Leveling control method and system for lifting posture of robot

Abstract

The invention discloses a leveling control method and system for the lifting posture of a robot. The method comprises the steps that an actual angle is obtained through a sensor; a deviation signal of the angular velocity is obtained through an outer ring controller; and a speed signal is generated as final output through an outer ring controller, so that corresponding control actions are performed. By introducing the BP fuzzy neural PID control algorithm, accurate leveling of the lifting posture of the hot-line work robot is achieved, the problem of posture inclination caused by uneven winding and sudden slippage of the rope is effectively solved, the stability of the robot in the lifting process is improved, winding of the traction rope is avoided, and the work efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of robot posture control, and in particular to a leveling control method and system for a robot's lifting posture. Background Art

[0002] When the live working robot is lifted or lowered, there are two key problems that may cause the robot's posture to change or tilt seriously, or even cause the traction rope to be entangled, seriously affecting the efficiency of going online. The first problem is that it is impossible to ensure that the rope is completely wound on the four drums, resulting in unequal rope radius and posture tilt; the second problem is that the rope is stacked at one end of the drum. When the rope suddenly slides to the side, it will cause the robot's posture to change suddenly. Summary of the invention

[0003] In view of the above-mentioned problems, the present invention is proposed.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a leveling control method for the lifting posture of a robot, comprising:

[0005] Get the actual angle through the sensor;

[0006] The deviation signal of angular velocity is obtained through the outer loop controller;

[0007] Through the outer loop controller, a speed signal is generated as the final output, so as to perform corresponding control actions.

[0008] As a preferred solution of the leveling control method of the robot lifting posture described in the present invention, the live working robot has five degrees of freedom during the lifting process, namely vertical movement, rolling movement and pitching movement.

[0009] As a preferred solution of the leveling control method of the robot lifting posture described in the present invention, during the lifting process of the live working robot, the leveling of the robot when it goes online is controlled by a BP fuzzy neural PID control algorithm.

[0010] As a preferred solution of the robot lifting posture leveling control method of the present invention, wherein: the outer loop controller includes, the input signal is the desired angle value and the actual angle value measured by the posture sensor;

[0011] Calculate the angle deviation: the difference between the expected angle and the actual angle;

[0012] The angle deviation is taken as input, and after the PID controller operation, an angular velocity value is obtained as output; and the difference between the expected value and the feedback signal of the inner loop is calculated to obtain the angular velocity deviation signal.

[0013] As a preferred solution of the leveling control method of the lifting posture of the robot described in the present invention, wherein: the inner loop controller includes, the input signal is the angular velocity deviation: the angular velocity deviation signal output by the outer loop angle controller and the actual angular velocity measured by the posture sensor;

[0014] The angular velocity deviation is used as input, and after being calculated by the PID controller, a velocity signal is generated as the final output;

[0015] The speed signal is used to control the motor to drive the reel to rotate, thereby achieving robot posture leveling control.

[0016] As a preferred solution of the robot lifting posture leveling control method described in the present invention, the specific process of the fuzzy PID algorithm in the outer loop controller is:

[0017] The output value y(t) is obtained according to the sensor data, and it is compared with the input value r(t) to obtain the system error e(t), and the error change rate e is calculated at the same time. c (t);

[0018] The error e)t) and the error change rate e c (t) Perform fuzzification and map it to the corresponding domain;

[0019] Select the membership function and calculate e(t) and e c (t) Membership degree in the respective fuzzy subsets;

[0020] Construct a fuzzy rule base and use the fuzzy rules to p , K I and K D Perform fuzzy reasoning;

[0021] By defuzzifying the obtained fuzzy output data, the specific K p , K I and K D The value of

[0022] Among them, K p , K I and K D They represent the proportional coefficient, integral coefficient, and differential coefficient respectively.

[0023] As a preferred solution of the robot lifting posture leveling control method described in the present invention, wherein: in the outer loop controller, the input of the fuzzy PID controller is the angle deviation e i and the rate of change of angle deviation e lc , the output is the change in proportional coefficient ΔK p , integral coefficient change ΛK I And the differential coefficient change ΔKD ;

[0024] When the error is large, adjust K according to the positive and negative conditions of the error and the error change rate. p , K I and K D .

[0025] A robot lifting posture leveling control system using the method of the present invention is characterized by:

[0026] The acquisition unit obtains the actual angle through the sensor;

[0027] An outer loop control unit obtains an angular velocity deviation signal through an outer loop controller;

[0028] The inner loop control unit generates a speed signal as the final output through the outer loop controller, thereby performing corresponding control actions.

[0029] A computer device comprises: a memory and a processor; the memory stores a computer program, wherein: the processor implements the steps of any one of the methods of the present invention when executing the computer program.

[0030] A computer-readable storage medium stores a computer program, wherein: when the computer program is executed by a processor, the steps of any one of the methods of the present invention are implemented.

[0031] Beneficial effects of the invention: The robot lifting posture leveling control method provided by the invention realizes precise leveling of the lifting posture of the live working robot by introducing the BP fuzzy neural PID control algorithm, effectively solves the posture tilt problem caused by uneven rope winding and sudden slippage, improves the stability of the robot lifting process, avoids the winding of the traction rope, and improves the working efficiency and safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0033] Figure 1 An overall flow chart of a robot lifting posture leveling control method provided by the first embodiment of the present invention;

[0034] Figure 2 A robot posture leveling control flow chart that is the same as the robot lifting posture leveling control method provided by the first embodiment of the present invention;

[0035] Figure 3 A layout diagram of a hoisting mechanism reel for a leveling control method for a robot lifting posture provided by the first embodiment of the present invention;

[0036] Figure 4 A force analysis diagram of a reel of a leveling control method for a robot lifting posture provided by the first embodiment of the present invention;

[0037] Figure 5 A fuzzy PID algorithm flow chart of a robot lifting posture leveling control method provided by the first embodiment of the present invention;

[0038] Figure 6 A fuzzy PID control block diagram of a robot lifting posture leveling control method provided by the first embodiment of the present invention;

[0039] Figure 7 A fuzzy neural network PID algorithm flow chart of a robot lifting posture leveling control method provided by the first embodiment of the present invention;

[0040] Figure 8 A comparison diagram of step response simulation results of two control models in a leveling control method for a robot lifting posture provided by the first embodiment of the present invention;

[0041] Fig. 9 A comparison diagram of step signal interference simulation results of two control models in a leveling control method for the lifting posture of a robot provided in the first embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0043] Example 1, reference Figure 1-7 , as an embodiment of the present invention, provides a leveling control method for a robot lifting posture, comprising:

[0044] The lifting mechanism is located between the walking arm and the control box. The distribution of the four drums is as follows: Figure 3As shown. A bidirectional thread groove is provided on the reciprocating screw to ensure that each drum automatically moves in the opposite direction after the screw runs to the end of the thread groove, so as to achieve uniform distribution of the rope as much as possible to reduce the risk of sudden changes in posture. The traction rope placement hole of the drum is designed to just accommodate the knotted rope, and a placement groove is provided on the side of the placement hole to prevent the rope from being pulled out during the ascent of the robot, thereby ensuring the safety of the robot and avoiding falling accidents. The four traction rope positioning holes are located above the lifting mechanism and are composed of two fixed pulleys, which are distributed in a square shape. The traction rope is passed through the positioning holes to prevent the rope from sliding back and forth or even entangled in the walking arm or the end of the operation during the lifting process of the robot, causing the operation to fail. L1, L2, L3, and L4 are 4 drums respectively.

[0045] The live working robot has five degrees of freedom during the lifting process, namely vertical movement, rolling movement and pitching movement. In order to ensure that the robot's posture remains stable and controllable during the lifting process, the tension on the four reels must be adjusted in real time. The magnitude of the tension depends on the current rotation speed of each reel. To this end, the four reels will be driven by four independently running servo motors.

[0046] (1) Vertical lifting movement

[0047] The vertical lifting of the robot is achieved by the motor driving the reels to rotate. When the reels L1, L2, L3, and L4 rotate at the same speed, the robot will remain vertical and achieve ascending or descending movement. If they rotate at different speeds, the robot's posture will tilt.

[0048] (2) Left and right rolling motion

[0049] The robot's left and right rolling is achieved by rotating the reels at different speeds. When reels L2 and L3 rotate at the same speed, and reels L1 and L4 rotate at the same speed, the robot will maintain a balanced state. If the rotation speed of reels L2 and L3 increases, or the rotation speed of reels L1 and L4 decreases, the robot will tilt to the left. Conversely, if the rotation speed of reels L1 and L4 increases, or the rotation speed of reels L2 and L3 decreases, the robot will tilt to the right.

[0050] (3) Forward and backward pitch motion

[0051] The robot's forward and backward pitch is achieved by rotating the reels at different speeds. When reels L3 and L4 rotate at the same speed, and reels L1 and L2 rotate at the same speed, the robot will maintain a balanced state. If the rotation speed of reels L3 and L4 increases, or the rotation speed of reels L1 and L2 decreases, the robot will tilt forward. Conversely, if the rotation speed of reels L1 and L2 increases, or the rotation speed of reels L3 and L4 decreases, the robot will tilt backward.

[0052] The lifting and lowering of the robot mainly relies on four motors. By adjusting the speed of the four motors, each drum can control the pulling and releasing force of the traction rope. The entire operation process must ensure sufficient driving force to ensure that the robot can stably and safely complete the up and down line tasks. Therefore, it is particularly important to perform a force analysis on the drum of the lifting device. After analysis, the exact parameters of the required motor can be determined and the motor can be selected. The force analysis of the drum is as follows: Figure 4 shown.

[0053] The lifting and leveling control process of the live working robot is as follows: Figure 2 Firstly, the robot structure and electromechanical transmission are analyzed to determine the motor selection, and then the BP fuzzy neural network PID control algorithm is used to perform leveling control on the posture of the machine during the lifting process.

[0054] S1: Get the actual angle through the sensor.

[0055] It should be said that live working robots need to overcome posture tilt problems caused by various reasons during the lifting process, so a control algorithm needs to be introduced to solve these problems. The BP fuzzy neural PID control algorithm is an innovative algorithm that combines fuzzy control and neural networks. It has the ability to self-learn fuzzy concepts. It has the advantages of both controls: strong robustness, strong adaptability, high precision, and self-learning ability. It performs better than fuzzy PID in practical applications. Therefore, this method is selected to control the leveling of the robot when it goes online.

[0056] This requires obtaining the current state through sensors and using the output value of the sensor as the current state for control.

[0057] S2: Obtain the angular velocity deviation signal through the outer loop controller.

[0058] The outer loop controller includes: input signals are the expected angle value and the actual angle value measured by the attitude sensor; calculate the angle deviation: the difference between the expected angle and the actual angle; use the angle deviation as input, and obtain an angular velocity value as output through PID controller operation; and calculate the difference between the expected value and the feedback signal of the inner loop to obtain an angular velocity deviation signal.

[0059] It should be said that fuzzy PID is a control algorithm based on fuzzy logic. Compared with the traditional PID controller, fuzzy PID takes into account the fuzzy relationship between the control input and output variables, and can better adapt to complex control environments. In the traditional PID controller, the three parameters of proportional, integral and differential are manually adjusted according to the mathematical model and experience of the system, while the fuzzy PID controller uses fuzzy logic to handle the fuzzy relationship between input and output, which can automatically adjust these parameters more flexibly to adapt to nonlinear control systems.

[0060] The working principle of the fuzzy PID controller is to fuzzify the input and output into fuzzy sets, use fuzzy rules to infer the fuzzy sets of the output, and then convert the fuzzy output into specific control actions through the process of defuzzification. This control method can better cope with the characteristics of system nonlinearity, uncertainty and fuzziness.

[0061] The main operation mode of the Mamdani-type fuzzy controller includes the following steps: first, define the input and output variables and divide them into fuzzy sets; then, based on expert knowledge or experience, determine a set of fuzzy rules to describe the relationship between the input variables and the output variables; then, use the fuzzy reasoning method to combine the fuzzy set of the input variables and the fuzzy rule set to obtain the fuzzy set of the output variables; finally, defuzzify and map the fuzzy set of the fuzzy output variables to specific output values. According to the specific output values ​​obtained by defuzzification, perform corresponding control actions. The fuzzy PID algorithm flow chart is shown in the figure. Figure 5 Specifically:

[0062] (1) The output value y(t) is obtained based on the sensor data, and is compared with the input value r(t) to obtain the system error e(t), and the error change rate e is calculated at the same time. c (t).

[0063] (2) The error e(t) and the error change rate e c (t) Fuzzify them and map them into corresponding domains.

[0064] (3) Select an appropriate membership function and calculate e(t) and e c (t) Membership in their respective fuzzy subsets (4) Construct a fuzzy rule base and use the fuzzy rules to p , K I and K D Perform fuzzy reasoning. The fuzzy rule base contains a series of rules that describe how to adjust PID parameters under different error and error change rate conditions.

[0065] (5) By defuzzifying the obtained fuzzy output data, the specific K p , K I and K D The defuzzification process converts the fuzzy output into a specific value, which will be used as the parameter of the PID controller.

[0066] Depend on Figure 6 It can be seen that the input of the fuzzy PID controller is the angle deviation e i and the rate of change of angle deviation e lc , the output is the change in proportional coefficient ΔK p , integral coefficient change ΔK I And the differential coefficient change ΔK D These data are fed back to the outer loop control module through the fuzzy controller to adjust the data.

[0067] When the error is large, the following methods can be used to adjust K according to the positive and negative conditions of the error and the error change rate: p , K I and K D , the adjustment rules are as follows:

[0068] (1) When the product of the error and the error change rate is greater than zero (the error is moving in a larger direction): increase K p The value of K can enhance the response speed and sensitivity of the system and reduce the error more quickly. p This may cause the system to over-regulate and oscillate, so it needs to be adjusted carefully; reduce K I The value of K can be shortened to reduce the integral response time and speed up the system to eliminate the steady-state error. I This can prevent the system from being unstable due to a long integration process, but a too small K I May cause the system to be sensitive to noise or produce overregulation; increasing K D The value of K can enhance the system's suppression of the error change rate and reduce the system's oscillation and shock. D It can better adapt to the rapid changes of errors, but too large K D This may cause the system to be over-suppressed. (2) When the product of the error and the error change rate is less than zero (the error is moving in a smaller direction): increase K p The value of K can enhance the system's sensitivity to errors and speed up the system's response. p It can reduce the error faster, but too large K p This may cause the system to over-regulate and oscillate, so it needs to be adjusted carefully; increasing K I The value of K can enhance the integral response of the system and help eliminate steady-state errors. IThe system maintains the integral action for a longer time, gradually reducing the error, but too large K I This may cause the system to respond too slowly or produce overshoot; reduce K D The value of K can reduce the system's sensitivity to the error change rate and reduce oscillation and shock. D It can better adapt to the change of error, but too small K D This may cause the system to be sensitive to noise or weaken the suppression effect. Based on previous experience in parameter adjustment, the fuzzy domain range of the angle deviation e is set to [-3,3], and the angle deviation change rate e is set to c The fuzzy domain range is [-2.5, 2.5], the fuzzy subsets of input and output are {NB, NM, NS, ZO, PS, PM, PB}, and the input and output variables both use triangular membership functions.

[0069] The fuzzy PID rule inference table is established as shown in Table 1-3:

[0070] Table 1ΔK p Fuzzy rule reasoning table

[0071]

[0072]

[0073] Table 2ΔK I Fuzzy rule reasoning table

[0074]

[0075] Table 3ΔK D Fuzzy rule reasoning table

[0076]

[0077] According to the above table, by comparing e and e c Perform fuzzification and fuzzy reasoning calculations to obtain the corresponding fuzzy output, use the centroid method to defuzzify, and use the following formula to adjust the parameters.

[0078]

[0079] Among them, K p0 ,K l0 ,K l0 is the initial value of each PID parameter; K p , K I , K D The value adjusted by fuzzy control.

[0080] During the robot lifting process, according to the detected e and e c, adjust the three parameters of PID in real time, and control the speed of the motor by the adjusted parameters in the following formula.

[0081]

[0082] Where: k is the sampling number, T is the sampling time, and e(k) is the motion deviation at the current moment.

[0083] e(k)=ry

[0084] e c (k) = e(k) - e(k-1)

[0085] During the actual lifting process, the robot will be affected by the drum, causing the posture to change. Even though the diameter of the drum has been increased on the basis of the previous generation to reduce the number of turns of the traction rope, in actual tests, the rope will still be stacked and wound on one side, and suddenly slide to the side, causing the robot's posture to change suddenly. In addition, it is impossible to ensure that the rope is wound around the drum very regularly, and the diameter of the drum will also change accordingly, and the changes of the four drums are different, which will affect the stability of the robot's posture. The ordinary fuzzy PID algorithm cannot adjust the robot's posture in time, and cannot ensure the safety of the robot's up and down lines. For this reason, the outer loop PID controller in the original fuzzy PID controller is changed to a BP fuzzy neural network algorithm with self-learning ability.

[0086] BP fuzzy neural network PID (Backpropagation Fuzzy Neural Network PID) is a PID control method that combines fuzzy control and BP neural network. This method uses fuzzy neural network for nonlinear mapping and fuzzy reasoning, and combines the BP neural network's error back propagation algorithm for parameter optimization to achieve more precise control. By combining the self-learning ability of neural network and the reasoning ability of fuzzy control, the BP fuzzy neural network PID algorithm can better adapt to nonlinear systems and improve control performance. The algorithm can optimize PID parameters through neural network training, thereby achieving more accurate and stable control. The algorithm flow chart is shown below. Figure 7 shown.

[0087] The specific BP fuzzy neural network PID algorithm process is as follows:

[0088] (1) Determine the number of nodes and connection mode of the input layer, fuzzification layer, rule layer and output layer of the neural network. The fuzzification layer is used to map the input variables to fuzzy subsets, the rule layer is used to store fuzzy rules, and the output layer is used to calculate the output of the controller.

[0089] (2) Initialize the center value, width, and weight of the output layer of the activation function, and select the learning efficiency and inertia factor. The center value and width of the activation function determine the shape and range of the fuzzy subset, and the output layer weight is used to calculate the output of the controller.

[0090] (3) Reasoning about the fuzzy process and calculating the error. Map the input variables to fuzzy subsets, and obtain the fuzzy output according to the fuzzy rules. Then, the fuzzy output is converted into a specific controller output value through defuzzification processing, and the error between the actual output value and the expected output value is calculated.

[0091] (4) Adjust the parameters and weights of the activation function according to the error until the learning is completed. Use the error information to update the parameters of the fuzzy neural network, including the center value and width of the activation function and the output layer weights. Usually, an optimization algorithm such as gradient descent is used to update the parameters to minimize the error.

[0092] In order to solve the shortcomings of traditional fuzzy PID controller in self-learning ability, as well as the slow convergence speed and low control accuracy in the control process, the self-learning function of BP neural network is integrated with fuzzy PID control technology to construct a new controller design scheme to solve many of the above problems.

[0093] The network topology of the BP fuzzy neural network is described as follows: Input layer: This layer contains 2 nodes, which are the two input value errors e and the error change rate e. c , the role of this layer is to pass the input signal to the next layer.

[0094]

[0095] Where: is the input value of the first layer node; is the output value of the first layer node; x m is the mth input variable; x mn is the membership of the mth input variable on the nth fuzzy subset. Fuzzification layer: This layer contains 2 inputs, corresponding to 7 linguistic variable values. The function of this layer is to generate a membership function for each linguistic variable value.

[0096]

[0097] Where: is the input value of the second layer node; is the output value of the second layer node; c m m is the membership function center of the mth input signal in the nth fuzzy subset; σ mn is the basis width of the membership function on the nth fuzzy subset of the mth input signal; is the linguistic variable value of the mth input variable on the nth fuzzy subset. Fuzzy regularization layer: This layer contains 49 nodes, each of which corresponds to a fuzzy rule and is used to calculate its fitness for the rule.

[0098]

[0099] Where: is the input value of the third layer node; is the output value of the third layer node; The nth input of the first layer i The linguistic variable value corresponding to the fuzzy subset; It is the language variable value corresponding to the n2th fuzzy subset of the second input of the upper layer; n1=1,2,...,7; n2=1,2,...,7; ι is the rule point, l=1,2,...,49. Normalization layer: This layer contains 49 nodes, and its function is to perform normalization calculation on the output of the third layer.

[0100]

[0101] Where: is the input value of the third layer node; is the output value of the third layer node. Output layer: This layer is used to perform clarification operations. The three neuron nodes correspond to K p , K I , K D .

[0102]

[0103] Where: is the input value of the fifth layer node; is the output value of the fifth-layer node; h is the number of nodes in the fifth layer, h = 1, 2, 3; ω mn is the network weight of the output layer.

[0104] Output:

[0105]

[0106] Optimize the BP fuzzy neural PID algorithm. Given the output value of the system is r(t), the actual output value of the system is y(t), and the network error function is defined as:

[0107]

[0108] Use gradient optimization algorithm to optimize c mn , σ ad ,ω dd Three parameters:

[0109]

[0110] Among them, k is the number of iterations, α is the inertia factor, and η is the learning rate.

[0111] S3: Generate a speed signal as the final output through the outer loop controller, so as to perform corresponding control actions.

[0112] The input signal is the angular velocity deviation: the angular velocity deviation signal output by the outer ring angle controller and the actual angular velocity measured by the attitude sensor; the angular velocity deviation is used as input, and after calculation by the PID controller, a speed signal is generated as the final output; the speed signal is used to control the motor to drive the reel to rotate, thereby realizing the robot attitude leveling control.

[0113] Embodiment 2 is an embodiment of the present invention, which provides a leveling control system for the lifting posture of a robot, including: a collection unit, which obtains the actual angle through a sensor.

[0114] The outer loop control unit obtains the angular velocity deviation signal through the outer loop controller.

[0115] The inner loop control unit generates a speed signal as the final output through the outer loop controller, thereby performing corresponding control actions.

[0116] If the above functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0117] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0118] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0119] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0120] Example 3 is an embodiment of the present invention, which provides a leveling control method for the lifting posture of a robot. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0121] The pitch angle fuzzy PID controller was constructed using MATLAB Simulink software. The existing pitch angle fuzzy PID controller was adjusted and improved, while the parameter settings of the inner loop PID controller remained unchanged. At the outer loop PID controller level, a more advanced BP fuzzy neural network controller was used to replace the traditional fuzzy controller.

[0122] The step response simulation analysis of the two control methods is carried out, and the results are as follows: Figure 8 As shown in the figure, when the system receives a step signal, the fuzzy PID controller responds slowly, and it takes about 4 seconds to reach a stable state, and there will be obvious oscillations, and its overshoot is also large. In contrast, the BP fuzzy neural network PID responds faster, and it only takes 2.4 seconds to reach a stable state, and its overshoot is smaller, and the system is more stable. Therefore, the BP fuzzy neural network PID controller will perform better in robot leveling operations.

[0123] When the system reaches a stable state, a step signal interference is added at the 10th second to simulate the posture change conditions that the robot may encounter. The simulation results of the anti-interference ability of the two control methods are compared. Fig. 9 shown.

[0124] As can be seen from the figure, when the system receives a step signal interference, the fuzzy PID controller responds slowly, and it takes about 4 seconds to return to a stable state. In contrast, the BP fuzzy neural network PID responds faster, taking only 3 seconds to return to a stable state, and its oscillation is smaller and more stable. Therefore, the BP fuzzy neural network PID controller will perform better when the robot responds to emergencies.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A leveling control method for a robot's lifting posture, characterized in that: include: Get the actual angle through the sensor; The deviation signal of angular velocity is obtained through the outer loop controller; Through the outer loop controller, a speed signal is generated as the final output, so as to perform corresponding control actions.

2. The robot lifting posture leveling control method according to claim 1, characterized in that: The live working robot has five degrees of freedom during the lifting process, namely vertical movement, rolling movement and pitching movement.

3. The robot lifting posture leveling control method according to claim 2, characterized in that: During the lifting process of the live working robot, the BP fuzzy neural PID control algorithm is used to control the leveling of the robot when it goes online.

4. The robot lifting posture leveling control method according to claim 3, characterized in that: The outer loop controller includes, the input signals are the desired angle value and the actual angle value measured by the attitude sensor; Calculate the angle deviation: the difference between the expected angle and the actual angle; The angle deviation is taken as input, and after the PID controller operation, an angular velocity value is obtained as output; and the difference between the expected value and the feedback signal of the inner loop is calculated to obtain the angular velocity deviation signal.

5. The robot lifting posture leveling control method according to claim 4, characterized in that: The inner loop controller includes, the input signal is the angular velocity deviation: the angular velocity deviation signal output by the outer loop angle controller and the actual angular velocity measured by the attitude sensor; The angular velocity deviation is used as input, and after being calculated by the PID controller, a velocity signal is generated as the final output; The speed signal is used to control the motor to drive the reel to rotate, thereby achieving robot posture leveling control.

6. The robot lifting posture leveling control method according to claim 4, characterized in that: Before the outer loop controller, the specific process of the fuzzy PID algorithm is as follows: The output value y(t) is obtained according to the sensor data, and it is compared with the input value r(t) to obtain the system error e(t), and the error change rate e is calculated at the same time. c (t); The error e(t) and the error change rate e c (t) Perform fuzzification and map it to the corresponding domain; Select the membership function and calculate e(t) and e c (t) Membership degree in the respective fuzzy subsets; Construct a fuzzy rule base and use the fuzzy rules to p , K I and K D Perform fuzzy reasoning; By defuzzifying the obtained fuzzy output data, the specific K p , K I and K D The value of Among them, K p , K I and K D They represent the proportional coefficient, integral coefficient, and differential coefficient respectively.

7. The robot lifting posture leveling control method according to claim 5, characterized in that: In the outer loop controller, the input of the fuzzy PID controller is the angle deviation e i and the rate of change of angle deviation e lc , the output is the change in proportional coefficient ΔK p , integral coefficient change ΛK I And the differential coefficient change ΔK D ; When the error is large, adjust K according to the positive and negative conditions of the error and the error change rate. p , K I and K D .

8. A robot lifting posture leveling control system using the method as claimed in any one of claims 1 to 7, characterized in that: The acquisition unit obtains the actual angle through the sensor; An outer loop control unit obtains an angular velocity deviation signal through an outer loop controller; The inner loop control unit generates a speed signal as the final output through the outer loop controller, thereby performing corresponding control actions.

9. A computer device comprising: A memory and a processor; the memory stores a computer program, characterized in that: when the processor executes the computer program, the steps of the leveling control method of the robot's lifting posture are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by a processor, implements the steps of a method for leveling the lifting posture of a robot.

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