Master-slave space mining robot adaptive control system based on iavoa

Abstract

The application discloses a master-slave space mining robot adaptive control system based on IAVOA, which comprises the following steps: a master-slave kinematic model of the space mining robot is established according to the structure and motion characteristics of the space mining robot, and then a trajectory tracking control system of the space mining robot is established; in the controller design process, the direct feedback linearization theory is adopted to realize the global accurate linearization of the nonlinear system of the space mining robot, and the kinematic problem of the space mining robot is greatly simplified; the optimal initialization of the algorithm population is realized by introducing the Henon chaotic mapping population initialization and elite population strategy; the external storage library can help the vulture population to select more solution space, and avoid generating a large number of same non-inferior solutions; the adaptive optimization adjustment of the control parameter of the space mining robot control system can be realized, the complex working environment on the surface of the asteroid can be effectively coped with, and the accurate control of the motion of the system on the space mining robot can be realized.

Description

Technical Field

[0001] This invention relates to an adaptive control system, specifically an adaptive control system for a master-slave space mining robot based on IAVOA, belonging to the field of aerospace technology. Background Technology

[0002] Currently, with the continuous development of the economy and technological strength of countries around the world, the consumption of important energy sources such as oil, natural gas, and coal is increasing day by day. The Earth's important mineral resources, including ferrous metals, heavy metals, and light metals, are extremely limited and are facing depletion.

[0003] Beyond Earth, there are abundant mineral and usable resources. Celestial bodies such as the Moon, Mars, and near-Earth asteroids contain rich mineral resources and rare metals. Therefore, in order to solve the problem of increasingly depleted Earth's resources, it is crucial to design multifunctional and highly reliable space mining robots and achieve precise control over their operations, grasping, and flipping behaviors on the surfaces of celestial bodies in outer space.

[0004] Currently, the control systems of my country's space mining robots have the following shortcomings:

[0005] (1) Research on space robots that take near-Earth asteroids as their research object is in its early stages. There are few robot control systems for this environment, and it is impossible to achieve precise control of the operation process of space mining robots.

[0006] (2) Given the rugged and varied surface environment of celestial bodies in outer space, space mining robots are prone to slippage and rotation during operation, which places high demands on the control system. The current control system has weak adaptive capabilities and is difficult to meet the actual needs of space mining robots. Summary of the Invention

[0007] The purpose of this invention is to provide an adaptive control system for a master-slave space mining robot based on IAVOA in order to solve at least one of the above-mentioned technical problems.

[0008] This invention achieves the above objective through the following technical solution: an adaptive control system for a master-slave space mining robot based on IAVOA, the adaptive control system for the master-slave space mining robot comprising:

[0009] A kinematic model of a six-wheeled space mining robot was established as the research object;

[0010] The six-wheeled space mining robot includes a differential mechanism, a suspension assembly, and space mining robot wheels. The suspension assembly is connected to both ends of the differential mechanism. The suspension assembly includes a main suspension and a secondary suspension connected to the main suspension. The space mining robot wheels are rotatably connected to both the main suspension and the secondary suspension via steering motors. Each space mining robot wheel is connected to a drive motor.

[0011] The control method of the master-slave space mining robot adaptive control system includes:

[0012] Step 1: Taking a six-wheeled space mining robot as the research object, assuming that the overall material of the space mining robot is uniform and the center of mass is located at the geometric center of the vehicle body, establish the kinematic model of the space mining robot;

[0013] Step 2: In the process of motion control of the space mining robot, in order to describe its trajectory tracking process, a trajectory following model of master and slave space mining robots is introduced, and a virtual robot that maintains a constant distance from the master space mining robot and whose direction of movement is always consistent is introduced.

[0014] Step 3: Simplify the master-slave space mining robot control model by using coordinate transformation to establish a coordinate system with the slave space mining robot as the master.

[0015] Step 4: Complete the kinematic modeling of the space mining robot and establish its motion controller. At the same time, use the IAVOA optimization algorithm to adaptively optimize the control system of the space mining robot.

[0016] Step 5: For the random initialization of the vulture population, Henon chaotic mapping population initialization and elite population strategy are introduced to improve the African vulture algorithm, and thereby optimize the selection of parameters for the master-slave space mining robot adaptive control system.

[0017] As a further aspect of the present invention: the kinematic model of the space mining robot established in step one is as follows:

[0018]

[0019] Where v(t) is the linear velocity of the space mining robot, w(t) is the angular velocity of the space mining robot, and l is the vertical distance between the front wheel axle of the space mining robot and the center of mass of the vehicle body.

[0020] During the movement of the space mining robot, the relationship between its linear velocity v(t), angular velocity w(t), and the velocities of its left and right drive wheels is as follows:

[0021]

[0022]

[0023] Where S is the radius of rotation of the space mining robot during its rotational motion, and v l (t) and v r (t) The linear velocity of the left and right wheels of the space mining robot, where H represents the distance between the centers of the left and right wheels.

[0024] As a further aspect of the present invention: In step two, G1, G2, and G3 represent the centroid coordinates of the slave robot, the virtual robot, and the master robot, respectively, v a (t) and w a (t) represent the linear velocity and angular velocity of the space mining robot, respectively. b (t) and w b (t) represent the linear velocity and angular velocity of the virtual space mining robot, respectively. c (t) and w c (t) represent the linear velocity and angular velocity of the main space mining robot, respectively;

[0025] Using the main space mining robot as a reference, the pose information of the virtual space mining robot is as follows:

[0026]

[0027] Differentiating the above equation, we obtain the kinematic model of the virtual space mining robot as follows:

[0028]

[0029] Based on the positional relationship between the virtual robot and the main robot, the following following error model is established:

[0030]

[0031] Among them, e x (t) and e y (t) represents the positional deviation between the virtual robot and the slave robot, e θ (t) represents the angular deviation between the virtual robot and the slave robot;

[0032] Therefore, we can conclude that:

[0033]

[0034] Differentiating both sides of the above equation, we get:

[0035]

[0036] As a further aspect of the present invention: in step three, the coordinate transformation equations are as follows:

[0037]

[0038] Where Z1(t) and Z2(t) represent the position errors of the robot and the virtual robot in the new coordinate system, respectively;

[0039] Combining the above equations, we can obtain the following positional error between the master and slave space mining robots:

[0040]

[0041] Among them, P1 and P2 are the lateral and longitudinal position differences of the master and slave space mining robots, respectively;

[0042] Differentiating the above equation, we get:

[0043]

[0044] Differentiating the above equation, we get:

[0045]

[0046] Combining the above equations, we can obtain

[0047]

[0048] From θ a (t)=θ c (t)-e θ Thus, the derivative model of the position error between the master and slave space mining robots is obtained as follows:

[0049]

[0050] Once the linear velocity and angular velocity of the main space mining robot are obtained using the above formula, the trajectory of the main space mining robot can be accurately tracked by controlling the linear velocity and angular velocity of the secondary space mining robot.

[0051] The master-slave space mining robot's follower model is written in the following form:

[0052]

[0053] Among them, P 12 =[P1, P2] T u a =[v a (t), w a (t)] T u c =[v c (t), w c (t)] t ;

[0054]

[0055]

[0056] Based on the direct feedback linearization theory, when there is a distance error between the master space mining robot and the slave space mining robot, the input of the slave space mining robot is defined as:

[0057] u a =A -1 (e-Bu c (16) Where e is defined as the auxiliary control input, and is defined as:

[0058]

[0059] Here, k1 and k2 are defined as the controller gains of the space mining robot. This indicates the desired lateral and longitudinal spacing between the master and slave space mining robots;

[0060] Therefore, the master-slave space mining robot's follower model is simplified as follows:

[0061]

[0062] Direct feedback linearization theory only requires establishing a virtual control input quantity and obtaining a nonlinear feedback compensation law to achieve global accurate linearization of the nonlinear system. The key to achieving linearization of the nonlinear system by the direct feedback linearization method is to select a suitable virtual control quantity to counteract the nonlinear factors in the original system, thereby achieving linearization.

[0063] As a further aspect of the present invention: in step four, the IAVOA optimization algorithm specifically includes:

[0064] The African Vulture Optimization Algorithm (AVOA) is based on the lifestyle of African vultures and simulates their foraging and navigation behaviors.

[0065] First, initialize the population, calculate the fitness of all solutions, select the best solution as the best vulture in the first group, select the second best solution as the best vulture in the second group, and move other solutions to the best solutions in the first and second groups; in each fitness iteration, the entire population will be recalculated.

[0066]

[0067] In the formula, R(i) represents the positions of vultures other than the best and second-best vultures; BestV1 and BestV2 represent the best and second-best vulture positions, respectively; L1 and L2 represent the parameters to be measured between 0 and 1, and their sum is 1; p i The probability of selecting the best vulture; F iFor the fitness of other vultures; use a roulette wheel to obtain the probability of selecting the best solution, and select each best solution for each group;

[0068]

[0069] Calculating the hunger rate of vultures; mathematical modeling inspired by the speed at which vultures become full or hungry, and also used to transition from the exploration phase to the development phase; the decreasing satiety rate is used to simulate this behavior;

[0070]

[0071]

[0072] Where F represents the vulture's hunger rate, t represents the current iteration number, T represents the maximum number of iterations, z is a random number between -1 and 1 that changes with each iteration, h is a random number between -2 and 2, and rand1 is a random number between 0 and 1. When the z value drops below 0, it indicates that the vulture is hungry; if the z value increases to 0, it indicates that the vulture is full. The proportion of the total number of vultures decreases, and the decrease is greater with each repetition. When the value of F is greater than 1, the vulture searches for food in different areas, entering the exploration phase; if the value of F is less than 1, the vulture enters the development phase, searching for food near the optimal solution.

[0073] During the exploration phase, vultures examine different random areas throughout the population, selecting one strategy based on two different strategies and using a parameter called P1. This parameter must be assigned a value between 0 and 1 before the search operation to determine how to use either strategy; to select which strategy to use during the exploration phase, a random number between 0 and 1 needs to be generated. The process is shown in the following formula:

[0074]

[0075] P(i+1)=R(i)-|X×R(i)-P(i)|×F (24)

[0076] In the formula, the vulture randomly searches for food within a random distance of one of the two optimal groups, where P(i+1) is the vulture's position vector in the next iteration, and F is the vulture's satiety rate obtained in the current iteration. X is the place where the vulture randomly moves to protect the food from other vultures. X is used as a coefficient vector to increase the random movement, which changes in each iteration and is obtained using the formula X = 2rand, where rand is a random number between 0 and 1; P(i) is the vulture's current vector position.

[0077] P(i+1)=R(i)-F+rand2×((ub-lb)×rand3+lb) (25)

[0078] In the formula, rand2 and rand3 are random values ​​between 0 and 1, and lb and ub represent the upper and lower bounds of the variable; by creating a high random coefficient on the scale of the search environment to increase diversity and search different search space regions, the movement of vultures is simulated in this way.

[0079] Upon entering the development phase, when the value of F is between 0.5 and 1, the vulture population enters the first stage of development. In the first stage, two different rotational flight and encirclement strategies are implemented; P2 is used to determine the choice of each strategy, and this value should be between 0 and 1; at the start of this stage, [the following is a process / function] is generated. It is a random number between 0 and 1; if the number is greater than or equal to parameter P2, the siege strategy will be implemented slowly; however, if the random number is less than parameter P2, the rotation flight strategy will be executed; the process is as follows:

[0080]

[0081] When |F|≥0.5, vultures have relatively sufficient energy; when many vultures gather at a food source, they may cause serious conflicts over food acquisition; in such cases, physically strong vultures prefer not to share food with other vultures.

[0082] On the other hand, weaker vultures attempt to tire out healthier vultures by gathering around them and instigating minor conflicts in order to obtain food from the healthier vultures. This process is modeled as follows:

[0083] P(i+1)=|X×R(i)-P(i)|×(F+rand4)-(R(i)-P(i)) (27)

[0084] In the formula, rand4 is a random number between 0 and 1, used to increase the randomness coefficient. P(i) is the current position vector of the vulture, through which the distance between the current vulture and one of the best vultures in the two groups can be obtained;

[0085] Vultures often perform rotating flight to simulate spiral motion; a spiral model has been used for mathematical modeling of rotating flight; in this method, a spiral equation is established between all vultures and one of the two best vultures:

[0086]

[0087] P(i+1)=R(i)-(S1+S2) (29)

[0088] In the formula, rand5 and rand6 are random numbers between 0 and 1; S1 and S2 are used to update the position of the vulture;

[0089] If |F| < 0.5, then this phase of the algorithm is executed. At the start of this phase, randP3 is generated, a random number between 0 and 1; the vulture swarm launches an aggressive struggle for food, as shown in the following equation:

[0090]

[0091] All vulture movements toward food sources were examined. Sometimes, vultures would go hungry, and there would be intense competition for food. Several types of vultures might accumulate at a single food source. This movement of vultures is described by the following formula:

[0092]

[0093]

[0094] Meanwhile, the lead vulture becomes hungry and weak, lacking the energy to fight the other vultures; on the other hand, the other vultures become aggressive in their search for food; they move in different directions toward the lead vulture, modeling this movement:

[0095] P(i+1)=R(i)-|R(i)-P(i)|×F×Levy(d) (33)

[0096] In the formula, Levy(d) is the Levy flight mechanism, which is used to improve the effectiveness of the vulture population in the formula. Its step direction is completely random and isotropic, and the step size is a heavy-tailed distribution.

[0097] As a further aspect of this invention: In step five, Henon chaotic mapping theory is a nonlinear theory with characteristics such as nonlinearity, initial value sensitivity, randomness, and ergodicity. It can traverse all states without repetition within a specified range according to its own laws. Henon chaotic mapping is generated in 2D space and is a typical discrete chaotic mapping. Its dynamic formula is as follows:

[0098]

[0099] As can be seen from the above formula, the four parameters x0, y0, a, and b determine the state of the Henon chaotic map, which is more complex than the 1-dimensional chaotic map. When a = 1.4 and b = 0.3, the strong randomness of the generated chaotic sequence is guaranteed when the function enters the chaotic state. The chaotic map initialization population is obtained through the above mapping method.

[0100] An elite population strategy is adopted, merging the chaotic mapping initialization population and the regular initialization population, calculating the fitness of each initial vulture, sorting them, and selecting the top N elite individuals. The elite individual sequence is as follows:

[0101] x i ={x1, x2, ..., x N}, i = 1 ~ N (35)

[0102] Where x1 = BestV1, x2 = BestV2; N is the number of vultures in the population.

[0103] The beneficial effects of this invention are:

[0104] 1) Based on the structure and motion characteristics of the space mining robot, a master-slave kinematic model of the space mining robot was first established, and then a trajectory tracking control system for the space mining robot was established. At the same time, in the process of controller design, considering the complex working conditions of the space mining robot, the direct feedback linearization theory was adopted to realize the global accurate linearization of the nonlinear system of the space mining robot, which greatly simplifies the kinematic problem of the space mining robot.

[0105] 2) Regarding the controller parameters, the IAVOA optimization algorithm is used for optimization selection. The IAVOA optimization algorithm differs from the basic algorithm by introducing Henon chaotic mapping population initialization and elite population strategy, which realizes the optimal initialization of the algorithm population. This makes the algorithm not only have good global optimization performance, but also effectively avoid getting trapped in local optima. Moreover, the external repository can help the vulture population select more solution spaces, avoid generating a large number of identical non-dominated solutions, and has good convergence.

[0106] 3) By adopting the above-mentioned IAVOA optimization algorithm, the control parameters of the space mining robot control system can be adaptively optimized and adjusted, effectively coping with the complex working environment on the asteroid surface, and realizing the system's precise control over the movement of the space mining robot. Attached Figure Description

[0107] Figure 1 This is a three-dimensional model of the "six-wheeled space mining robot," the subject of this invention.

[0108] Figure 2 This is a schematic diagram of the movement of the space mining robot in this invention;

[0109] Figure 3 This is a schematic diagram of the master-slave following system of the space mining robot in this invention;

[0110] Figure 4 This is a schematic diagram of the master-slave following system of the space mining robot in the new coordinate system of the present invention;

[0111] Figure 5 This is a system control block diagram of the IAVOA-based master-slave space mining robot adaptive control system in this invention;

[0112] Figure 6 This is a flowchart of the African vulture optimization algorithm in this invention;

[0113] Figure 7 This is a schematic diagram illustrating the food competition behavior of a vulture population in the African vulture algorithm of this invention;

[0114] Figure 8 This is a flowchart of the IAVOA algorithm in this invention;

[0115] Figure 9 This is a simulation diagram of the convergence curve of the first test function in this embodiment of the invention under multiple intelligent optimization algorithms;

[0116] Figure 10 This is a simulation diagram of the convergence curve of the second test function under multiple intelligent optimization algorithms in an embodiment of the present invention;

[0117] Figure 11 This is a simulation diagram of the convergence curve of the third test function in this embodiment of the invention under multiple intelligent optimization algorithms;

[0118] Figure 12 This is a simulation diagram of the convergence curve of the fourth test function in this embodiment of the invention under multiple intelligent optimization algorithms.

[0119] In the diagram: 1. Wheel of the space mining robot; 2. Main suspension; 3. Secondary suspension; 4. Drive motor; 5. Steering motor; 6. Differential mechanism. Detailed Implementation

[0120] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0121] Example 1

[0122] like Figures 1 to 8 As shown, an adaptive control system for a master-slave space mining robot based on IAVOA is presented.

[0123] The master-slave space mining robot adaptive control system includes:

[0124] A kinematic model of a six-wheeled space mining robot was established, taking the six-wheeled space mining robot as the research object. The six-wheeled space mining robot includes a differential mechanism 6, a suspension assembly, and space mining robot wheels 1. The suspension assembly is connected to both ends of the differential mechanism 6. The suspension assembly includes a main suspension 2 and a secondary suspension 3 connected to the main suspension 2. The space mining robot wheels 1 are rotatably connected to the main suspension 2 and the secondary suspension 3 through a steering motor 5. Each space mining robot wheel 1 is connected to a drive motor 4.

[0125] The control method of the master-slave space mining robot adaptive control system includes:

[0126] Step 1: Taking a six-wheeled space mining robot as the research object, assuming that the overall material of the space mining robot is uniform and the center of mass is located at the geometric center of the vehicle body, establish the kinematic model of the space mining robot;

[0127] Step 2: In the process of motion control of the space mining robot, in order to describe its trajectory tracking process, a trajectory following model of master and slave space mining robots is introduced, and a virtual machine that maintains a constant distance from the master space mining robot and whose direction of motion is always consistent is introduced.

[0128] Step 3: Simplify the master-slave space mining robot control model by using coordinate transformation to establish a coordinate system with the slave space mining robot as the master.

[0129] Step 4: Complete the kinematic modeling of the space mining robot and establish its motion controller. At the same time, use the IAVOA optimization algorithm to adaptively optimize the control system of the space mining robot.

[0130] Step 5: For the random initialization of the vulture population, Henon chaotic mapping population initialization and elite population strategy are introduced to improve the African vulture algorithm, and thereby optimize the selection of parameters for the master-slave space mining robot adaptive control system.

[0131] Example 2

[0132] In addition to all the technical features included in Embodiment 1, this embodiment also includes:

[0133] The kinematic model of the space mining robot established in step one is as follows:

[0134]

[0135] Where v(t) is the linear velocity of the space mining robot, w(t) is the angular velocity of the space mining robot, and l is the vertical distance between the front wheel axle of the space mining robot and the center of mass of the vehicle body.

[0136] During the movement of the space mining robot, the relationship between its linear velocity v(t), angular velocity w(t), and the velocities of its left and right drive wheels is as follows:

[0137]

[0138]

[0139] Where S is the radius of rotation of the space mining robot during its rotational motion, and v l (t) and v r (t) The linear velocity of the left and right wheels of the space mining robot, where H represents the distance between the centers of the left and right wheels.

[0140] In step two, G1, G2, and G3 represent the centroid coordinates of the slave robot, the virtual robot, and the master robot, respectively. a (t) and w a (t) represent the linear velocity and angular velocity of the space mining robot, respectively. b (t) and w b (t) represent the linear velocity and angular velocity of the virtual space mining robot, respectively. c (t) and w c (t) represent the linear velocity and angular velocity of the main space mining robot, respectively;

[0141] Using the main space mining robot as a reference, the pose information of the virtual space mining robot is as follows:

[0142]

[0143] Differentiating the above equation, we obtain the kinematic model of the virtual space mining robot as follows:

[0144]

[0145] Based on the positional relationship between the virtual robot and the main robot, the following following error model is established:

[0146]

[0147] Among them, e x (t) and e y (t) represents the positional deviation between the virtual robot and the slave robot, e θ (t) represents the angular deviation between the virtual robot and the slave robot;

[0148] Therefore, we can conclude that:

[0149]

[0150] Differentiating both sides of the above equation, we get:

[0151]

[0152] In step three, the coordinate transformation equations are as follows:

[0153]

[0154] Where Z1(t) and Z2(t) represent the position errors of the robot and the virtual robot in the new coordinate system, respectively;

[0155] Combining the above equations, we can obtain the following positional error between the master and slave space mining robots:

[0156]

[0157] Among them, P1 and P2 are the lateral and longitudinal position differences of the master and slave space mining robots, respectively;

[0158] Differentiating the above equation, we get:

[0159]

[0160] Differentiating the above equation, we get:

[0161]

[0162] Combining the above equations, we can obtain

[0163]

[0164] From θ a (t)=θ c (t)-e θ Thus, the derivative model of the position error between the master and slave space mining robots is obtained as follows:

[0165]

[0166] Once the linear velocity and angular velocity of the main space mining robot are obtained using the above formula, the trajectory of the main space mining robot can be accurately tracked by controlling the linear velocity and angular velocity of the secondary space mining robot.

[0167] The master-slave space mining robot's follower model is written in the following form:

[0168]

[0169] Among them, P 12 =[P1, P2] T u a =[v a (t), w a (t)] T uc =[v c (t), w c (t)] T ;

[0170]

[0171]

[0172] Based on the direct feedback linearization theory, when there is a distance error between the master space mining robot and the slave space mining robot, the input of the slave space mining robot is defined as:

[0173] u a =A -1 (e-Bu c (51)

[0174] Where e is defined as the auxiliary control input, and is defined as:

[0175]

[0176] Here, k1 and k2 are defined as the controller gains of the space mining robot. This indicates the desired lateral and longitudinal spacing between the master and slave space mining robots;

[0177] Therefore, the master-slave space mining robot's follower model is simplified as follows:

[0178]

[0179] Direct feedback linearization theory only requires establishing a virtual control input to obtain a nonlinear feedback compensation law, thereby achieving global accurate linearization of the nonlinear system. The key to linearizing a nonlinear system using the direct feedback linearization method is to select an appropriate virtual control quantity to counteract the nonlinear factors in the original system, thus achieving linearization.

[0180] Step four, the IAVOA optimization algorithm specifically includes:

[0181] The African Vulture Optimization Algorithm (AVOA) is based on the lifestyle of African vultures and simulates their foraging and navigation behaviors.

[0182] First, initialize the population, calculate the fitness of all solutions, select the best solution as the best vulture in the first group, select the second best solution as the best vulture in the second group, and move other solutions to the best solutions in the first and second groups; in each fitness iteration, the entire population will be recalculated.

[0183]

[0184] In the formula, R(i) represents the positions of vultures other than the best and second-best vultures; BestV1 and BestV2 represent the best and second-best vulture positions, respectively; L1 and L2 represent the parameters to be measured between 0 and 1, and their sum is 1; p i The probability of selecting the best vulture; F i For the fitness of other vultures; use a roulette wheel to obtain the probability of selecting the best solution, and select each best solution for each group;

[0185]

[0186] Calculating the hunger rate of vultures; mathematical modeling inspired by the speed at which vultures become full or hungry, and also used to transition from the exploration phase to the development phase; the decreasing satiety rate is used to simulate this behavior;

[0187]

[0188]

[0189] Where F represents the vulture's hunger rate, t represents the current iteration number, T represents the maximum number of iterations, z is a random number between -1 and 1 that changes with each iteration, h is a random number between -2 and 2, and rand1 is a random number between 0 and 1. When the z value drops below 0, it indicates that the vulture is hungry; if the z value increases to 0, it indicates that the vulture is full. The proportion of the total number of vultures decreases, and the decrease is greater with each repetition. When the value of F is greater than 1, the vulture searches for food in different areas, entering the exploration phase; if the value of F is less than 1, the vulture enters the development phase, searching for food near the optimal solution.

[0190] During the exploration phase, vultures examine different random areas throughout the population, selecting one strategy based on two different strategies and using a parameter called P1. This parameter must be assigned a value between 0 and 1 before the search operation to determine how to use either strategy; to select which strategy to use during the exploration phase, a random number between 0 and 1 needs to be generated. The process is shown in the following formula:

[0191]

[0192] P(i+1)=R(i)-|X×R(i)-P(i)|×F (59)

[0193] In the formula, the vulture randomly searches for food within a random distance of one of the two optimal groups, where P(i+1) is the vulture's position vector in the next iteration, and F is the vulture's satiety rate obtained in the current iteration. X is the place where the vulture randomly moves to protect the food from other vultures. X is used as a coefficient vector to increase the random movement, which changes in each iteration and is obtained using the formula X = 2rand, where rand is a random number between 0 and 1; P(i) is the vulture's current vector position.

[0194] P(i+1)=R(i)-F+rand2×((ub-lb)×rand3+lb) (60)

[0195] In the formula, rand2 and rand3 are random values ​​between 0 and 1, and lb and ub represent the upper and lower bounds of the variable; by creating a high random coefficient on the scale of the search environment to increase diversity and search different search space regions, the movement of vultures is simulated in this way.

[0196] Upon entering the development phase, when the value of F is between 0.5 and 1, the vulture population enters the first stage of development. In the first stage, two different rotational flight and encirclement strategies are implemented; P2 is used to determine the choice of each strategy, and this value should be between 0 and 1; at the start of this stage, [the following is a process / function] is generated. It is a random number between 0 and 1; if the number is greater than or equal to parameter P2, the siege strategy will be implemented slowly; however, if the random number is less than parameter P2, the rotation flight strategy will be executed; the process is as follows:

[0197]

[0198] When |F|≥0.5, vultures have relatively sufficient energy; when many vultures gather at a food source, they may cause serious conflicts over food acquisition; in such cases, physically strong vultures prefer not to share food with other vultures.

[0199] On the other hand, weaker vultures attempt to tire out healthier vultures by gathering around them and instigating minor conflicts in order to obtain food from the healthier vultures. This process is modeled as follows:

[0200] P(i+1)=|X×R(i)-P(i)|×(F+rand4)-(R(i)-P(i)) (62)

[0201] In the formula, rand4 is a random number between 0 and 1, used to increase the randomness coefficient. P(i) is the current position vector of the vulture, through which the distance between the current vulture and one of the best vultures in the two groups can be obtained;

[0202] Vultures often perform rotating flight to simulate spiral motion; a spiral model has been used for mathematical modeling of rotating flight; in this method, a spiral equation is established between all vultures and one of the two best vultures:

[0203]

[0204] P(i+1)=R(i)-(S1+S2) (64)

[0205] In the formula, rand5 and rand6 are random numbers between 0 and 1; S1 and S2 are used to update the position of the vulture;

[0206] If |F| < 0.5, then this phase of the algorithm is executed. At the beginning of this phase, the following steps are taken: This is a random number between 0 and 1; the vulture population launched an aggressive struggle involving sieges and competition for food, as shown in the following formula:

[0207]

[0208] All vulture movements toward food sources were examined. Sometimes, vultures would go hungry, and there would be intense competition for food. Several types of vultures might accumulate at a single food source. This movement of vultures is described by the following formula:

[0209]

[0210]

[0211] Meanwhile, the lead vulture becomes hungry and weak, lacking the energy to fight the other vultures; on the other hand, the other vultures become aggressive in their search for food; they move in different directions toward the lead vulture, modeling this movement:

[0212] P(i+1)=R(i)-|R(i)-P(i)|×F×Levy(d) (68)

[0213] In the formula, Levy(d) is the Levy flight mechanism, which is used to improve the effectiveness of the vulture population in the formula. Its step direction is completely random and isotropic, and the step size is a heavy-tailed distribution.

[0214] In step five, Henon chaotic mapping theory is a nonlinear theory with characteristics such as nonlinearity, initial value sensitivity, randomness, and ergodicity. It can traverse all states without repetition within a specified range according to its own laws. Henon chaotic mapping is generated in 2-dimensional space and is a typical discrete chaotic mapping. Its dynamic formula is as follows:

[0215]

[0216] As can be seen from the above formula, the four parameters x0, y0, a, and b determine the state of the Henon chaotic map, which is more complex than the 1-dimensional chaotic map. When a = 1.4 and b = 0.3, the strong randomness of the generated chaotic sequence is guaranteed when the function enters the chaotic state. The chaotic map initialization population is obtained through the above mapping method.

[0217] An elite population strategy is adopted, merging the chaotic mapping initialization population and the regular initialization population, calculating the fitness of each initial vulture, sorting them, and selecting the top N elite individuals. The elite individual sequence is as follows:

[0218] x i ={x1, x2, ..., x N}, i = 1 ~ N (70)

[0219] Where x1 = BestV1, x2 = BestV2; N is the number of vultures in the population.

[0220] Example 3

[0221] like Figure 9 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used test functions. To verify the performance of the IAVOA algorithm, the theoretical optimal value of the function is 0, and the search region is defined as [-30, 30]. The IAVOA optimization algorithm is compared with the basic African Vulture optimization algorithm, as well as classic algorithms such as Particle Swarm Optimization, Gray Wolf Optimization, and Genetic Algorithm, to test the performance of the improved African Vulture optimization algorithm. To ensure fairness in the testing, the population size for each algorithm is set to 30, and the maximum number of iterations is set to 400.

[0222] It can be seen that the IAVOA algorithm has a faster optimization speed compared to other algorithms.

[0223] Example 4

[0224] like Figure 10 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used test functions. To verify the performance of the IAVOA algorithm, the theoretical optimal value of the function is 0, and the search region is defined as [-100, 100]. The classic algorithms used for comparison are the same as those in the previous embodiments, with the population size of each algorithm set to 30 and the maximum number of iterations set to 400.

[0225] It can be seen that the IAVOA algorithm has a faster optimization speed compared to other algorithms.

[0226] Example 5

[0227] like Figure 11 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes: selecting commonly used test functions. To verify the performance of the IAVOA algorithm, the theoretical optimal value of the function is 0, and the search region is defined as [-1.28, 1.28]. The classic algorithms used for comparison are the same as those in the previous embodiments, with the population size of each algorithm set to 30 and the maximum number of iterations set to 400.

[0228] It can be seen that the IAVOA algorithm has a faster optimization speed compared to other algorithms.

[0229] Example 6

[0230] like Figure 12 As shown, in addition to all the technical features included in Embodiment 1, this embodiment also includes:

[0231] Select commonly used test functions

[0232]

[0233] To verify the performance of the IAVOA algorithm, the theoretical optimal value of the function is 0, and the search region of the function is defined as [-32, 32]. The classic algorithm used for comparison is the same as that in the previous embodiment. The population size of each algorithm is set to 30, and the maximum number of iterations is set to 400.

[0234] It can be seen that the IAVOA algorithm has a faster optimization speed compared to other algorithms.

[0235] Based on the above embodiments, the conclusion is as follows:

[0236] For four classic test functions, the IAVOA optimization algorithm can find the optimal value of the function at a significantly faster speed than traditional optimization algorithms. After achieving optimal improvements in population initialization, the algorithm achieves a balance between the exploration and development phases. The algorithm not only has good global optimization performance but also effectively avoids getting trapped in local optima. Furthermore, the external repository helps the vulture population select from a wider solution space, avoiding the generation of a large number of identical non-dominated solutions, and exhibits good convergence.

[0237] By employing the aforementioned IAVOA optimization algorithm, the control parameters of the space mining robot's control system can be adaptively optimized and adjusted, effectively improving the performance of the space mining robot's motion control system.

[0238] Working principle: First, based on the structure and motion characteristics of the space mining robot, a master-slave kinematic model of the space mining robot was established, and then a trajectory tracking control system for the space mining robot was established. In the controller design process, direct feedback linearization theory was adopted to achieve global accurate linearization of the nonlinear system of the space mining robot, which greatly simplifies the kinematic problem of the space mining robot.

[0239] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0240] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An adaptive control system for a master-slave space mining robot based on IAVOA, characterized in that: The master-slave space mining robot adaptive control system includes: A kinematic model of a six-wheeled space mining robot was established as the research object; The six-wheeled space mining robot includes a differential mechanism (6), a suspension assembly, and space mining robot wheels (1). The suspension components are respectively connected to the two ends of the differential mechanism (6). The suspension components include a main suspension (2) and a secondary suspension (3) connected to the main suspension (2). The main suspension (2) and the secondary suspension (3) are rotatably connected to a space mining robot wheel (1) via a steering motor (5). Each space mining robot wheel (1) is connected to a drive motor (4). The control method of the master-slave space mining robot adaptive control system includes: Step 1: Taking a six-wheeled space mining robot as the research object, assuming that the overall material of the space mining robot is uniform and the center of mass is located at the geometric center of the vehicle body, establish the kinematic model of the space mining robot; Step Two: During the motion control of the space mining robot, to describe its trajectory tracking process, a master-slave space mining robot trajectory following model is introduced, and a virtual robot that maintains a constant distance from the master space mining robot and whose direction of movement is always consistent is introduced; in Step Two, respectively using... , , Represents the centroid coordinates of the robot, virtual robot, and main robot. and These represent the linear velocity and angular velocity of the space mining robot, respectively. and These represent the linear velocity and angular velocity of the virtual space mining robot, respectively. and These represent the linear velocity and angular velocity of the main space mining robot, respectively. Using the main space mining robot as a reference, the pose information of the virtual space mining robot is as follows: ； Differentiating the above equation, we obtain the kinematic model of the virtual space mining robot as follows: ； Based on the positional relationship between the virtual robot and the main robot, the following following error model is established: ； in, and This indicates the positional deviation between the virtual robot and the slave robot. This indicates the angular deviation between the virtual robot and the slave robot; Therefore, we can conclude that: ； Differentiating both sides of the above equation, we get: ； Step 3: Simplify the master-slave space mining robot control model by using coordinate transformation to establish a coordinate system with the slave space mining robot as the master. Step 4: Complete the kinematic modeling of the space mining robot, and use it to establish the motion controller for the space mining robot. Simultaneously, adopt... The optimization algorithm adaptively optimizes the control system of the space mining robot; Step 5: For the random initialization of the vulture population, Henon chaotic mapping population initialization and elite population strategy are introduced to improve the African vulture algorithm, and thereby optimize the selection of parameters for the master-slave space mining robot adaptive control system.

2. The master-slave space mining robot adaptive control system according to claim 1, characterized in that: The kinematic model of the space mining robot established in step one is as follows: ； in, For the linear velocity of the space mining robot, For the angular velocity of the space mining robot, This is the vertical distance between the front axle of the space mining robot and the center of mass of the vehicle body. The linear velocity of the space mining robot during its movement angular velocity The relationship between the speeds of the left and right drive wheels is as follows: ； ； in, The radius of rotation for a space mining robot during rotational motion. and The linear velocity of the left and right wheels of the space mining robot. This indicates the distance between the centers of the left and right wheels.

3. The master-slave space mining robot adaptive control system according to claim 1, characterized in that: In step three, the coordinate transformation equation can be obtained as follows: ； in, and These represent the position errors of the robot and the virtual robot in the new coordinate system, respectively. Combining the above equations, we can obtain the following positional error between the master and slave space mining robots: ； in, and These represent the lateral and longitudinal positional differences between the master and slave space mining robots, respectively. Differentiating the above equation, we get: ； Differentiating the above equation, we get: ； Combining the above equations, we can obtain ； Depend on Thus, the derivative model of the positional error between the master and slave space mining robots is obtained as follows: ； Once the linear velocity and angular velocity of the main space mining robot are obtained using the above formula, the trajectory of the main space mining robot can be accurately tracked by controlling the linear velocity and angular velocity of the secondary space mining robot. The master-slave space mining robot's follower model is written in the following form: ； in, , , ; ； ； Based on the direct feedback linearization theory, when there is a distance error between the master space mining robot and the slave space mining robot, the input of the slave space mining robot is defined as: ； in, Defined as an auxiliary control input, defined as: ； Among them, , Defined as the controller gain of a space mining robot. , This indicates the desired lateral and longitudinal spacing between the master and slave space mining robots; Therefore, the master-slave space mining robot's follower model is simplified as follows: ； Direct feedback linearization theory only requires establishing a virtual control input quantity and obtaining a nonlinear feedback compensation law to achieve global accurate linearization of the nonlinear system. The key to achieving linearization of the nonlinear system by the direct feedback linearization method is to select a suitable virtual control quantity to counteract the nonlinear factors in the original system, thereby achieving linearization.

4. The master-slave space mining robot adaptive control system according to claim 1, characterized in that: In step four, The optimization algorithms specifically include: First, initialize the population, calculate the fitness of all solutions, select the best solution as the best vulture in the first group, select the second best solution as the best vulture in the second group, and move other solutions to the best solutions in the first and second groups; in each fitness iteration, the entire population will be recalculated. ； In the formula, For the positions of vultures other than the best and second-best vultures; , These are the optimal and second-optimal vulture positions, respectively. , These are the parameters to be measured, which are between 0 and 1, and their sum is 1. To determine the probability of selecting the best vulture; For the fitness of other vultures; use a roulette wheel to obtain the probability of selecting the best solution, and select each best solution for each group; ； Calculating the hunger rate of vultures; mathematical modeling inspired by the speed at which vultures become full or hungry, and also used to transition from the exploration phase to the development phase; the decreasing satiety rate is used to simulate this behavior; , in, Indicates the vulture's hunger rate. Indicates the current iteration number. Indicates the maximum number of iterations. It is a random number between -1 and 1 that changes with each iteration. It is a random number between -2 and 2. It is a random number between 0 and 1; when When the value drops below 0, it indicates that the vulture is hungry. When the value increases to 0, it indicates that the vultures are full; the proportion of the total vulture population is decreasing, and the decrease is greater with each repetition; when When the value is greater than 1, the vulture searches for food in different areas, entering the exploration phase; if... When the value is less than 1, the vulture enters the development phase and searches for food near the optimal solution. During the exploration phase, vultures throughout the population examine different random areas, based on two different strategies and using a method called... The parameter is used to select either strategy. This parameter must be assigned a value between 0 and 1 before the search operation to determine how to use either strategy. To select which strategy to use during the exploration phase, a random number between 0 and 1 needs to be generated. The process is shown in the following formula: ； In the formula, the vultures randomly search for food at a random distance from one of the two optimal groups; in, It is the vulture's position vector in the next iteration. It is the vulture's satiety rate obtained in the current iteration. These are areas where vultures randomly move to protect their food from other vultures. This is used as a coefficient vector to increase the random motion, which changes in each iteration, and is expressed using the formula... Obtain, among which It is a random number between 0 and 1; This is the vulture's current vector position; ； In the formula, and A random value between 0 and 1. and The upper and lower bounds of the variable are represented; the movement of the vulture is simulated by creating a highly random coefficient on the scale of the search environment to increase diversity and search different regions of the search space. After entering the development phase, when When the value is between 0.5 and 1, the vulture population enters the first stage of development, in which two different rotational flight and encirclement strategies are implemented. This is used to determine the choice of each strategy, and the value should be between 0 and 1; at the beginning of this phase, the following is generated: It is a random number between 0 and 1; if the random number is greater than or equal to the parameter If the random number is less than the parameter, the siege strategy will be implemented slowly; however, if the random number is less than the parameter... If so, a rotational flight strategy is executed; the process is shown in the following formula: ； when At this time, vultures have relatively abundant energy; when many vultures gather at one food source, they may cause serious conflicts over food acquisition; in such cases, the stronger vultures prefer not to share food with other vultures. On the other hand, weaker vultures attempt to tire out healthier vultures by gathering around them and instigating minor conflicts in order to obtain food from the healthier vultures. This process is modeled as follows: ； In the formula, These are random numbers between 0 and 1, used to increase the randomness factor. This is the current position vector of the vulture, through which the distance between the current vulture and one of the best vultures in the two groups can be obtained; Vultures often perform rotating flight to simulate spiral motion; a spiral model has been used for mathematical modeling of rotating flight; in this method, a spiral equation is established between all vultures and one of the two best vultures: ； ； ； In the formula, and It is a random number between 0 and 1; and Update the vulture's location; if Then, at the beginning of this stage of the algorithm, the following steps are generated: This is a random number between 0 and 1; the vulture population launched an aggressive struggle involving sieges and competition for food, as shown in the following formula: ； All vulture movements toward food sources were examined. Sometimes, vultures would go hungry, and there would be intense competition for food. Several types of vultures might accumulate at a single food source. This movement of vultures is described by the following formula: ； ； Meanwhile, the lead vulture becomes hungry and weak, lacking the energy to fight the other vultures; on the other hand, the other vultures become aggressive in their search for food; they move in different directions toward the lead vulture, modeling this movement: ； In the formula, The Levi flight mechanism is used to improve the effectiveness of the vulture population in the formula. Its step direction is completely random and isotropic, and the step size is a heavy-tailed distribution.

5. The master-slave space mining robot adaptive control system according to claim 1, characterized in that: In step five, Henon chaotic mapping theory is a nonlinear theory that can traverse all states without repetition within a specified range according to its own laws. Henon chaotic mapping is generated in 2-dimensional space and is a typical discrete chaotic mapping. Its dynamic formula is as follows: ； From the above formula, we can see that... , , and These four parameters determine the state of the Henon chaotic map, which is more complex than that of a 1D chaotic map; taking , This ensures the strong randomness of the generated chaotic sequence when the function enters a chaotic state; The chaotic mapping initialization population is obtained through the above mapping method; An elite population strategy is adopted, merging the chaotic mapping initialization population and the regular initialization population, calculating the fitness of each initial vulture, sorting them, and selecting the top ones. There are 1 elite individual, and the sequence of elite individuals is as follows: ； in, , ; This represents the number of vultures in the population.

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