Preset-time queuing control method for vehicle systems under narrow tunnel constraints

Abstract

This invention discloses a preset-time formation control method for vehicle systems under narrow tunnel constraints, belonging to the field of unmanned vehicle control and cooperative formation control technology. The method includes: setting vehicle position constraints and performance constraints for formation errors; integrating nonlinear command filtering techniques with parameter estimation mechanisms; designing a preset-time fault-tolerant formation control strategy by combining generalized fuzzy neural networks and Butterworth low-pass filtering techniques; and achieving stable and safe formation structure maintenance for heterogeneous vehicle systems within a preset time. This invention solves the problems of difficult constraints in general curved tunnels, non-preset convergence time, complex controller design, and difficulty in non-affine fault tolerance, achieving safe, efficient, and preset-time convergent formation control.

Description

Technical Field

[0001] This invention belongs to the field of unmanned vehicle control and cooperative formation control technology, specifically relating to a preset time formation control method for vehicle systems under the constraint of narrow tunnels. Background Technology

[0002] In recent years, with the rapid pace of urbanization, the number of vehicles on urban roads has increased dramatically, leading to increasingly severe traffic congestion and frequent traffic accidents. To address these challenges, there has been a growing focus on intelligent transportation systems and vehicle automation technologies, with vehicle platooning control gradually becoming an important research direction. Placing control aims to achieve efficient traffic management and safe operation through the coordination of multiple vehicles.

[0003] In real-world traffic scenarios, vehicle platooning systems typically need to complete platoon reconfiguration or trajectory tracking within a limited timeframe. Insufficient convergence speed can lead to platoon breakup, collisions, or significant tracking errors, threatening the safety of the entire system. Therefore, improving the system's rapid response capability in complex traffic environments while ensuring system stability has become a critical issue in platooning control. Furthermore, during platooning operations, the state of vehicles, especially their positions, usually needs to be constrained to avoid collision risks in narrow tunnels or other restricted passageways.

[0004] To address the above issues, some formation control strategies have been proposed in existing research. A typical approach is to introduce logarithmic / tangential obstacle Lyapunov functions to constrain the vehicle's position, thereby effectively avoiding collisions between the vehicle and the tunnel boundary while maintaining system stability. Theoretically, this type of method has a good safety guarantee mechanism, but it has the following shortcomings: (1) The control structure of this type of method is complex and it is mainly applicable to simple constraint areas enclosed by two straight lines, making it difficult to describe more complex tunnel geometries; (2) This type of method is difficult to apply gradually shrinking dynamic constraints to the tracking error, and it is usually impossible to pre-set the convergence time of the system during the design phase, which limits its applicability in application scenarios with transient performance and time requirements; (3) Traditional backstepping control methods require repeated differentiation of the virtual control function, which leads to "computational complexity" problems, increases the complexity of controller implementation, and limits its application in real-time vehicle systems; (4) Most existing formation control methods assume that the actuators are fault-free or only consider linear affine faults, making it difficult to handle non-affine actuator faults that may occur in actual vehicle operation (such as nonlinear failure of the accelerator / brake pedal, sensor drift, etc.). Due to their non-affine nature, this type of fault causes an algebraic loop between the controller input and the fault output, making it difficult to directly apply traditional adaptive fault-tolerant control methods and reducing the robustness and safety of the system.

[0005] Therefore, how to effectively constrain general curved and narrow tunnels, accurately control the transient and steady-state performance of formation errors, and handle fault-tolerant processing of non-affine actuator failures while ensuring system stability and reducing the complexity of controller design are the technical problems that urgently need to be solved in the field of vehicle formation control. Summary of the Invention

[0006] The purpose of this invention is to overcome the shortcomings of the existing technology and provide a preset time formation control method for vehicle systems under narrow tunnel constraints. This method solves the problems of difficult constraints in general curved tunnels, inability to preset convergence time, complex controller design, and difficulty in non-affine fault tolerance, and achieves safe, efficient, and preset time convergence formation control.

[0007] The objective of this invention is achieved through the following technical solution:

[0008] A preset time-based queuing control method for vehicle systems under narrow tunnel constraints includes the following steps:

[0009] Step 1: Obtain the state information of a heterogeneous vehicle system consisting of a leader and multiple followers, and establish a dynamic model of the vehicle system with non-affine actuator faults.

[0010] Step 2: Obtain the preset tunnel boundary information and system performance requirements, and transform the vehicle position constraints into a formation error system with preset performance funnel constraints;

[0011] Step 3: Construct a virtual controller by combining the formation error system with preset performance funnel constraints, and perform filtering processing on the virtual controller based on a nonlinear command filter to avoid direct differentiation calculation of the virtual controller;

[0012] Step 4: Based on the Butterworth low-pass filter and the generalized fuzzy neural network, the key unknown functions in the vehicle system dynamics model affected by unknown nonlinear terms and actuator failures are approximated to obtain approximation signals.

[0013] Step 5: Based on the formation error system with preset performance funnel constraints, the filtered output, and the approximation signal, design an actual controller and apply the actual controller to the following vehicle to control the vehicle system to be stable within a preset time and not to collide with the tunnel boundary, and to prevent the formation error from overshooting.

[0014] Furthermore, in step 1, the vehicle system dynamics model is established as follows:

[0015] (1)

[0016] in, and They represent the first The position and speed of the vehicle Indicates mechanical efficiency. Indicates the first The quality of the vehicle Indicates the wheel radius. This indicates the throttle / brake input affected by a non-affine actuator malfunction. The air drag coefficient, It is the acceleration due to gravity. The rolling resistance coefficient, For positive integers, It is an unknown nonlinear function;

[0017] The fault model for the non-affine actuator is as follows:

[0018] (2)

[0019] in, For control inputs affected by non-affine actuator failures, For the control input to be designed, Represents a non-affine nonlinear fault function; Indicates the time when the actuator failure occurred. For at any time Time distribution corresponding to actuator failures;

[0020] The faults include sudden change faults and initial faults.

[0021] Furthermore, in step 2, the specific process of transforming the vehicle's position constraints into a formation error system with preset performance funnel constraints is as follows:

[0022] Based on the upper and lower boundaries of the tunnel defined by the function, positional constraints are constructed as follows:

[0023] (3)

[0024] in It is a bounded, continuously differentiable function. and These represent the upper and lower boundary curves of the tunnel, respectively.

[0025] Using coordinate transformation, the formula is as follows:

[0026] (4)

[0027] Based on the desired relative formation position, the formation error is defined as follows:

[0028] (5)

[0029] in, and For positive integers, Indicates the number of followers. It is a time-varying function representing the desired formation pattern between the leader and followers, and It is a bounded function, that is , For positive integers, The position of the leader is a continuously differentiable function that satisfies... ;

[0030] By employing a funnel control method, a performance funnel set for constraining the formation error is constructed as follows:

[0031] (6)

[0032] in, For the preset time performance funnel set, For time variables, For formation error, For the performance funnel function, the It is a bounded function and , It is a positive constant; in addition, it is defined as... And satisfy , It is a bounded, continuous, and differentiable function. and Let the positive constants represent the functions respectively. The upper and lower bounds;

[0033] When the formation error satisfies the performance funnel set, it ensures that the formation error does not overshoot and that the vehicle does not collide with the boundary of a narrow tunnel.

[0034] Furthermore, in step 3, the virtual control signal in the control process is filtered based on a nonlinear command filter, specifically including:

[0035] (1) Define constraint error and build a virtual controller for:

[0036] (twenty four)

[0037] in, The speed of the lead vehicle is represented by a continuously differentiable function. It is a nonlinear function. For positive integers, Indicates the number of followers. It is a nonlinear function. for The derivative, For parameter estimation, and nonlinear terms for:

[0038]

[0039] in, and For positive integers, For positive integers, The ratio of even integers to odd integers. It is a nonlinear function. and It is a positive number;

[0040] Design parameters The update rate is:

[0041] (25)

[0042] in, Positive design parameters;

[0043] (2) Define the error surface signal and boundary layer error ;

[0044] (3) Construct a nonlinear command filter:

[0045] (9)

[0046] in, This is the virtual control signal for the filter. This is an intermediate control signal. are filter parameters and For positive integers, It is a nonlinear function. It is a nonlinear function. and Assuming it is a positive constant, for The upper realm, for The estimated value, with an estimation error of ;

[0047] The adaptive update law is as follows:

[0048] (10)

[0049] in, Positive design parameters, constants as well as .

[0050] Furthermore, in step 4, an approximator based on a combination of a Butterworth low-pass filter and a generalized fuzzy neural network is constructed, specifically including:

[0051] (1) Define the key unknown function as an intermediate composite function that includes unknown system parameters and fault effects, and realize the online estimation and updating of relevant parameters through an adaptive update law;

[0052] (2) Introduce a Butterworth low-pass filter to filter the input variables of the composite function to obtain the filtered signal;

[0053] (3) A generalized fuzzy neural network is used to approximate the filtered signal. The filtered input signal is used as the network input to approximate the unknown intermediate composite nonlinear function, and the final approximation output is obtained as follows:

[0054] (19)

[0055] in, Let the ideal weight vector be that of the generalized fuzzy neural network. For generalized fuzzy basis functions, To approximate the error and satisfy , It is a positive number.

[0056] Furthermore, in step 5, the specific process of calculating the actual controller and controlling the vehicle system includes:

[0057] (1) Define constraint error Based on the preset time Lyapunov stability condition, and combined with the approximation signal and the filtered output of the nonlinear command filter, an actual controller acting on the vehicle actuator is constructed.

[0058] (2) Input the control signal generated by the actual controller to the actuator of the vehicle system so that all closed-loop signals are bounded within a preset time and control the formation error to always be within the performance funnel and prevent all vehicles from colliding with the tunnel boundary.

[0059] The actual controller design for:

[0060] (30)

[0061] in, It is a nonlinear function and satisfies , and Let the positive constants represent the functions respectively. The upper and lower bounds, Bounded nonlinear functions and , It is a constant. for The derivative, For parameter estimation;

[0062] Furthermore, it satisfies the following at the initial moment. And nonlinear terms for

[0063]

[0064] in, It is a nonlinear function. and It is a positive number;

[0065] Meanwhile, design parameters The update rate is:

[0066] (31)

[0067] in, The design parameters are positive.

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. This invention introduces bounded, continuously differentiable functions to describe the upper and lower boundary curves of a tunnel, and designs corresponding coordinate transformation strategies to convert complex curved boundary constraints into easily manageable formation error forms. Compared to existing methods that can only handle straight boundary conditions, this invention is applicable to narrow tunnel scenarios with arbitrary curved boundaries, significantly improving the engineering applicability of the method and enabling formation control technology to be truly applied to confined passage environments with complex geometries.

[0070] 2. This invention combines a funnel control framework with a preset time control theory. By designing a performance funnel function, it dynamically constrains the formation error, ensuring that the formation error has no overshoot throughout the formation process, while also achieving active control over the error convergence speed. Based on the preset time stability theory, the system can achieve stable convergence within a time preset by the designer, meeting the differentiated convergence time requirements of different application scenarios and solving the problem that existing methods cannot predict the convergence time during the design phase.

[0071] 3. This invention introduces a nonlinear command filter, avoiding the repeated differentiation of the virtual control function required by traditional backstepping control methods. This design significantly reduces the computational complexity of the controller, making the control law expression more concise and easier to implement in real time in automotive embedded systems, effectively improving the real-time performance and engineering feasibility of the control system.

[0072] 4. This invention addresses the technical challenge of algebraic loop problems caused by non-affine actuator failures by combining a generalized fuzzy neural network with Butterworth low-pass filtering technology. By introducing a low-pass filter to smooth the control input and using the filtered signal to replace the original control input in the neural network approximation, the algebraic loop problem is effectively solved. This design enables the system to maintain stable formation under both sudden and initial fault conditions, significantly enhancing the system's fault tolerance to non-affine actuator failures and improving the safety of formation control.

[0073] 5. Through the comprehensive application of the technical means of this invention, stable formation of heterogeneous vehicle systems (vehicles with different masses and different dynamic characteristics) can be achieved within a preset time, while ensuring that: the vehicle positions always meet the constraints of curved narrow tunnels to avoid collision risks; the formation error is always kept within the preset performance funnel boundary, and there is no overshoot during the convergence process; the system has fault tolerance capability for non-affine actuator failures and can maintain formation stability when a failure occurs. Attached Figure Description

[0074] To more intuitively illustrate the technical solutions adopted in the embodiments of the present invention, the relevant drawings are briefly described below. It should be understood that these drawings are only used to illustrate some specific embodiments of the present invention and do not constitute a limitation on the scope of the present invention. Those skilled in the art can also obtain other forms of drawings or improved solutions based on these drawings without any creative effort.

[0075] Figure 1 This is a flowchart of the present invention;

[0076] Figure 2 This is a schematic diagram of the control principle of the present invention;

[0077] Figure 3 This is a schematic diagram of the communication topology in this invention;

[0078] Figure 4 This is a schematic diagram showing the positions of the following vehicle and the leading vehicle in this invention;

[0079] Figure 5 This is a schematic diagram illustrating the speeds of the following vehicle and the lead vehicle in this invention;

[0080] Figure 6 This is a schematic diagram of formation error in this invention;

[0081] Figure 7 This is a schematic diagram of the control input in this invention. Detailed Implementation

[0082] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. It should be understood that the described embodiments are only some examples of the present invention, and not all of them. Those skilled in the art can conceive of various different structures or design schemes based on these drawings and descriptions without creative effort, and these should all fall within the protection scope of the present invention. The components in the embodiments shown in the drawings can be arranged and designed in various ways. Therefore, the detailed description below does not constitute a limitation on the protection scope of the present invention, but rather a description of some preferred embodiments of the present invention.

[0083] It should be noted that in the following figures, the same or similar reference numerals and letters are used to indicate parts that are similar in function or structure. Therefore, once an element has been defined or described in one figure, it usually does not need to be described again in subsequent figures.

[0084] This invention addresses the problems of complex control design and limited applicability of existing vehicle formation control methods in narrow tunnel constraints. It proposes a pre-time formation control scheme based on a performance funnel and a nonlinear command filter. First, by combining the funnel control method with appropriate coordinate transformation, the complex controller design of existing obstacle-based Lyapunov function methods is avoided. Furthermore, it overcomes the limitation of existing methods that can only guarantee vehicle position constraints within a simple region enclosed by two straight lines, making the proposed method applicable to general curved narrow tunnel constraints. Second, a nonlinear command filter is introduced into the controller design to improve the system's dynamic response characteristics, further enhancing the control system's rapid response capability. Finally, while ensuring stable convergence of the controlled system within a pre-set time, the scheme achieves zero overshoot in formation error and satisfies the applied narrow tunnel constraints.

[0085] like Figure 1-2 As shown, a preset time formation control method for a vehicle system under the constraint of a narrow tunnel includes the following steps:

[0086] Step 101: Set vehicle position constraints and formation error performance constraints;

[0087] Step 102: Integrate nonlinear command filtering techniques with parameter estimation mechanisms;

[0088] Step 103: Combining generalized fuzzy neural networks and Butterworth low-pass filtering technology, design a preset time-tolerant formation control strategy;

[0089] Step 104: Ensure that the heterogeneous vehicle system stabilizes and maintains a safe formation structure within a preset time.

[0090] Furthermore, in step 101, consider a leader and A heterogeneous vehicle system composed of followers. vehicle ( The dynamics of ) are described by the following nonlinear model:

[0091] (1)

[0092] in, and They represent the first The position and speed of the vehicle Indicates mechanical efficiency. Indicates the first The quality of the vehicle Indicates the wheel radius. This indicates the throttle / brake input affected by a non-affine actuator malfunction. The air drag coefficient, It is the acceleration due to gravity. The rolling resistance coefficient, For positive integers, It is an unknown nonlinear function.

[0093] Control inputs affected by non-affine actuator failure for:

[0094] (2)

[0095] in, For the control input to be designed, This represents a non-affine nonlinear fault function. Indicates the time when the actuator failure occurred. For at any time The time distribution corresponding to actuator failures. In this invention, both sudden change failures and initial failures are considered.

[0096] (1) Sudden change fault

[0097]

[0098] (2) Initial Fault

[0099]

[0100] in This indicates the rate of evolution of an unknown fault.

[0101] Assumption 1: There exists an unknown nonlinear function. This makes the following inequality true, namely .

[0102] The following lemma is given to lay the theoretical foundation for subsequent controller design and stability analysis;

[0103] Lemma 1: An unknown continuous function can be approximated by the following generalized fuzzy neural network.

[0104]

[0105] in, Let the ideal weight vector be that of the generalized fuzzy neural network. For generalized fuzzy basis functions, This is the approximation error.

[0106] Lemma 2: Consider the system If it exists And satisfy ,in This indicates that the controlled system is stable within the actual preset time, and the convergence time is... The region of convergence is .

[0107] Lemma 3: For any definition in Interval variables The following inequalities hold.

[0108] in, , As a constant, and .

[0109] Lemma 4: For any variable as well as The following inequalities hold.

[0110]

[0111] in and Is with Relevant constants.

[0112] Lemma 5: If the variable and constants Then the following inequality holds.

[0113]

[0114] Furthermore, in step 101, the preset time-based formation control method for the vehicle system, which addresses the position and performance constraints of the vehicle system, is as follows:

[0115] 1) Position constraints

[0116] To ensure that the vehicle does not collide with the tunnel boundary during operation, the following constraints are defined:

[0117] (3)

[0118] in, It is a bounded, continuously differentiable function. and These represent the upper and lower boundary curves of the tunnel, respectively. This constraint prevents vehicles from colliding with the tunnel boundaries during movement.

[0119] Define the following coordinate transformation

[0120] (4)

[0121] The formation error regarding vehicle positions is defined as follows:

[0122] (5)

[0123] in, and For positive integers, Indicates the number of followers. It is a time-varying function representing the desired formation pattern between the leader and followers, and It is a bounded function, that is , For positive integers, The position of the leader is a continuously differentiable function that satisfies... .

[0124] 2) Performance Constraints

[0125] In addition to position constraints (3), formation errors can be further constrained by adopting a funnel control framework. The transient and steady-state behavior of the function. Consider a bounded, continuous, differentiable function.

[0126] ,in and Let the positive constants represent the functions respectively. The upper and lower bounds. Furthermore, define... , It is a bounded function and , It is a positive integer. If it satisfies Then formation error Always stay within the performance funnel, where the performance funnel set is

[0127] (6)

[0128] Furthermore, in step 102, the following coordinate transformation is defined.

[0129] (7)

[0130] (8)

[0131] in, For error surface, This is the virtual control signal for the filter. For boundary layer error, This is an intermediate control signal, and its detailed structure will be given later.

[0132] To ensure the stability of the controlled system within a preset time and to avoid computational complexity issues in the control design process, the following nonlinear command filter is introduced:

[0133] (9)

[0134] in, are filter parameters and , It is a nonlinear function. and as well as For positive integers, The ratio of even integers to odd integers. It is a nonlinear function. and Assuming it is a positive constant, for The upper realm, for The estimated value, with an estimation error of .

[0135] design The adaptive update law is:

[0136] (10)

[0137] in, Positive design parameters and It is a positive number.

[0138] Furthermore, in step 103, the definition is...

[0139] (11)

[0140] Among them, at the initial time, it satisfies .

[0141] Regarding (4) time Taking the derivative, we get:

[0142] (12)

[0143] Using (12) and (5) regarding time Taking the derivative, we get:

[0144] (13)

[0145] in, For an unknown nonlinear function, , It is a nonlinear function. It is a positive number.

[0146] Furthermore, based on (13) and on (11) regarding time Taking the derivative, we get:

[0147] (14)

[0148] in, It is an unknown nonlinear function.

[0149] According to Lemma 1, the unknown nonlinear function It can be identified by the following generalized fuzzy neural network, then we have

[0150] (15)

[0151] in, Let the ideal weight vector be that of the generalized fuzzy neural network. For generalized fuzzy basis functions, To approximate the error and satisfy , It is a positive number.

[0152] Further, define

[0153] (16)

[0154] Among them, the function And satisfy , and Let the positive constants represent the functions respectively. upper and lower bounds of the function It is bounded and , It is a constant. Furthermore, it satisfies the following at the initial time: .

[0155] Regarding (7) time Taking the derivative, we get:

[0156] (17)

[0157] Using (17), and regarding (16) time Taking the derivative, we get:

[0158] (18)

[0159] in, For unknown nonlinear functions and .

[0160] For unknown nonlinear functions ,because Dependent on controller If a generalized fuzzy neural network is used directly Approximating it leads to algebraic loop problems. To solve this problem, we introduce... ,in It is a Butterworth low-pass filter. Furthermore, the nonlinear function can be approximated using the following generalized fuzzy neural network. :

[0161] (19)

[0162] in, Let the ideal weight vector be that of the generalized fuzzy neural network. For generalized fuzzy basis functions, To approximate the error and satisfy , It is a positive number.

[0163] Step 1: Select the following Lyapunov functions:

[0164] (20)

[0165] in, To estimate the error, For parameters The estimated value.

[0166] Regarding (20) time Taking the derivative and from (14), we can obtain

[0167] (twenty one)

[0168] Regarding (21), we can further obtain using Young's inequality.

[0169] (twenty two)

[0170] (twenty three)

[0171] Where parameters , It is a nonlinear function.

[0172] Design a virtual controller for:

[0173] (twenty four)

[0174] in, The speed of the leading vehicle is a continuously differentiable function, and

[0175]

[0176] Design parameters The update rate is:

[0177] (25)

[0178] in, The design parameters are positive.

[0179] Combining (21)-(25) we can further obtain

[0180] (26)

[0181] Step 2: Select the following Lyapunov functions:

[0182] (27)

[0183] in, To estimate the error, For parameters The estimated value.

[0184] Regarding (27) time Taking the derivative, we get:

[0185] (28)

[0186] Regarding (28), we can further obtain using Lemma 3:

[0187] (29)

[0188] Among them, parameters , It is a nonlinear function.

[0189] Design a practical controller for:

[0190] (30)

[0191] in,

[0192]

[0193] Design parameters The update rate is:

[0194] (31)

[0195] in, Positive design parameters;

[0196] Combining (28)-(31), we can further obtain:

[0197] (32)

[0198] Furthermore, based on Lemma 4 and Lemma 5, we know that:

[0199] (33)

[0200] in, , Is with Relevant positive numbers.

[0201] Furthermore, the following general Lyapunov functions are selected.

[0202] (34)

[0203] definition And based on (32)-(34), we can further obtain

[0204] (35)

[0205] in, ,and

[0206] .

[0207] Based on Lemma 3, we can further obtain... This means that as time approaches infinity, .

[0208] Then, based on (35) and Lemma 2, it can be seen that the controlled system is actually time-stable, and the region of convergence is... The convergence time satisfies Furthermore, it is possible to obtain... as well as This indicates that the formation error satisfies the constraints imposed by the performance funnel. Furthermore, based on... It can be known ,in , for The minimum singular value. Furthermore, based on (4), we can further obtain... This indicates that the vehicle will not collide with the tunnel boundary during its movement.

[0209] Furthermore, in step 104, simulation verification is performed using Matlab software. Consider a heterogeneous vehicle system consisting of four followers and one leader. The parameters of the nonlinear vehicle system model are as follows.

[0210] , , ,

[0211] , , , ;

[0212] , , , ;

[0213] , , ;

[0214] The fault function is selected as follows:

[0215]

[0216] The fault model is:

[0217] (1) Sudden change fault

[0218]

[0219] (2) Initial Fault

[0220]

[0221] The initial conditions are selected as follows , , , , , , , , , .

[0222] The design parameters and smoothing function are selected as follows: , , , , ;

[0223] The function for narrow tunnels is selected as follows: ;

[0224] The funnel boundary function is set as follows: , .

[0225] Simulation results are as follows Figure 3-7 As shown, Figure 3 The communication topology between vehicles is shown. Figure 4 The map shows the position trajectories of the following and lead vehicles. Figure 5 Speed ​​trajectory diagrams of the following and lead vehicles were plotted. Figure 6 The formation error trajectory diagram is shown. Figure 7 The control input trajectory diagram of the vehicle is presented. The comprehensive simulation results show that the method proposed in this invention can ensure that the position and speed of the following vehicle effectively follow the position and speed of the leader vehicle within a preset time, that the vehicle does not collide with the narrow tunnel boundary during operation, and that the formation error remains within the preset performance funnel boundary. This further verifies the comprehensive performance of the designed control strategy in ensuring system convergence speed, formation accuracy, and safety.

[0226] Compared with existing linear, finite-time, or fixed-time command filter technologies, this invention, by designing a nonlinear preset-time command filter, not only avoids the problem of repeated differentiation of the virtual control function in backstep control and reduces the strong dependence of existing filters on design parameters, but also allows designers to pre-set the convergence time of relevant errors during the controller design stage, thereby significantly improving the designability, implementation efficiency, and dynamic performance of the control system.

[0227] Unlike existing collision avoidance formation control strategies based on obstacle function control methods, this invention proposes a preset-time formation control strategy based on a performance funnel. By introducing funnel technology and appropriate coordinate transformations, this invention reduces the complexity of control design while ensuring that vehicles do not collide with typical curved, narrow tunnels during operation. Furthermore, throughout the entire motion, the formation error remains within the performance funnel boundary, achieving dual constraints on transient and convergence performance. This significantly improves the overall control performance, reliability, and engineering feasibility of the system.

[0228] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0229] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A pre-set time platoon control method for a vehicle system under tight tunnel constraints, characterized by, Includes the following steps: Step 1: Obtain the state information of a heterogeneous vehicle system consisting of a leader and multiple followers, and establish a dynamic model of the vehicle system with non-affine actuator faults. Step 2: Obtain the preset tunnel boundary information and system performance requirements, and transform the vehicle position constraints into a formation error system with preset performance funnel constraints; Step 3: Construct a virtual controller by combining the formation error system with preset performance funnel constraints, and perform filtering processing on the virtual controller based on a nonlinear command filter to avoid direct differentiation calculation of the virtual controller; Step 4: Based on the Butterworth low-pass filter and the generalized fuzzy neural network, the key unknown functions in the vehicle system dynamics model affected by unknown nonlinear terms and actuator failures are approximated to obtain approximation signals. Step 5: Based on the formation error system with preset performance funnel constraints, the filtered output, and the approximation signal, design an actual controller and apply the actual controller to the following vehicle to control the vehicle system to be stable within a preset time and not to collide with the tunnel boundary, and to prevent the formation error from overshooting.

2. The preset time formation control method for a vehicle system under narrow tunnel constraints according to claim 1, characterized in that, In step 1, the vehicle system dynamics model is established as follows: (1) in, and They represent the first The position and speed of the vehicle Indicates mechanical efficiency. Indicates the first The quality of the vehicle Indicates the wheel radius. This indicates the throttle / brake input affected by a non-affine actuator malfunction. The air drag coefficient, It is the acceleration due to gravity. The rolling resistance coefficient, For positive integers, It is an unknown nonlinear function; The fault model for the non-affine actuator is as follows: (2) in, For control inputs affected by non-affine actuator failures, For the control input to be designed, Represents a non-affine nonlinear fault function; Indicates the time when the actuator failure occurred. For at any time Time distribution corresponding to actuator failures; The faults include sudden change faults and initial faults.

3. The preset time formation control method for a vehicle system under narrow tunnel constraints according to claim 1, characterized in that, In step 2, the specific process of transforming the vehicle's position constraints into a formation error system with preset performance funnel constraints is as follows: Based on the upper and lower boundaries of the tunnel defined by the function, positional constraints are constructed as follows: (3) in It is a bounded, continuously differentiable function. and These represent the upper and lower boundary curves of the tunnel, respectively. Using coordinate transformation, the formula is as follows: (4) Based on the desired relative formation position, the formation error is defined as follows: (5) in, and For positive integers, Indicates the number of followers. It is a time-varying function representing the desired formation pattern between the leader and followers, and It is a bounded function, that is , For positive integers, The position of the leader is a continuously differentiable function that satisfies... ; By employing a funnel control method, a performance funnel set for constraining the formation error is constructed as follows: (6) in, For the preset time performance funnel set, For time variables, For formation error, For the performance funnel function, the It is a bounded function and , It is a positive constant; in addition, it is defined as... And satisfy , It is a bounded, continuous, and differentiable function. and Let the positive constants represent the functions respectively. The upper and lower bounds; When the formation error satisfies the performance funnel set, it ensures that the formation error does not overshoot and that the vehicle does not collide with the boundary of a narrow tunnel.

4. The preset time formation control method for a vehicle system under narrow tunnel constraints according to claim 1, characterized in that, In step 3, the virtual control signal in the control process is filtered based on a nonlinear command filter, specifically including: (1) Define constraint error and build a virtual controller for: (24) in, The speed of the lead vehicle is represented by a continuously differentiable function. It is a nonlinear function. For positive integers, Indicates the number of followers. It is a nonlinear function. for The derivative, For parameter estimation, and nonlinear terms for: in, and For positive integers, For positive integers, The ratio of even integers to odd integers. It is a nonlinear function. and It is a positive number; Design parameters The update rate is: (25) in, Positive design parameters; (2) Define the error surface signal and boundary layer error ; (3) Construct a nonlinear command filter: (9) in, This is the virtual control signal for the filter. This is an intermediate control signal. are filter parameters and For positive integers, It is a nonlinear function. It is a nonlinear function. and Assuming it is a positive constant, for The upper realm, for The estimated value, with an estimation error of ; The adaptive update law is as follows: (10) in, Positive design parameters, constants as well as .

5. The preset time formation control method for a vehicle system under narrow tunnel constraints according to claim 1, characterized in that, In step 4, an approximator based on a combination of a Butterworth low-pass filter and a generalized fuzzy neural network is constructed, specifically including: (1) Define the key unknown function as an intermediate composite function that includes unknown system parameters and fault effects, and realize the online estimation and updating of relevant parameters through an adaptive update law; (2) Introduce a Butterworth low-pass filter to filter the input variables of the composite function to obtain the filtered signal; (3) A generalized fuzzy neural network is used to approximate the filtered signal. The filtered input signal is used as the network input to approximate the unknown intermediate composite nonlinear function, and the final approximation output is obtained as follows: (19) in, Let the ideal weight vector be that of the generalized fuzzy neural network. For generalized fuzzy basis functions, To approximate the error and satisfy , It is a positive number.

6. The preset time formation control method for a vehicle system under narrow tunnel constraints according to claim 1, characterized in that, Step 5, the specific process of calculating the actual controller and controlling the vehicle system includes: (1) Define constraint error Based on the preset time Lyapunov stability condition, and combined with the approximation signal and the filtered output of the nonlinear command filter, an actual controller acting on the vehicle actuator is constructed. (2) Input the control signal generated by the actual controller to the actuator of the vehicle system so that all closed-loop signals are bounded within a preset time and control the formation error to always be within the performance funnel and prevent all vehicles from colliding with the tunnel boundary. The actual controller design for: (30) in, It is a nonlinear function and satisfies , and Let the positive constants represent the functions respectively. The upper and lower bounds, Bounded nonlinear functions and , It is a constant. for The derivative, For parameter estimation; Furthermore, it satisfies the following at the initial moment. And nonlinear terms for in, It is a nonlinear function. and It is a positive number; Meanwhile, design parameters The update rate is: (31) in, The design parameters are positive.

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