Momentum wheel self-balancing vehicle control system and method based on linear active disturbance rejection control
By adopting a linear self-immune control system in the momentum wheel self-balancing vehicle, combined with PI and LADRC controllers, the problems of low control accuracy and insufficient stability in the prior art are solved, and higher control accuracy and better stability are achieved.
Patent Information
- Application Number
- CN202411598849.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-11
- Publication Date
- 2025-03-18
AI Technical Summary
In the prior art, when the momentum wheel self-balancing vehicle faces problems such as insufficient assembly accuracy and misalignment of the axis, the control accuracy of the PID controller is not high, and its performance is limited in maintaining the stability of the self-balancing vehicle.
The momentum wheel self-balancing vehicle control system based on linear self-immunity control is adopted, combined with the speed ring PI controller and the upright ring LADRC controller, through a linear expansion state observer and a differential tracker, the input signal end is designed to reduce the impact of parameter changes, and improve the system's tracking and immunity performance.
The control accuracy of the momentum wheel self-balancing vehicle is improved, the inclination swing amplitude and torque fluctuations are reduced, and the performance of maintaining stability is enhanced, so that the self-balancing vehicle can maintain upright balance for a longer time.
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Figure CN119644820A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to a control system, in particular to a control system and method for a momentum wheel self-balancing vehicle based on linear self-disturbance rejection control. Background Art
[0002] The momentum wheel self-balancing vehicle is a popular self-balancing vehicle in recent years. It is easier to control than a two-wheeled self-balancing vehicle, and is safer and has a wider range of applications. The momentum wheel self-balancing vehicle relies on the angular momentum provided by the momentum wheel module to maintain balance. In practical applications, the rider does not need to touch the ground with his feet to maintain balance. Among them, the way the momentum wheel module provides angular momentum is to rely on the brushless DC motor to drive the momentum wheel to rotate. Therefore, the control of the brushless DC motor in the momentum wheel module is the key to keeping the momentum wheel self-balancing vehicle stable.
[0003] The application of PID controller in momentum wheel self-balancing car is very common. PID controller has the advantages of simple structure and mature engineering application. However, in actual application, due to the limitation of the manufacturing process level of momentum wheel self-balancing car, problems will inevitably occur during the assembly process: insufficient assembly precision leads to looseness between parts, and the axis is not aligned and causes jitter, which will affect the parameters of the momentum wheel balancing car. The control accuracy of PID controller is not high when facing these controlled objects with parameter changes, and the performance of PID controller in maintaining the stability of the self-balancing car is limited. Summary of the invention
[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to provide a control system and method for a momentum wheel self-balancing vehicle based on linear self-disturbance rejection control.
[0005] To achieve the above object, the present invention provides the following technical solutions:
[0006] A control system for a momentum wheel self-balancing vehicle based on linear self-disturbance rejection control comprises a speed loop PI controller and an upright loop LADRC controller; the speed loop PI controller is electrically connected to the upright loop LADRC controller, and the upright loop LADRC controller is electrically connected to the momentum wheel self-balancing vehicle; wherein the upright loop LADRC controller comprises a linear extended state observer, a differential tracker and a control input signal terminal; in the linear self-disturbance rejection control, the controlled object is regarded as an integral series system, and the part of the actual system different from the integral series system is regarded as a total disturbance; the total disturbance is regarded as an extended state of the system, and a linear extended state observer is designed to observe the total disturbance, and then compensated back to the control input signal terminal of the controlled object, so as to achieve the goal of reducing the influence of the parameter change part.
[0007] Furthermore, other system states estimated by the linear extended state observer are used to design state feedback in a differential tracker to improve the tracking and anti-disturbance performance of the system.
[0008] A control method for a momentum wheel self-balancing vehicle based on linear active disturbance rejection control, the control method comprising the following steps:
[0009] Step 1: Use the system identification method to identify the order of the system model from the motor input uθ to the inclination angle θ of the momentum wheel self-balancing vehicle. The model obtained by identification is:
[0010]
[0011] Among them, Θ(s) and U θ (s) are θ(t) and u θ Laplace transform of (t);
[0012] Step 2: Using the identified system order, a second-order upright loop LADRC controller is designed. The expression of the linear extended state observer in the second-order upright loop LADRC controller is:
[0013]
[0014] Among them, z1 is the estimated value of the inclination angle of the momentum-wheel self-balancing vehicle, z2 is the estimated value of the inclination angular velocity, and z3 is the estimated value of the total disturbance; the estimated value z1 of the inclination angle and the estimated value z2 of the angular velocity of the momentum-wheel self-balancing vehicle are the inputs of the differential tracker; l1, l2 and l3 are the observer error feedback gains to be adjusted; b0 is the controlled object gain parameter to be designed in the linear active disturbance rejection control.
[0015] The expression of the differential tracker is:
[0016] u0(t)=k p (u ω (t)-z1(t))-k d z2(t)
[0017] The control input signal terminal u θ The expression of (t) is:
[0018]
[0019] The estimated value of the inclination z1 is subtracted from the given input uω of the vertical loop obtained by the velocity loop and multiplied by the corresponding coefficient to obtain a part of the differential tracker output. The estimated value of the inclination angular velocity z2 is directly multiplied by the corresponding coefficient to obtain another part of the differential tracker output. The two parts of the differential tracker output are added together to obtain the output u0 of the differential tracker; the output u0 of the differential tracker minus the estimated value of the total disturbance z3(t) is divided by b0 to obtain the control input u0; k p With k d are the differential tracker parameters to be designed;
[0020] Step 3: Convert the second-order LADRC into a dual-degree-of-freedom feedback structure. The converted dual-degree-of-freedom feedback structure is:
[0021]
[0022] Among them, U ω (s) is u ω Laplace transform of (t);
[0023] Step 4: Based on the double-freedom feedback control structure of the second-order LADRC, the controller implementation method is obtained, namely:
[0024] C1(s)=K r (s)K1(s)
[0025] C2(s)=K1(s)K2(s)
[0026] Among them, K r (s), K1(s) and K2(s) are:
[0027]
[0028] After adjusting the parameters of the speed loop PI controller and the vertical loop LADRC controller, K r (s), K1(s) and K2(s) as well as the PI controller are discretized and written into the momentum wheel self-balancing vehicle in the form of differential equations to achieve the control of the momentum wheel self-balancing vehicle to achieve upright balance.
[0029] Compared with the prior art, the present invention has the following beneficial effects:
[0030] 1. Compared with the conventional momentum wheel self-balancing vehicle using cascade PID control, the present invention has higher control accuracy. Specifically, when the self-balancing vehicle recovers to an upright balanced state, the tilt swing amplitude is smaller and the torque fluctuation is smaller. Therefore, the momentum wheel self-balancing vehicle based on LADRC has higher accuracy than the momentum wheel self-balancing vehicle based on cascade PID control.
[0031] 2. Compared with the conventional momentum wheel self-balancing vehicle using cascade PID control, the present invention has better performance in maintaining stability. Specifically, the self-balancing vehicle can maintain an upright position for a longer time and has a stronger ability to maintain an upright and stable state. Therefore, the momentum wheel self-balancing vehicle based on LADRC has better performance in maintaining stability than the momentum wheel self-balancing vehicle based on cascade PID control. BRIEF DESCRIPTION OF THE DRAWINGS
[0032] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.
[0033] Figure 1 This is a double-free feedback control structure diagram of the second-order LADRC in the present invention.
[0034] Figure 2 The figure is a general implementation block diagram of the control method of the momentum wheel self-balancing vehicle based on LADRC of the present invention.
[0035] Figure 3 This is a comparison diagram of the inclination angle θ within 2s in the present invention.
[0036] Figure 4 This is a comparison diagram of the motor angular velocity ω within 2s in the present invention.
[0037] Figure 5 This is a comparison diagram of the inclination angle θ of the present invention within 50 seconds.
[0038] Figure 6 This is a comparison diagram of the motor angular velocity ω within 50 seconds of the present invention. DETAILED DESCRIPTION
[0039] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.
[0040] Embodiment 1:
[0041] like Figures 1 to 6As shown, a control system of a momentum wheel self-balancing vehicle based on linear self-disturbance rejection control includes a speed loop PI controller and an upright loop LADRC controller. The speed loop PI controller is electrically connected to the upright loop LADRC controller, and the upright loop LADRC controller is electrically connected to the momentum wheel self-balancing vehicle; wherein the upright loop LADRC controller includes a linear extended state observer, a differential tracker and a control input signal terminal; in the linear self-disturbance rejection control, the controlled object is regarded as an integral series system, and the part of the actual system that is different from the integral series system is regarded as the total disturbance; the total disturbance is regarded as the extended state of the system, and a linear extended state observer is designed to observe the total disturbance, and then compensate it back to the control input signal terminal of the controlled object, so as to achieve the goal of reducing the influence of the parameter change part.
[0042] It is understandable that the momentum wheel self-balancing vehicle has an upright ring gyroscope. The momentum wheel self-balancing vehicle is used as a controlled object. Further, other states of the system estimated by the linear extended state observer are used to design state feedback in the differential tracker to improve the tracking and anti-disturbance performance of the system. It should be noted that the total disturbance includes the part of the controlled object where the parameters change.
[0043] The present invention uses LADRC for the upright ring of the momentum wheel self-balancing vehicle to overcome the influence of parameter changes of the self-balancing vehicle on maintaining uprightness. At the same time, the use of LADRC improves the performance of the self-balancing vehicle in maintaining stability.
[0044] It should be noted that the integral link in the speed loop PI controller can eliminate steady-state errors. In motor control, the purpose of the speed loop is to maintain the motor speed at a constant value, even when the load changes. The integral action ensures that the motor speed can be stabilized at the set value after long-term operation without error accumulation. In addition, after the motor starts or the load changes, there may be a static error, that is, the difference between the actual speed and the set speed. The PI controller can gradually reduce this static error through the integral action until it is eliminated.
[0045] The speed loop PI controller also has strong anti-interference ability. When the motor load changes, the speed loop PI controller can better adapt to these changes and adjust the control amount through integral action to maintain the stability of the speed.
[0046] It is understandable that the PID controller can eliminate steady-state errors through integral action, while the PD controller cannot do this. In addition, the PD controller may cause large overshoot, while the PID controller can predict and suppress overshoot by introducing differential action, thereby reducing the overshoot amount. The PID controller can improve the stability and control accuracy of the system while ensuring fast response through the combination of proportional, integral and differential actions.
[0047] Embodiment 2:
[0048] A control method for a momentum wheel self-balancing vehicle based on linear anti-disturbance control, the control method comprising the following steps:
[0049] Step 1: Use the system identification method to identify the order of the system model from the motor input uθ to the inclination angle θ of the momentum wheel self-balancing vehicle. The model obtained by identification is:
[0050]
[0051] Among them, Θ(s) and U θ (s) are θ(t) and u θ (t) is the Laplace transform of
[0052] Step 2: Use the identified order of the system to design a second-order upright loop LADRC controller. In the controller, select the inclination angle θ and its derivative of the self-balancing vehicle in the nominal system "integrated series system" As two states x1 and x2; the different parts of the self-balancing vehicle system and the "integrated series system" are regarded as the total disturbance f, and expanded to the third state x f , we get the augmented state space equation of the self-balancing vehicle system. In order to design the linear extended state observer (LESO), we add the observer estimation error term to the augmented state space equation and get the core LESO of LADRC. Using LESO to estimate the three states and The corresponding three state estimation values z1, z2 and z3 are fed back to obtain the differential tracker and control input respectively.
[0053] The expression of the linear extended state observer in the second-order upright loop LADRC controller is:
[0054]
[0055] The expression of the differential tracker is:
[0056] u0(t)=k p (u ω (t)-z1(t))-k d z2(t)
[0057] The expression of the control input signal uθ(t) is:
[0058]
[0059] The control input signal uθ(t) and the output inclination angle θ of the momentum wheel car are used as the input of the observer LESO; z1 is the estimated value of the inclination angle of the momentum wheel self-balancing car, z2 is the estimated value of the inclination angular velocity, and z3 is the estimated value of the total disturbance; the estimated value of the inclination angle z1 and the estimated value of the angular velocity z2 of the momentum wheel self-balancing car are the inputs of the differential tracker, where the estimated value of the inclination angle z1 is consistent with the given input u of the upright ring ω Subtract and multiply by the corresponding coefficient to get a part of the differential tracker output, the estimated value of the inclination angular velocity z2 is directly multiplied by the corresponding coefficient to get another part of the differential tracker output, and the two parts of the differential tracker output are added to get the output u0 of the differential tracker; the output u0 of the differential tracker minus the estimated value of the total disturbance z3(t) and then divided by b0 to get the control input u0. l1, l2 and l3 are the observer error feedback gains to be adjusted; b0 is the controlled object gain parameter to be designed in the linear active disturbance rejection control; k p With k d are the parameters of the differential tracker to be designed.
[0060] Step 3: Convert the second-order LADRC into a dual-DOF feedback structure, perform Laplace transformation on the LES0, differential tracker and control input of the second-order LADRC, and solve the control output u0 with respect to the given input u of the vertical loop. ω The relationship between the tilt angle θ of the momentum wheel trolley and the dual-degree-of-freedom feedback structure is obtained. The converted dual-degree-of-freedom feedback structure is:
[0061]
[0062] Among them, U ω (s) is u ω (t) is the Laplace transform of
[0063] Step 4: Based on the double-freedom feedback control structure of the second-order LADRC, the controller is implemented as follows: Figure 1 As shown in Figure 1, a dual-freedom feedback control structure for realizing the second-order LADRC is given, that is, C1(s) and C2(s) are decomposed as follows:
[0064] C1(s)=K r (s)K1(s)
[0065] C2(s)=K1(s)K2(s)
[0066] Among them, K r (s), K1(s) and K2(s) are:
[0067]
[0068] like Figure 2As shown in the figure, a general implementation block diagram of a control method for a momentum wheel self-balancing vehicle based on LADRC is given. After the parameters of the speed loop PI controller and the upright loop LADRC controller are tuned, K r (s), K1(s) and K2(s) as well as the PI controller are discretized and written into the momentum wheel self-balancing vehicle in the form of differential equations to achieve the control of the momentum wheel self-balancing vehicle to achieve upright balance.
[0069] The working process of the control system of the present invention is:
[0070] The given angular velocity signal ω rd Input to the controller input; given angular velocity signal ω at time k rd Subtract the speed ω(k) of the momentum wheel self-balancing car motor at time k and input it into the speed loop PI controller to obtain the PI controller output u at time k ω (k). The PI controller output at time k is u ω (k) is input into the LADRC controller via K r The output is subtracted from the output of K2 at time k to obtain the error signal e(k) at time k. The error signal e(k) at time k is passed through K1 to obtain the control input signal uθ(k) at time k. After the momentum wheel self-balancing car inputs the control input signal uθ(k) at time k, it obtains the inclination signal θ(k+1) and the motor angular velocity signal ω(k+1) at time k+1, which are used to obtain the control signal at the next moment; the parameters of LADRC and the parameters of the PI controller are adjusted to improve the performance of the momentum wheel self-balancing car to maintain stability.
[0071] Compared with the traditional momentum wheel balancing car using cascade PID control, the present invention has higher control accuracy and improved anti-interference performance. When the self-balancing car recovers the upright balance state, the tilt angle swing amplitude is smaller and the motor control torque fluctuation is smaller; at the same time, the stability performance is better, that is, the self-balancing car can stay upright for a longer time and has a stronger ability to maintain an upright and stable state.
[0072] Application examples:
[0073] In order to verify the actual effect of the present solution, in this embodiment, the control method of the momentum wheel self-balancing vehicle based on LADRC provided by the present invention and the traditional control method of the momentum wheel self-balancing vehicle based on series PID are respectively used to carry out simulation experiments on the self-balancing vehicle to maintain balance. The parameters used in the simulation are shown in Tables 1 and 2.
[0074] Table 1 Parameter values of vertical ring LADRC controller
[0075] parameter Parameter Value parameter Parameter Value parameter Parameter Value <![CDATA[b0]]> 1.2 <![CDATA[k p ]]> 100 <![CDATA[k d ]]> 20 parameter Parameter Value parameter Parameter Value parameter Parameter Value <![CDATA[l1]]> 90 <![CDATA[l2]]> 2700 <![CDATA[l3]]> 27000
[0076] Table 2 Parameter values of vertical ring PD controller
[0077] parameter Parameter Value parameter Parameter Value <![CDATA[k p，PD ]]> -2000 <![CDATA[k d,PD ]]> -200
[0078] During the simulation, the control method of the momentum wheel self-balancing vehicle based on LADRC provided by the present invention was compared with the traditional control method of the momentum wheel self-balancing vehicle based on series PID. Two experiments were conducted, with the experimental time being 2s and 50s respectively. Figure 3 and Figure 4 The comparison diagram of the 2s inclination angle θ and the motor angular velocity ω are shown respectively; Figure 5 and Figure 6 The comparison diagram of the inclination angle θ and the motor angular velocity ω within 50s are shown respectively. The experimental conditions of the two experiments are that the inclination angle θ has an initial value of 10 degrees and there is a power of 10 -5 , Gaussian white noise with a sampling time of 0.1. Since the momentum wheel self-balancing car needs to be controlled to maintain upright balance, the given reference speed signal ω is 0.
[0079] In order to compare the controller effects, the integral absolute error (IAE) is used to evaluate the quality of the controller. Figure 3 The red solid line in FIG. 1 is a control method for a self-balancing vehicle with a momentum wheel based on LADRC provided by the present invention, and the blue solid line is a control method for a self-balancing vehicle with a momentum wheel based on a series PID. Figure 2 and Figure 3 The performance indicators corresponding to Table 3 can be obtained, and it can be seen that the controller provided by the present invention is better than PID, and the inclination angle and motor angular velocity of the momentum wheel self-balancing car return to the given value of 0 more smoothly, are less affected by noise, and have higher control accuracy. Figure 5 and Figure 6 It can be seen that the momentum wheel self-balancing car under PID control cannot always maintain balance and will slowly fall over time, but under the control of LADRC, the momentum wheel self-balancing car can always maintain balance and stability. Therefore, compared with the traditional momentum wheel self-balancing car control method based on series PID, the momentum wheel self-balancing car control method based on LADRC provided by the present invention has a greater advantage in maintaining the stability of the momentum wheel self-balancing car, and at the same time, the control accuracy is higher, the inclination swing amplitude is smaller, and the torque fluctuation is smaller.
[0080] Table 3 Comparison of controller effects under noise
[0081]
[0082] The above embodiments are preferred implementation modes of the present invention and are only used to facilitate the explanation of the present invention. They are not intended to limit the present invention in any form. Any person with ordinary knowledge in the relevant technical field, if they do not depart from the scope of the technical features of the present invention, can make equivalent embodiments by partial changes or modifications made by the technical contents disclosed in the present invention, and they still fall within the scope of the technical features of the present invention without departing from the technical features of the present invention.
Claims
1. A control system for a momentum wheel self-balancing vehicle based on linear anti-disturbance control, characterized in that: It includes a speed loop PI controller and an upright loop LADRC controller; the speed loop PI controller is electrically connected to the upright loop LADRC controller, and the upright loop LADRC controller is electrically connected to the momentum wheel self-balancing vehicle; wherein the upright loop LADRC controller includes a linear expansion state observer, a differential tracker and a control input signal terminal; In linear active disturbance rejection control, the controlled object is regarded as an integral series system, and the part of the actual system that is different from the integral series system is regarded as the total disturbance; the total disturbance is regarded as the expanded state of the system, and a linear expanded state observer is designed to observe the total disturbance, which is then compensated back to the control input signal end of the controlled object to achieve the goal of reducing the impact of the parameter change part.
2. The control system of the momentum wheel self-balancing vehicle based on linear active disturbance rejection control according to claim 1, characterized in that: Other system states estimated by the linear extended state observer are used to design state feedback in a differential tracker to improve the tracking and anti-disturbance performance of the system.
3. A control method for a momentum wheel self-balancing vehicle based on linear anti-disturbance control, characterized in that: The control method comprises the following steps: Step 1: Use the system identification method to input u to the motor of the momentum wheel self-balancing vehicle θ The order of the system model with an inclination angle θ is identified, and the model obtained by identification is: Among them, Θ(s) and U θ (s) are θ(t) and u θ Laplace transform of (t); Step 2: Using the identified system order, a second-order upright loop LADRC controller is designed. The expression of the linear extended state observer in the second-order upright loop LADRC controller is: Among them, z1 is the estimated value of the inclination angle of the momentum-wheel self-balancing vehicle, z2 is the estimated value of the inclination angular velocity, and z3 is the estimated value of the total disturbance; the estimated value z1 of the inclination angle and the estimated value z2 of the angular velocity of the momentum-wheel self-balancing vehicle are the inputs of the differential tracker; l1, l2 and l3 are the observer error feedback gains to be adjusted; b0 is the controlled object gain parameter to be designed in the linear active disturbance rejection control. The differential tracker and the control input signal terminal u θ The expressions of (t) are: u0(t)=k p (u ω (t)-z1(t))-k d z2(t); The estimated value of the inclination angle z1 is equal to the vertical loop given input u obtained by the velocity loop ω Subtract and multiply by the corresponding coefficient to get a part of the differential tracker output, the estimated value of the inclination angular velocity z2 is directly multiplied by the corresponding coefficient to get another part of the differential tracker output, and the two parts of the differential tracker output are added together to get the output u0 of the differential tracker; the output u0 of the differential tracker minus the estimated value z3(t) of the total disturbance is divided by b0 to get the control input u0; k p With k d are the differential tracker parameters to be designed; Step 3: Convert the second-order LADRC into a dual-degree-of-freedom feedback structure. The converted dual-degree-of-freedom feedback structure is: Among them, U ω (s) is u ω Laplace transform of (t); Step 4: Based on the double-freedom feedback control structure of the second-order LADRC, the controller implementation method is obtained, namely: C1(s)=K r (s)K1(s) C2(s)=K1(s)K2(s) Among them, K r (s), K1(s) and K2(s) are: After adjusting the parameters of the speed loop PI controller and the vertical loop LADRC controller, K r (s), K1(s) and K2(s) as well as the PI controller are discretized and written into the momentum wheel self-balancing vehicle in the form of differential equations to achieve the control of the momentum wheel self-balancing vehicle to achieve upright balance.