Loading vehicle control method and system of fuzzy observer

Abstract

The invention relates to a loading vehicle control method for a fuzzy observer, and belongs to the technical field of loading vehicle control, and the method comprises the following steps: S1, employing a camera carried on a loading vehicle to shoot an image; s2, inputting the image into a T-S dynamical model, and calculating an expected vehicle driving state; s3, estimating an actual vehicle driving state through the vehicle state information by using a fuzzy observer; s3, comparing an expected vehicle driving state with an actual vehicle driving state through a composite nonlinear feedback control model based on parallel distribution compensation, and calculating a feedback control quantity; and S4, inputting the feedback control quantity into an industrial personal computer of the loading vehicle, and controlling the operation of the loading vehicle.

Description

Technical Field

[0001] The present invention belongs to the technical field of loader control and relates to a loader control method and system of a fuzzy observer. Background Art

[0002] With the continuous development of the mining and metallurgy industry, mining operations are facing increasing challenges. Loaders are a key piece of equipment in mining and metallurgy, responsible for loading ore and other materials onto trucks or conveyors. However, traditional manual loading methods have numerous shortcomings in terms of efficiency, safety, and cost.

[0003] Secondly, mining operations require efficient and continuous operations to meet production demands. Autonomous loaders can operate 24 / 7, significantly improving production efficiency. Furthermore, automated loaders enable precise path planning and material handling, reducing human error and improving loading accuracy and consistency.

[0004] However, the application of autonomous loader applications in mining and metallurgy scenarios still faces many control issues:

[0005] 1. The terrain in mining areas is usually uneven, with obstacles such as slopes, potholes, and rocks. Robust control algorithms need to be developed to maintain stable and efficient operations under different load and terrain conditions.

[0006] 2. The dynamic characteristics of the loader are complex, including nonlinearity, time-varying and multivariable coupling problems. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to propose a fuzzy observer-based automatic driving control method for a loader truck in order to address the problems of uneven terrain, slopes, potholes, rocks and other obstacles in mining scenarios, and complex dynamic characteristics of the loader truck.

[0008] In order to achieve the above object, the present invention provides the following technical solutions:

[0009] A loader control method using a fuzzy observer comprises the following steps:

[0010] S1: Use the camera on the loader to capture images of the road and surrounding environment;

[0011] S2: Input the image into the TS dynamics model to calculate the desired vehicle driving state;

[0012] S3: Using the fuzzy observer, the actual vehicle driving state is estimated based on the vehicle state information;

[0013] S3: Compare the desired vehicle driving state with the actual vehicle driving state through a composite nonlinear feedback control model based on parallel distributed compensation, and calculate the feedback control variable;

[0014] S4: Input the feedback control quantity into the industrial computer of the loader to control the operation of the loader.

[0015] Furthermore, the TS dynamics model in step S2 adds fuzzy rules to the ordinary dynamics model to correct the calculation of the vehicle lateral force:

[0016]

[0017] F yf =h1(|α f |)α f +h2(|α f |)α f

[0018] F yr =h1(|α f |)α r +h2(|α f |)α r

[0019] where α f represents the sideslip angle of the front wheel, α r Indicates the sideslip angle of the rear wheel; C f1 and C f2 Respectively represent |α f |is M1 and |α f |is the cornering stiffness of the front wheel in M2; C r1 and C r2 Respectively represent |α f |is M1 and |α f |is the cornering stiffness of the rear wheel in M2; F yf represents the lateral tire force on the front wheel, F yr represents the lateral tire force of the rear wheel; h1 and h2 represent |α f |is M1 and |α f The membership function in the case of |is M2 is specifically expressed as follows:

[0020]

[0021] θ i Indicates the deflection angle;

[0022] The improved dynamic model obtained is written in the form of vehicle path tracking as follows:

[0023]

[0024] Among them C f Indicates the cornering stiffness of the front wheel, C r represents the cornering stiffness of the rear wheel, l f Indicates the distance from the vehicle's center of gravity to the front axle, l r represents the distance from the center of gravity of the vehicle to the rear axle, m represents the total mass of the vehicle, I z represents the yaw moment of inertia of the vehicle, δ f Indicates the front wheel turning angle; x Indicates the longitudinal speed of the vehicle; Indicates the lateral offset, represents the heading error, ρ represents the curvature radius;

[0025] The above equation can be written as the following form:

[0026]

[0027] in represents the displacement in the x direction, A i represents the parameter matrix determined by the least squares method, B i represents the parameter matrix determined by the least squares method, sat(u) represents the system saturation control input vector, B ρi represents the parameter matrix determined by the least squares method, and C represents the constant matrix;

[0028] The composite nonlinear feedback control model based on parallel distributed compensation is developed around the state equation form.

[0029] Furthermore, the fuzzy observer estimates the vehicle driving state through the vehicle state information, which is used as the feedback quantity in the feedback control to form a closed loop. The fuzzy observer is used to observe e and ψ:

[0030]

[0031] u represents the control input variable, i.e., the steering angle; represents the estimation error, represents the membership function, represents the unit estimation error, Indicates that the error of y is limited to a numerical range. represents the estimation error in the y direction;

[0032] e and ψ are the observed results, which are used to compare with the results calculated by the TS dynamic model. The control is performed by a composite nonlinear feedback control model based on parallel distributed compensation, so that the results observed by the fuzzy observer are close to the results calculated by the TS dynamic model.

[0033] Furthermore, in the composite nonlinear feedback control model based on parallel distributed compensation, the parallel distributed compensation PDC formula is as follows:

[0034]

[0035] in represents the membership function, K j A fuzzy set representing feedback.

[0036] In another aspect, the present invention provides a loader control system using a fuzzy observer, comprising:

[0037] Camera: mounted on the loader and used to capture images;

[0038] TS dynamics model: used to receive the image and calculate the expected vehicle driving state;

[0039] Fuzzy observer: used to estimate the actual vehicle driving state through vehicle state information;

[0040] A composite nonlinear feedback control model based on parallel distributed compensation is used to compare the desired vehicle driving state with the actual vehicle driving state and calculate the feedback control quantity to control the loader.

[0041] On the other hand, the present invention provides an electronic device comprising a memory and a processor; the memory is used to store a computer program; the processor is used to implement the fuzzy observer loading vehicle control method as described in any one of the above items when executing the computer program.

[0042] In another aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the loader control method using the fuzzy observer as described above is implemented.

[0043] The beneficial effects of the present invention are as follows: the method can effectively obtain the state of the loader during driving, so that obstacles can be effectively avoided during driving, and the desired location can be accurately reached, thereby ensuring the equipment safety of the loader itself and the safety of workers and other equipment in the operating area, and improving the efficiency of loading and unloading operations. The method realizes refined safety management and production process control in the mining area, further improves production efficiency, maintains the safety of production order, and at the same time realizes the improvement of the operating efficiency of the loader. The present invention transparently monitors the loader and improves the efficiency of ground transportation control.

[0044] Other advantages, objects, and features of the present invention will be described in part in the following description and, in part, will be apparent to those skilled in the art upon examination of the following description or may be learned from practice of the present invention. The objects and other advantages of the present invention may be realized and obtained through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be described in detail below with reference to the accompanying drawings, in which:

[0046] Figure 1 This is the framework diagram of the loader control system with fuzzy observer. DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present invention by means of specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] It should be noted that the illustrations provided in the following embodiments are merely schematic illustrations of the basic concept of the present invention. Therefore, the illustrations only show components related to the present invention and are not drawn according to the number, shape, and size of components in actual implementation. In actual implementation, the type, quantity, and proportion of each component may be changed arbitrarily, and the component layout may also be more complex.

[0049] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the embodiments of the present invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring the embodiments of the present invention.

[0050] like Figure 1 As shown, the present invention provides a loader control method based on a fuzzy observer, and the control block diagram includes a camera signal input, a TS dynamic model, a composite nonlinear feedback control model based on parallel distributed compensation, a fuzzy observer and a loader body.

[0051] Camera: Obtains the path following status through image information and inputs it into the TS dynamics model to obtain the desired vehicle driving state.

[0052] TS dynamic model: After obtaining the camera image information, calculations are performed to obtain the desired state.

[0053] Compared with the ordinary dynamics model, fuzzy rules are added to correct the calculation of vehicle lateral force (mainly correcting the nonlinear part)

[0054]

[0055] F yf =h1(|α f |)α f +h2(|α f |)α f

[0056] F yr =h1(|α f |)α r +h2(|α f |)α r

[0057] Its membership function h is as follows:

[0058]

[0059] The improved dynamic model obtained is written in the form of vehicle path tracking as follows:

[0060]

[0061] The above equation can be written as the following form:

[0062]

[0063] The subsequent control algorithm is based on this state equation form.

[0064] A composite nonlinear feedback control model based on parallel distributed compensation is proposed: the desired state is obtained from the TS dynamics model, the actual state is calculated from the fuzzy observer, and the feedback control variable is calculated to control the loader.

[0065] Fuzzy observer: estimates the vehicle's driving state through vehicle state information and uses it as feedback in feedback control to form a closed loop.

[0066] The design of the observer is mainly used to observe e and ψ

[0067]

[0068] This e and ψ are the observed results, which are used to compare with the results calculated by the dynamic model. The purpose of control is to make the observed results (actual results) close to the calculated results (ideal results).

[0069] The parallel distributed compensation (PDC) formula is as follows:

[0070]

[0071] Loading truck: the actual controlled object.

[0072] In the above embodiments, references to "this embodiment" in the specification indicate that a particular feature, structure, or characteristic described in conjunction with the embodiment is included in at least some embodiments, but not necessarily all embodiments. Multiple occurrences of "this embodiment" do not necessarily refer to the same embodiment.

[0073] In the above embodiments, although the invention has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory structures (e.g., dynamic RAM (DRAM)) may be used with the embodiments discussed. The embodiments of the present invention are intended to encompass all such alternatives, modifications, and variations that fall within the broad scope of the appended claims.

[0074] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, any one of the methods in this embodiment is implemented.

[0075] This embodiment also provides an electronic terminal, including: a processor and a memory;

[0076] The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory, so that the terminal executes any one of the methods in this embodiment.

[0077] Regarding the computer-readable storage medium in this embodiment, those skilled in the art will appreciate that all or part of the steps in the aforementioned method embodiments can be implemented using hardware associated with the computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When executed, the program performs the steps in the aforementioned method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0078] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store computer programs, the communication interface is used for communication, and the processor and the transceiver are used to run computer programs so that the electronic terminal executes the various steps of the above method.

[0079] In this embodiment, the memory may include a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0080] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0081] The present invention can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments that include any of the above.

[0082] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media, including storage devices.

[0083] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the purpose and scope of the technical solutions, which should all be included in the scope of the claims of the present invention.

Claims

1. A loader control method using a fuzzy observer, characterized in that: The following steps are involved: S1: Use the camera on the loader to capture images of the driving road and surrounding environment; S2: Input the image into the TS dynamics model to calculate the desired vehicle driving state; S3: using the fuzzy observer to estimate the actual vehicle driving state through the vehicle state information; S3: Compare the desired vehicle driving state with the actual vehicle driving state through a composite nonlinear feedback control model based on parallel distributed compensation, and calculate the feedback control amount; S4: Input the feedback control quantity into the industrial computer of the loader to control the operation of the loader.

2. The loader control method of the fuzzy observer according to claim 1 is characterized in that: The TS dynamics model in step S2 is a general dynamics model with fuzzy rules added to correct the calculation of the vehicle lateral force: F yf =h1(|α f |)a f +h2(|a f |)a f F yr =h1(|α f |)a r +h2(|a f |)a r where α f represents the sideslip angle of the front wheel, α r Indicates the side slip angle of the rear wheel; C f1 and C f2 Respectively represent |α f |is M1 and |α f |isM2 front wheel cornering stiffness; C r1 and C r2 Respectively represent |α f |is M1 and |α f |is the cornering stiffness of the rear wheel in M2; F yf represents the lateral tire force of the front wheel, F yf represents the lateral tire force of the rear wheel; h1 and h2 represent |α f |is M1 and |α f The membership function in the case of |isM2 is specifically expressed as follows: θ i Indicates the deflection angle; The improved dynamic model obtained is written in the form of vehicle path tracking as follows: Among them C f Indicates the cornering stiffness of the front wheel, C r represents the cornering stiffness of the rear wheel, l f Indicates the distance from the vehicle's center of gravity to the front axle, l r represents the distance from the center of gravity of the vehicle to the rear axle, m represents the total mass of the vehicle, and I z represents the yaw moment of inertia of the vehicle, δ f Indicates the front wheel turning angle; v x Indicates the longitudinal speed of the vehicle; Indicates the lateral offset, represents the heading error, ρ represents the curvature radius; The above equation can be written as the state equation as follows: in represents the displacement in the x direction, A i represents the parameter matrix determined by the least squares method, B i represents the parameter matrix determined by the least squares method, sat(u) represents the system saturation control input vector, B ρi represents the parameter matrix determined by the least squares method, and C represents the constant matrix; The composite nonlinear feedback control model based on parallel distributed compensation is carried out around the state equation form.

3. The loader control method of the fuzzy observer according to claim 1 is characterized in that: The fuzzy observer estimates the vehicle driving state through the vehicle state information, which is used as the feedback quantity in the feedback control to form a closed loop. The fuzzy observer is used to observe e and ψ: u represents the control input variable, i.e., the steering angle; represents the estimation error, represents the membership function, represents the unit estimation error, Indicates that the error of y is limited to a numerical range. represents the estimation error in the y direction; e and ψ are observed results, which are used to compare with the results calculated by the TS dynamics model. The control is performed through a composite nonlinear feedback control model based on parallel distributed compensation, so that the results observed by the fuzzy observer are close to the results calculated by the TS dynamics model.

4. The loader control method of the fuzzy observer according to claim 1 is characterized in that: In the composite nonlinear feedback control model based on parallel distributed compensation, the parallel distributed compensation PDC formula is as follows: in represents the membership function, K j A fuzzy set representing feedback.

5. A loader control system of a fuzzy observer, characterized in that: include: Camera: mounted on the loading vehicle, used to capture images; TS dynamics model: used to receive the image and calculate the expected vehicle driving state; Fuzzy observer: used to estimate the actual vehicle driving state through vehicle state information; Composite nonlinear feedback control model based on parallel distributed compensation: used to compare the expected vehicle driving state with the actual vehicle driving state, and calculate the feedback control amount to control the loader.

6. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the fuzzy observer loading vehicle control method according to any one of claims 1 to 4 when executing the computer program.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the loader vehicle control method of the fuzzy observer according to any one of claims 1 to 4 is implemented.

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