An intelligent motion control method based on adaptive look-ahead distance

Abstract

The application discloses an intelligent motion control method based on self-adaptive forward distance, which adopts an elliptical two-type fuzzy path tracking control method based on an improved integral line-of-sight IILOS (Improved Integral Line-of-Sight, IILOS). The method comprehensively considers the characteristics of ship maneuvering characteristics and the disturbance of the planning area environment, introduces the concept of uncertainty interval (FOU) to consider the influence of the environment on the control, first uses the ship motion state information and the ship motion control model to design the heading controller, and then presents the noise collected by the environment variable or the sensor in the interval, and then uses the controller to accurately control it, solves or partially solves the control problem of the unmanned ship under the uncertain disturbance, makes the path tracking of the unmanned ship more smooth, and makes certain reference to the selection and determination of the membership function of the two-type fuzzy system and the selection of the parameters.

Description

Technical Field

[0001] The present invention relates to the field of motion control technology for intelligent unmanned boats, and in particular to an intelligent motion control method based on adaptive foresight distance. A path tracking control method of a type-II elliptical fuzzy controller based on an improved integral sight-range guidance strategy is designed. Background Art

[0002] With the in-depth integration of autonomous navigation technology and unmanned surface vehicles, the characteristics of unmanned boats such as strong maneuverability, low cost, high efficiency and high convenience have become increasingly prominent in the application of the water transportation industry, especially in the civilian field. In the ever-changing marine environment, how to accurately, quickly and smoothly track the desired path is one of the key technologies to ensure the safety and completion of the mission of unmanned surface vehicles (USVs). Therefore, a set of path tracking motion control methods that can be applied to complex environments is one of the core elements of the development of unmanned boats.

[0003] Common control methods include optimal control, model predictive control, sliding film control, PID control, artificial intelligence methods, and hybrid algorithms. Commonly used guidance methods for unmanned vehicles include line of sight (LOS) and improved LOS, azimuth guidance, and moving target prediction.

[0004] During the implementation of the present invention, the inventors of this application discovered that existing mainstream control methods and guidance strategies have at least the following technical problems:

[0005] For example, PID and backstepping methods struggle to guarantee control accuracy due to the high degree of nonlinearity present during UAV navigation. Model predictive control struggles to achieve both precision and real-time performance. While robust, sliding membrane control struggles to handle chattering. Fuzzy logic systems offer excellent nonlinear system handling capabilities, but the computational complexity increases dramatically with increasing accuracy. Intelligent methods like reinforcement learning lack supporting theory and are understudied. Therefore, more current approaches combine the strengths of multiple algorithms to achieve better control results. As for guidance methods, most currently rely on pure geometric tracking, failing to consider the UAV's current state, namely its motion characteristics and current speed. Consequently, path tracking is not smooth and can lead to greater tracking errors when subjected to significant external disturbances such as wind, waves, and currents. Summary of the Invention

[0006] The present invention provides an intelligent motion control method based on adaptive foresight distance, which is used to solve or at least partially solve the technical problem of poor control effect in the methods in the prior art.

[0007] In order to solve the above technical problems, the technical solution provided by the present invention is:

[0008] An intelligent motion control method based on adaptive forward-looking distance, comprising:

[0009] The membership function is set based on the elliptic type II fuzzy control theory, and samples are obtained according to the set membership function. The optimal interval is obtained by finding the optimal switching point for the obtained samples.

[0010] An improved integral line-of-sight guidance strategy is used to calculate the foresight distance, and the path tracking process is converted into heading control based on the adaptive foresight distance.

[0011] Based on the obtained optimal interval and the converted heading control, an elliptical type II controller is obtained;

[0012] The obtained elliptical type II controller is used to control the motion of the ship.

[0013] In one embodiment, the optimal interval is obtained by finding the optimal switching point for the acquired samples, including obtaining the optimal interval using the following formula:

[0014]

[0015] Where, is the interval set under the jth rule, Y cos is The set of intervals defined by two points, are the left and right transition points under the i-th rule, y l ,y r are the left and right transition points of the final output set, and They are the left and right transition points of the output space under the first rule, and is the left and right transition point of the final output set under the th rule, q=1,2…K; is the interval set under the j-th rule, f j The expression is Its specific meaning is the t-norm product of the lower bound membership function of p inputs corresponding to rule j. represents the t-norm product connector, The expression is Its specific meaning is the t-norm product of the upper bound membership function of p inputs corresponding to rule j. f j and Together they constitute the interval set under the jth rule y l ,yr Determined by formulas (2) and (3)

[0016]

[0017]

[0018] In one embodiment, an improved integrated line-of-sight guidance strategy is used to calculate an adaptive foresight distance, and a path tracking process is converted into heading control based on the adaptive foresight distance, including:

[0019] The foresight distance is calculated using the following formula:

[0020]

[0021] Among them, k1 and k2 are proportional parameters, satisfying k1>0, k2>0, Δ1 is the set minimum foresight distance, v represents the navigation speed, and e1 represents the tracking error;

[0022] The path tracking process is converted into heading control using the following formula:

[0023]

[0024] α d is the desired heading angle, α k It is the angle between the ship's navigation path and due north.

[0025] In one embodiment, the obtained elliptical type II controller is used to control the motion of a ship, including:

[0026] The current attitude of the ship is input into the elliptical type II controller, and the throttle command or rudder angle command is calculated by the elliptical type II controller as output to control the ship.

[0027] Compared with the prior art, the advantages and beneficial technical effects of the present invention are as follows:

[0028] This paper selects input fuzzy sets based on considerations of the ship's maneuvering characteristics, uses language mechanisms to construct an inference engine suitable for the current environment, employs an improved integral sight-of-sight guidance strategy to calculate foresight distance, and converts the path tracking process into heading control based on adaptive foresight distance. Finally, a suitable elliptical type-2 controller is established, using the ship's current attitude as input via sensors and throttle or rudder angle commands calculated by the controller as output, thereby achieving the purpose of controlling the speed and heading of the unmanned boat. This approach solves the control problem of unmanned boats under uncertain interference, making the path tracking of the unmanned boat smoother and improving the control effect. It also provides a reference for the selection of membership functions and the selection and determination of parameters for type-2 fuzzy systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0030] Figure 1 1 is a schematic diagram of the overall process of an intelligent motion control method based on adaptive foresight distance provided by an embodiment of the present invention;

[0031] Figure 2 This is a framework diagram of the Type-1 and Type-2 fuzzy logic systems provided by an embodiment of the present invention;

[0032] Figure 3 The calculation y provided in the embodiment of the present invention l and y r Schematic diagram of the conversion point;

[0033] Figure 4 Schematic diagram of LOS guidance in the prior art;

[0034] Figure 5 Schematic diagram of LOS guidance based on forward-looking distance in an embodiment of the present invention;

[0035] Figure 6 1 is a block diagram of a path tracking system based on the IILOS guidance strategy in an embodiment of the present invention;

[0036] Figure 7 Schematic diagram of an elliptic membership function in an embodiment of the present invention;

[0037] Figure 8 Schematic diagram of the multi-straight line ship path tracking effect based on IILOS in an embodiment of the present invention. DETAILED DESCRIPTION

[0038] The purpose of this invention is to provide an intelligent motion control method for uncertain interference. The method selects input fuzzy sets based on the ship's maneuverability characteristics, uses language mechanisms to construct an inference engine appropriate to the current environment, and finally establishes a suitable elliptical type-2 controller. The current attitude of the ship is obtained from sensors as input, and the throttle command or rudder angle command calculated by the controller is used as output to achieve the purpose of controlling the speed and direction of the unmanned vehicle.

[0039] In order to achieve the above object, the main concepts of the present invention are:

[0040] This paper presents an elliptical type-II fuzzy path tracking control method based on the Improved Integral Line-of-Sight (IILOS) guidance law. This method comprehensively considers the ship's maneuvering characteristics and the environmental disturbances in the planning area. It introduces the concept of uncertainty intervals (FOUs) to account for the impact of the environment on control. First, a heading controller is designed using the ship's motion state information and the ship's motion control model. Environmental variables or sensor noise are concretized and represented in the intervals, and then the controller is used to precisely control them.

[0041] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0042] An embodiment of the present invention provides an intelligent motion control method based on adaptive foresight distance, comprising:

[0043] The membership function is set based on the elliptic type II fuzzy control theory, and samples are obtained according to the set membership function. The optimal interval is obtained by finding the optimal switching point for the obtained samples.

[0044] An improved integral line-of-sight guidance strategy is used to calculate the foresight distance, and the path tracking process is converted into heading control based on the adaptive foresight distance.

[0045] Based on the obtained optimal interval and the converted heading control, an elliptical type II controller is obtained;

[0046] The obtained elliptical type II controller is used to control the motion of the ship.

[0047] See Figure 1 , is a schematic diagram of the overall flow of an intelligent motion control method based on adaptive foresight distance provided by an embodiment of the present invention.

[0048] First, the type-2 fuzzy logic system and its key technologies are introduced.

[0049] Zadeh (often called the father of fuzzy logic) first proposed the fuzzy logic system in 1965 and introduced type-2 fuzzy logic in 1975, marking the beginning of its historical development. This is because as system complexity increases, the accuracy of conventional fuzzy control depends on the refinement of fuzzy rules. Complex rules significantly increase computational complexity, naturally turning interest to methods that can handle more complex situations. This is the reason for the introduction of type-2 fuzzy logic. Type-2 fuzzy control also consists of four components: 1. Fuzzification interface; 2. Knowledge base; 3. Fuzzy reasoning; and 4. Defuzzification interface. Unlike conventional fuzzy control, type-2 fuzzy has distinct fuzzy set outputs. This is due to the fuzziness of the membership of type-2 fuzzy itself. However, this fuzziness gives the entire system greater freedom and potential for handling uncertainty and nonlinearity.

[0050] Its potential lies in: 1. More degrees of freedom: Compared with general fuzzy systems, it offers more modeling and design freedom. 2. Due to the presence of the foot of uncertainty (FOU), precise design of the placement of each membership function is unnecessary, as a Type-2 fuzzy set (T2FS) contains countless Type-1 fuzzy sets (T1FS). 3. Type-2 fuzzy systems exhibit significantly better noise and interference immunity than conventional systems. 4. Type-2 fuzzy systems can better represent uncertainty. Since each Type-2 fuzzy set is ultimately defined by embedding an uncertainty interval (FOU) on its corresponding Type-1 parameter, defining the midpoint of the uncertainty interval—that is, utilizing the intermediate set TR—can greatly simplify computations. However, currently, no performance criteria exist for selecting membership functions. Therefore, this paper proposes the elliptical membership function as a reference. Its parameter and width decoupling characteristics facilitate subsequent quantitative analysis of certain aspects of its properties. A comparison of several common membership functions is also provided, highlighting their respective advantages and disadvantages. The following introduces Type-2 fuzzy logic from two perspectives.

[0051] 1. Type II fuzzy system

[0052] like Figure 2 As shown in Figure 2, there is a clear distinction between the output sets of a type-two fuzzy system (T2FLS). For a T1FLS, the output is a clear value, while for a T2FLS, the output set needs to pass through a type-reducer (TR) to reduce the type-two fuzzy output set to a type-one fuzzy output set, which can then be passed to a defuzzifier to obtain a clear output.

[0053] First, let's briefly introduce the Type-1 system. As we know, the construction of Type-2 fuzzy rules is almost the same as that of Type-1. The rule-based Mamdani fuzzy system contains a set of rules R(1,2,…K). For a model with p inputs and one output, the output under the lth rule has the structure shown in Equation (1):

[0054]

[0055] where x1, x2, … x p are input variables, x1∈X1,x2∈X2,…x p ∈X p ; y is the output, y∈Y; the superscript l represents the lth rule, y l Indicates the output under l rules; F i l is the i-th input x i Input membership function under rule l, where (i=1,…p); is the output membership function under rule l; where (l=1,…K).

[0056] The main difference between Type-1 and Type-2 systems is that Type-1 uses T1FS, while the latter uses at least one T2FS. It can be defined by formula (2):

[0057]

[0058] Where X is the domain of the fuzzy variable, and u is called the interval-type membership function, where is a type-2 fuzzy set Membership function. It has a lower bound membership function and upper bound membership function

[0059] The reasoning of interval-type II fuzzy can be expressed by formula (3), where and f l The expression is defined by (4) and (5); the interval of b is defined by (6) and (7); where is the t-norm connector.

[0060]

[0061]

[0062]

[0063]

[0064]

[0065] S2: Type-Reducer

[0066] In a type-2 fuzzy system, the type-reducer (TR) plays a crucial role. It converts the type-2 fuzzy output set produced by the inference process into a type-1 fuzzy output set, ultimately resulting in a clear output value. The first step is to obtain the centroid of the type-2 fuzzy set, typically represented as an interval called a TR set. The TR centroid is a popular method in theoretical research. While the traditional Karnik-Mendel (KM) algorithm is extremely accurate, it typically requires 2-6 iterations to converge. The following briefly introduces the KM type reducer.

[0067] The KM algorithm is an iterative process that can obtain [ l The uncertainty interval for the centroid of an interval-type II fuzzy logic system given by [r] is similar to the defuzzification of a type I fuzzy system. Based on the type I defuzzification process, Karnik et al. proposed several common types of reducers for performing T2FS: height and height-corrected TR, centroid TR, and center set TR. As we know, the choice of defuzzification method has a significant impact on the results. While height and height-corrected TR have relatively simple execution processes, they return different results when a single rule in the rule base is triggered. Center of mass TR requires more computation because, for each new system input, it must merge the FSs generated by all rules to obtain the centroid of the FS generated by that input. Finally, center set TR is a commonly used method that requires fewer operations than center of mass TR. This is because the a priori calculation of each fuzzy set is independent of changes in the system's input variables and can serve as a constant for center set TR. Therefore, the necessary process after each new input is introduced to the system is to perform a weighted average of the stored centroids based on the combination of the upper and lower trigger levels of each rule. Since the center set TR inevitably needs to calculate the centroid of each subsequent type-2 fuzzy set once, the present invention selects the center TR method as the main type reducer, and first introduces its simple principle.

[0068] Similar to the centroid defuzzification process, the centroid TR first obtains K samples from the Type-2 fuzzy set. Since the uncertainty interval (FOU) of the Type-2 fuzzy set is embedded in multiple Type-1 fuzzy sets, to calculate TR, we must first obtain two Type-1 fuzzy sets whose centroids are closest to the upper and lower bounds of the Type-2 fuzzy set centroid. Figure 3 in Taking fuzzy sets as an example, this process first uses the upper and lower bounds of sampling to find the optimal value of the most appropriate switching point [L, R]. Figure 3 Parts (a) and (b) represent the calculation of y l 、y r Schematic diagram of the conversion point.

[0069] The selection of candidate points is as follows (8) and (9):

[0070]

[0071]

[0072] In the formula They are The fuzzy set corresponds to the upper bound membership function and the lower bound membership function; y i represents the output under the i-th rule. K is the number of discrete points. The values ​​of L and R are determined by equations (10) and (11):

[0073]

[0074]

[0075] The second equation in (10) and (11) is the conversion point y l and y r Approximate calculation formula. The exact conversion point calculation formula is shown in formulas (12) and (13)

[0076]

[0077]

[0078] Find the transition point y l and y r The principle of left and right bounds is simple, depending on whether the transition is from a higher or lower point, such as y l , it must be the minimum value of the entire fuzzy set, so its left side is a larger value and its right side is a smaller value, but calculating all candidate centroid points is undoubtedly a less efficient way, so Table 1 gives a more optimal iterative method.

[0079] Table 1 KM iterative algorithm

[0080]

[0081]

[0082] However, many scholars still believe that the iterative process is too time-consuming. In this invention, the centroid TR is used to calculate the simpler one. Similar to the centroid defuzzification process, it is also necessary to obtain K samples from the membership function of the type-2 fuzzy system. By finding the optimal switching point, the optimal interval is obtained as shown in formula (14), where Y cos is The specific expression of the interval set defined by two points is:

[0083]

[0084] Where, is the interval set of the jth sample, Y cos is The set of intervals defined by two points, are the left and right transition points under the i-th rule,

[0085]

[0086]

[0087] Finally, the defuzzification is performed through formula (17) to obtain the clear value:

[0088]

[0089] Description: y l ,y r are the left and right transition points of the final output set (or output space), and They are the left and right transition points of the output space under the first rule. Similarly, and is the left-right transition point in the output space under the i-th (where i = 1, ... K) rule, where q = 1, 2 ... K; is the interval set under the j-th rule, where f j The expression is Its specific meaning is the t-norm product of the lower bound membership function corresponding to p inputs in rule j (where j = 1, ...K), represents the t-norm product connector, The expression is Its specific meaning is the t-norm product of the upper bound membership function corresponding to p inputs in rule j (where j = 1, ... K). Similarly, we can determine and define f K and expression, It is the t-norm product of the upper bound membership function of the p inputs corresponding to the rule K, f j and Together they constitute the interval set under the jth (where j = 1, ...K) rule j and i represent the count or number of the rule, which is for the convenience of describing a certain rule.

[0090] In one embodiment, an improved integrated line-of-sight guidance strategy is used to calculate an adaptive foresight distance, and a path tracking process is converted into heading control based on the adaptive foresight distance, including:

[0091] The foresight distance is calculated using the following formula:

[0092]

[0093] Among them, k1 and k2 are proportional parameters, satisfying k1>0, k2>0, Δ1 is the set minimum foresight distance, v represents the navigation speed, and e1 represents the tracking error;

[0094] The path tracking process is converted into heading control using the following formula:

[0095]

[0096] α d is the desired heading angle, α k It is the angle between the ship's navigation path and due north.

[0097] Specifically, the LOS algorithm is one of the most widely used algorithms in geometric algorithms. Its main parameters are only the setting of the front sight distance and the determination of the turning point radius of the unmanned boat. Figure 4 express.

[0098] Figure 4 In the LOS guidance diagram shown, it is assumed that the position of the unmanned boat in the spatial coordinate system o-xyz is L(x,y), and the starting point and ending point of the reference path currently tracked by the unmanned boat are L k (x k ,y k ), L k+1 (x k+1 ,y k+1 ). Then the LOS angle It can be obtained by formula (18):

[0099]

[0100] The range of the LOS angle is between - and π, and the size of this angle is only related to the current position of the unmanned vehicle and the desired path.

[0101] The more commonly used strategy is the guidance strategy based on the forward range, and its principle diagram is shown in Figure 5To express it, the position of the unmanned boat in the space coordinate system is L(x,y), and the starting point and ending point of the reference path currently tracked are L k (x k ,y k ), L k+1 (x k+1 ,y k+1 ). The angle between the path direction and the true north direction is α k ,In order to facilitate calculation later, the current heading of the unmanned boat is converted into a coordinate system with the path direction as the reference. The distance from the unmanned boat to the foot of the current expected path is e, and the expected heading point of the current path is set to (x los ,y los ), and the distance from the projection of the ship's current position on the current path is Δ. Δ is called the visual distance and is one of the key parameters of the LOS guidance algorithm. Generally, it is two to three times the ship's length. The ultimate goal of the LOS guidance algorithm is to calculate the angle between the desired path direction and the true north direction, that is, the desired heading angle. The LOS guidance algorithm is shown in Equation (19):

[0102]

[0103] α d is the desired heading angle.

[0104] The LOS algorithm aims to minimize the error between the current heading angle and the desired heading angle, ultimately enabling the UAV to track the desired trajectory. However, the look-ahead distance setting directly affects the smoothness, convergence speed, and overshoot of the UAV's tracking path. Therefore, if the vessel is far from the desired track, a judgment mechanism can be set to set the look-ahead distance to a very small value, forcing the vessel to initially track along the vertical line. Once the distance reaches a certain range, the look-ahead distance can be reset.

[0105] When the unmanned boat has finished tracking the current path, the end point of the current path is L k+1 (x k+1 ,y k+1 ) becomes the starting point of the next path, and the unmanned boat turns to track L k+1 (x k+1 ,y k+1 ), L k+2 (x k+2 ,y k+2 ), so a switching mechanism is needed to enable the unmanned boat to switch the target path to be tracked. Generally, the following algorithm is used to switch the desired path.

[0106] At this time, for the setting involving the turning point radius, it is assumed that the end point of the current tracking path of the unmanned boat is L k+1 (x k+1 ,y k+1 ), Lk+1 (x k+1 ,y k+1 ) as the center and a circle with a radius R0 of the turning point. When the UAV's current position L(x, y) satisfies the following condition (20), the UAV switches to the next tracking path. The size of R0 can be changed according to the UAV's maneuverability and the final effect of the controller, and is generally set to twice the length of the ship.

[0107]

[0108] However, the above-mentioned LOS guidance strategy has the following problems. When the USV is far away from the tracking segment and the speed of the USV changes, the fixed foresight distance will cause the tracking smoothness to deteriorate. For example, an excessively large foresight distance will cause the path tracking convergence speed to slow down, thereby increasing the tracking error of the subsequent path, while an excessively small foresight distance will increase the overshoot of tracking the current path. The calculation of the foresight distance must simultaneously consider the USV speed v and the current tracking error e of the USV. When the tracking error is large, regardless of the USV speed, a smaller foresight distance should be selected to quickly approach the desired path. As the tracking error decreases, it is necessary to increase the foresight distance according to the current speed of the ship. When the speed is relatively fast, the foresight distance needs to be quickly increased to a larger value to reduce overshoot. In this way, the USV can smoothly approach the target path. Therefore, the present invention proposes an improved integral LOS guidance strategy. The formula for calculating the visual distance is shown in (21), where e1 represents the tracking error. According to formula (21), the guidance algorithm is calculated using formula (22). Therefore, the final form of the guidance algorithm is as shown in formula (22):

[0109]

[0110]

[0111] Where k1 and k2 are proportional parameters, satisfying k1>0 and k2>0, and Δ1 is the minimum look-ahead distance. Formula (21) shows that the look-ahead distance is primarily determined by the tracking error. When the tracking error is small, the look-ahead distance is proportional to the speed, thereby reducing the system's overshoot and the distance required to approach the path. Formula (22) converts the path tracking process into heading control.

[0112] In one embodiment, initializing the generator includes:

[0113] The current attitude of the ship is input into the elliptical type II controller, and the throttle command or rudder angle command is calculated by the elliptical type II controller as output to control the ship.

[0114] In the specific implementation process, the path tracking process is first converted into heading control. The specific conversion process is shown in the above equations (20) and (21). Then, the ship heading controller is designed using the aforementioned elliptic type II fuzzy control theory so that the ship reaches the expected path. Specifically, Figure 6 shown.

[0115] The implementation process is as follows: first, the visual distance Δ and error e are calculated based on the ship's expected path point parameters and current position parameters, and then the expected heading angle α is calculated according to formula (22): d ,Then the type-two fuzzy control theory is used to design the heading controller, and finally the ship path tracking control is realized.

[0116] The method provided by the present invention is described below through specific experimental data.

[0117] Lab 1. Unmanned Boat Multi-Linear Path Tracking

[0118] Establishing a ship elliptic type II fuzzy controller, including the design of membership function and inference rules

[0119] Influenced by the triangular membership function formula, the elliptic membership function can be simplified as shown in Equation (23) and Equation (24):

[0120]

[0121]

[0122] Where c and d represent the center of gravity and width of the ellipse, respectively, x is the input vector, and the parameters a1 and a2 directly determine the uncertainty interval size of the proposed ellipse membership function, whose range is determined by formula (25):

[0123]

[0124] Here we take a1=1.2, a2=0.8, and design the type II fuzzy rules as shown in Table 2. The membership function is as follows: Figure 7 {NB, NM, NS, ZE, PS, PM, PB} means {negative large, negative medium, negative small, zero, positive small, positive medium, positive large}

[0125] Table 2 Fuzzy rules table

[0126]

[0127] The fuzzy set is represented by seven linguistic variables, whose post-order parameters are NB = -1, NM = -0.7, NS = -0.3, ZE = 0, PS = 0.3, PM = 0.7, PB = 1, and the function membership is symmetrical. The sum of adjacent function memberships is 1. Therefore, this embodiment will have 49 fuzzy rules, as shown in Table 2. In addition, this embodiment defines When the time is right, steer left. When it is negative, steer to the right. For example, the first rule Corresponding to the right steering, the heading error is negative at this time. In order to maintain the desired tracking, it is necessary to accelerate the steering in the opposite direction. Therefore, the output corresponding to the rule is NB, which means fast steering to the right.

[0128] Experiment 2, multi-line path tracking experiment

[0129] In order to test the guidance effect of the improved integrated line-of-sight guidance strategy (IILOS guidance algorithm) proposed in this invention, the LOS guidance strategy of the prior art is replaced by the IILOS guidance strategy, where the turning points are (100, 15); (100, 300); (0, 400); (-100, 300); (-100, 15). The expected speed in the experiment is set to 2.0 m / s. The IILOS parameters are set to: k1 = 0.8, k2 = 0.01, Δ1 = 0.1. The actual ship test results are as follows: Figure 8 Part (a) is a schematic diagram showing the comparison between the actual path and the expected path, part (b) is a schematic diagram showing the comparison between the tracking error, part (c) is a schematic diagram showing the comparison between the actual heading and the reference heading, part (d) is a schematic diagram showing the change in rudder angle, part (e) is a schematic diagram showing the comparison between the actual speed and the expected speed, and part (f) is a schematic diagram showing the change in throttle command.

[0130] Regarding the steering controller, the steering system is specifically composed of a DC servo motor, a DC servo electric push rod, a steel wire flexible shaft, etc. Its mechanical structure is relatively simple. The DC servo electric push rod drives the steel wire flexible shaft to move horizontally, thereby pushing the outboard motor to deflect to achieve rudder angle control, and PID control is used for closed-loop control of the rudder angle.

[0131] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0132] Obviously, those skilled in the art may make various changes and modifications to the embodiments of the present invention without departing from the spirit and scope of the embodiments of the present invention. Thus, if such changes and modifications of the embodiments of the present invention fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. An intelligent motion control method based on adaptive foresight distance, characterized in that: include: The membership function is set based on the elliptic type II fuzzy control theory, and samples are obtained according to the set membership function. The optimal interval is obtained by finding the optimal switching point for the obtained samples. An improved integral line-of-sight guidance strategy is used to calculate the foresight distance, and the path tracking process is converted into heading control based on the adaptive foresight distance. Based on the obtained optimal interval and the converted heading control, an elliptical type II controller is obtained; The obtained elliptical type II controller is used to control the motion of the ship; Among them, an improved integral line-of-sight guidance strategy is used to calculate the adaptive foresight distance, and the path tracking process is converted into heading control based on the adaptive foresight distance, including: The foresight distance is calculated using the following formula: in, is a scale parameter that satisfies , is the minimum foresight distance set, Indicates the sailing speed, represents the tracking error; The path tracking process is converted into heading control using the following formula: is the desired heading angle, is the angle between the ship's navigation path and the true north direction, is the distance from the unmanned boat to the foot of the current desired path.

2. The intelligent motion control method based on adaptive foresight distance according to claim 1, wherein: The optimal interval is obtained by finding the optimal switching point for the obtained samples, including using the following formula to obtain the optimal interval: (1) Where, For the The set of intervals under the rule, is The set of intervals defined by two points, 、 For the There are two transition points on the left and right under the rule. are the left and right transition points of the final output set, and They are the left and right transition points of the output space under the first rule, and is the left and right transition point of the final output set under the rule, ; For the The set of intervals under the rule, The expression is , its specific meaning is The lower bound membership function of the input corresponding to rule j norm product, express Norm product connector, The expression is , its specific meaning is The upper bound membership function of the input corresponding to rule j norm product, and Together they constitute the interval set under the jth rule ; Determined by formulas (2) and (3) (2) (3)。 3. The intelligent motion control method based on adaptive foresight distance according to claim 1, characterized in that: The obtained elliptical type II controller is used to control the motion of the ship, including: The current attitude of the ship is input into the elliptical type II controller, and the throttle command or rudder angle command is calculated by the elliptical type II controller as output to control the ship.

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