Distributed adaptive fault-tolerant mixed-order cooperative queue control strategy

Abstract

The invention discloses a distributed self-adaptive fault-tolerant mixed-order cooperative queue control strategy, belongs to the field of intelligent control, and mainly relates to a distributed self-adaptive fault-tolerant mixed-order cooperative queue control strategy for a mixed-order cooperative queue control system under the conditions of actuator fault and saturation, uncertainty and external disturbance. The stability of the whole queue is ensured through a self-adaptive fault-tolerant control strategy based on a self-adaptive disturbance observer and neural network control, and the control performance and robustness of the queue are improved. The method comprises the following steps: establishing a mixed order queue system model with actuator fault and saturation, uncertainty and external disturbance; establishing a queue cooperative tracking error; establishing an adaptive disturbance observer; establishing a distributed mixed-order sliding mode surface; establishing a neural network adaptive mechanism; and establishing a distributed adaptive fault-tolerant mixed-order cooperative queue control strategy. The method is used for cooperative queue intelligent control.

Description

[0001] A distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy, belonging to the field of intelligent control, mainly addresses the stability of a hybrid-order cooperative queue control system under conditions of actuator failure and saturation, uncertainty, and external disturbances. This is achieved through an adaptive fault-tolerant control strategy based on an adaptive disturbance observer and neural network control, thereby improving the queue's control performance and robustness. The invention includes: establishing a hybrid-order queue system model with actuator failure and saturation, uncertainty, and external disturbances; establishing a queue cooperative tracking error; establishing an adaptive disturbance observer; establishing a distributed hybrid-order sliding surface; establishing a neural network adaptive mechanism; and establishing a distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy. This invention is used for intelligent control of cooperative queues. Technical Field

[0002] This invention belongs to the field of intelligent control and mainly relates to a distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy. Background Technology

[0003] With the increasing intelligence of complex industrial systems, system operating efficiency, energy consumption control, and operational safety have become key factors restricting overall performance. Cooperative control, as a core technology for multi-agent systems, effectively improves task execution consistency, reduces total system energy consumption, and enhances operational reliability, attracting widespread attention. However, existing cooperative control methods still suffer from insufficient control accuracy, poor robustness, and weak adaptability when facing uncertainties, actuator failures, and external disturbances in real-world industrial environments. Furthermore, existing research often bases controller design on homogeneous dynamic models, while agents in real-world systems are often heterogeneous, exhibiting hybrid-order structures (such as simultaneously containing second- and third-order dynamics), and the state variables of different agents may differ in type and quantity. The control problem of such hybrid-order cooperative control systems has not been fully studied, and existing control strategies under homogeneous assumptions are difficult to apply directly. On the other hand, most existing fault-tolerant control methods model actuator faults as fixed constants, neglecting the time-varying efficiency decay and nonlinear fault characteristics caused by wear, aging, overheating, and other factors during actual operation. This limits the reliability of the controller in long-term operation. Furthermore, internal parameter uncertainties, unmodeled dynamics, and external time-varying disturbances further increase the design difficulty of the cooperative controller. Traditional linear observers and fixed-parameter adaptive mechanisms struggle to accurately estimate and compensate for complex disturbances and faults. Existing methods still lack systematic control architecture design and stability assurance mechanisms, resulting in cooperative control performance failing to meet expectations in practical applications. In summary, to address the heterogeneity, nonlinear time-varying actuator faults, and external disturbances in modern industrial systems, existing cooperative control methods need further improvement and refinement. A robust and high-precision adaptive fault-tolerant control method suitable for mixed-order system structures needs to be developed to ensure stable operation of the cooperative control system in complex environments and broaden its application scenarios. This invention addresses this technical need. Summary of the Invention

[0004] The purpose of this invention is to address the problems existing in current research on cooperative queue control based on a single-order dynamic model. Specifically, practical cooperative queue control systems often exhibit heterogeneity, with their dynamic characteristics manifesting as a mixed-order structure (e.g., simultaneously containing second- and third-order dynamics), thus limiting their applicability and effectiveness. Furthermore, most existing fault-tolerant control methods model actuator faults as fixed constants, neglecting the time-varying efficiency decay and nonlinear fault characteristics of actuators due to wear, aging, overheating, and other factors during actual operation, thereby limiting the reliability of the controller in long-term operation. This invention provides a distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy.

[0005] A distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy is proposed. Its characteristic is that the dynamic model incorporates nonlinear, time-varying, and unknown actuator faults, and simultaneously includes second-order and third-order dynamics. By developing an adaptive fault-tolerant control method suitable for hybrid-order queue structures, possessing strong robustness and high precision, the stable operation of the cooperative queue control system in complex environments is ensured, expanding its application scenarios. The control strategy includes the following steps:

[0006] Step 1: Establish a mixed-order queue system model with actuator failures and saturation, uncertainties, and external disturbances:

[0007] Second-order queue system model

[0008]

[0009] Three-order queue system model

[0010]

[0011] Where i is the sequence number, x p，i (t), x v，i (t) and x a,i (t) represents the position, velocity, and acceleration of the i-th agent, respectively, and t represents time. It is the uncertainty that the system is subject to, u i (t) is the control input, 0 < θ i ≤1 represents the degree of actuator saturation, ρ i (t) is the actuator efficiency factor. Let M1 represent the set of external disturbances, M2 represent the set of second-order agents, and M3 represent the set of third-order agents.

[0012] Step 2: Establish queue-based collaborative error tracking:

[0013] Coordinated tracking error

[0014]

[0015] Among them, e p,i e v，i and e a，i x represents the tracking error for position, velocity, and acceleration, respectively. d，i =x p，0 -i·d is the adjacent distance between agents, d is the safety distance, and x is the distance between agents. p，0 x v，0 and x a，0 For the position, velocity, and acceleration of a leader adjacent to the agent, It is a neighboring agent of agent i. It is a neighboring agent of agent i with third order, a ij Let b represent the adjacency matrix. i Let represent the connection weight between agent i and the leader, and M represent the set containing all agents.

[0016] Step 3: Establish an adaptive disturbance observer:

[0017] Adaptive Disturbance Observer

[0018]

[0019] in, This represents the dynamic error of the adaptive perturbation observer. and It is x v，i (t) and x a，i The estimated value of (t).

[0020]

[0021] Where, ξ i (t) is a bounded unknown constant. and yes and ξ i The estimated value of (t), α s and α ξ All of them are constants greater than zero.

[0022] Step 4: Establish a distributed hybrid sliding surface:

[0023] Distributed hybrid sliding surface

[0024]

[0025] Among them, s i For sliding mode error, and All are sliding mode control coefficients.

[0026] Step 5: Establish a neural network adaptive mechanism:

[0027] Neural Network Approximation

[0028]

[0029] in, and Both are activation functions of neural networks, ω f，i and ω ρ，i All of these are ideal weights for the neural network. and respectively and ρi The estimated value of (t), ε f,i and ε ρ，i All of these are approximation errors of the neural network.

[0030] Neural Network Adaptive Mechanism

[0031]

[0032] in, and ω respectively f，i and ω ρ，i The estimated value, d i It is the in-degree of agent i, p i >0, Γ f，i >0, Γ ρ，i >0, and All are gain values ​​greater than zero.

[0033] Step Six: Establish a distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy:

[0034] Distributed adaptive fault-tolerant hybrid-order cooperative queue control law

[0035]

[0036] Where c is the control gain value.

[0037] Effects of the embodiments of the present invention:

[0038] The beneficial effects of this invention lie in effectively solving the problems of mixed-order queue structures, actuator nonlinear time-varying faults, and external disturbances in existing cooperative queue control methods. This invention is applicable to mixed-order queue structures, ensuring stable operation of the cooperative queue control system in complex environments and enhancing system robustness, while also expanding the application scenarios of cooperative queue control. Attached Figure Description

[0039] Figure 1 This is a flowchart illustrating the cooperative queue control method described in Specific Implementation Method 1. Detailed Implementation

[0040] Specific implementation method one: Combining Figure 1 This embodiment describes a distributed adaptive fault-tolerant hybrid-level cooperative queue control strategy, which includes the following steps:

[0041] Step 1: Establish a mixed-order queue system model with actuator failures and saturation, uncertainties, and external disturbances:

[0042] Second-order queue system model

[0043]

[0044] Three-order queue system model

[0045]

[0046] Where i is the sequence number, x p，i (t), x v，i (t) and x a，i (t) represents the position, velocity, and acceleration of the i-th agent, respectively, and t represents time. It is the uncertainty that the system is subject to, u i (t) is the control input, 0 < θ i ≤1 represents the degree of actuator saturation, ρ i (t) is the actuator efficiency factor. Let M1 represent the set of external disturbances, M2 represent the set of second-order agents, and M3 represent the set of third-order agents.

[0047] Step 2: Establish queue-based collaborative error tracking:

[0048] Coordinated tracking error

[0049]

[0050] Among them, e p，i e v，i and e a，i x represents the tracking error for position, velocity, and acceleration, respectively. d，i =x p，0 -i·d is the adjacent distance between agents, d is the safety distance, and x is the distance between agents. p，0 x v，0 and x a，0 For the position, velocity, and acceleration of a leader adjacent to the agent, It is a neighboring agent of agent i. It is a neighboring agent of agent i with third order, a ij Let b represent the adjacency matrix. i Let represent the connection weight between agent i and the leader, and M represent the set containing all agents.

[0051] Step 3: Establish an adaptive disturbance observer:

[0052] Adaptive Disturbance Observer

[0053]

[0054] in, This represents the dynamic error of the adaptive perturbation observer. and It is x v，i (t) and x a，i The estimated value of (t).

[0055]

[0056] Where, ξ i (t) is a bounded unknown constant. and yes and ξ i The estimated value of (t), α s and α ξ All are positive numbers greater than zero.

[0057] Step 4: Establish a distributed hybrid sliding surface:

[0058] Distributed hybrid sliding surface

[0059]

[0060] Among them, s i For sliding mode error, and All are sliding mode control coefficients.

[0061] Step 5: Establish a neural network adaptive mechanism:

[0062] Neural Network Approximation

[0063]

[0064] in, and Both are activation functions of neural networks, ω f，i and ω ρ，i All of these are ideal weights for the neural network. and respectively and ρ i The estimated value of (t), ε f，i and ε ρ，i All of these are approximation errors of the neural network.

[0065] Neural Network Adaptive Mechanism

[0066]

[0067] in, and ω respectively f，i and ω ρ，i The estimated value, d i It is the in-degree of agent i, pi >0, Γ f，i >0, Γ ρ，i >0, and All are gain values ​​greater than zero.

[0068] Step Six: Establish a distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy:

[0069] Distributed adaptive fault-tolerant hybrid-order cooperative queue control law

[0070]

[0071] Where c is the control gain value.

[0072] Effects of the embodiments of the present invention:

[0073] The beneficial effects of this invention lie in effectively solving the problems of mixed-order queue structures, actuator nonlinear time-varying faults, and external disturbances in existing cooperative queue control methods. This invention is applicable to mixed-order queue structures, ensuring stable operation of the cooperative queue control system in complex environments and enhancing system robustness, while also expanding the application scenarios of cooperative queue control.

Claims

1. A distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy, characterized by: The dynamic model incorporates nonlinearity, time-varying characteristics, and unknown actuator faults, and includes both second- and third-order dynamics. To address this, an adaptive fault-tolerant control method with strong robustness and high precision, suitable for mixed-order queue structures, is developed to ensure the stable operation of the cooperative queue control system in complex environments and to broaden its application scenarios. The control strategy includes the following steps: Step 1: Establish a mixed-order queue system model with actuator failures and saturation, uncertainties and external disturbances; Step 2: Establish a queue for collaborative error tracking; Step 3: Establish an adaptive disturbance observer; Step 4: Establish a distributed hybrid sliding surface; Step 5: Establish a neural network adaptive mechanism; Step 6: Establish a distributed adaptive fault-tolerant hybrid-order cooperative queue control strategy; In step one, Second-order queue system model Three-order queue system model Where i is the sequence number, x p，i (t), x v，i (t) and x a，i (t) represents the position, velocity, and acceleration of the i-th agent, respectively, and t represents time. It is the uncertainty that the system is subject to, u i (t) is the control input, 0 < θ i ≤1 represents the degree of actuator saturation, ρ i (t) is the actuator efficiency factor. Let M1 represent the set of external disturbances, M2 represent the set of second-order agents, and M3 represent the set of third-order agents. In step two, Coordinated tracking error Where ep, i, e v，i and e a，i x represents the tracking error for position, velocity, and acceleration, respectively. d，i =x p,0 -i·d is the adjacent distance between agents, d is the safety distance, and x is the distance between agents. p，0 x v，0 and x a，0 For the position, velocity, and acceleration of a leader adjacent to the agent, It is a neighboring agent of agent i. It is a neighboring agent of agent i with third order, a ij Let b represent the adjacency matrix. i Let represent the connection weight between agent i and the leader, and M represent the set containing all agents. In step three Adaptive Disturbance Observer in, This represents the dynamic error of the adaptive perturbation observer. and It is x v，i (t) and x a，i The estimated value of (t). Where, ξ i (t) is a bounded unknown constant. and yes and ξ i The estimated value of (t), α s and α ξ All of them are constants greater than zero. In step four, Distributed hybrid sliding surface Among them, s i For sliding mode error, and All are sliding mode control coefficients. In step five, Neural Network Approximation in, and Both are activation functions of neural networks, ω f，i and ω ρ，i All of these are ideal weights for the neural network. and respectively and ρ i The estimated value of (t), ε f，i and ε ρ，i All of these are approximation errors of the neural network. Neural Network Adaptive Mechanism in, and ω respectively f，i and ω ρ，i The estimated value, d i It is the in-degree of agent i, p i >0, Г f，i >0, Г ρ，i >0, and All are gain values ​​greater than zero. In step six, Distributed adaptive fault-tolerant hybrid-order cooperative queue control law Where c is the control gain value.

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