot to effectively improve the robot motion control performance; the control mode is divided according to the error change condition so as to reasonably simulate the control behavior of a human; compared with the traditional PID control method, the variable structure control method has a certain intelligence and improves the walking motion control quality of the robot.

Description

Variable structure control method of wheeled mobile robot

Technical Field

The invention relates to a control algorithm of a wheeled mobile robot, in particular to a variable structure control method of the wheeled mobile robot.

Background

The robot has great research significance in the aspects of production, study and research, the wheel type robot research is a branch of the robot research, belongs to the research category of the walking intelligent robot, and relates to a plurality of leading-edge subjects such as computers, automatic control, sensing and perception, wireless communication, precision machinery, bionic materials and the like. With the continuous development of science and technology and the improvement of material life, people want to be free from heavy, dangerous and repeated work. Therefore, robots are widely used, and the research on the motion of the robots is more and more focused.

The motion control problem of the mobile robot is the most basic problem in robot research and the problem that the motion control problem can be solved only by means of a control theory in the robot research. For a control system including a control object, the theoretical research of the control problem includes two aspects, namely the analysis problem of the control system and the comprehensive problem of the control system. In analyzing the problem, the qualitative behavior (such as controllability, observability, stability and the like) and quantitative change rules of the control system are determined according to the known control input action. In the synthetic problem, the control input actions, i.e. control algorithms, that need to be applied to the control object are determined in accordance with the desired form of controlled system motion or some performance indicator, as opposed to the analytical problem.

To improve the motion performance of the robot, the design and optimization of the underlying motion control algorithm become key. The well designed motion control system becomes an important target for perfecting the wheeled robot system.

PID is the most common control method, and the determination of three parameters of the PID controller has a qualitative calculation method. For a proportional controller, its input is proportional to its output, both without delay in time; the integral action is adopted to solve the steady-state error, but the phase lag of the system is increased, and the response speed of the system is seriously weakened. Effective control can be achieved by starting integration only when the error falls within a certain range. When its coefficient of action is too large, the system tends to be unstable. However, if the coefficient is too small, the system will be slow. On the premise of system stability, the proportion adjustment is increased, so that the steady-state error can be reduced, but the error cannot be eliminated; by adopting differential control, the control effect is rapid, the method is generally suitable for a system with time lag and has the effect of advance adjustment, but if the numerical value is not properly selected, the input value of the control system can oscillate repeatedly, so that the system can never reach the preset value. In addition, the PID controller lacks an intelligent sensing mechanism, the real-time performance is not strong, the running track is not accurate and stable enough, and different control methods cannot be adopted for different states.

In order to improve the self-adaptive capacity of the mobile robot in walking, a fuzzy proportional integral control algorithm combining a fuzzy algorithm and proportional integral is proposed in documents and is applied to an autonomously developed four-wheel mobile robot, but the regular area of the method is not easy to divide, and the simulation effect is not easy to achieve in practical application.

By analyzing a mobile robot kinematic model, the prior vision-based two-wheeled robot uses a PID control method of parameter fuzzy self-tuning to be applied to the mobile robot kinematic control, but the parameter of the method is not easy to tune and is not easy to realize in practice.

In order to prevent a large tracking error and deviation from a predetermined path due to oversteer when a wheeled mobile robot tracks a path in a large curve, a course tracking control method that can adapt to a large steering is proposed in the literature, in which the absolute direction of the left wheel slip angle of the robot is used as feedback information of a controller. However, in practical applications, a single deflection angle is used as feedback, and a center offset is not used as feedback information, so that a good control effect cannot be achieved.

There is a document that proposes to design a fuzzy controller based on a fusion function to perform robot walking control. The method can reduce fuzzy rules, but the design of the fuzzy controller is complex, and the fuzzy controller in practical application cannot be well designed.

The proper variable structure control can enable the robot system to reach the specified switching surface within a limited time, thereby realizing sliding mode control. However, in the actual system, due to the inevitable inertia of the switching device, the variable structure control system switches back and forth in different control logics, so that the actual sliding mode control does not accurately occur on the switching surface, and severe shaking of the system is easily caused, thereby being a great obstacle in the practical application.

The mobile robot control algorithm is different from that of a general system. Conventional automatic control algorithms are often designed for specific application conditions and are only used to perform a specific function. The robot control algorithm needs to complete multiple functions, and all robot control systems are almost the fusion of multiple control algorithms.

Therefore, a control algorithm which can not only keep the excellent characteristics of the robot but also improve the adaptability of the traditional control algorithm when the robot is controlled to walk is urgently needed.

The patent provides a new variable structure control method which integrates multi-mode control, rule control and PID control and is used for controlling the walking motion of a wheeled mobile robot.

Disclosure of Invention

In view of the above, in order to solve the above problems, the present invention provides a control algorithm that can not only maintain the excellent characteristics of the robot, but also improve the adaptivity of the conventional control algorithm when controlling the robot to walk; the new variable structure control method fusing the multi-mode control, the rule control and the PID control is used for controlling the walking motion of the wheeled mobile robot.

The purpose of the invention is realized as follows:

the invention provides a variable structure control method of a wheeled mobile robot, wherein the wheeled mobile robot comprises a left wheel, a right wheel, a driver, a front-row magnetic sensor, a rear-row magnetic sensor, a wheeled mobile robot body and an RFID reader-writer, wherein the driver controls the rotating speed of a motor, the driver comprises linear walking control and turning control, and the linear walking control controls the linear movement of the robot and corrects the advancing direction of the robot according to the difference information of a left wheel driving motor and a right wheel driving motor of the robot; and the turning control is to determine the information required by the turning of the robot according to the turning direction information provided by the RFID tag and the turning position information provided by the magnetic strip, and to control the turning action by adopting an in-situ right-angle turning mode.

Further, the linear walking control is in a variable structure control mode and comprises an inner control ring and an outer control ring, and the inner control ring controls the linear movement of the robot by adopting a multi-mode PID control method; the outer control ring corrects the advancing direction of the trolley by adopting a control method combining PID control and regular control; the inner control loop and the outer control loop are switched according to the following conditions: when the direction angle theta is equal to 0 and the central offset eta is less than d, the inner control ring controls the advance of the robot, otherwise, the outer control ring adjusts the advance direction of the robot; wherein d is the interval between two adjacent magnetic sensors;

further, the multi-modal PID control method includes the steps of:

s1: computing a coefficient K in a multi-modal PID control_p、K_i、K_d，

Wherein K_pDenotes the proportionality coefficient, K_iDenotes the integral coefficient, K_dRepresents a differential coefficient;

s2: inputting the collected robot movement information y (k), wherein y (k) is the rotating speed difference between the left wheel and the right wheel at the current moment;

s3: calculating a deviation e (k) ═ r (k) — y (k),

wherein y (k) represents the input quantity of the current sampling; r (k) represents a given input quantity; e (k) represents the deviation of the given input quantity and the current sampling input quantity;

s4: the control amount is calculated by the following formula:

u(k)＝u(k-1)+K_p(e(k)-e(k-1))+K_ie(k)+K_d(e(k)-2e(k-1)+e(k-2))

wherein e (k) represents the deviation of the given input quantity and the current sampling input quantity; e (k-1) represents the deviation between the given input amount and the last input amount; e (k-2) represents the deviation between the given quantity and the last sampled input quantity; u (k) represents a control amount to be output; u (k-1) represents the control amount of the last output;

s5: outputting a control quantity u (k), and driving the motor to control the motion of the robot through the robot driving module;

s6: modifying the deviation through the following formula, setting the deviation as the next deviation, and setting the next deviation as the last deviation:

e(k)→e(k-1)，e(k-1)→e(k-2)；

s7: judging whether the sampling time arrives, if not, recording the sampling time until the sampling time is finished, and entering the next step;

s8: if the sampling time is finished, returning to the step S2 for the next sampling input;

further, in the step of calculating the controlled variable, before calculating the controlled variable, PID modes are divided according to the change of the difference between the rotating speeds of the left wheel and the right wheel of the robot, and the PID mode division is performed as follows:

when the rotating speed difference of the left wheel and the right wheel is smaller than a preset minimum threshold value, using PID control;

when the rotating speed difference of the left wheel and the right wheel is larger than a preset maximum threshold value, P control is used;

when the rotating speed difference of the left wheel and the right wheel is between a preset minimum threshold value and a preset maximum threshold value, PI control is used;

the preset minimum threshold value is 1r/min, and the preset maximum threshold value is 2 r/min;

further, the outer control loop comprises the steps of:

s21: collecting position deviation signals of the robot by using magnetic sensors and magnetic stripe positions at the front end and the rear end of the robot and calculating the posture of the robot;

s22: judging whether the robot posture deviates or not, if not, keeping the original posture of the robot to move;

s23: if deviation occurs, the control quantity is calculated by adopting the following formula:

e(k)＝k₁η(k)+k₂θ(k)

u(k)＝u(k-1)+K_p(e(k)-e(k-1))+K_ie(k)+K_d(e(k)-2e(k-1)+e(k-2))

wherein k is₁Proportionality coefficient, k, representing the amount of center shift₂Scale factor, k, representing the azimuth₁、k₂Is determined according to the pose of the robot; e (k) represents the deviation of the given input quantity and the current sampling input quantity; e (k-1) represents the deviation between the given input amount and the last input amount; e (k-2) represents the deviation between the given quantity and the last sampled input quantity; u (k) represents a control amount to be output; u (k-1) represents the last output quantity; eta (k) represents the distance between the line and the center of the path in the robot detected this time; θ (k) represents a direction angle at which the robot is detected this time;

s24: determining the steering of the robot according to the deviation angle of the robot detected by the sensor;

s25: after the bogie is determined to be steered, voltage is sent to corresponding ports of the driver according to the control quantity, so that the rotating speed of motors of two driving wheels of the robot and the steering of the wheels are adjusted;

further, the method also comprises the following steps:

s9: when the robot runs out of the range of the magnetic stripe in the running process, forward video navigation is carried out, and a forward video navigation system provides information of offset and center offset distance for a motion control system;

further, the turning control includes the steps of:

s11: when the robot walks in a straight line, turning direction information of the RFID label is obtained through the RFID reader-writer, and turning position information provided by the magnetic stripe is detected through the magnetic sensor;

s12: stopping the robot after the robot moves for a preset extension time;

s13: judging and determining the turning direction information of the robot according to the zone bits provided by the FRID label;

s14: according to the turning direction information, enabling one corresponding wheel to rotate and the other wheel to stop;

s15: detecting the magnetic stripe, and judging whether the magnetic stripe is positioned between the front row magnetic sensor and the rear row magnetic sensor; if the magnetic strip is not positioned between the front row magnetic sensor and the rear row magnetic sensor, returning to the step S14 to continue turning;

s16: if the magnetic strip is positioned between the front row magnetic sensor and the rear row magnetic sensor, the turning is stopped;

further, the step of calculating the deviation in S3 includes calculating a deviation amount of the angle and the offset distance;

further, the center offset amount is calculated by the following formula:

$\mathrm{\&eta;}=\frac{{\mathrm{<figure-callout\; id="1"\; label="L"\; filenames="HDA0000070300860000041.png"\; state="\{\{state\}\}">L</figure-callout>}}_1+{\mathrm{<figure-callout\; id="2"\; label="L"\; filenames="HDA0000070300860000041.png"\; state="\{\{state\}\}">L</figure-callout>}}_2}{2}$

the azimuth angle is calculated by the following formula:

$\tan \mathrm{\&theta;}=\frac{L_1-L_2}{L}$

wherein L is₁Indicating the distance, L, from the centerline sensed by a front-row magnetic sensor sensing a magnetic stripe₂The distance between the front row of magnetic sensors sensing the magnetic strips and the center line is represented, and the distance between the front row of magnetic sensors and the rear row of magnetic sensors is represented by L.

The invention has the advantages that: the multi-mode PID control is adopted, the input quantity of the control method is the rotating speed information of two wheels, an inner control loop is formed, and the linear movement of the robot is controlled; the method also combines regular control and PID control, the input quantity of the control method is the position (direction angle and center offset) information of the robot, and an outer control loop is formed to correct the advancing direction of the robot; when the switching condition of the inner control ring and the outer control ring is met, the inner control ring controls the advance of the robot, otherwise, the outer control ring adjusts the advance direction of the robot; the control mode of combining PID control and regular control is determined according to the special working principle of a motor driver, and the algorithm adopts different control algorithms and corresponding control parameters aiming at different states of the robot, so that the motion control performance of the robot is effectively improved.

The multi-mode PID control method provided by the invention divides the control modes according to the change condition of the error, simulates the control experience and skill of a human to a certain extent, more reasonably simulates the control behavior of the human, has certain intelligence compared with the traditional PID control method, and improves the control quality of the walking motion of the robot.

Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

Drawings

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings, in which:

FIG. 1 is a control schematic diagram of linear automatic motion control;

FIG. 2 is a block diagram of a PID control flow;

FIG. 3 is a schematic view of rule 1 of the heading attitude of the vehicle;

FIG. 4 is a schematic diagram of rule 2 for a car heading pose;

FIG. 5 is a schematic diagram of rule 3 for a car heading pose;

FIG. 6 is a schematic view of rule 4 of the heading attitude of the vehicle;

FIG. 7 is a schematic view of rule 5 for the heading attitude of the cart;

FIG. 8 is a schematic view of rule 6 for the heading attitude of the cart;

FIG. 9 is a diagram of a navigation system architecture;

FIG. 10 is a schematic view of the turn magnetic stripe and RFID tag location arrangement;

fig. 11 is a process flow of the car turning control.

1 represents a magnetic stripe induction line, 2 represents a front-row magnetic sensor, 3 represents a rear-row magnetic sensor, 4 represents a center offset η, 5 represents a robot advancing direction angle θ, 6 represents an RFID reader, 7 represents a magnetic stripe, 8 represents a turn information prompting magnetic stripe, 9 represents an RFID tag, and 10 represents a magnetic stripe mounting groove.

Detailed Description

The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings; it should be understood that the preferred embodiments are illustrative of the invention only and are not limiting upon the scope of the invention.

The invention provides a variable structure control method of a wheeled mobile robot, wherein the wheeled mobile robot comprises a left wheel, a right wheel, a driver, a front-row magnetic sensor, a rear-row magnetic sensor, a wheeled mobile robot body and an RFID reader-writer, wherein the driver controls the rotating speed of a motor, the driver comprises linear walking control and turning control, and the linear walking control controls the linear movement of the robot and corrects the advancing direction of the robot according to the difference information of a left wheel driving motor and a right wheel driving motor of the robot; and the turning control is to determine the information required by the turning of the robot according to the turning direction information provided by the RFID tag and the turning position information provided by the magnetic strip, and to control the turning action by adopting an in-situ right-angle turning mode.

FIG. 1 is a control schematic diagram of linear automatic motion control; as shown in the figure, as a further improvement of the above embodiment, the linear walking control is a variable structure control mode, and includes an inner control loop and an outer control loop, and the inner control loop controls the linear movement of the robot by adopting a multi-mode PID control method; the outer control ring corrects the advancing direction of the trolley by adopting a control method combining PID control and regular control; the inner control loop and the outer control loop are switched according to the following conditions:when the direction angle theta is equal to 0 and the central offset eta is less than d, the inner control ring controls the advance of the robot, otherwise, the outer control ring adjusts the advance direction of the robot; where d is the spacing between two adjacent magnetic sensors, K₁Proportionality coefficient, K, representing the amount of center shift₂A scaling factor representing the azimuth.

FIG. 2 is a block diagram of a PID control flow; as shown in the figure, as a further improvement of the above embodiment, the multi-modal PID control method includes the steps of:

s1: computing a coefficient K in a multi-modal PID control_p、K_i、K_d，

Wherein K_pDenotes the proportionality coefficient, K_iDenotes the integral coefficient, K_dRepresents a differential coefficient;

s2: inputting the collected robot movement information y (k), wherein y (k) is the rotating speed difference between the left wheel and the right wheel at the current moment;

s3: calculating a deviation e (k) ═ r (k) — y (k),

wherein y (k) represents the input quantity of the current sampling; r (k) represents a given input quantity; e (k) represents the deviation of the given input quantity and the current sampling input quantity;

s4: the control amount is calculated by the following formula:

u(k)＝u(k-1)+K_p(e(k)-e(k-1))+K_ie(k)+K_d(e(k)-2e(k-1)+e(k-2))

wherein e (k) represents the deviation of the given input quantity and the current sampling input quantity; e (k-1) represents the deviation between the given input amount and the last input amount; e (k-2) represents the deviation between the given quantity and the last sampled input quantity; u (k) represents a control amount to be output; u (k-1) represents the control amount of the last output;

s5: outputting a control quantity u (k), and driving the motor to control the motion of the robot through the robot driving module;

s6: modifying the deviation through the following formula, setting the deviation as the next deviation, and setting the next deviation as the last deviation:

e(k)→e(k-1)，e(k-1)→e(k-2)；

s7: judging whether the sampling time arrives, if not, recording the sampling time until the sampling time is finished, and entering the next step;

s8: if the sampling time is over, the process returns to step S2 to perform the next sampling input.

As a further improvement of the above embodiment, the step of calculating the controlled variable further divides the modes of the PID according to the variation of the rotation speed difference between the left wheel and the right wheel of the robot before calculating the controlled variable, and the PID mode division is performed as follows:

when the rotating speed difference of the left wheel and the right wheel is smaller than a preset minimum threshold value, PID control is used, namely proportional, integral and differential control rules are adopted for control;

when the rotating speed difference of the left wheel and the right wheel is larger than a preset maximum threshold value, P control is used, namely, a proportional control rule is adopted for control;

when the rotating speed difference of the left wheel and the right wheel is between a preset minimum threshold value and a preset maximum threshold value, PI control is used, namely proportional and integral control rules are adopted for control;

the preset minimum threshold value is 1r/min, and the preset maximum threshold value is 2 r/min.

Fig. 9 is a structure diagram of a navigation system, in which the distribution position of the RFID reader/writer 6 on the car and the arrangement mode of the magnetic stripe 7 on the road are shown, and the turning control process is specifically as follows: when the navigation system reads the RFID tag 9 during straight-line walking, the navigation system obtains the turning direction information. When the magnetic sensors detect 4 or more than 4 magnetic stripes 8 which prompt turning information, the trolley is defined as detecting a turning mark, the trolley stops immediately, the turning direction information is taken out of the navigation system, one corresponding wheel rotates, the other wheel stops, the magnetic sensors are detected simultaneously, and when the front rear row magnetic sensors sense that the magnetic stripes arranged in the magnetic stripe mounting grooves 10 are positioned in the middle, the trolley continues to advance. And after the turning is finished, emptying the turning direction information of the navigation system. After the turning direction information of the navigation system is cleared, if the magnetic sensor reads 4 or more than 4 magnetic stripes 8 which prompt turning information, the robot does not turn, so as to avoid misoperation of the robot. As shown in the figure: as a further refinement of the above embodiment, the outer control loop comprises the steps of:

s21: collecting position deviation signals of the robot by using magnetic sensors and magnetic stripe positions at the front end and the rear end of the robot and calculating the posture of the robot;

s22: judging whether the robot posture deviates or not, if not, keeping the original posture of the robot to move;

s23: if deviation occurs, the control quantity is calculated by adopting the following formula:

e(k)＝k₁η(k)+k₂θ(k)

u(k)＝u(k-1)+K_p(e(k)-e(k-1))+K_ie(k)+K_d(e(k)-2e(k-1)+e(k-2))

wherein k is₁Proportionality coefficient, k, representing the amount of center shift₂Scale factor, k, representing the azimuth₁、k₂Is determined according to the pose of the robot; e (k) represents the deviation of the given input quantity and the current sampling input quantity; e (k-1) represents the deviation between the given input amount and the last input amount; e (k-2) represents the deviation between the given quantity and the last sampled input quantity; u (k) represents a control amount to be output; u (k-1) represents the last output quantity; eta (k) represents the distance between the line and the center of the path in the robot detected this time; θ (k) represents a direction angle at which the robot is detected this time;

wherein k is₁，k₂The value of (a) is determined by field trial and error according to the attitude of the trolley.

S24: determining the steering of the robot according to the deviation angle of the robot detected by the sensor;

s25: and after the steering of the trolley is determined, voltage is sent to the corresponding port of the driver according to the control quantity, so that the rotating speed of the motors of the two driving wheels of the robot and the steering of the wheels are adjusted.

As a further improvement of the above embodiment, the calculating of the deviation in the step S3 includes calculating a deviation amount of both an angle and a deviation distance, and the center deviation amount is calculated by the following formula:

$\mathrm{\&eta;}=\frac{{\mathrm{<figure-callout\; id="1"\; label="L"\; filenames="HDA0000070300860000041.png"\; state="\{\{state\}\}">L</figure-callout>}}_1+{\mathrm{<figure-callout\; id="2"\; label="L"\; filenames="HDA0000070300860000041.png"\; state="\{\{state\}\}">L</figure-callout>}}_2}{2}$

the azimuth angle is calculated by the following formula:

$\tan \mathrm{\&theta;}=\frac{L_1-L_2}{L}$

wherein L is₁Indicating the distance, L, from the centerline sensed by a front-row magnetic sensor sensing a magnetic stripe₂The distance between the front row of magnetic sensors sensing the magnetic strips and the center line is represented, and the distance between the front row of magnetic sensors and the rear row of magnetic sensors is represented by L.

L₁，L₂Is a signed number, specifying that the left bias is negative and the right bias is positive.

FIGS. 3-8 are schematic views of the vehicle's heading attitude; in the figure, a magnetic strip induction line 1, a front row magnetic sensor 2, a rear row magnetic sensor 3, a center offset eta 4 and a robot advancing direction angle theta 5 are provided, and the posture of the robot is described by the following rules:

FIG. 3 is a schematic view of rule 1 of the heading attitude of the vehicle; as shown, rule 1: l is₁＜0，L₂If the correction is more than 0, the trolley should be corrected to the left. When θ is 0 and η is less than d, the straight-line travel correction control is performed. d is the interval between two adjacent magnetic sensors.

FIG. 4 is a schematic diagram of rule 2 for a car heading pose; as shown, rule 2: l is₁＞0，L₂If less than 0, the trolley should be corrected to the right. When θ is 0 and η is less than d, the straight-line travel correction control is performed.

FIG. 5 is a schematic diagram of rule 3 for a car heading pose; as shown, rule 3: l is₁＜0，L₂If theta is less than 0 and theta is more than 0, the trolley keeps the moving direction unchanged. When it becomes the posture rule 2, the correction procedure of the posture rule 2 is executed.

FIG. 6 is a schematic view of rule 4 of the heading attitude of the vehicle; as shown, rule 4: l is₁＜0，L₂If theta is less than 0, and the trolley should be corrected to the left. When θ is 0 and η is less than d, the straight-line travel correction control is performed.

FIG. 7 is a schematic view of rule 5 for the heading attitude of the cart; as shown, rule 5: l is₁＞0，L₂If the angle is more than 0 and theta is less than 0, the trolley keeps the moving direction unchanged. When the posture rule 1 is changed, the correction routine of the rule 1 is executed.

FIG. 8 is a schematic view of rule 6 for the heading attitude of the cart; as shown, rule 6: l is₁＞0，L₂If theta is greater than 0, and the trolley should be corrected to the right. When θ is 0 and η is less than d, the straight-line travel correction control is performed.

As a further improvement of the above embodiment, the method further comprises the following steps:

s9: when the robot runs out of the range of the magnetic stripe in the running process, forward video navigation is carried out, and the forward video navigation system provides information of offset and center offset distance for the motion control system.

FIG. 10 is a schematic view of the turn magnetic stripe and RFID tag location arrangement; fig. 11 shows a car turning control flow, where 8 shows 4 magnetic stripes for presenting turning information, 9 shows RFID tags, and 10 shows magnetic stripe mounting slots.

As shown in the drawing, as a further improvement of the above embodiment, the turning control includes the steps of:

s11: when the robot walks in a straight line, turning direction information of the RFID label is obtained through the RFID reader-writer, and turning position information provided by the magnetic stripe is detected through the magnetic sensor;

s12: stopping the robot after the robot moves for a preset extension time;

s13: judging and determining the turning direction information of the robot according to the zone bits provided by the FRID label;

s14: according to the turning direction information, enabling one corresponding wheel to rotate and the other wheel to stop;

s15: detecting the magnetic stripe, and judging whether the magnetic stripe is positioned between the front row magnetic sensor and the rear row magnetic sensor; if the magnetic strip is not positioned between the front row magnetic sensor and the rear row magnetic sensor, returning to the step S14 to continue turning;

s16: and if the magnetic strip is positioned between the front row magnetic sensor and the rear row magnetic sensor, the turning is stopped.

The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and it is apparent that those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (3)

1. The variable structure control method of the wheeled mobile robot comprises a left wheel, a right wheel, a driver, a front-row magnetic sensor, a rear-row magnetic sensor, a wheeled mobile robot body and an RFID reader-writer, wherein the driver controls the rotation speed of a motor, the RFID reader-writer is arranged at the front end of a trolley, a magnetic stripe is arranged in a magnetic stripe mounting groove in the center of a road, an RFID label is further arranged on the road, a turning information magnetic stripe is further arranged at a turning position of the road, the front-row magnetic sensor is arranged at the front end of the trolley, and the rear-row magnetic sensor is arranged at the rear end of the trolley, and the variable structure control method is characterized in that: the robot comprises a linear walking control unit and a turning control unit, wherein the linear walking control unit controls the robot to move linearly and corrects the advancing direction of the robot according to the rotating speed difference and the voltage difference of a left wheel driving motor and a right wheel driving motor of the robot; the turning control is to determine the information required by the turning of the robot according to the turning direction information provided by the RFID tag and the turning position information provided by the magnetic stripe, and the turning action is controlled by adopting an in-situ right-angle turning mode;

the linear walking control is in a variable structure control mode and comprises an inner control ring and an outer control ring, wherein the inner control ring controls the linear movement of the robot by adopting a multi-mode PID control method; the outer control ring corrects the advancing direction of the trolley by adopting a control method combining PID control and regular control; the inner control loop and the outer control loop are switched according to the following conditions: when the direction angle theta is equal to 0 and the central offset eta is less than d, the inner control ring controls the advance of the robot, otherwise, the outer control ring adjusts the advance direction of the robot; wherein d is the interval between two adjacent magnetic sensors;

the center offset is calculated by the following formula:

$\mathrm{\&eta;}=\frac{L_1+L_2}{2}$

the direction angle is calculated by the following formula:

$\tan \mathrm{\&theta;}=\frac{L_1-L_2}{L}$

wherein,L₁indicating the distance, L, from the centerline sensed by a front-row magnetic sensor sensing a magnetic stripe₂The distance between the front row of magnetic sensors sensing the magnetic stripes and the central line is represented, and the distance between the front row of magnetic sensors and the rear row of magnetic sensors is represented by L;

the multi-modal PID control method comprises the following steps:

s1: computing a coefficient K in a multi-modal PID control_p、K_i、K_d，

Wherein K_pDenotes the proportionality coefficient, K_iDenotes the integral coefficient, K_dRepresents a differential coefficient;

the turning control includes the steps of:

s11: when the robot walks in a straight line, turning direction information of the RFID label is obtained through the RFID reader-writer, and turning position information provided by the magnetic stripe is detected through the magnetic sensor;

s12: stopping the robot after the robot moves for a preset extension time;

s13: judging and determining the turning direction information of the robot according to the zone bits provided by the FRID label;

s14: according to the turning direction information, enabling one corresponding wheel to rotate and the other wheel to stop;

s15: detecting the magnetic stripe, and judging whether the magnetic stripe is positioned between the front row magnetic sensor and the rear row magnetic sensor; if the magnetic strip is not positioned between the front row magnetic sensor and the rear row magnetic sensor, returning to the step S14 to continue turning;

s16: if the magnetic strip is positioned between the front row magnetic sensor and the rear row magnetic sensor, the turning is stopped;

s2: inputting the collected robot movement information y (k), wherein y (k) is the rotating speed difference between the left wheel and the right wheel at the current moment;

s3: calculating a deviation e (k) ═ r (k) — y (k),

wherein y (k) represents the input quantity of the current sampling; r (k) represents a given input quantity; e (k) represents the deviation of the given input quantity and the current sampling input quantity;

s4: the control amount is calculated by the following formula:

u(k)＝u(k-1)+K_p(e(k)-e(k-1))+K_ie(k)+K_d(e(k)-2e(k-1)+e(k-2))

wherein e (k) represents the deviation of the given input quantity and the current sampling input quantity; e (k-1) represents the deviation between the given input amount and the last input amount; e (k-2) represents the deviation between the given quantity and the last sampled input quantity; u (k) represents a control amount to be output; u (k-1) represents the control amount of the last output;

s5: outputting a control quantity u (k), and driving the motor to control the motion of the robot through the robot driving module;

s6: modifying the deviation through the following formula, setting the deviation as the next deviation, and setting the next deviation as the last deviation:

e(k)→e(k-1)，e(k-1)→e(k-2);

s7: judging whether the sampling time arrives, if not, recording the sampling time until the sampling time is finished, and entering the next step;

s8: if the sampling time is finished, returning to the step S2 for the next sampling input;

in the step of calculating the controlled variable, before calculating the controlled variable, the modes of the PID are divided according to the change of the difference between the rotating speeds of the left wheel and the right wheel of the robot, and the PID mode division is performed according to the following method:

when the rotating speed difference of the left wheel and the right wheel is smaller than a preset minimum threshold value, using PID control;

when the rotating speed difference of the left wheel and the right wheel is larger than a preset maximum threshold value, P control is used;

when the rotating speed difference of the left wheel and the right wheel is between a preset minimum threshold value and a preset maximum threshold value, PI control is used;

the preset minimum threshold value is 1r/min, and the preset maximum threshold value is 2 r/min;

the outer control loop comprises the steps of:

s21: collecting position deviation signals of the robot by using magnetic sensors and magnetic stripe positions at the front end and the rear end of the robot and calculating the posture of the robot;

s22: judging whether the robot posture deviates or not, if not, keeping the original posture of the robot to move;

s23: if deviation occurs, the control quantity is calculated by adopting the following formula:

e(k)＝k₁η(k)+k₂θ(k)

u(k)＝u(k-1)+K_p(e(k)-e(k-1))+K_ie(k)+K_d(e(k)-2e(k-1)+e(k-2))

wherein k is₁Proportionality coefficient, k, representing the amount of center shift₂Proportional coefficient, k, representing angle of orientation₁、k₂Is determined according to the pose of the robot; e (k) represents the deviation of the given input quantity and the current sampling input quantity; e (k-1) represents the deviation between the given input amount and the last input amount; e (k-2) represents the deviation between the given quantity and the last sampled input quantity; u (k) represents a control amount to be output; u (k-1) represents the last output quantity; eta (k) represents the distance between the line and the center of the path in the robot detected this time; θ (k) represents a direction angle at which the robot is detected this time;

s24: determining the steering of the robot according to the deviation angle of the robot detected by the sensor;

s25: and after the steering of the trolley is determined, voltage is sent to the corresponding port of the driver according to the control quantity, so that the rotating speed of the motors of the two driving wheels of the robot and the steering of the wheels are adjusted.

2. The method of controlling a variable structure of a wheeled mobile robot according to claim 1, characterized in that: further comprising the steps of:

s9: when the robot runs out of the range of the magnetic stripe in the running process, forward video navigation is carried out, and the forward video navigation system provides information of offset and center offset distance for the motion control system.

3. The method of controlling a variable structure of a wheeled mobile robot according to claim 2, characterized in that: and calculating the deviation in the step S3 as the deviation of the left and right wheel rotating speed sampling quantity from a given value.

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