Distributed fault-tolerant cooperative control strategy based on nonlinear observer

Abstract

The invention discloses a distributed fault-tolerant cooperative control strategy based on a nonlinear observer, belongs to the field of intelligent control, and mainly aims at estimating and compensating actuator faults, unknown nonlinearity and external disturbance in a queue in queue control. And the whole queue is ensured to keep the consistency, stability and passing efficiency of advancing under the condition of a non-zero initial spacing error. The method comprises the following steps: establishing a queue system model with actuator faults, unknown nonlinearity and external disturbance; establishing a queue tracking error; establishing a queue sliding mode error; establishing a queue coupling sliding mode error; establishing a nonlinear observer for external disturbance of the queue; establishing a neural network estimation mechanism for unknown nonlinearity in the queue; establishing a self-adaptive parameter estimation mechanism aiming at the queue actuator fault; and establishing a distributed fault-tolerant queue control strategy based on a nonlinear observer, a neural network estimation mechanism and an adaptive parameter estimation mechanism. The method is used for queue cooperative intelligent control.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control and mainly relates to a distributed fault-tolerant collaborative control strategy of a nonlinear observer. Background Art

[0002] With the rapid growth of vehicle ownership, my country's road traffic system is facing increasingly serious problems, including traffic congestion, traffic accidents, environmental pollution, and energy crises. The development of intelligent transportation systems (ITS) offers a potential solution to these issues. Intelligent platoon cooperative control, as a key component in building ITS, has become a research focus. Platoon cooperative control aims to ensure that platoons maintain a desired spacing and travel at a consistent and stable speed. This not only significantly improves traffic flow and enhances safety, but also effectively reduces energy consumption. However, current research on platoon cooperative control has limitations. Existing research often assumes a constant actuator failure coefficient when analyzing actuator failures. However, during actual vehicle operation, actuators are affected by factors such as wear, aging, overcurrent, and overvoltage, resulting in a time-varying failure coefficient that is difficult to accurately predict. Furthermore, existing platoon disturbance observation techniques often require the prior knowledge of the boundary values of disturbance parameters. However, in complex real-world environments, these critical parameters are often difficult to obtain directly or even impossible to accurately measure. This lack of prior knowledge directly limits the practical application of existing technologies. Based on the above analysis, in order to adapt to the higher standards and requirements of the iterative development of intelligent systems for queue collaborative control technology, the existing queue collaborative control methods need to be further innovated and improved. Summary of the Invention

[0003] The present invention aims to address the difficulties in accurately predicting the time-varying actuator failure coefficients in platoon cooperative control, as well as the difficulty or even inability to directly obtain the key information required for platoon disturbance observation in real-world scenarios, which limits the practical application of existing methods. This invention proposes a distributed fault-tolerant cooperative control strategy based on a nonlinear observer.

[0004] A distributed fault-tolerant cooperative control strategy based on a nonlinear observer is characterized by being able to estimate and compensate for actuator failures, unknown nonlinearities, and external disturbances in the queue, while ensuring that the entire queue maintains consistency, stability, and efficiency under the condition of non-zero initial spacing errors. The control strategy includes the following steps:

[0005] Step 1: Build a queue system model with actuator failures, unknown nonlinearities, and external disturbances:

[0006] Queue system model

[0007] (1)

[0008] in, is the serial number, For time, and Representing the The position and speed of the vehicle, Indicates the The acceleration of the car, Represents the vehicle mass, Indicates the transmission efficiency of the engine, represents the tire radius, represents the actuator failure coefficient, is the control input, represents unknown nonlinearity, represents the engine time constant, ,in, and , represents the symbolic function, represents a natural constant, and The parameters are The upper and lower bounds of , is the lumped disturbance term, is the approximation error, is the actuator bias fault, , represents external disturbances.

[0009] Step 2: Establish queue tracking error:

[0010] Queue tracking error

[0011] (2)

[0012] in, represents the queue tracking error, and is the internal state variable, Indicates the distance between adjacent vehicles. Indicates the vehicle length, ,in, represents the static distance between adjacent vehicles, represents the safety factor, is the speed of the pilot car, is the absolute value of the vehicle’s maximum possible deceleration, is a parameter The lower bound, , , They are all normal numbers. , The natural constant The exponential function with base , for The value at the initial moment, and They are First and second differentials at the initial time.

[0013] Step 3: Establish queue sliding mode error:

[0014] Queue sliding mode error

[0015] (3)

[0016] in, , , is a positive constant, is the sliding mode error, is the integration variable, ,in, , Expressed as

[0017] (4)

[0018] in, Indicates the pre-parking distance. represents the maximum speed of the vehicle, Maintaining the queue The maximum permissible distance to ensure stability during driving. , is the cosine function.

[0019] Step 4: Establish queue coupling sliding mode error:

[0020] Queue coupling sliding mode error

[0021] (5)

[0022] in, is the queue coupling sliding mode error, is the number of vehicles, .

[0023] Step 5: Establish a nonlinear observer for external disturbances of the queue:

[0024] Nonlinear observer

[0025] (6)

[0026] in, represents the observer gain, For the The acceleration of the car, represents the acceleration of the pilot car, , for The estimated value of is the internal state variable, represents the error compensator, which is expressed as

[0027] (7)

[0028] in, It represents the error between the true value of the disturbance and the estimated value.

[0029] Step 6: Establish a neural network estimation mechanism for unknown nonlinearities in the queue:

[0030] Neural network estimation mechanism

[0031] (8)

[0032] in, , , , , and are all constants greater than zero. is the hyperbolic tangent function, and They are and The estimated value of , is the ideal weight of the neural network, is the neural network approximation error, is the activation function.

[0033] Step 7: Establish an adaptive parameter estimation mechanism for queue executor failures:

[0034] Adaptive parameter estimation mechanism

[0035] (9)

[0036] in, , , , express estimated value.

[0037] Step 8. Establish a distributed fault-tolerant queue control strategy based on nonlinear observer, neural network estimation mechanism and adaptive parameter estimation mechanism:

[0038] Distributed fault-tolerant queue control law

[0039] in, , , is a positive constant, Expressed as

[0040]

[0041] for The second-order differential of .

[0042] The present invention effectively addresses the practical limitations of existing methods, such as the difficulty in accurately predicting the time-varying actuator failure coefficient in platoon cooperative control, and the difficulty or even inability to directly obtain the key information required for platoon disturbance observation in real-world scenarios. This approach estimates and compensates for actuator failures, unknown nonlinearities, and external disturbances within the platoon. This approach ensures consistent movement across the entire platoon under conditions of non-zero initial spacing errors, improving traffic flow, enhancing platoon safety, and reducing energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 Schematic diagram of the control method described in the first embodiment DETAILED DESCRIPTION

[0044] Specific implementation method 1: Combination Figure 1 This embodiment describes a distributed fault-tolerant cooperative control strategy based on a nonlinear observer, and the control strategy includes the following steps:

[0045] Step 1: Build a queue system model with actuator failures, unknown nonlinearities, and external disturbances:

[0046] Queue system model

[0047] (1)

[0048] in, is the serial number, For time, and Representing the The position and speed of the vehicle, It is The acceleration of the car, Represents the vehicle mass, Indicates the transmission efficiency of the engine, represents the tire radius, represents the actuator failure coefficient, is the control input, represents unknown nonlinearity, represents the engine time constant, ,in, and , represents the symbolic function, represents a natural constant, and The parameters are The upper and lower bounds of , is the lumped disturbance term, is the approximation error, is the actuator bias fault, , represents external disturbances.

[0049] Step 2: Establish queue tracking error:

[0050] Queue tracking error

[0051] (2)

[0052] in, represents the queue tracking error, and is the internal state variable, represents the distance between adjacent vehicles, Indicates the vehicle length, ,in, represents the static distance between adjacent vehicles, represents the safety factor, is the speed of the pilot car, is the absolute value of the vehicle's maximum possible deceleration, is a parameter The lower bound, , , They are all normal numbers. , The natural constant The exponential function with base , for The value at the initial moment, and They are First and second differentials at the initial time.

[0053] Step 3: Establish queue sliding mode error:

[0054] Queue sliding mode error

[0055] (3)

[0056] in, , , is a positive constant, is the sliding mode error, is the integration variable, ,in, , Expressed as

[0057] (4)

[0058] in, Indicates the pre-parking distance. represents the maximum speed of the vehicle, To maintain the vehicle queue The maximum permissible distance to ensure stability during driving. , is the cosine function.

[0059] Step 4: Establish queue coupling sliding mode error:

[0060] Queue coupling sliding mode error

[0061] (5)

[0062] in, is the queue coupling sliding mode error, is the number of vehicles, .

[0063] Step 5: Establish a nonlinear observer for external disturbances of the queue:

[0064] Nonlinear observer

[0065] (6)

[0066] in, represents the observer gain, For the The acceleration of the car, represents the acceleration of the pilot car, , for The estimated value of is the internal state variable, represents the error compensator, which is expressed as

[0067] (7)

[0068] in, It represents the error between the true value of the disturbance and the estimated value.

[0069] Step 6: Establish a neural network estimation mechanism for unknown nonlinearities in the queue:

[0070] Neural network estimation mechanism

[0071] (8)

[0072] in, , , , , and are all constants greater than zero. is the hyperbolic tangent function, and They are and The estimated value of , is the ideal weight of the neural network, is the neural network approximation error, is the activation function.

[0073] Step 7: Establish an adaptive parameter estimation mechanism for queue executor failures:

[0074] Adaptive parameter estimation mechanism

[0075] (9)

[0076] in, , , , express estimated value.

[0077] Step 8. Establish a distributed fault-tolerant queue control strategy based on nonlinear observer, neural network estimation mechanism and adaptive parameter estimation mechanism:

[0078] Distributed fault-tolerant queue control law

[0079] in, , , is a positive constant, Expressed as

[0080]

[0081] for The second-order differential of .

[0082] Effects of this implementation:

[0083] The proposed control strategy effectively addresses the practical limitations of existing methods, such as the difficulty in accurately predicting the time-varying actuator failure coefficient in platoon control, and the difficulty or even inability to directly obtain the key information required for platoon disturbance observation in real-world scenarios. It also estimates and compensates for actuator failures, unknown nonlinearities, and external disturbances within the platoon. This approach ensures consistent movement across the entire platoon even with non-zero initial spacing errors, improving traffic flow, enhancing platoon safety, and reducing energy consumption.

Claims

1. A distributed fault-tolerant cooperative control strategy based on nonlinear observer, characterized by: The control strategy is capable of estimating and compensating for actuator failures, unknown nonlinearities, and external disturbances in platoon control, and ensuring that the entire platoon maintains consistency, stability, and efficiency under the condition of non-zero initial spacing error. The control strategy includes the following steps: Step 1: Establish a queue system model with actuator failure, unknown nonlinearity and external disturbance; Step 2: Establish queue tracking error; Step 3: Establish queue sliding mode error; Step 4: Establish queue coupling sliding mode error; Step 5: Establish a nonlinear observer for external disturbances of the queue; Step 6: Establish a neural network estimation mechanism for unknown nonlinearities in the queue; Step 7: Establish an adaptive parameter estimation mechanism for queue executor failures; Step 8. Establish a distributed fault-tolerant queue control strategy based on nonlinear observer, neural network estimation mechanism and adaptive parameter estimation mechanism.

2. A distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step 1, Modeling a queuing system with actuator failures, unknown nonlinearities, and external disturbances (1) in, is the serial number, For time, and Representing the The position and speed of the vehicle, It is The acceleration of the car, Represents the vehicle mass, Indicates the transmission efficiency of the engine, represents the tire radius, represents the actuator failure coefficient, is the control input, represents unknown nonlinearity, represents the engine time constant, ,in, and , represents the symbolic function, represents a natural constant, and The parameters are The upper and lower bounds of , is the lumped disturbance term, is the approximation error, is the actuator bias fault, , represents external disturbances.

3. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step 2, Queue tracking error (2) in, represents the queue tracking error, and is the internal state variable, represents the distance between adjacent vehicles, Indicates the vehicle length, ,in, represents the static distance between adjacent vehicles, represents the safety factor, is the speed of the pilot car, is the absolute value of the vehicle's maximum possible deceleration, is a parameter The lower bound, , , They are all normal numbers. , The natural constant The exponential function with base , for The value at the initial moment, and They are First and second differentials at the initial time.

4. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step three, Queue sliding mode error (3) in, , , is a positive constant, is the sliding mode error, is the integration variable, , , Expressed as (4) in, Indicates the pre-parking distance. represents the maximum speed of the vehicle, To maintain the vehicle queue The maximum permissible distance to ensure stability during driving. , is the cosine function.

5. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step 4, Queue coupling sliding mode error (5) in, is the queue coupling sliding mode error, is the number of vehicles, .

6. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step five, Nonlinear observer (6) in, represents the observer gain, For the The acceleration of the car, represents the acceleration of the pilot car, , for The estimated value of is the internal state variable, represents the error compensator, which is expressed as (7) in, It represents the error between the true value of the disturbance and the estimated value.

7. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step six, Neural network estimation mechanism (8) in, , , , , and are all constants greater than zero. is the hyperbolic tangent function, and They are and The estimated value of , is the ideal weight of the neural network, is the neural network approximation error, is the activation function.

8. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step seven, Adaptive parameter estimation mechanism (9) in, , , , express estimated value.

9. The distributed fault-tolerant cooperative control strategy based on nonlinear observer according to claim 1, characterized in that: In the step eight, Distributed fault-tolerant queue control law in, , , is a positive constant, Expressed as for The second-order differential of .

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