CMG unmanned motorcycle anti-interference method, device, equipment and storage medium based on disturbance frequency separation

Abstract

Disclosed are a method, apparatus, device, and storage medium for CMG unmanned motorcycle anti-disturbance based on disturbance frequency separation. The method includes: estimating the system state and roll channel disturbance based on the precession control variable and measurement output using a disturbance observer; separating the disturbance into low-frequency disturbances and high-frequency disturbances; calculating the actual state equilibrium point based on the low-frequency disturbances; generating a preliminary precession control variable based on the estimated system state and the actual state equilibrium point; performing high-frequency disturbance feedforward compensation on the preliminary precession control variable based on the high-frequency disturbance to generate a new precession control variable; and applying the newly generated precession control variable to the CMG unmanned motorcycle system. The present invention can also drive the precession angle back to zero in the presence of disturbances, avoiding CMG angular momentum saturation. It also achieves decoupling of steering control from CMG control, significantly enhancing the motorcycle's anti-disturbance capability under various disturbances.

Description

Technical Field

[0001] The present disclosure relates to the technical field of CMG (control moment gyro) unmanned motorcycles, and in particular to a CMG unmanned motorcycle anti-interference method, device, equipment and storage medium based on disturbance frequency separation. Background Art

[0002] Unmanned motorcycle robots offer the advantages of high speed, high maneuverability, and excellent maneuverability, making them suitable for a variety of tasks in structured environments such as cities and roads, as well as unstructured environments such as mountains and grasslands. However, they are unstable at low speeds. Common mechanisms to address this instability include control moment gyros (CMGs), reaction wheels, mass pendulums, and negative drag torque. Compared to the other three methods, CMGs can output greater torque, and therefore have attracted considerable attention.

[0003] The basic components of a CMG include a high-inertia flywheel, a motor that drives the flywheel's rotation, a frame connecting the rotating motor and flywheel, and a motor that drives the frame and flywheel's precession. The fundamental principle behind its torque output is the law of angular momentum. Specifically, when a high-speed rotating flywheel is driven into precession, it generates a gyroscopic torque along a third axis that is orthogonal to both its rotation and precession. Figure 1 is a schematic diagram of CMG, where IΩ is the rotational angular momentum, is the precession angular velocity, the dot operator above the precession angle γ represents the derivative, and M is the output torque.

[0004] Balance control for CMG-based autonomous motorcycles involves controlling the gyroscopic torque output by the CMG to counteract gravity and restore the vehicle to its equilibrium position when the vehicle tilts. However, the CMG is limited by angular momentum saturation, meaning that when the precession angle approaches 90 degrees, it cannot generate torque in the desired output axis direction. This means that the integral of the CMG output torque is limited, and it cannot continuously output torque for long periods of time. Therefore, in order for the CMG motorcycle to withstand larger disturbances and cope with disturbances in unknown directions, a key goal of CMG control is to control the CMG precession axis to return to a preset position while maintaining vehicle balance.

[0005] There are already some control methods for CMG-based unmanned motorcycles or bicycles.

[0006] One existing technology is the linear state feedback method. Classic linear control methods build a state-space model of the system, calculate the observer feedback gain to stabilize the observation error system, and then estimate the unknown state based on the input and output. Finally, calculate the controller feedback gain to stabilize the closed-loop system or achieve the desired performance. Methods for calculating feedback gains include pole placement and the linear quadratic regulator method.

[0007] This linear state feedback method only ensures system stability, but lacks robustness and disturbance rejection. Because it lacks a dedicated disturbance rejection mechanism, non-zero mean disturbances can lead to tracking errors, meaning the CMG precession angle (referred to as the frame angle in some literature) cannot be tracked to zero. Figures 2(a) and (b) show experimental results based on this technique from different publications.

[0008] The second existing technology is active disturbance rejection control (ADRC). Active rejection control (ADRC) is a disturbance estimation and compensation method for systems with disturbances. Based on state feedback, ADRC treats the disturbance as an additional state to create an expanded state vector. The observer poles are configured to stabilize the expanded observation error system, thereby estimating the unknown disturbance. The opposite of the estimated disturbance is then added to the control input to offset the disturbance.

[0009] However, the disadvantage of the anti-disturbance control method is that the CMG precession angle will also be biased under disturbance. More seriously, if the disturbance continues to act, the CMG angular momentum will be saturated and the balance control ability will be lost. Figure 3 It can be seen that the precession angle ( Figure 3 The α angle) produces a bias.

[0010] The reason is that the ADACS method is only applicable to situations where the disturbance is a matched disturbance (a matched disturbance refers to a situation where the disturbance coefficient matrix and the input coefficient matrix columns are linearly correlated in the state-space equation). However, the disturbance in CMG's unmanned motorcycle is a non-matched disturbance, so directly using this method will fail.

[0011] The third existing technology is a solution based on disturbance compensation and control allocation. It uses an extended state observer to observe disturbances, then uses control allocation technology to coordinate the output torques of the steering and CMG to compensate for the disturbances. At the same time, a precession angle penalty term is added to the objective function of the control allocation to promote the precession angle to approach zero.

[0012] There are two disadvantages of the third existing technology: first, it can only promote the return of the precession angle to zero, and there is no theoretical guarantee that the precession angle will completely return to zero; second, it relies on the distribution between steering control and CMG control, and does not decouple the steering control and CMG control, resulting in deviations in the tracking path that depends on steering.

[0013] Therefore, it is necessary to develop an anti-disturbance control technology solution for CMG unmanned motorcycles, which can drive the precession angle back to zero in the presence of disturbances, avoid CMG angular momentum saturation, and at the same time not couple the steering angle control. Summary of the Invention

[0014] This disclosure proposes an anti-disturbance control method for a CMG unmanned motorcycle based on disturbance frequency separation, which can drive the precession angle back to zero when there is a disturbance, avoid CMG angular momentum saturation, and achieve decoupling of steering control and CMG control.

[0015] According to one embodiment of the present disclosure, a disturbance frequency separation-based CMG unmanned motorcycle anti-disturbance control method is proposed, comprising:

[0016] According to the current CMG precession control quantity u and the motion state measurement output y of the CMG unmanned vehicle, the estimated system state is obtained. and roll channel disturbances The system state includes the precession angle γ, the roll angle Roll angular velocity

[0017] The estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances

[0018] Based on low-frequency perturbations Calculate the actual state equilibrium point of the system

[0019] Based on the estimated system state and the actual state balance point Generate preliminary precession control

[0020] Based on high-frequency perturbations Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u;

[0021] The newly generated precession control variable u is applied to the CMG unmanned motorcycle system to achieve anti-disturbance control.

[0022] In some possible implementations, the estimated system state is obtained based on the current CMG precession control value u and the motion state measurement output y of the CMG unmanned vehicle. and roll channel disturbances include:

[0023] Define the expanded state vector γ is the precession angle, represents the roll angle, represents the roll angular velocity, d represents the roll channel disturbance, and the expanded system equation is established:

[0024]

[0025] Where δ represents the handlebar angle, v represents the rear wheel speed, g(·) is the nonlinear function of the system roll dynamics, and p is the derivative of the roll channel disturbance d. is the expansion system function;

[0026] Based on the expanded system equation, the following expanded state observation equation is established:

[0027]

[0028] in, is the expanded state vector The estimated value of , L is the observation gain matrix, and C is the output matrix;

[0029] The error equation is obtained:

[0030]

[0031] in,

[0032] Determine the observation gain matrix L so that A o -LC is stable and the error e converges, then The first three components are used as the estimated system state And at this time The fourth component of is the estimated roll channel disturbance

[0033] In some possible implementations, the estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances include:

[0034] Estimation of the roll channel disturbance Through the low-pass filter, the low-frequency disturbance is obtained The transfer function of the low-pass filter is Where T is an adjustable time constant parameter used to adjust the cutoff frequency of the filter, and s is the complex variable of the Laplace transform;

[0035] Estimation of the roll channel disturbance Through the high-pass filter, high-frequency disturbance is obtained The transfer function of the high-pass filter is

[0036] In some possible implementations, at the actual state equilibrium point of the computing system , the precession angle balance point is set to the preset zero position.

[0037] In some possible implementations, based on low-frequency perturbations Calculate the actual state equilibrium point of the system include:

[0038] The zero point of the roll angle balance point function is solved based on the Newton iteration method. The roll angle balance point function is as follows:

[0039]

[0040] Among them, δ t and v t is the input command, g(·) is the nonlinear function of the system roll dynamics;

[0041] The roll angle at the end of the Newton iteration method The value of the roll angle balance point And get the actual state equilibrium point

[0042] In some possible implementations, based on the estimated system state and the actual state balance point Generate preliminary precession control include:

[0043] exist Linearize the following system state space equation at u=0:

[0044]

[0045] Where f(x) represents the system dynamics function, h(x) is the control input matrix, and D is the constant vector;

[0046] definition and Building a closed-loop controller Where K is the feedback gain matrix, and the feedback gain matrix K is adjusted so that the closed-loop matrix A c -B c K is stable, and A c -B c When K reaches a stable As the initial precession control

[0047] In some possible implementations, based on high frequency perturbations Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u, including:

[0048] The high-frequency disturbance feedforward compensation function is calculated according to the following formula:

[0049]

[0050] Among them, I b is the moment of inertia parameter, I fz is the flywheel moment of inertia parameter, Ω is the rotation angular velocity;

[0051] Generate a new precession control variable u according to the following formula:

[0052]

[0053] According to one embodiment of the present disclosure, a CMG unmanned motorcycle anti-disturbance control device based on disturbance frequency separation is also proposed, comprising:

[0054] The disturbance observer is used to obtain the estimated system state based on the current CMG precession control quantity u and the motion state measurement output y of the CMG unmanned vehicle and roll channel disturbances The system state includes the precession angle γ, the roll angle Roll angular velocity

[0055] High and low frequency separators for the estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances

[0056] Perturbation equilibrium point calculator for low-frequency perturbations Calculate the actual state equilibrium point of the system

[0057] Controller for system states based on estimates and the actual state balance point Generate preliminary precession control

[0058] High frequency disturbance feedforward compensator for high frequency disturbance based on Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u;

[0059] The anti-disturbance controller is used to apply the newly generated precession control variable u to the CMG unmanned motorcycle system to achieve anti-disturbance control.

[0060] According to one embodiment of the present disclosure, an electronic device is further proposed, comprising a memory and a processor, wherein the memory is used to store computer instructions executable on the processor, and the processor is used to implement any of the above methods when executing the computer instructions.

[0061] According to an embodiment of the present disclosure, a computer-readable storage medium is further provided, on which a computer program is stored. When the program is executed by a processor, the method described in any one of the above items is implemented.

[0062] The CMG unmanned motorcycle anti-disturbance control scheme based on disturbance frequency separation proposed in this disclosure ensures that the precession angle returns to zero even in the presence of disturbances by performing frequency separation on the disturbances and employing different compensation strategies, thereby avoiding CMG angular momentum saturation and improving the system's anti-disturbance capability. At the same time, this scheme achieves decoupled control of the CMG and steering, ensuring that the CMG's anti-disturbance control does not affect the steering-based trajectory tracking control, providing greater flexibility for the practical application of the unmanned motorcycle. Furthermore, the scheme's design combines the accuracy of zero-position tracking of the precession angle under low-frequency disturbance components with the rapidity of anti-disturbance control under high-frequency disturbance components, enhancing the motorcycle's anti-disturbance capability under various disturbances.

[0063] Other features and advantages of the present disclosure are described in detail below. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the specification and, together with the description, serve to explain the principles of the specification.

[0065] Figure 1 A schematic diagram of a CMG is shown.

[0066] FIG2(a) and FIG2(b) are experimental results based on a certain prior art.

[0067] Figure 3 This is an experimental result based on another prior art.

[0068] Figure 4 A schematic diagram of the geometric relationship of the CMG unmanned motorcycle system is shown.

[0069] Figure 5 A flow chart of a CMG unmanned motorcycle anti-interference method based on disturbance frequency separation according to an embodiment of the present disclosure is shown.

[0070] Figure 6 A control block diagram of a CMG unmanned motorcycle anti-disturbance device based on disturbance frequency separation according to an exemplary embodiment of the present disclosure is shown.

[0071] Figure 7 It is a schematic structural diagram of an electronic device according to at least one embodiment of the present disclosure. DETAILED DESCRIPTION

[0072] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.

[0073] The disclosed embodiments may be applied to a computer system / server that is operable with numerous other general-purpose or special-purpose computing system environments or configurations. Examples of well-known computing systems, environments, and / or configurations suitable for use with the computer system / server include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments including any of the above, among others.

[0074] Computer systems / servers may be described in the general context of computer system-executable instructions, such as program modules, executed by a computer system. Generally, program modules may include routines, programs, objects, components, logic, data structures, and the like, that perform specific tasks or implement specific abstract data types. Computer systems / servers may be implemented in a distributed cloud computing environment where tasks are performed by remote processing devices linked through a communications network. In a distributed cloud computing environment, program modules may be located on local or remote computer system storage media, including storage devices.

[0075] The meanings of the CMG unmanned motorcycle modeling parameters involved in this disclosure are shown in Table 1.

[0076] Table 1 Modeling parameters

[0077]

[0078] In addition, unless otherwise indicated, the dot above the symbol in this disclosure (i.e. · ) represents derivation; the broken line (i.e. ^) above the symbol in this disclosure represents the estimated value.

[0079] Figure 4 The geometric relationship diagram of the CMG unmanned motorcycle is shown, and the location of key parameters in the motorcycle structure is marked. The dynamic equation of the CMG unmanned motorcycle can be established using the Newton-Euler method:

[0080]

[0081] This paper adopts a decoupled control method for steering and CMG. Specifically, steering is responsible for tracking the trajectory planned by the upper layer, and CMG is responsible for the balance control of the roll angle of the unmanned motorcycle. The design goal of this paper is to control the roll angle to reach a specified angle and control the precession angle of the CMG back to zero. Therefore, the system state of the unmanned motorcycle can be defined as The input of the system is the precession angular velocity of the CMG, that is Considering that the roll channel of the system is subject to a disturbance d, the state space equation of the system can be written as:

[0082]

[0083] Figure 5 The flowchart of the CMG unmanned vehicle anti-interference method based on disturbance frequency separation according to one embodiment of the present disclosure is shown. As shown in the figure, the method includes steps 1 to 6.

[0084] Step 1: Based on the current CMG precession control quantity u and the CMG unmanned motorcycle motion state measurement output y, the estimated system state is obtained. and roll channel disturbances The system state includes the precession angle γ, the roll angle Roll angular velocity

[0085] The CMG precession control value u in this embodiment can be a precession angular velocity control value, a precession joint torque control value, or a precession acceleration control value, etc., which can realize the control of CMG precession. Those skilled in the art can choose which specific parameter to use as the CMG precession control value u according to actual conditions.

[0086] The measurement output y may include the precession angle γ, the roll angle Handlebar angle δ, rear wheel forward speed v, roll angular velocity The forward acceleration and / or rear wheel torque are measurement quantities that reflect the motion state of the CMG unmanned motorcycle. Those skilled in the art can determine which motion state quantities to measure as needed.

[0087] This step can be achieved through a disturbance observer, the goal of which is to estimate the unknown state and disturbance based on the precession control quantity and motion state measurement quantity of the unmanned vehicle system. For example, in one example, its input can be the current precession angular velocity control quantity and measurement output y of the CMG unmanned vehicle, and the measurement output y can include the precession angle γ, the roll angle The output is the estimated value of the system state. and the perturbation estimate The disturbance observer used in the present disclosure can be continuous or discrete, and can be full-order or reduced-order. The specific implementation form is not limited, such as Kalman filter, Lumberg observer, extended state observer, sliding mode observer, nonlinear disturbance observer, etc. Those skilled in the art can select an appropriate observer according to their needs.

[0088] In some embodiments, the following extended state observer can be used to estimate the system state: and roll channel disturbances

[0089] First, define the expanded state vector γ is the precession angle, represents the roll angle, represents the roll angular velocity, d represents the roll channel disturbance, and the following expanded system equation is established:

[0090]

[0091] Where δ represents the handlebar angle, v represents the rear wheel speed, g(·) is the nonlinear function of the system roll dynamics, and p is the derivative of the roll channel disturbance d. is the expansion system function.

[0092] The second step is to establish the following expanded state observation equation based on the expanded system equation:

[0093]

[0094] in, In expansion state The estimated value of , L is the observation gain matrix, and C is the output matrix.

[0095] Then we get the error equation:

[0096]

[0097] in,

[0098] Finally, determine the observation gain matrix L so that A o -LC is stable and the error e converges, then The first three components are used as the estimated system state And at this time The fourth component of is the estimated roll channel disturbance

[0099] For example, when determining the observation gain matrix L, L can be calculated by pole placement method or other methods so that A o-LC is stable, then as long as p is bounded, L can be further adjusted so that e converges to 0 under a given error, realizing the observation of the disturbance, that is,

[0100] According to this embodiment, by considering the disturbance as an additional state to obtain an expanded state vector, configuring the observer poles to make the expanded observation error system stable to estimate the unknown disturbance, it is possible to effectively achieve simultaneous estimation of the system state and disturbance.

[0101] Step 2: Perturb the estimated roll channel Separation into low-frequency disturbances and high-frequency disturbances

[0102] This step can be achieved by using high and low frequency separation filters, the goal is to separate the disturbance Mid- and low-frequency components and high-frequency components Then different methods are used for compensation. Its input is the disturbance estimated in the previous step The output is a low frequency disturbance and high-frequency disturbances The separation of low-frequency and high-frequency disturbances can be achieved using various types of filters, such as digital or analog, discrete or continuous, finite impulse response (FIR) or infinite impulse response (IIR) filters. For example, Butterworth filters, Chebyshev filters, elliptic filters, windowed filters, and raised cosine filters can be used. In addition, the high-frequency and low-frequency parts of the filter do not have to completely cover all frequencies.

[0103] In some embodiments, a combination of a first-order low-pass filter and a high-pass filter may be used. Through the low-pass filter, the low-frequency disturbance is obtained The transfer function of the low-pass filter is:

[0104]

[0105] Where T is an adjustable time constant parameter used to adjust the cutoff frequency of the filter, and s is the complex variable of the Laplace transform;

[0106] The estimation of the roll channel disturbance can be made Through the high-pass filter, high-frequency disturbance is obtained The transfer function of the high-pass filter is (1.7)

[0108] The cutoff frequency of the filter can be matched to the system characteristics by selecting an appropriate time constant T.

[0109] According to this embodiment, the mean of the high-frequency disturbance and the low-frequency disturbance satisfies In addition, the low-frequency perturbation also satisfies the boundedness, that is, These characteristics lay the foundation for the subsequent adoption of different compensation strategies.

[0110] In other embodiments, other means besides filters may be used to convert the disturbance Separation into low-frequency disturbances and high-frequency disturbances For example, the characteristics of the disturbance can be learned based on learning methods, and the disturbance can be divided into slow-changing or fast-changing disturbances according to the characteristics of the disturbance. As long as the purpose of separating the slow-changing disturbance and the fast-changing disturbance can be achieved so that they can be compensated separately later, it will be sufficient.

[0111] Step 3, based on low-frequency perturbation Calculate the actual state equilibrium point of the system

[0112] This step can be implemented by a perturbation equilibrium point calculator, whose goal is to estimate the low-frequency perturbation To estimate the actual equilibrium point of the system, the inventor abstracted its essence as solving the zero point of a nonlinear equation, whose input is a low-frequency disturbance Upper level instruction v t and δ t , the output is the estimated equilibrium point Upper level instruction v t and δ t It can be understood as the desired motion state determined manually or given by the upper-level path planner, that is, the target forward speed and the target turning angle. Those skilled in the art can use various zero-point solving methods that they think are suitable to solve the zero point of the equation.

[0113] The target equilibrium point of the system can be obtained by OK. Among them, [v t , δ t ] is provided by the input command signal. In some embodiments, in order to obtain the maximum anti-interference ability of the system, the CMG precession angle balance point can be set to a preset zero position, that is, γ t It is a preset reference position. It will be understood by those skilled in the art that the preset zero position here does not represent the geometric level, mechanical zero or other meanings of 0, but rather the optimal balance position selected according to the specific working characteristics of the CMG. In this case, the roll balance point is estimated. The target equilibrium point of the system can be determined.

[0114] In some embodiments, the Newton iteration method may be used to solve the roll balance point. First, the zero point of the roll angle balance point function may be solved based on the Newton iteration method. The roll angle balance point function is shown as follows:

[0115]

[0116] Among them, δ t and v t is the input command, g(·) is the nonlinear function of the rolling dynamics of the unmanned motorcycle system. Specifically, the zero point guess initial value can be given according to the Newton iteration method. Given an error limit ε, perform the following iterations:

[0117]

[0118] Until the iteration termination condition is met

[0119] The roll angle at the end of the Newton iteration method The value of is used as the estimated roll angle balance point Then determine the actual state equilibrium point of the system

[0120] According to this embodiment, the Newton iteration method is used to calculate the balance point, and the CMG precession angle balance point is set to a preset zero position, which ensures that the system has the maximum anti-interference ability. In addition, the input of the balance point calculator is only low-frequency disturbance. Helps prevent the system from over-responding to high-frequency disturbances.

[0121] In other embodiments, instead of using the above model-based calculation method, a learning-based method may be used to calculate the actual state equilibrium point based on simulation or experimental data fitting.

[0122] Step 4: Based on the estimated system state and the actual state balance point Generate preliminary precession control

[0123] This step can be implemented by the controller, whose goal is to stabilize the unmanned motorcycle at the estimated disturbance equilibrium point, and the input is the estimated actual state equilibrium point and system status The output is the preliminary precession control quantity Those skilled in the art may adopt any applicable controller, such as a continuous or discrete controller. The specific implementation may adopt, for example, a state feedback controller, an LQR controller, a sliding mode controller, an MPC controller, an H2 / H∞ controller, a reinforcement learning controller, etc.

[0124] In one embodiment, a linear state feedback controller may be used. Linearize the following system state space equation at u=0:

[0125]

[0126] Where f(x) represents the system dynamics function, h(x) is the control input matrix, and D is the constant vector. Refer to the above formula (1.2).

[0127] For example, we can use the first-order Taylor expansion and obtain the following after linearization:

[0128]

[0129] Defining state deviation and Then we have:

[0130]

[0131] Building a closed-loop controller Where K is the feedback gain matrix, and the feedback gain matrix K is adjusted so that the closed-loop matrix A c -B c K is stable, and A c -B c When K reaches a stable As the initial precession control

[0132] According to this embodiment, the nonlinear system is linearized at the actual state equilibrium point, and the state feedback method is used to achieve the convergence of the system state to the equilibrium point. Therefore, in d h = 0, that is, when there is only low-frequency disturbance, the controller Under the effect, there are It can ensure that the precession angle γ returns to zero.

[0133] Step 5, based on high-frequency perturbations Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u.

[0134] This step can be implemented by a high-frequency disturbance feedforward compensator, the goal of which is to compensate for high-frequency disturbances, and its input is the preliminary precession control quantity High-frequency disturbance d h , the output is the new actual precession control quantity u.

[0135] In some embodiments, high-frequency disturbance feedforward compensation is performed, and the high-frequency disturbance feedforward compensation function can be calculated according to the following formula:

[0136]

[0137] Among them, I b is the moment of inertia parameter, I fz is the flywheel moment of inertia parameter, Ω is the rotation angular velocity;

[0138] And generate a new precession control variable u according to the following formula:

[0139]

[0140] After adding state feedback control and high-frequency disturbance feedforward compensation to the state space equation of the unmanned motorcycle system, the following closed-loop system equation is obtained:

[0141]

[0142] The entire closed-loop system includes core control modules such as unmanned motorcycle system, state feedback control, and high-frequency disturbance feedforward supplement, as well as auxiliary control modules such as disturbance observation, frequency separation, and disturbance equilibrium point calculation.

[0143] According to this embodiment, since (A c -B c K) stable, The mean is 0, so The steady-state value will not deviate from the The channel moved to channel, achieving anti-interference of the roll angle channel.

[0144] In some embodiments, the high-frequency disturbance feedforward compensation function may not fully compensate for the high-frequency disturbance, and may refine the estimated high-frequency disturbance, such as adding weight coefficients, shielding specific frequencies, etc., depending on actual needs or experimental results.

[0145] The anti-disturbance control method for a CMG unmanned motorcycle based on disturbance frequency separation proposed in this embodiment ensures that the precession angle returns to zero even in the presence of disturbances by performing frequency separation on the disturbances and employing different compensation strategies, thereby avoiding CMG angular momentum saturation and improving the system's anti-disturbance capability. At the same time, this method achieves decoupled control of the CMG and steering, ensuring that the CMG's anti-disturbance control does not affect the steering-based trajectory tracking control, providing greater flexibility for the practical application of the unmanned motorcycle. Furthermore, the design of this method, which combines the accuracy of precession angle zero tracking under low-frequency disturbance components with the rapidity of anti-disturbance control under high-frequency disturbance components, enhances the motorcycle's anti-disturbance capability under various disturbances.

[0146] According to another embodiment of the present disclosure, a CMG unmanned motorcycle anti-disturbance control device based on disturbance frequency separation is also proposed, including:

[0147] The disturbance observer is used to obtain the estimated system state based on the current CMG precession control quantity u and the motion state measurement output y of the CMG unmanned vehicle and roll channel disturbances The system state includes the precession angle γ, the roll angle Roll angular velocity

[0148] High and low frequency separators for the estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances

[0149] Perturbation equilibrium point calculator for low-frequency perturbations Calculate the actual state equilibrium point of the system

[0150] Controller for system states based on estimates and the actual state balance point Generate preliminary precession control

[0151] High frequency disturbance feedforward compensator for high frequency disturbance based on Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u;

[0152] The anti-disturbance controller is used to apply the newly generated precession control variable u to the CMG unmanned motorcycle system to achieve anti-disturbance control.

[0153] Figure 6 A control block diagram of a CMG unmanned motorcycle anti-disturbance device based on disturbance frequency separation, according to an exemplary embodiment of the present disclosure, is shown. As shown, the device includes a CMG motorcycle, a controller, a high-frequency disturbance feedforward compensator, a disturbance observer, a filter for high- and low-frequency separation, and a disturbance balance point calculator.

[0154] The disturbance observer is used to estimate the unknown state and disturbance based on the system's precession control variable and measurement variable. In this example, its input is the precession angular velocity control variable u and the measurement output of the CMG unmanned motorcycle. The output is the estimated system state and the estimated disturbance

[0155] This exemplary embodiment uses a filter to separate high and low frequencies. Its input is the disturbance estimate output by the disturbance observer. Separate it into high-frequency disturbances and low-frequency disturbances

[0156] The perturbation equilibrium point calculator is used to calculate the perturbation equilibrium point based on the estimated low-frequency perturbation. Estimate the actual equilibrium point of the system. Its input is a low-frequency disturbance and upper level instruction v t , δ t , the output is the estimated actual state equilibrium point

[0157] The controller is used to stabilize the GMC motorcycle at the estimated disturbance equilibrium point. The input is the actual state equilibrium point and observation status The output is the initial precession angular velocity control quantity

[0158] The high-frequency disturbance feedforward compensator is used to compensate for high-frequency disturbances. Its input is the initial precession angular velocity control quantity and high-frequency disturbances The output is the new precession angular velocity control variable u.

[0159] The CMG motorcycle is the controlled object, and its input is the precession angular velocity control variable u, and its output includes the precession angle γ, roll angle The handlebar angle δ and the rear wheel speed v are both affected by the external disturbance d.

[0160] pass Figure 6 The control block diagram clearly illustrates the closed-loop control structure of the entire system and the signal transmission relationships between its various components. The CMG motorcycle, controller, and high-frequency disturbance feedforward compensator form the basic closed-loop control structure, while the disturbance observer, filter, and disturbance equilibrium point calculator provide the necessary parameters and signals for implementing this closed-loop control.

[0161] For other details and beneficial effects of this embodiment, please refer to the relevant introduction above and will not be repeated here.

[0162] Figure 7 An electronic device provided for at least one embodiment of the present disclosure includes a memory and a processor, wherein the memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the CMG unmanned motorcycle anti-interference control method based on disturbance frequency separation described in any embodiment or implementation method of the present disclosure when executing the computer instructions.

[0163] At least one embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the CMG unmanned motorcycle anti-disturbance control method based on disturbance frequency separation as described in any embodiment or implementation of the present disclosure.

[0164] Those skilled in the art will appreciate that one or more embodiments of this specification may be provided as a method, system, or computer program product. Thus, one or more embodiments of this specification may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, one or more embodiments of this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0165] The various embodiments in this specification are described in a progressive manner. Similar portions between the various embodiments can be referenced to each other. Each embodiment focuses on the differences between the other embodiments. In particular, the data processing device embodiment is generally similar to the method embodiment, so its description is relatively simple. For relevant portions, refer to the description of the method embodiment.

[0166] The foregoing description of this specification describes specific embodiments. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in an order different from that described in the embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order shown or sequential order to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0167] Embodiments of the subject matter and functional operations described in this specification may be implemented in the following: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. Embodiments of the subject matter described in this specification may be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier to be executed by a data processing device or to control the operation of the data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal, such as a machine-generated electrical, optical, or electromagnetic signal, which is generated to encode information and transmit it to a suitable receiver device for execution by the data processing device. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them.

[0168] The processes and logic flows described in this specification can be performed by one or more programmable computers executing one or more computer programs to perform the corresponding functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can be implemented as, special purpose logic circuitry, such as an FPGA (field programmable gate array) or an ASIC (application-specific integrated circuit).

[0169] Computers suitable for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit will receive instructions and data from a read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as magnetic disks, magneto-optical disks, or optical disks, or the computer will be operably coupled to such mass storage devices to receive data from them or to transmit data to them, or both. However, a computer does not necessarily have such devices. In addition, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0170] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and storage devices, including, for example, semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD ROM and DVD-ROM disks. The processor and memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0171] Although this specification includes many specific implementation details, these should not be interpreted as limiting the scope of any invention or the scope of protection claimed, but are mainly used to describe the features of specific embodiments of specific inventions. Certain features described in multiple embodiments within this specification may also be implemented in combination in a single embodiment. On the other hand, the various features described in a single embodiment may also be implemented separately in multiple embodiments or in any suitable sub-combination. In addition, although features may work in certain combinations as described above and even initially claimed as such, one or more features from the claimed combination may be removed from the combination in some cases, and the claimed combination may point to a sub-combination or a variation of the sub-combination.

[0172] Similarly, although operations are depicted in a particular order in the accompanying drawings, this should not be understood as requiring that these operations be performed in the particular order shown or performed sequentially, or that all illustrated operations be performed to achieve the desired results. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system modules and components in the above-described embodiments should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product, or packaged into multiple software products.

[0173] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the particular order shown or sequential sequence to achieve the desired results. In some implementations, multitasking and parallel processing may be advantageous.

[0174] The above description is merely a preferred embodiment of one or more embodiments of this specification and is not intended to limit one or more embodiments of this specification. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of one or more embodiments of this specification shall be included in the scope of protection of one or more embodiments of this specification.

Claims

1. A CMG unmanned motorcycle anti-disturbance control method based on disturbance frequency separation, characterized in that: include: According to the current CMG precession control quantity u and the motion state measurement output y of the CMG unmanned vehicle, the estimated system state is obtained. and roll channel disturbances The system state includes the precession angle γ, the roll angle Roll angular velocity The estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances Based on low-frequency perturbations Calculate the actual state equilibrium point of the system Based on the estimated system state and the actual state balance point Generate preliminary precession control Based on high-frequency perturbations Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u; The newly generated precession control variable u is applied to the CMG unmanned motorcycle system to achieve anti-disturbance control.

2. The method according to claim 1, characterized in that According to the current CMG precession control quantity u and the motion state measurement output y of the CMG unmanned vehicle, the estimated system state is obtained. and roll channel disturbances include: Define the expanded state vector γ is the precession angle, represents the roll angle, represents the roll angular velocity, d represents the roll channel disturbance, and the expanded system equation is established: Where δ represents the handlebar angle, v represents the rear wheel speed, g(·) is the nonlinear function of the system roll dynamics, and p is the derivative of the roll channel disturbance d. is the expansion system function; Based on the expanded system equation, the following expanded state observation equation is established: in, is the expanded state vector The estimated value of , L is the observation gain matrix, and C is the output matrix; The error equation is obtained: in, Determine the observation gain matrix L so that A o -LC is stable and the error e converges, then The first three components are used as the estimated system state And at this time The fourth component of is the estimated roll channel disturbance 3. The method according to claim 1, characterized in that The estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances include: Estimation of the roll channel disturbance Through the low-pass filter, the low-frequency disturbance is obtained The transfer function of the low-pass filter is Where T is an adjustable time constant parameter used to adjust the cutoff frequency of the filter, and s is the complex variable of the Laplace transform; Estimation of the roll channel disturbance Through the high-pass filter, high-frequency disturbance is obtained The transfer function of the high-pass filter is 4. The method according to claim 1, wherein At the actual state equilibrium point of the computing system , the precession angle balance point is set to the preset zero position.

5. The method according to claim 4, characterized in that Based on low-frequency perturbations Calculate the actual state equilibrium point of the system include: The zero point of the roll angle balance point function is solved based on the Newton iteration method. The roll angle balance point function is as follows: Among them, δ t and v t is the input command, g(·) is the nonlinear function of the system roll dynamics; The roll angle at the end of the Newton iteration method The value of the roll angle balance point And get the actual state equilibrium point 6. The method according to claim 1, wherein Based on the estimated system state and the actual state balance point Generate preliminary precession control include: exist The following system state space equations are linearized at: Where f(x) represents the system dynamics function, h(x) is the control input matrix, and D is the constant vector; definition and Building a closed-loop controller Where K is the feedback gain matrix, and the feedback gain matrix K is adjusted so that the closed-loop matrix A c -B c K is stable, and A c -B c When K reaches a stable As the initial precession control 7. The method according to claim 6, characterized in that Based on high-frequency perturbations Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u, including: The high-frequency disturbance feedforward compensation function is calculated according to the following formula: Among them, I b is the moment of inertia parameter, I fz is the flywheel moment of inertia parameter, Ω is the rotation angular velocity; Generate a new precession control variable u according to the following formula:

8. A CMG unmanned motorcycle anti-disturbance control device based on disturbance frequency separation, characterized in that: include: The disturbance observer is used to obtain the estimated system state based on the current CMG precession control quantity u and the motion state measurement output y of the CMG unmanned vehicle and roll channel disturbances The system state includes the precession angle γ, the roll angle Roll angular velocity High and low frequency separators for the estimated roll channel disturbance Separation into low-frequency disturbances and high-frequency disturbances Perturbation equilibrium point calculator for low-frequency perturbations Calculate the actual state equilibrium point of the system Controller for system states based on estimates and the actual state balance point Generate preliminary precession control High frequency disturbance feedforward compensator for high frequency disturbance based on Initial precession control Perform high-frequency disturbance feedforward compensation to generate a new precession control variable u; The anti-disturbance controller is used to apply the newly generated precession control variable u to the CMG unmanned motorcycle system to achieve anti-disturbance control.

9. An electronic device, characterized in that: The device includes a memory and a processor, wherein the memory is used to store computer instructions that can be executed on the processor, and the processor is used to implement the method according to any one of claims 1 to 7 when executing the computer instructions.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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