A brt articulated steering quasi-fuzzy pd coordinated control method and an active articulated system thereof

Abstract

The application discloses a BRT hinged steering quasi-fuzzy PD coordination control method and an active hinged system thereof, and comprises the following steps: acquiring at least two groups of actual steering process data and performing simulation processing through establishing a simulation model; acquiring optimal output input simulation parameter values; quasi-fuzzifying a fuzzy controller; acquiring actual BRT execution control parameters; and starting an execution module. Through quasi-fuzzifying the first fuzzy controller and the second fuzzy controller, the calculation of the trajectory tracking layer and the bottom device execution layer control method is simplified, and the decision speed of the fuzzy control method is improved, so that the active hinged system can quickly and timely respond to the execution signal, and the active hinged system can actively adjust according to the change of the vehicle body mass, the speed and the trajectory tracking precision.

Description

Technical Field

[0001] This invention relates to a quasi-fuzzy PD coordinated control method for BRT articulated steering and its active articulation system, belonging to the field of BRT fuzzy control technology. Background Technology

[0002] BRT (Bus Rapid Transit) buses are a new type of public transportation system that falls between road and rail public transportation. The articulated system connects the two rigid car bodies of the articulated bus, allowing them to communicate. Currently, BRT buses mainly consist of two car bodies connected by a damped articulated mechanism. The front and rear cars respectively handle steering and driving functions. The existing articulated devices in BRT buses adjust the damping of two hydraulic cylinders in the hydraulic system based on the real-time angle and angular velocity between the front and rear car bodies around a turntable bearing. When the BRT bus turns, the articulated device generates a passive rotational torque around the center of the turntable bearing. This torque affects the driving characteristics of the front and rear car bodies: if the rotational torque generated by the passive articulation system is too small, at higher speeds, the rear car body is prone to laterally pushing out the central axle, causing sideslip and potentially leading to a folding collision between the front and rear car bodies; if the rotational torque is too large, the rotation of the front and rear car bodies around the turntable bearing will be hindered or delayed, increasing the turning radius of the BRT bus and affecting its maneuverability.

[0003] To improve the reliability of traditional articulated systems, Chinese patent CN 103640628 B proposes a semi-active articulated system suitable for BRT buses. Its innovation lies in installing a cylindrical helical preload spring on each of the hydraulic arms on both sides of the traditional articulated system. When the two car bodies of the BRT bus rotate relative to each other, the combined action of hydraulic damping force and spring force generates a rotational torque around the turntable bearing, compensating for the discontinuous and unstable nature of the hydraulic damping force and enhancing the handling stability of the BRT bus. However, the hydraulic damping torque and spring elastic torque provided by the semi-active articulated system are both passive torques and cannot actively adjust to changes in car body mass, speed, and trajectory tracking accuracy, inevitably limiting the improvement of the BRT bus's handling stability.

[0004] During the operation of BRT buses, it is necessary to track the trajectory of the vehicle body. The trajectory tracking control layer sends execution commands to the underlying device execution layer. However, due to the time-varying nonlinear dynamic characteristics of BRT buses and the structural nonlinear characteristics of each execution device, the design difficulty of the control methods of the trajectory tracking control layer and the underlying device execution layer is increased. Furthermore, since BRT buses operate at high speeds, the execution signals for trajectory tracking in the rapid transit system should meet high real-time requirements at high sampling frequencies. However, the complex trajectory tracking control method increases the solution pressure on the hardware processor, resulting in enhanced control time delay of the system. This causes each execution device to be unable to accurately output reference values ​​in real time. The greater the time delay, the greater the tracking error of the lateral motion of each vehicle body. Summary of the Invention

[0005] Purpose of the invention: To address the shortcomings of existing technologies, this invention provides a pseudo-fuzzy PD coordinated control method for BRT articulated steering and its active articulation system. This invention reduces the design difficulty and solution pressure of the control methods for the trajectory tracking control layer and the underlying device execution layer by pseudo-fuzzifying the fuzzy controller. Furthermore, by applying this method through the active articulation system, the active adjustment of the articulated rotation process is achieved, thereby increasing the reliability of the articulation system.

[0006] Technical solution: A quasi-fuzzy PD coordinated control method for BRT articulated steering, comprising the following steps:

[0007] S1: Obtain at least two sets of actual steering process data by establishing a simulation model and perform simulation processing: The acquisition module collects the lateral displacement of the front vehicle's center of gravity, the velocity of the front vehicle, the lateral displacement of the rear vehicle's center of gravity, and the velocity of the rear vehicle, and then performs simulation processing to obtain the lateral displacement error Δy1 of the front vehicle and the rate of change of the lateral displacement error of the front vehicle. Lateral displacement error of rear vehicle body Δy2, rate of change of lateral displacement error of rear vehicle body The actual steering angle, actual steering torque, and actual articulation torque of the vehicle in front are collected and then processed in a simulation.

[0008] S2: Simulate the data obtained in S1 to obtain the fuzzy control input and output parameter values ​​of the first and second fuzzy controllers, and compare them to obtain the optimal fuzzy control input and output simulation parameter values:

[0009] The lateral displacement error Δy1 and the rate of change of the lateral displacement error of the front vehicle body obtained from the simulation in S1 are used as the basis for the calculation. Lateral displacement error of rear vehicle body Δy2, rate of change of lateral displacement error of rear vehicle body The simulated desired control parameters, which are used as input parameters for the first fuzzy controller, are processed and output as input parameters for the first PD controller and the second PD controller, respectively. and simulation desired control parameters and

[0010] The first PD controller outputs the simulated desired steering angle δ of the vehicle in front. d By combining the actual steering angle of the vehicle in front collected in S1, the simulation error Δδ of the steering angle of the vehicle in front and the simulation rate of change of the steering angle error of the vehicle in front are obtained. This value is used as the input parameter of the second fuzzy controller, and after processing, the simulation desired control parameters are obtained as the input parameters of the third PD controller. and

[0011] By analyzing and comparing the trajectory tracking control accuracy of the front and rear vehicles in each simulation model, the optimal fuzzy control input and output simulation parameter values ​​are obtained.

[0012] S3: Quasi-fuzzification of the first and second fuzzy controllers: Construct nonlinear regression models of the output variables of the first and second fuzzy controllers with respect to the input variables and their related variables. Obtain the optimal fuzzy control input and output simulation parameter values ​​of the first and second fuzzy controllers in S2. Then, combine the least squares method to fit the designed variable parameters in the nonlinear regression model to obtain the expected control parameters of the first PD controller. Desired control parameters of the second PD controller and and the desired control parameters of the third PD controller and

[0013] S4: Obtain the actual BRT execution control parameters: based on the expected control parameters of the first PD controller obtained from S3. Desired control parameters of the second PD controller and and the desired control parameters of the third PD controller and Obtain the desired steering angle δ of the vehicle in front. d Desired hinge torque M d and desired steering torque M t ;

[0014] S5: Start the execution module: Combine the actual steering angle of the preceding vehicle, the actual articulation torque, and the actual steering torque obtained in S1 with the desired steering angle δ of the preceding vehicle in S4. d Desired hinge torque M d and desired steering torque M t The parameters are compared and optimized, and the optimized control parameters are sent to the execution module for execution.

[0015] This invention first constructs a BRT vehicle dynamics simulation model, optimizes the trajectory tracking accuracy of the front and rear BRT vehicles through simulation, obtains the optimal fuzzy control input and output parameter values ​​of the first fuzzy controller in the trajectory tracking control layer and the second fuzzy controller in the underlying device execution layer, and constructs a nonlinear regression model of the output variables of the first and second fuzzy controllers with respect to the input variables and their related variables. The least squares method is then used to fit the designed variable parameters in the nonlinear regression model to obtain the desired control parameters of the first, second, and third PD controllers. This involves quasi-fuzzifying the first and second fuzzy controllers to reduce the original computational load. Finally, the desired steering angle δ of the front vehicle is obtained based on the desired control parameters of the first, second, and third PD controllers. d Desired hinge torque M d and desired steering torque M t Afterwards, the designed BRT articulated steering quasi-fuzzy PD coordinated controller was compiled into the ECU for real vehicle testing. The simulated lateral displacement of the front vehicle's center of gravity, lateral displacement of the rear vehicle's center of gravity, front vehicle steering angle, steering torque, and articulated torque signals were compared with the actual relevant acquired signals. The actual control parameters were optimized and adjusted, and the optimized and updated control parameters were sent to the ECU to actively adjust the actual values ​​towards the desired values. This increased the reliability of the underlying device execution layer and the mobility and controllability of the BRT bus.

[0016] In the preferred embodiment, the specific steps for obtaining the optimal fuzzy control input and output simulation parameter values ​​in S2 are as follows: analyze and compare the trajectory tracking control accuracy of the front vehicle and the rear vehicle in the simulation model in S1, and obtain the optimal fuzzy control input and output simulation parameter values ​​of the first fuzzy controller and the second fuzzy controller when the trajectory tracking error is minimized.

[0017] Preferably, the specific steps for obtaining the desired control parameters of the first PD controller in step S3 are as follows:

[0018] The design variable matrix of the regression model is selected as follows: All variables are selected as first-order. The design variable matrix is ​​fitted using the least squares method and the optimal fuzzy control input-output parameter data. The desired control parameter F(k) of the first PD controller is obtained through data fitting. p1 ) and F(k d1 ):

[0019] F(k p1 (=(X1)) T X1) -1 X1 T k p1

[0020] Where X1 is the variable matrix, T is the transpose matrix symbol, and kp1 The simulation expectation scaling factor for the first PD controller;

[0021] F(k d1 )=(X1 T X1) -1 X1 T k d1

[0022] Where X1 is the variable matrix, T is the transpose matrix symbol, and k d1 The simulation expected differential coefficients of the first PD controller;

[0023] The design variable matrix of the regression model is selected as follows: The design variable matrix is ​​fitted using the least squares method and optimal fuzzy control input-output parameter data. The desired control parameters F(k) of the second PD controller are obtained through data fitting. p2 ) and F(k d2 ):

[0024] F(k p2 )=(X1 T X1) -1 X1 T k p2

[0025] Where X1 is the variable matrix, T is the transpose matrix symbol, and k p2 The simulation expectation scaling factor for the second PD controller;

[0026] F(k d2 )=(X1 T X1) -1 X1 T k d2

[0027] Where X1 is the variable matrix, T is the transpose matrix symbol, and k d2 The simulation expected differential coefficients of the second PD controller;

[0028] The design variable matrix of the regression model is selected as follows: The design variable matrix is ​​fitted using the least squares method and optimal fuzzy control input-output parameter data. The desired control parameters F(k) of the third PD controller are then obtained through data fitting. p3 ) and F(k d3 ):

[0029] F(k p3 )=(X2 T X2) -1 X2 T k p3

[0030] Where X2 is the variable matrix, T is the transpose matrix symbol, and k p3 The simulation expectation scaling factor for the third PD controller;

[0031] F(k d3 )=(X2 T X2) -1 X2 T k d3

[0032] Where X2 is the variable matrix, T is the transpose matrix symbol, and k d3 The simulation expected differential coefficients of the third PD controller are given.

[0033] The preferred option is that the specific steps in S4 are as follows:

[0034] Obtain the desired steering angle δ of the vehicle in front d :

[0035]

[0036] Where, δ d Let Δy1 be the desired steering angle of the vehicle in front, and Δy1 be the lateral displacement error of the vehicle in front. This represents the rate of change of the lateral displacement error of the front vehicle body. and These are the desired control parameters for the first PD controller;

[0037] Obtain the desired hinge torque M d :

[0038]

[0039] Among them, M d Let Δy2 be the desired articulation torque, and Δy2 be the lateral displacement error of the rear vehicle body. The rate of change of the lateral displacement error of the rear vehicle body. and These are the desired control parameters for the second PD controller;

[0040] Obtain the desired steering torque M t :

[0041]

[0042] Among them, M t Let Δδ be the desired steering torque, and Δδ be the steering angle error of the vehicle in front. The rate of change of the steering angle error of the vehicle in front. and These are the desired control parameters for the third PD controller.

[0043] An active articulation system for implementing a quasi-fuzzy PD coordinated control method for BRT articulated steering includes an ECU processor, a front frame, and a rear frame. The front frame and the rear frame are connected by an articulation mechanism. A drive device is mounted on the front frame. The ECU processor sends control signals to the drive device, and the drive device controls the articulation mechanism by receiving the signals.

[0044] This invention sends the active adjustment signal received by the ECU processor from the trajectory tracking layer to the drive device of the underlying device execution layer, namely the steering system and the active articulation system. The drive device controls the execution of the steering system and the active articulation system in the underlying device execution layer through the received active adjustment signal, namely the active steering torque and the active articulation torque, thereby improving the maneuverability and controllability of the BRT.

[0045] In a preferred embodiment, to achieve the hinge between the front frame and the rear frame, the hinge mechanism is a turntable bearing, which includes an inner bearing and an outer bearing sleeved outside the inner bearing. The inner bearing is fixedly connected to the rear frame, and the outer bearing is fixedly connected to the front frame. The front frame and the rear frame rotate relative to each other through the inner bearing and the outer bearing.

[0046] In a preferred embodiment, in order to control the active articulation system, the drive device includes a transmission mechanism, an active adjustment mechanism, and a motor connected in sequence, wherein the motor drives the active adjustment mechanism to drive the transmission mechanism to reset the front frame and the rear frame.

[0047] In a preferred embodiment, for transmission purposes, the transmission mechanism includes a rack and a gear that mesh with each other, the rack being mounted on the front frame and the gear being connected to an inner bearing via a rear frame.

[0048] In a preferred embodiment, to achieve active adjustment, the active adjustment mechanism includes a hydraulic assembly comprising a double-rod piston cylinder. The double-rod piston cylinder includes piston rods mounted on both sides of the cylinder, and the piston rods are fixedly connected to the front frame. The ECU processor controls the motor to actively adjust the double-rod piston cylinder to adjust the active rotational torque. Through the active adjustment signal sent by the ECU processor, the hydraulic assembly controls the generation of a rotational torque around the central axis of the turntable bearing via a rack and pinion transmission structure, thereby achieving the purpose of active adjustment.

[0049] In a preferred embodiment, to provide adjustment assistance during active adjustment, preload springs are installed on both piston rods. The preload springs provide damping torque during the bus's turning phase and elastic torque during the slewing phase to achieve the slewing of the front and rear bus bodies.

[0050] Beneficial effects: This invention simplifies the calculation of the control methods for the BRT bus trajectory tracking layer and the underlying device execution layer by quasi-fuzzifying the first and second fuzzy controllers. While not affecting the output results, it reduces the overall computational load and improves the decision-making speed of the fuzzy control method. It also meets the requirements for application in high-sampling-frequency, high-response systems. This allows the active articulation system to respond quickly and accurately to the execution signal. By executing the signal through the active articulation system, coordinated control of the active steering angle and active turning torque is achieved. The active articulation system can actively adjust according to changes in vehicle mass, speed, and trajectory tracking accuracy, improving the control stability of the BRT bus. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0052] Figure 1 This is a flowchart of the method of the present invention;

[0053] Figure 2 This is a front view of the active articulation system of the present invention;

[0054] Figure 3 This is a reverse view of the active articulation system of the present invention;

[0055] Figure 4 This is a schematic diagram of the turntable bearing of the present invention;

[0056] Figure 5 This is a schematic diagram of the installation of the turntable bearing and the front frame of the present invention. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] In the description of this invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0059] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can include direct contact between the first and second features, or contact between the first and second features through another feature between them. Furthermore, "above," "over," and "on top" of the second feature includes the first feature directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature includes the first feature directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0060] like Figure 1 As shown, a BRT articulated steering quasi-fuzzy PD coordinated control method includes the following steps:

[0061] S1: Obtain at least two sets of actual steering process data by establishing a simulation model and perform simulation processing: The acquisition module collects the lateral displacement of the front vehicle's center of gravity, the velocity of the front vehicle, the lateral displacement of the rear vehicle's center of gravity, and the velocity of the rear vehicle, and then performs simulation processing to obtain the lateral displacement error Δy1 of the front vehicle and the rate of change of the lateral displacement error of the front vehicle. Lateral displacement error of rear vehicle body Δy2, rate of change of lateral displacement error of rear vehicle body The actual steering angle, actual steering torque, and actual articulation torque of the vehicle in front are collected and then processed in a simulation.

[0062] By constructing a dynamic model of the BRT with active articulation and steering, the dynamic state equations of the whole vehicle are derived with the front vehicle's sideslip angle, front vehicle's yaw angle, rear vehicle's sideslip angle, and rear vehicle's yaw angle as state variables, and the front vehicle's steering angle and articulation turning torque as control variables. A BRT bus driver model, a dynamic model of the front vehicle's steering mechanism, and a dynamic model of the active articulation device are constructed. Based on the BRT vehicle parameters, a BRT whole vehicle dynamic simulation model is established in the MATLAB / Simulink environment. The data acquisition module collects the actual front vehicle steering angle, actual steering torque, and actual articulation torque, and collects the lateral displacement of the front vehicle's center of gravity and the lateral displacement of the rear vehicle's center of gravity for simulation processing, i.e., simulation model identification and parameter correction, so that the simulation model can truly reflect the kinematic and dynamic characteristics of the BRT.

[0063] S2: Simulate the data obtained in S1 to obtain the fuzzy control input and output parameter values ​​of the first and second fuzzy controllers, and compare them to obtain the optimal fuzzy control input and output simulation parameter values:

[0064] The lateral displacement error Δy1 and the rate of change of the lateral displacement error of the front vehicle body obtained from the simulation in S1 are used as the basis for the calculation. Lateral displacement error of rear vehicle body Δy2, rate of change of lateral displacement error of rear vehicle body The simulated desired control parameters, which are used as input parameters for the first fuzzy controller, are processed and output as input parameters for the first PD controller and the second PD controller, respectively. and simulation desired control parameters and

[0065] The first PD controller outputs the simulated desired steering angle δ of the vehicle in front. d By combining the actual steering angle of the vehicle in front collected in S1, the simulation error Δδ of the steering angle of the vehicle in front and the simulation rate of change of the steering angle error of the vehicle in front are obtained. This value is used as the input parameter of the second fuzzy controller, and after processing, the simulation desired control parameters are obtained as the input parameters of the third PD controller. and

[0066] By analyzing and comparing the trajectory tracking control accuracy of the front and rear vehicles in each simulation model, the optimal fuzzy control input and output simulation parameter values ​​are obtained.

[0067] S3: Quasi-fuzzification of the first and second fuzzy controllers: Construct nonlinear regression models of the output variables of the first and second fuzzy controllers with respect to the input variables and their related variables. Obtain the optimal fuzzy control input and output simulation parameter values ​​of the first and second fuzzy controllers in S2. Then, combine the least squares method to fit the designed variable parameters in the nonlinear regression model to obtain the expected control parameters of the first PD controller. Desired control parameters of the second PD controller and and the desired control parameters of the third PD controller and

[0068] S4: Obtain the actual BRT execution control parameters: based on the expected control parameters of the first PD controller obtained from S3. Desired control parameters of the second PD controller and and the desired control parameters of the third PD controller and Obtain the desired steering angle δ of the vehicle in front. d Desired hinge torque M d and desired steering torque M t ;

[0069] Since the lateral displacement of the front vehicle body is most affected by the steering angle of the front vehicle, the PD controller for the desired steering angle of the front vehicle is designed using only the lateral displacement error of the front vehicle body and its rate of change.

[0070] Since the lateral displacement of the rear vehicle body is most affected by the articulation torque, the PD controller for the desired articulation torque is designed using only the lateral displacement error of the rear vehicle body and its rate of change.

[0071] S5: Start the execution module: Combine the actual steering angle of the preceding vehicle, the actual articulation torque, and the actual steering torque obtained in S1 with the desired steering angle δ of the preceding vehicle in S4. d Desired hinge torque M d and desired steering torque M t The parameters are compared and optimized, and the optimized control parameters are sent to the execution module for execution.

[0072] First, a BRT vehicle dynamics simulation model is constructed. The trajectory tracking accuracy of the front and rear BRT vehicles is optimized through simulation. The optimal fuzzy control input and output parameter values ​​for the first fuzzy controller in the trajectory tracking control layer and the second fuzzy controller in the underlying device execution layer are obtained. A nonlinear regression model of the output variables of the first and second fuzzy controllers with respect to the input variables and their related variables is constructed. The parameters of the designed variables in the nonlinear regression model are fitted using the least squares method to obtain the desired control parameters of the first, second, and third PD controllers. This is achieved by quasi-fuzzifying the first and second fuzzy controllers to reduce the original computational load. Finally, the desired steering angle δ of the front vehicle is obtained based on the desired control parameters of the first, second, and third PD controllers. d Desired hinge torque M d and desired steering torque M t Afterwards, the designed BRT articulated steering quasi-fuzzy PD coordinated controller was compiled into the ECU for real vehicle testing. The simulated lateral displacement of the front vehicle's center of gravity, lateral displacement of the rear vehicle's center of gravity, front vehicle steering angle, steering torque, and articulated torque signals were compared with the actual relevant acquired signals. The actual control parameters were optimized and adjusted, and the optimized and updated control parameters were sent to the ECU to actively adjust the actual values ​​towards the desired values. This increased the reliability of the underlying device execution layer and the mobility and controllability of the BRT bus.

[0073] The specific steps for obtaining the optimal fuzzy control input and output simulation parameter values ​​in S2 are as follows: analyze and compare the trajectory tracking control accuracy of the front and rear vehicles in the simulation model in S1, and obtain the optimal fuzzy control input and output simulation parameter values ​​of the first fuzzy controller and the second fuzzy controller when the trajectory tracking error is minimized.

[0074] The ability of the BRT front and rear vehicle bodies to track trajectories is improved by using a coordinated control method for BRT articulated steering. The smaller the lateral trajectory tracking error (i.e., lateral displacement error) of the front and rear vehicle bodies, the higher the trajectory tracking accuracy and the stronger the control capability of the coordinated control method. A first fuzzy controller and a second fuzzy controller fuzzy method are designed. The lateral displacement of the front and rear vehicle bodies is run and collected, and the lateral displacement error is calculated. By comparing the lateral displacement error values ​​of the front and rear vehicle bodies under each set of designed first and second fuzzy controller fuzzy methods, the optimal fuzzy method of the first and second fuzzy controllers is determined. Under the designed optimal first and second fuzzy controller fuzzy methods, a simulation model is run to obtain the optimal input and output simulation parameter values ​​of the first and second fuzzy controllers.

[0075] The specific steps for obtaining the desired control parameters of the first PD controller in S3 are as follows:

[0076] The design variable matrix of the regression model is selected as follows: All variables are selected as first-order. The design variable matrix is ​​fitted using the least squares method and the optimal fuzzy control input-output parameter data. The desired control parameter F(k) of the first PD controller is obtained through data fitting. p1 ) and F(k d1 ):

[0077] F(k p1 )=(X1 T X1) -1 X1 T k p1

[0078] Where X1 is the variable matrix, T is the transpose matrix symbol, and k p1 The simulation expectation scaling factor for the first PD controller;

[0079] F(k d1 )=(X1 T X1) -1 X1 T k d1

[0080] Where X1 is the variable matrix, T is the transpose matrix symbol, and k d1 The simulation expected differential coefficients of the first PD controller;

[0081] The design variable matrix of the regression model is selected as follows: The design variable matrix is ​​fitted using the least squares method and optimal fuzzy control input-output parameter data. The desired control parameters F(k) of the second PD controller are obtained through data fitting. p2 ) and F(k d2 ):

[0082] F(k p2 )=(X1 T X1) -1 X1 T k p2

[0083] Where X1 is the variable matrix, T is the transpose matrix symbol, and k p2 The simulation expectation scaling factor for the second PD controller;

[0084] F(k d2 )=(X1 T X1) -1 X1 T k d2

[0085] Where X1 is the variable matrix, T is the transpose matrix symbol, and k d2 The simulation expected differential coefficients of the second PD controller;

[0086] The design variable matrix of the regression model is selected as follows: The design variable matrix is ​​fitted using the least squares method and optimal fuzzy control input-output parameter data. The desired control parameters F(k) of the third PD controller are then obtained through data fitting. p3 ) and F(k d3 ):

[0087] F(k p3 )=(X2 T X2) -1 X2 T k p3

[0088] Where X2 is the variable matrix, T is the transpose matrix symbol, and k p3 The simulation expectation scaling factor for the third PD controller;

[0089] F(k d3 )=(X2 T X2) -1 X2 T k d3

[0090] Where X2 is the variable matrix, T is the transpose matrix symbol, and k d3 The simulation expected differential coefficients of the third PD controller are given.

[0091] The specific steps in S4 are as follows:

[0092] Obtain the desired steering angle δ of the vehicle in front d :

[0093]

[0094] Where, δ d Let Δy1 be the desired steering angle of the vehicle in front, and Δy1 be the lateral displacement error of the vehicle in front. This represents the rate of change of the lateral displacement error of the front vehicle body. and These are the desired control parameters for the first PD controller;

[0095] Obtain the desired hinge torque M d :

[0096]

[0097] Among them, M d Let Δy2 be the desired articulation torque, and Δy2 be the lateral displacement error of the rear vehicle body. The rate of change of the lateral displacement error of the rear vehicle body. and These are the desired control parameters for the second PD controller;

[0098] Obtain the desired steering torque M t :

[0099]

[0100] Among them, M t Let Δδ be the desired steering torque, and Δδ be the steering angle error of the vehicle in front. The rate of change of the steering angle error of the vehicle in front. and These are the desired control parameters for the third PD controller.

[0101] A first fuzzy controller is designed in the trajectory tracking layer. The first fuzzy controller is then pseudofuzzified to obtain the desired control parameters of the first PD controller and the second PD controller, thereby obtaining the desired steering angle δ of the vehicle in front. d and the desired hinge torque M d The data is then sent to the underlying device execution layer via the ECU processor. This underlying device execution layer includes the front vehicle steering system and the active articulation system. For the front vehicle steering system, the desired front vehicle steering angle δ is... d and the rate of change of the steering angle error of the vehicle in front Δδ By combining the design of the second fuzzy controller, quasi-fuzzification is performed on the second fuzzy controller to obtain the desired control parameters of the third PD controller, thereby obtaining the desired steering torque M. t Based on the calculated desired steering torque M t The reference current of the steering motor of the preceding vehicle is derived by reverse engineering and sent to the steering motor of the preceding vehicle via the ECU controller to control the actual steering torque, thus achieving the tracking of the actual steering angle to the desired steering angle in the steering system of the preceding vehicle; for the active articulated system, the desired articulation torque M is calculated... d The reference current of the motor in the active articulation system is derived by reverse engineering and sent to the motor in the active articulation system through the ECU controller. In this embodiment, a permanent magnet synchronous motor is used to control the hydraulic thrust and combine it with the elastic force of the rigid preload spring to obtain the actual articulation torque, thereby realizing the tracking of the actual articulation torque to the desired articulation torque in the active articulation system.

[0102] An active articulation system for implementing a quasi-fuzzy PD coordinated control method for BRT articulated steering includes an ECU processor 1, a front frame 2, and a rear frame 3. The front frame 2 and the rear frame 3 are connected by an articulation mechanism 4. A drive device 5 is mounted on the front frame 2. The ECU processor 1 sends control signals to the drive device 5, and the drive device 5 controls the articulation mechanism 4 by receiving the signals.

[0103] The active adjustment signal received by the ECU processor from the trajectory tracking layer is sent to the drive unit of the underlying device execution layer. The drive unit controls the execution of the steering system and active articulation system in the underlying device execution layer through the received active adjustment signal, namely active steering torque and active articulation torque, thereby improving the maneuverability and controllability of the BRT.

[0104] like Figure 2-5 As shown, in order to achieve the hinge between the front frame and the rear frame, the hinge mechanism 4 is a turntable bearing. The turntable bearing includes an inner bearing 41 and an outer bearing 42 sleeved outside the inner bearing 41. The inner bearing 41 is fixedly connected to the rear frame 3, and the outer bearing 42 is fixedly connected to the front frame 2. The front frame 2 and the rear frame 3 rotate relative to each other through the inner bearing 41 and the outer bearing 42.

[0105] In order to control the active articulation system, the drive device 5 includes a transmission mechanism 51, an active adjustment mechanism 52 and a motor 53 connected in sequence. The motor 53 drives the active adjustment mechanism 52 to drive the transmission mechanism 51 to reset the front frame 2 and the rear frame 3.

[0106] For transmission, the transmission mechanism 51 includes a rack 511 and a gear 512 that mesh with each other. The rack 511 is mounted on the front frame 2, and the gear 512 is connected to the inner bearing 41 through the rear frame 3.

[0107] To achieve active adjustment, the active adjustment mechanism 52 includes a hydraulic assembly, which includes a double-rod piston cylinder 521. The double-rod piston cylinder 521 includes piston rods 522 mounted on both sides of the cylinder. The piston rods 522 are fixedly connected to the front frame 2. The ECU processor 1 controls the motor 53 to actively adjust the double-rod piston cylinder 521 to adjust the active rotational torque. Through the active adjustment signal sent by the ECU processor, the hydraulic assembly controls the generation of a rotational torque around the central axis of the turntable bearing via a rack and pinion transmission structure, thereby achieving the purpose of active adjustment.

[0108] To provide adjustment assistance during active adjustment, preload springs 6 are installed on the piston rods 522 on both sides. The preload springs provide damping torque during the bus's turning phase and elastic torque during the slewing phase to achieve the slewing of the front and rear bus bodies.

[0109] When the BRT bus is traveling in a straight line, the turntable bearing acts as a connection, the ECU processor controls the permanent magnet synchronous motor to be in a non-working state, and the preload spring is in its original state.

[0110] like Figure 2 As shown, when the BRT bus turns right, the rear frame rotates counterclockwise around the turntable bearing, the double-rod piston cylinder moves to the left relative to the front frame, the preload spring on the left is compressed, and the spring on the right is stretched; when the BRT bus turns left, the rear frame rotates clockwise around the turntable bearing, the double-rod piston cylinder moves to the right relative to the front frame, the preload spring on the left is stretched, and the spring on the right is compressed.

[0111] When the BRT bus turns right and returns to center, the rear frame rotates clockwise around the turntable bearing, the double-rod piston cylinder moves to the right relative to the front of the bus, and the compressed and stretched pre-tensioned springs gradually return to their original state; when the BRT bus turns left and returns to center, the rear frame rotates counterclockwise around the turntable bearing, the double-rod piston cylinder moves to the left relative to the front of the bus, and the compressed and stretched pre-tensioned springs gradually return to their original state.

[0112] The ECU processor controls the permanent magnet synchronous motor to adjust the output force of the double-rod piston cylinder. Combined with the spring force, the resultant force generates a rotational torque around the central axis of the turntable bearing through the transmission mechanism. This enables the coordinated control of the active steering angle and the active rotational torque to adjust the trajectory tracking of the front and rear bodies of the BRT bus.

[0113] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0114] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A quasi-fuzzy PD coordinated control method for BRT articulated steering, characterized in that: Includes the following steps: S1: Obtain actual steering process data by establishing a simulation model and perform simulation processing: The acquisition module collects the lateral displacement of the front vehicle's center of gravity, the velocity of the front vehicle, the lateral displacement of the rear vehicle's center of gravity, and the velocity of the rear vehicle, and then performs simulation processing to obtain the lateral displacement error of the front vehicle. , Change rate of lateral displacement error of the front vehicle body Lateral displacement error of the rear vehicle body , Rate of change of lateral displacement error of rear vehicle body The actual steering angle, actual steering torque, and actual articulation torque of the vehicle in front are collected and then processed through simulation. S2: Simulate the data obtained in S1 to obtain the fuzzy control input and output parameter values ​​of the first and second fuzzy controllers, and compare them to obtain the optimal fuzzy control input and output simulation parameter values: The lateral displacement error of the front vehicle body obtained from the simulation in S1 , Change rate of lateral displacement error of the front vehicle body Lateral displacement error of the rear vehicle body , Rate of change of lateral displacement error of rear vehicle body The simulated desired control parameters, which are used as input parameters for the first fuzzy controller, are processed and output as input parameters for the first PD controller and the second PD controller, respectively. , and simulation desired control parameters and ; The first PD controller outputs the simulated desired steering angle of the vehicle in front. The simulation error of the steering angle of the vehicle in front is obtained by combining the actual steering angle of the vehicle in front collected in S1. Simulated rate of change of steering angle error of the vehicle in front This value is used as the input parameter of the second fuzzy controller, and after processing, the simulation expected control parameters are obtained as the input parameters of the third PD controller. and ; By analyzing and comparing the trajectory tracking control accuracy of the front and rear vehicles in each simulation model, the optimal fuzzy control input and output simulation parameter values ​​are obtained. S3: Quasi-fuzzification of the first and second fuzzy controllers: Construct nonlinear regression models of the output variables of the first and second fuzzy controllers with respect to the input variables and their related variables. Obtain the optimal fuzzy control input and output simulation parameter values ​​of the first and second fuzzy controllers in S2. Then, combine the least squares method to fit the designed variable parameters in the nonlinear regression model to obtain the expected control parameters of the first PD controller. , The desired control parameters of the second PD controller and and the desired control parameters of the third PD controller and ; S4: Obtain the actual BRT execution control parameters: based on the expected control parameters of the first PD controller obtained from S3. , Desired control parameters of the second PD controller and and the desired control parameters of the third PD controller and Obtain the desired steering angle of the vehicle in front. Desired hinge torque and desired steering torque ; S5: Start the execution module: Combine the actual forward vehicle steering angle, actual articulation torque, and actual steering torque obtained in S1 with the desired forward vehicle steering angle in S4. Desired hinge torque and desired steering torque The parameters are compared and optimized, and the optimized control parameters are sent to the execution module for execution.

2. The BRT articulated steering quasi-fuzzy PD coordinated control method according to claim 1, characterized in that: The specific steps for obtaining the optimal fuzzy control input and output simulation parameter values ​​in S2 are as follows: analyze and compare the trajectory tracking control accuracy of the front and rear vehicles in the simulation model in S1, and obtain the optimal fuzzy control input and output simulation parameter values ​​of the first fuzzy controller and the second fuzzy controller when the trajectory tracking error is minimized.

3. The BRT articulated steering quasi-fuzzy PD coordinated control method according to claim 1, characterized in that: The specific steps for obtaining the desired control parameters of the first PD controller in S3 are as follows: The design variable matrix of the regression model is selected as follows: All variables were chosen to be of order 1. The design variable matrix was fitted using the least squares method and the optimal fuzzy control input-output parameter data. The desired control parameters of the first PD controller were obtained through data fitting. and : ； in, Let T be a matrix of variables, and T be the symbol for the transpose of the matrix. The simulation expectation scaling factor for the first PD controller; ； in, Let T be a matrix of variables, and T be the symbol for the transpose of the matrix. The simulation expected differential coefficients of the first PD controller; The design variable matrix of the regression model is selected as follows: The design variable matrix is ​​fitted using the least squares method and optimal fuzzy control input-output parameter data. The desired control parameters of the second PD controller are obtained through data fitting. and : ； in, Let T be a matrix of variables, and T be the symbol for the transpose of the matrix. The simulation expectation scaling factor for the second PD controller; ； in, Let T be a matrix of variables, and T be the symbol for the transpose of the matrix. The simulation expected differential coefficients of the second PD controller; The design variable matrix of the regression model is selected as follows: The design variable matrix is ​​fitted using the least squares method and optimal fuzzy control input-output parameter data. The desired control parameters of the third PD controller are obtained through data fitting. and : ； in, Let T be a matrix of variables, and T be the symbol for the transpose of the matrix. The simulation expectation scaling factor for the third PD controller; ； in, Let T be a matrix of variables, and T be the symbol for the transpose of the matrix. The simulation expected differential coefficients of the third PD controller are given.

4. The BRT articulated steering quasi-fuzzy PD coordinated control method according to claim 1, characterized in that: The specific steps in S4 are as follows: Obtain the desired steering angle of the vehicle in front : ； in, To achieve the desired turning angle of the vehicle in front, This represents the lateral displacement error of the front vehicle body. This represents the rate of change of the lateral displacement error of the front vehicle body. and These are the desired control parameters for the first PD controller; Obtain the desired hinge torque : ； in, For the desired hinge torque, This refers to the lateral displacement error of the rear vehicle body. This represents the rate of change of the lateral displacement error of the rear vehicle body. and These are the desired control parameters for the second PD controller; Obtain the desired steering torque : ； in, For the desired steering torque, This is the steering angle error of the vehicle in front. The rate of change of the steering angle error of the vehicle in front. and These are the desired control parameters for the third PD controller.

5. An active articulated system for implementing the BRT articulated steering quasi-fuzzy PD coordinated control method according to any one of claims 1-4, comprising an ECU processor (1), a front frame (2), and a rear frame (3), characterized in that: The front frame (2) and the rear frame (3) are connected by a hinge mechanism (4). A drive device (5) is installed on the front frame (2). The ECU processor (1) sends a control signal to the drive device (5). The drive device (5) controls the hinge mechanism (4) by receiving the control signal.

6. The active articulated system for implementing the quasi-fuzzy PD coordinated control method for BRT articulated steering according to claim 5, characterized in that: The hinge mechanism (4) is a turntable bearing, which includes an inner bearing (41) and an outer bearing (42) sleeved outside the inner bearing (41). The inner bearing (41) is fixedly connected to the rear frame (3), and the outer bearing (42) is fixedly connected to the front frame (2). The front frame (2) and the rear frame (3) rotate relative to each other through the inner bearing (41) and the outer bearing (42).

7. The active articulated system for implementing the quasi-fuzzy PD coordinated control method for BRT articulated steering according to claim 6, characterized in that: The drive device (5) includes a transmission mechanism (51), an active adjustment mechanism (52) and a motor (53) connected in sequence. The motor (53) drives the active adjustment mechanism (52) to drive the transmission mechanism (51) to reset the front frame (2) and the rear frame (3).

8. The active articulated system for implementing the quasi-fuzzy PD coordinated control method for BRT articulated steering according to claim 7, characterized in that: The transmission mechanism (51) includes a rack (511) and a gear (512) that mesh with each other. The rack (511) is mounted on the front frame (2), and the gear (512) is connected to the inner bearing (41) through the rear frame (3).

9. The active articulated system for implementing the quasi-fuzzy PD coordinated control method for BRT articulated steering according to claim 8, characterized in that: The active adjustment mechanism (52) includes a hydraulic assembly, which includes a double-rod piston cylinder (521). The double-rod piston cylinder (521) includes piston rods (522) mounted on both sides of the double-rod piston cylinder (521). The piston rods (522) are fixedly connected to the front frame (2). The ECU processor (1) controls the motor (53) to actively adjust the double-rod piston cylinder (521) to adjust the active rotation torque.

10. The active articulated system for implementing the quasi-fuzzy PD coordinated control method for BRT articulated steering according to claim 9, characterized in that: Preload springs (6) are installed on the piston rods (522) on both sides respectively.

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