Unmanned bicycle motion control method, system and unmanned bicycle with balancing flywheel

Abstract

The application discloses an unmanned bicycle motion control method and system with a balance flywheel and an unmanned bicycle, and solves the control problem of the unmanned bicycle under the condition that system parameters cannot be accurately measured. The method decouples the control of the unmanned bicycle into three parallel tasks of speed control, steering control and balance control, adopts a proportional-integral (PI) controller to realize the speed control, adopts a first-order filter to realize the steering control, and adopts an adaptive neural network controller to realize the balance control. The control method proposed by the application effectively solves the balance control problem of the unmanned bicycle under different speeds and different steering angles under the condition that system modeling is inaccurate, and can identify uncertain parameters and functions in the system model.

Description

Technical Field

[0001] This invention belongs to the technical field of unmanned bicycles, specifically relating to a motion control method, system, and unmanned bicycle with a balancing flywheel. Background Technology

[0002] Unmanned bicycles are a typical type of mobile robot, requiring speed, steering, and balance control during movement. To enable the unmanned bicycle to maintain balance when stationary and improve its balance adjustment capability during movement, a balance flywheel can be introduced to provide lateral control torque to the bicycle body. When the speed and steering angle of the unmanned bicycle are not zero, the bicycle body is subjected to centrifugal force. The magnitude of the centrifugal force varies with different speeds and steering angles, and this centrifugal force is a disturbance input for the balance control system. This results in different balance control characteristics under different speed and steering angle conditions. According to existing solutions, to counteract the impact of centrifugal force on the bicycle's balance, feedforward control must be introduced into the balance flywheel to suppress centrifugal force disturbances. To achieve feedforward control, the disturbance must be accurately modeled so that the controller can fully compensate for it. With the development of control theory, control schemes for unmanned bicycles with balancing flywheels have become quite mature, provided the system's dynamics modeling is relatively accurate. The mainstream approach is based on feedback linearization, which decouples and linearizes the system's dynamic equations, allowing the feedforward control section of the balance controller to completely cancel out disturbance inputs and its own nonlinearity. Balance control is achieved through methods such as PID control, sliding mode control, and LQR pole placement. However, in actual controller design, uncertainties exist, including modeling errors, parameter measurement errors, changes in vehicle load weight and center of gravity, and errors caused by battery voltage drops. These uncertainties affect controller performance. These factors have not received sufficient attention in current mainstream control schemes. The system modeling errors caused by these factors prevent the feedforward control section of the balance controller from completely canceling out the effects of disturbance inputs and nonlinear dynamics, leading to significant control errors and even causing the unmanned bicycle to lose balance. Therefore, introducing an adaptive neural network controller into the balance controller of an unmanned bicycle to identify nonlinear dynamics and disturbance inputs online is a significant research topic. Summary of the Invention

[0003] The main objective of this invention is to address the problem that the inability to accurately model unmanned bicycles in the prior art makes it difficult to design balance controllers. This invention provides a motion control method, system, and unmanned bicycle with a balance flywheel. It uses a proportional-integral controller to achieve speed control, a proportional controller to achieve steering angle control, and an adaptive neural network controller to achieve balance control. This controller can learn the unknown parameters and unknown functions in the system dynamics model online, so that the controller design does not depend on accurate system modeling and can work under conditions where the system structure is uncertain.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] In a first aspect, the present invention provides a motion control method for an unmanned bicycle with a balancing flywheel, comprising the following steps:

[0006] S1. Establish the dynamic model of the unmanned bicycle:

[0007]

[0008] Where t is time, I0 is the roll inertia of the system, m0 is the mass of the system, h is the height of the system's center of gravity, g is the acceleration due to gravity, α is the rake angle of the fork, a, b, and c are constants related to the dimensions of the autonomous bicycle, and θ(t) is the tilt angle. It is the tilt angular velocity. It is angular acceleration, and v(t) is the vehicle speed. and These are the first and second derivatives of the velocity, respectively, and δ(t) is the front wheel steering angle. It is the derivative of the front wheel steering angle; R m1 L m1 and J m1 These are the resistance, inductance, and rotor moment of inertia of the balancing motor, K. e1 K t1 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the balanced motor, respectively; w b (t) is the rotational speed of the balancing motor. and These are the first and second derivatives of the balancing motor speed, u. m1 (t) is the control voltage of the balancing motor; R m2 L m2 and J m2 These are the resistance, inductance, and rotor moment of inertia of the rear-wheel drive motor, K. e2 K t2 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the rear-wheel drive motor, u m2(t) is the drive voltage of the rear wheel drive motor; τ f It is the maximum static friction torque, Sgn(·) is the sign function; τ δ It is the servo response time constant, u δ (t) is the steering angle command of the servo motor;

[0009] S2. Design a segmented, continuous desired velocity reference signal v d (t), using a PI controller to control the voltage u of the rear wheel drive motor. m2 (t), so that the speed v(t) of the unmanned bicycle follows v d (t); Design a segmented, continuous desired steering angle reference signal δ d (t), using a first-order filter to generate the servo steering angle command u δ (t), causing the steering angle δ(t) to follow δ d (t);

[0010] S3. Based on the aforementioned dynamic model, an adaptive neural network controller is designed to control the balance of the unmanned bicycle. The adaptive neural network controller consists of a command filter and a controller, which controls the input voltage of the balancing motor. The command filter is used to generate a reference signal for the tilt angle, and the filter is implemented as follows:

[0011]

[0012] Where y d π(t) is the output of the command filter and also the reference signal for the tilt angle. π1(t) and π2(t) are the state variables of the filter, τ1 and τ2 are the time constants of the filter, and Γ is the input signal. η It is a constant, k 1η It is a constant gain. This is an estimation of the weights of a neural network, where S(v(t), δ(t)) is a Gaussian radial basis function:

[0013]

[0014] s k (v(t), δ(t)) represent the k-th neuron, [v i δ j ] T This is the center vector corresponding to the k-th neuron, i = 1, 2, ..., N1, j = 1, 2, ..., N2, where N1 and N2 are two positive integers representing the number of neurons arranged along two dimensions, N = N1 × N2 is the number of neurons, and k = 1, 2, ..., N, r v and r δ It is a constant used to control the width of neurons;

[0015] Define the output tracking error z1(t) = θ(t) - y d (t), The controller is constructed as follows:

[0016]

[0017] Where α1(t) is the virtual controller; and It is adaptive parameter estimation. It is neural network weight estimation. and These are their adaptive laws; γ1, γ2 and Γ r They are constants, σ1, σ2 and σ r It is a small constant used to ensure robustness.

[0018] As a preferred technical solution, in step S2, the PI controller specifically comprises:

[0019]

[0020] Where v d (t) represents the desired vehicle speed, e v (t) is the velocity deviation, k vp and k vi They are two positive numbers.

[0021] As a preferred technical solution, in step S2, the first-order filter specifically comprises:

[0022]

[0023] δ d (t) is the target turning angle, π δ (t) represents the filter state, τ uδ It is the filter time constant.

[0024] As a preferred technical solution, in step S2, the unmanned bicycle is in static balance mode, and the given desired speed reference signal is v. d (t) = 0, and the given desired steering angle reference signal is δ d (t) = 0; In motion balance mode, the given desired velocity reference signal is:

[0025]

[0026] As a preferred technical solution, in step S2, the given desired steering angle reference signal is:

[0027]

[0028] Secondly, the present invention provides a motion control system for an unmanned bicycle with a balance flywheel, which is applied to the motion control method for an unmanned bicycle with a balance flywheel, including a model building module, a reference signal setting module and a balance control module.

[0029] The model building module is used to establish the dynamic model of the unmanned bicycle:

[0030]

[0031] Where t is time, I0 is the roll inertia of the system, m0 is the mass of the system, h is the height of the system's center of gravity, g is the acceleration due to gravity, α is the rake angle of the fork, a, b, and c are constants related to the dimensions of the autonomous bicycle, and θ(t) is the tilt angle. It is the tilt angular velocity. υ(t) is angular acceleration, and υ(t) is the vehicle speed. and These are the first and second derivatives of the velocity, respectively, and δ(t) is the front wheel steering angle. It is the derivative of the front wheel steering angle; R m1 L m1 and J m1 These are the resistance, inductance, and rotor moment of inertia of the balancing motor, K. e1 K t1 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the balanced motor, respectively; ω b (t) is the rotational speed of the balancing motor. and These are the first and second derivatives of the balancing motor speed, u. m1 (t) is the control voltage of the balancing motor; R m2 L m2 and J m2 These are the resistance, inductance, and rotor moment of inertia of the rear-wheel drive motor, K. e2 K t2 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the rear-wheel drive motor, u m2 (t) is the drive voltage of the rear wheel drive motor; τ f It is the maximum static friction torque, Sgn(·) is the sign function; τ δ It is the servo response time constant, u δ (t) is the steering angle command of the servo motor;

[0032] The reference signal setting module is used to design a segmented, continuous desired velocity reference signal v. d (t), using a PI controller to control the voltage u of the rear wheel drive motor. m2(t), so that the speed v(t) of the unmanned bicycle follows v d (t); Design a segmented, continuous desired steering angle reference signal δ d (t), using a first-order filter to generate the servo steering angle command u δ (t), causing the steering angle δ(t) to follow δ d (t);

[0033] The balance control module is used to design an adaptive neural network controller to control the balance of the unmanned bicycle based on the dynamic model. The adaptive neural network controller consists of a command filter and a controller, and controls the input voltage of the balance motor. The command filter is used to generate a reference signal for the tilt angle, and the filter is implemented as follows:

[0034]

[0035] Where y d π(t) is the output of the command filter and also the reference signal for the tilt angle. π1(t) and π2(t) are the state variables of the filter, τ1 and τ2 are the time constants of the filter, and Γ is the input signal. η It is a constant, k 1η It is a constant gain. This is an estimation of the weights of a neural network, where S(v(t), δ(t)) is a Gaussian radial basis function:

[0036]

[0037] s k (v(t), δ(t)) represent the k-th neuron, [v i δ j ] T This is the center vector corresponding to the k-th neuron, i = 1, 2, ..., N1, j = 1, 2, ..., N2, where N1 and N2 are two positive integers representing the number of neurons arranged along two dimensions, N = N1 × N2 is the number of neurons, and k = 1, 2, ..., N, r v and r δ It is a constant used to control the width of neurons;

[0038] Define the output tracking error z1(t) = θ(t) - y d (t), The controller is constructed as follows:

[0039]

[0040] Where α1(t) is the virtual controller; and It is adaptive parameter estimation. It is neural network weight estimation. and These are their adaptive laws; γ1, γ2 and Γ r They are constants, σ1, σ2 and σ r It is a small constant used to ensure robustness.

[0041] Thirdly, the present invention provides a computer-readable storage medium storing a program, which, when executed by a processor, implements the motion control method for an unmanned bicycle with a balancing flywheel.

[0042] Fourthly, the present invention provides an unmanned bicycle, the unmanned bicycle comprising: a microcontroller, a card computer connected to the microcontroller, an inertial sensor, a servo motor, a balancing motor, and a rear wheel drive motor, wherein when the unmanned bicycle is in motion, the microcontroller executes the unmanned bicycle motion control method with a balancing flywheel.

[0043] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0044] 1. Under any motion state, the unmanned bicycle can maintain balance and keep the rotation speed of the balance flywheel stable near zero, thus solving the problem of unstable rotation speed of the balance flywheel caused by the non-minimum phase characteristics of the unmanned bicycle.

[0045] 2. This invention introduces adaptive neural network control technology. Given that the parameters of the unmanned bicycle cannot be accurately measured, it utilizes a neural network to model unknown nonlinear functions, eliminating the need for parameter measurement before controller design. The balance controller compensates for centrifugal force disturbances caused by vehicle speed and steering, ensuring system stability.

[0046] 3. Introducing a command filter into the adaptive neural network controller ensures that the reference signal for the tilt angle is second-order differentiable, making the control command sufficiently smooth. The filter state is used as the derivative of the reference signal in the controller, solving the problem that the derivative of the reference signal is unavailable. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0048] Figure 1 This is a hardware structure diagram of the motion control system for an unmanned bicycle with a balancing flywheel according to an embodiment of the present invention.

[0049] Figure 2 This is a structural diagram of the controller according to an embodiment of the present invention.

[0050] Figure 3 This is a tilt tracking curve diagram of an embodiment of the present invention in static equilibrium mode.

[0051] Figure 4 This is a graph showing the input voltage curve of the balancing motor control in static balancing mode according to an embodiment of the present invention.

[0052] Figure 5 This is a graph showing the speed curve of the balanced motor in static balance mode according to an embodiment of the present invention.

[0053] Figure 6 This is a graph showing the neural network weight estimation norm curve of the controller in static equilibrium mode according to an embodiment of the present invention.

[0054] Figure 7 This is a graph showing the neural network weight estimation norm curve of the virtual control law in static equilibrium mode according to an embodiment of the present invention.

[0055] Figure 8 This is a graph showing the vehicle speed change in motion balance mode according to an embodiment of the present invention.

[0056] Figure 9 This is a tilt tracking curve diagram in motion balance mode according to an embodiment of the present invention.

[0057] Figure 10 This is a graph showing the neural network weight estimation norm curve of the controller in motion balance mode according to an embodiment of the present invention.

[0058] Figure 11 This is a graph showing the neural network weight estimation norm curve of the virtual control law in motion balance mode according to an embodiment of the present invention.

[0059] Figure 12 This is a structural block diagram of the motion control system for an unmanned bicycle with a balancing flywheel according to an embodiment of the present invention. Detailed Implementation

[0060] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of the present application, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present application without creative effort are within the scope of protection of the present application.

[0061] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0062] This embodiment provides a motion control method for an unmanned bicycle with a balance flywheel, the method comprising the following steps:

[0063] S1. Establish the dynamic model of the unmanned bicycle:

[0064]

[0065] Where t is time, I0 is the roll inertia of the system, m0 is the mass of the system, h is the height of the system's center of gravity, g is the acceleration due to gravity, α is the rake angle of the fork, a, b, and c are constants related to the dimensions of the autonomous bicycle, and θ(t) is the tilt angle. It is the tilt angular velocity. It is the angular acceleration, and v(t) is the vehicle speed. and These are the first and second derivatives of the vehicle speed, respectively, and δ(t) is the front wheel steering angle. It is the derivative of the front wheel steering angle; R m1 L m1 and J m1 These are the resistance, inductance, and rotor moment of inertia of the balancing motor, K. e1 K t1 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the balanced motor, respectively; ω b (t) is the rotational speed of the balancing motor. and These are the first and second derivatives of the balancing motor speed, u. m1 (t) is the control voltage of the balancing motor; R m2 L m2 and J m2 These are the resistance, inductance, and rotor moment of inertia of the rear-wheel drive motor, K. e2 K t2 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the motor, u m2 (t) is the drive voltage of the rear wheel drive motor; τ f It is the maximum static friction torque, Sgn(·) is the sign function; τ δ It is the servo response time constant, u δ(t) is the steering angle command of the servo motor; none of the parameters need to be measured.

[0066] S2. Design a segmented, continuous desired velocity reference signal v d (t), using a PI controller to control the voltage u of the rear wheel drive motor. m2 (t), controlling the speed v(t) of the unmanned bicycle to follow v d (t); Design a segmented, continuous desired steering angle reference signal δ d (t), using a first-order filter to generate the servo steering angle command u δ (t), such that the steering angle δ(t) follows δ d (t); In this embodiment, the unmanned bicycle in static balance mode has a given desired speed reference signal of v. d (t) = 0, and the given desired steering angle reference signal is δ d (t) = 0; In motion balance mode, the given desired velocity reference signal is:

[0067]

[0068] The speed PI controller is specifically as follows:

[0069]

[0070] Where v(t) is the current vehicle speed, v d (t) represents the desired vehicle speed, e v (t) is the velocity deviation, k vp and k vi They are two positive numbers, u m2 (t) represents the voltage output of the controller. In this embodiment, the selected parameter is: k vp =2.5, k vi =0.4.

[0071] The given desired steering angle reference signal is:

[0072]

[0073] The first-order filter is specifically:

[0074]

[0075] Where u δ (t) is the angle command input to the servo motor, δ d (t) is the target turning angle, π δ (t) represents the filter state, τ uδ It is the filter time constant; in this embodiment, τ is selected. uδ =0.25.

[0076] S3. Based on the dynamic model, design an adaptive neural network controller to control the balance of the unmanned bicycle. The adaptive neural network controller consists of a command filter and a controller, which controls the input voltage of the balancing motor. The command filter is used to generate a reference signal for the tilt angle. The implementation of the filter is as follows:

[0077]

[0078] Where y d (t) is the output of the command filter and also the reference signal for the tilt angle; π1(t) and π2(t) are the state variables of the filter; τ1 and τ2 are the time constants of the filter; Γ η It is a constant, k 1η It is a constant gain. These are estimated values ​​of the neural network weights. In this embodiment, the parameters of the instruction filter are selected as: τ1 = 0.1, τ2 = 0.1, k η =-4.2, Γ r =0.1.

[0079] Where S(v(t), δ(t)) are Gaussian radial basis functions:

[0080]

[0081] s k (υ(t), δ(t)) represent the k-th neuron, [v i δ j ] T This is the center vector corresponding to the k-th neuron, i = 1, 2, ..., N1, j = 1, 2, ..., N2, where N1 and N2 are two positive integers representing the number of neurons arranged along two dimensions, N = N1 × N2 is the number of neurons, and k = 1, 2, ..., N, r v and r δ It is a constant used to control the width of neurons;

[0082] In this embodiment, N1 = 11, N2 = 9, and the radial basis function center point is:

[0083] [v1, v2, ..., v 10 v 11 ] T = [-2.5, -2.0, ..., 2.0, 2.5] T

[0084]

[0085] Where the radial base width is:

[0086] Define the output tracking error z1(t) = θ(t) - y d (t), The controller is constructed as follows:

[0087]

[0088] Where α1(t) is the virtual controller; and It is adaptive parameter estimation. It is neural network weight estimation. and These are their adaptive laws; γ1, γ2 and Γ r They are constants, σ1, σ2 and σ r These are small constants used to ensure robustness; in this embodiment, the controller parameters are selected as follows: c1 = 2.5, c2 = 7.5, γ1 = 0.05, γ2 = 0.05, σ1 = 0.0001, σ2 = 0.0001, Γ r =0.1.

[0089] In this unmanned bicycle, a DC motor installed in the middle of the vehicle body drives an inertial flywheel to provide balancing torque to the vehicle body. The rear wheel is driven by a DC motor and a belt, while the front wheel is controlled by a servo motor. Figure 1 This is a hardware system architecture diagram. The microcontroller is the control center of the entire system, mainly responsible for processing sensor signals and calculating control laws. The specific model is STM32F103C8T6. The inertial sensor is used to measure the system's tilt angle and tilt angular velocity; the specific model is MPU6050. The card-type computer is mainly responsible for calculating the output of the neural network; the specific model is Raspberry Pi 4B. The control signals calculated by the microcontroller are output to the corresponding motors. Figure 2 This is the controller structure diagram. Figure 2 The PI controller is used as the vehicle speed controller, the first-order filter is used as the steering controller, and the adaptive neural network controller is used as the balance controller.

[0090] The results of the static equilibrium experiment are as follows Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 As shown. Figure 3 This is the tilt tracking curve in static balance mode. Under the action of the controller, it takes about 6 seconds for the unmanned bicycle to adjust the tilt angle to the static balance point and maintain balance. Figure 4 This is the input voltage curve of the balancing motor control in static balancing mode. After an adjustment process of about 6 seconds, the control output is maintained at a stable value. Figure 5This is a graph of the balance motor speed in static balance mode. During the transient adjustment process of the first 6 seconds, the balance flywheel has a high speed due to the interaction of the balance control force. After the system stabilizes, the speed of the balance flywheel converges to near 0. Figure 6 This is a graph showing the neural network weight norm of the controller in static equilibrium mode. Figure 7 This is a graph showing the neural network weight norm of the virtual control law in static equilibrium mode. Figure 6 and Figure 7 This demonstrates the learning process of the adaptive neural network controller. During the first 6 seconds of transient operation, the neural network learns the unknown nonlinear dynamics. After the system stabilizes, the weight norm converges to a steady state. In summary, the results show that the controller enables the unmanned bicycle to maintain balance while stationary.

[0091] Results of the motion balance experiment as follows Figure 8 , Figure 9 , Figure 10 and Figure 11 As shown. Different target vehicle speeds υ are set at different times. d and different target turning angles δ d This allows the car to move within the environment. Figure 7 This is a speed change curve in motion balance mode. Under the action of the PI controller, the speed of the unmanned bicycle can track the target speed υ. d The unmanned bicycle can maintain its balance under varying speed conditions. Figure 8 This is the tilt tracking curve in motion balance mode, from Figure 8 It can be seen that under different vehicle speeds and steering angles, the controller can set the target tilt angle according to the current motion state, and the actual tilt angle of the unmanned bicycle can track the target tilt angle. At this time, the torque generated by the gravity and centrifugal force of the unmanned bicycle cancels each other out, and the unmanned bicycle can stably maintain balance. Figure 9 This is a neural network norm curve under motion balance mode. (Summary) Figure 7 , Figure 8 and Figure 9 It can be seen that unmanned bicycles can maintain balance during movement.

[0092] It should be noted that, for the sake of simplicity, the aforementioned method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously.

[0093] Based on the same concept as the unmanned bicycle motion control method with a balancing flywheel in the above embodiments, the present invention also provides an unmanned bicycle motion control system with a balancing flywheel, which can be used to execute the above-described unmanned bicycle motion control method with a balancing flywheel. For ease of explanation, the structural schematic diagram of the embodiment of the unmanned bicycle motion control system with a balancing flywheel only shows the parts related to the embodiments of the present invention. Those skilled in the art will understand that the illustrated structure does not constitute a limitation on the device, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0094] like Figure 12 As shown, in another embodiment of this application, an unmanned bicycle motion control system 100 with a balance flywheel is provided. The system includes a model building module 101, a reference signal setting module 102, and a balance control module 103.

[0095] The model building module 101 is used to establish a dynamic model of the unmanned bicycle:

[0096]

[0097] Where t is time, I0 is the roll inertia of the system, m0 is the mass of the system, h is the height of the system's center of gravity, g is the acceleration due to gravity, α is the rake angle of the fork, a, b, and c are constants related to the dimensions of the autonomous bicycle, and θ(t) is the tilt angle. It is the tilt angular velocity. It is angular acceleration, and v(t) is the vehicle speed. and These are the first and second derivatives of the velocity, respectively, and δ(t) is the front wheel steering angle. It is the derivative of the front wheel steering angle; R m1 L m1 and J m1 These are the resistance, inductance, and rotor moment of inertia of the balancing motor, K. e1 K t1 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the balanced motor, respectively; ω b (t) is the rotational speed of the balancing motor. and These are the first and second derivatives of the balancing motor speed, u. m1 (t) is the control voltage of the balancing motor; R m2 L m2 and J m2 These are the resistance, inductance, and rotor moment of inertia of the rear-wheel drive motor, K. e2 K t2These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the rear-wheel drive motor, u m2 (t) is the drive voltage of the rear wheel drive motor; τ f It is the maximum static friction torque, Sgn(·) is the sign function; τ δ It is the servo response time constant, u δ (t) is the steering angle command of the servo motor;

[0098] The reference signal setting module 102 is used to design a segmented, continuous desired velocity reference signal v. d (t), using a PI controller to control the voltage u of the rear wheel drive motor. m2 (t), so that the speed v(t) of the unmanned bicycle follows v d (t); Design a segmented, continuous desired steering angle reference signal δ d (t), using a first-order filter to generate the servo steering angle command u δ (t), causing the steering angle δ(t) to follow δ d (t);

[0099] The balance control module 103 is used to design an adaptive neural network controller to control the balance of the unmanned bicycle based on the dynamic model. The adaptive neural network controller consists of a command filter and a controller, and controls the input voltage of the balance motor. The command filter is used to generate a reference signal for the tilt angle, and the filter is implemented as follows:

[0100]

[0101] Where y d π(t) is the output of the command filter and also the reference signal for the tilt angle. π1(t) and π2(t) are the state variables of the filter, τ1 and τ2 are the time constants of the filter, and Γ is the input signal. η It is a constant, k 1η It is a constant gain. This is an estimation of the weights of a neural network, where S(v(t), δ(t)) is a Gaussian radial basis function:

[0102]

[0103] s k (v(t), δ(t)) represent the k-th neuron, [v i δ j ] T This is the center vector corresponding to the k-th neuron, i = 1, 2, ..., N1, j = 1, 2, ..., N2, where N1 and N2 are two positive integers representing the number of neurons arranged along two dimensions, N = N1 × N2 is the number of neurons, and k = 1, 2, ..., N, rv and r δ It is a constant used to control the width of neurons;

[0104] Define the output tracking error z1(t) = θ(t) - y d (t), The controller is constructed as follows:

[0105]

[0106] Where α1(t) is the virtual controller; and It is adaptive parameter estimation. It is neural network weight estimation. and These are their adaptive laws; γ1, γ2 and Γ r They are constants, σ1, σ2 and σ r It is a small constant used to ensure robustness.

[0107] It should be noted that the motion control system of the unmanned bicycle with a balancing flywheel of the present invention corresponds one-to-one with the motion control method of the unmanned bicycle with a balancing flywheel of the present invention. The technical features and beneficial effects described in the embodiments of the motion control method of the unmanned bicycle with a balancing flywheel described above are applicable to the embodiments of the motion control of the unmanned bicycle with a balancing flywheel. For details, please refer to the description in the embodiments of the method of the present invention, which will not be repeated here.

[0108] Furthermore, in the above embodiments of the unmanned bicycle motion control system with a balance flywheel, the logical division of each program module is only an example. In actual applications, the above functions can be assigned to different program modules as needed, for example, for the sake of corresponding hardware configuration requirements or software implementation convenience. That is, the internal structure of the unmanned bicycle motion control system with a balance flywheel is divided into different program modules to complete all or part of the functions described above.

[0109] In one embodiment, an unmanned bicycle is also provided, including a microcontroller, a card computer connected to the microcontroller, an inertial sensor, a servo motor, a balancing motor, and a rear wheel drive motor. When the unmanned bicycle is in motion, the microcontroller executes the unmanned bicycle motion control method with a balancing flywheel.

[0110] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments described above. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0111] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0112] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

Claims

1. A motion control method for an unmanned bicycle with a balancing flywheel, characterized in that, Includes the following steps: S1. Establish the dynamic model of the unmanned bicycle: Where t is time, I0 is the roll inertia of the system, m0 is the mass of the system, h is the height of the system's center of gravity, g is the acceleration due to gravity, α is the rake angle of the fork, a, b, and c are constants related to the dimensions of the autonomous bicycle, and θ(t) is the tilt angle. It is the tilt angular velocity. It is angular acceleration, and v(t) is the vehicle speed. and These are the first and second derivatives of the velocity, respectively, and δ(t) is the front wheel steering angle. It is the derivative of the front wheel steering angle; R m1 L m1 and J m1 These are the resistance, inductance, and rotor moment of inertia of the balancing motor, K. e1 K t1 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the balanced motor, respectively; ω b (t) is the rotational speed of the balancing motor. and These are the first and second derivatives of the balancing motor speed, u. m1 (t) is the control voltage of the balancing motor; R m2 L m2 and J m2 These are the resistance, inductance, and rotor moment of inertia of the rear-wheel drive motor, K. e2 K t2 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the rear-wheel drive motor, u m2 (t) is the drive voltage of the rear wheel drive motor; τ f It is the maximum static friction torque, Sgn(·) is the sign function; τ δ It is the servo response time constant, u δ (t) is the steering angle command of the servo motor; S2. Design a segmented, continuous desired velocity reference signal v d (t), using a PI controller to control the voltage u of the rear wheel drive motor. m2 (t), so that the speed v(t) of the unmanned bicycle follows v d (t); Design a segmented, continuous desired steering angle reference signal δ d (t), using a first-order filter to generate the servo steering angle command u δ (t), causing the steering angle δ(t) to follow δ d (t); S3. Based on the aforementioned dynamic model, an adaptive neural network controller is designed to control the balance of the unmanned bicycle. The adaptive neural network controller consists of a command filter and a controller, which controls the input voltage of the balancing motor. The command filter is used to generate a reference signal for the tilt angle, and the filter is implemented as follows: Where y d π(t) is the output of the command filter and also the reference signal for the tilt angle. π1(t) and π2(t) are the state variables of the filter, τ1 and τ2 are the time constants of the filter, and Γ is the input signal. η It is a constant, k 1η It is a constant gain. This is an estimation of the weights of a neural network, where S(v(t), δ(t)) is a Gaussian radial basis function: s k (v(t), δ(t)) represent the k-th neuron, [v i δ j ] T Let r be the center vector corresponding to the k-th neuron, i = 1, 2, ..., N1, j = 1, 2, ..., N2, where N1 and N2 are two positive integers representing the number of neurons arranged along two dimensions, N = N1 × N2 is the number of neurons, and k = 1, 2, ..., N. v and r δ It is a constant used to control the width of neurons; Define the output tracking error z1(t) = θ(t) - y d (t), The controller is constructed as follows: Where α1(t) is the virtual controller; and It is adaptive parameter estimation. It is neural network weight estimation. and These are their adaptive laws; γ1, γ2, and Γ r They are constants, σ1, σ2 and σ r It is a small constant used to ensure robustness.

2. The motion control method for an unmanned bicycle with a balancing flywheel according to claim 1, characterized in that, In step S2, the PI controller specifically comprises: Where v d (t) represents the desired vehicle speed, e v (t) is the velocity deviation, k vp and k vi They are two positive numbers.

3. The motion control method for an unmanned bicycle with a balancing flywheel according to claim 1, characterized in that, In step S2, the first-order filter specifically refers to: δ d (t) is the target turning angle, π δ (t) represents the filter state, τ uδ It is the filter time constant.

4. The motion control method for an unmanned bicycle with a balancing flywheel according to claim 1, characterized in that, In step S2, the unmanned bicycle is in static balance mode, and the given desired speed reference signal is v. d (t) = 0, and the given desired steering angle reference signal is δ d (t) = 0; In motion balance mode, the given desired velocity reference signal is:

5. The motion control method for an unmanned bicycle with a balance flywheel according to claim 1, characterized in that, In step S2, the given desired steering angle reference signal is:

6. A motion control system for an unmanned bicycle with a balancing flywheel, characterized in that, The motion control method for an unmanned bicycle with a balance flywheel, applicable to any one of claims 1-5, includes a model building module, a reference signal setting module, and a balance control module; The model building module is used to establish the dynamic model of the unmanned bicycle: Where t is time, I0 is the roll inertia of the system, m0 is the mass of the system, h is the height of the system's center of gravity, g is the acceleration due to gravity, α is the rake angle of the fork, a, b, and c are constants related to the dimensions of the autonomous bicycle, and θ(t) is the tilt angle. It is the tilt angular velocity. It is angular acceleration, and v(t) is the vehicle speed. and These are the first and second derivatives of the velocity, respectively, and δ(t) is the front wheel steering angle. It is the derivative of the front wheel steering angle; R m1 L m1 and J m1 These are the resistance, inductance, and rotor moment of inertia of the balancing motor, K. e1 K t1 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the balanced motor, respectively; ω b (t) is the rotational speed of the balancing motor. and These are the first and second derivatives of the balancing motor speed, u. m1 (t) is the control voltage of the balancing motor; R m2 L m2 and J m2 These are the resistance, inductance, and rotor moment of inertia of the rear-wheel drive motor, K. e2 K t2 These are the voltage-speed proportional coefficient and the current-torque proportional coefficient of the rear-wheel drive motor, u m2 (t) is the drive voltage of the rear wheel drive motor; τ f It is the maximum static friction torque, Sgn(·) is the sign function; τ δ It is the servo response time constant, u δ (t) is the steering angle command of the servo motor; The reference signal setting module is used to design a segmented, continuous desired velocity reference signal v. d (t), using a PI controller to control the voltage u of the rear wheel drive motor. m2 (t), so that the speed v(t) of the unmanned bicycle follows v d (t); Design a segmented, continuous desired steering angle reference signal δ d (t), using a first-order filter to generate the servo steering angle command u δ (t), causing the steering angle δ(t) to follow δ d (t); The balance control module is used to design an adaptive neural network controller to control the balance of the unmanned bicycle based on the dynamic model. The adaptive neural network controller consists of a command filter and a controller, and controls the input voltage of the balance motor. The command filter is used to generate a reference signal for the tilt angle, and the filter is implemented as follows: Where y d π(t) is the output of the command filter and also the reference signal for the tilt angle. π1(t) and π2(t) are the state variables of the filter, τ1 and τ2 are the time constants of the filter, and Γ is the input signal. η It is a constant, k 1η It is a constant gain. This is an estimation of the weights of a neural network, where S(v(t), δ(t)) is a Gaussian radial basis function: s k (v(t), δ(t)) represent the k-th neuron, [v i δ j ] T Let r be the center vector corresponding to the k-th neuron, i = 1, 2, ..., N1, j = 1, 2, ..., N2, where N1 and N2 are two positive integers representing the number of neurons arranged along two dimensions, N = N1 × N2 is the number of neurons, and k = 1, 2, ..., N. v and r δ It is a constant used to control the width of neurons; Define the output tracking error z1(t) = θ(t) - y d (t), The controller is constructed as follows: Where α1(t) is the virtual controller; and It is adaptive parameter estimation. It is neural network weight estimation. and These are their adaptive laws; γ1, γ2, and Γ r They are constants, σ1, σ2 and σ r It is a small constant used to ensure robustness.

7. A computer-readable storage medium storing a program, characterized in that, When the program is executed by the processor, it implements the motion control method for an unmanned bicycle with a balancing flywheel as described in any one of claims 1-5.

8. An unmanned bicycle, characterized in that, The unmanned bicycle includes: a microcontroller, a card computer connected to the microcontroller, an inertial sensor, a servo motor, a balancing motor, and a rear wheel drive motor. When the unmanned bicycle is in motion, the microcontroller executes the unmanned bicycle motion control method with a balancing flywheel as described in any one of claims 1-5.

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