Switching system dynamic quantization control method and equipment based on event-triggered communication

Abstract

The invention discloses a switching system dynamic quantization control method and device based on event-triggered communication, and the method comprises the steps: building a continuous time switching linear system model for a switching system, and giving a system stability guarantee mechanism; designing a dynamic quantizer for the continuous time switching linear system model, giving a dynamic quantization parameter and a dynamic quantization updating strategy, and cooperatively designing an event triggering mechanism according to a state variable of the system and the dynamic quantization parameter; and designing a switching law according to an event triggering mechanism, and adjusting dynamic quantization parameters of the system to realize stable control of the system. On the premise of ensuring the system performance, the communication frequency and calculation burden of the system can be reduced, the resource utilization efficiency of the system can be improved, the method can be widely applied to the fields of network control systems, industrial automation systems and the like, and an effective solution is provided for solving the control problem of a complex system.

Description

Technical Field

[0001] This invention relates to the fields of network control systems and quantitative communication, specifically a dynamic quantitative control method for switching systems based on event-triggered communication. Background Technology

[0002] Existing networked control systems mostly employ fixed-period sampling and static quantization strategies, which have significant limitations: traditional time-triggered mechanisms generate a large amount of redundant communication during system stability, resulting in wasted bandwidth resources, while during dynamic changes, untimely sampling leads to response lag; static quantization strategies struggle to balance high-precision control with low bandwidth usage, often facing the contradiction of insufficient accuracy or excessive communication load; in multi-condition switching scenarios, a single control model cannot adapt to the differences in characteristics of different operating modes, leading to mismatch and decreased stability during switching. Although some research has attempted to improve event-triggered or quantization methods, it has failed to effectively coordinate the synergistic relationship between the two and switching control. The fixed settings of event trigger thresholds and quantization accuracy can easily lead to high-frequency oscillations or error accumulation, and the energy mutations during switching transients further exacerbate the risk of system instability. In complex industrial environments with limited communication bandwidth, scarce computing resources, and variable operating conditions, there is an urgent need for an integrated control method that can collaboratively optimize trigger decisions, quantization accuracy, and mode switching to simultaneously meet the stringent requirements of real-time performance, robustness, and resource efficiency. Summary of the Invention

[0003] Purpose of the invention: This invention provides a dynamic quantitative control method for switching systems based on event-triggered communication to solve the problems of low resource utilization, prominent contradiction between accuracy and bandwidth, and multimodal mismatch in traditional networked control, thereby achieving efficient and reliable control under complex operating conditions.

[0004] Technical solution: A dynamic quantization control method for switching systems based on event-triggered communication, comprising the following steps:

[0005] (1) Collect the state variables and control inputs of the switching system, establish a continuous-time switching linear system model for the switching system, and provide a system stability guarantee mechanism;

[0006] (2) Design a dynamic quantizer for a class of continuous-time switching linear system models, give dynamic quantization parameters and dynamic quantization update strategy, and design an event triggering mechanism based on the system's state variables and dynamic quantization parameters.

[0007] (3) Based on the event triggering mechanism, design a switching law and adjust the dynamic quantization parameters of the system to achieve stable control of the system.

[0008] Furthermore, a continuous-time switching linear system model is established:

[0009]

[0010] in and These are the system's state variables and control inputs, respectively. and It is a constant matrix, n x and n u These are the system's state variable dimension and control input dimension, respectively. This is a segmented constant right continuous switching signal. To switch the number of subsystems, for any and t∈[t s ,t s+1 ), σ(t)=i represents the state of the switching interval [t] s ,t s+1 The i-th subsystem running on ) Let τ be the time switching sequence; for any two switching intervals τ d , satisfying t s+1 -t s ≥τ d , τ d This is the minimum stay time.

[0011] Furthermore, the system stability guarantee mechanism assumes that all subsystems have continuous state feedback and can be stabilized, that is, for each subsystem... There exists a corresponding controller gain matrix. Make A i +B i K i Let P be a Herwitz matrix. For a given scalar v ≥ 1, there exists a matrix P. i >0, Q i >0 and Make:

[0012] (A i +B i K i ) T P i +P i (A i +B i K i )<-Q i

[0013]

[0014] The controller gain matrix K is determined by solving the above system of inequalities. i Sum of matrix P i Q i and The system stability guarantee mechanism ensures that the energy of each subsystem decreases continuously during its working phase, and the energy jump at the moment of switching is limited by a factor of v, avoiding energy accumulation caused by switching, thereby ensuring the global stability of the closed-loop system under any switching sequence.

[0015] Furthermore, the dynamic quantizer is designed as follows:

[0016]

[0017] in It is a piecewise constant function. and Let q represent the quantization range and quantization error of the dynamic quantizer, respectively, satisfying: when |x(t)|≤△μ(t), μ(t) (x(t))=0; when △μ(t)<|x(t)|≤Mμ(t), |q μ(t) (x(t))-x(t)|≤△μ(t); when |x(t)|>Mμ(t), q μ(t) (x(t))>(M-△)μ(t); μ(t)>0 is the dynamic quantization scaling function.

[0018] Furthermore, the following event triggering mechanism is designed by combining the system's state variables and dynamic quantization parameters:

[0019] t k+1 =inf{t>t k :|x(t k )-x(t)|≥θμ(t)-△μ(t k )}

[0020] in This represents the sampled time series generated by the event-triggered mechanism, where θ>0 is a given event-triggered threshold parameter, and inf is the infimum function;

[0021] The system state variables are output after passing through event triggers and dynamic quantizers. Control input is When the formula setting conditions of the event triggering mechanism are met, the dynamic quantization scaling function μ(t) of the system is updated and the control input u(t) is updated.

[0022] Furthermore, the dynamic quantization parameters include the quantization range M, the quantization error Δ, the event trigger threshold parameter θ, and the dynamic quantization scaling function μ(t), satisfying:

[0023]

[0024] The dynamic quantization update strategy includes the update law of the dynamic quantization scaling function μ(t):

[0025]

[0026] in

[0027]

[0028] sgn represents the sign function, β i Both β and N(t,t) are positive design parameters. k ) is the interval (t) k The number of switching times within [t], λ min (·) represents the smallest eigenvalue of the matrix, λ max (·) represents the largest eigenvalue of the matrix, T k =t k+1 -t k The sampling interval is denoted as .

[0029] Furthermore, to avoid dynamic quantizer saturation, the system's Lyapunov function is selected:

[0030]

[0031] Define the level set as follows:

[0032]

[0033] The initial state satisfies In the case of sampling time Based on the sampling interval [t] k ,t k+1 Whether or not there is a switch depends on two situations:

[0034] In the first case, within the two sampling intervals [t] k ,t k+1 No switching occurred within N(t,t) k ) = 0, which can be derived by combining the dynamic quantizer and the event triggering mechanism:

[0035]

[0036] Combining the established update law for the dynamic quantization scaling function μ(t), we can obtain:

[0037]

[0038] Further V i (x(t k+1 ))≤λ min (P i M 2 μ 2 (t k+1 ),ensure

[0039] In the second case, within the two sampling intervals [t] k ,t k+1 Any number of switching occurs within N(t,t) k If )>0, similar to the first case, we can obtain...

[0040] Combining the established dynamic quantization scaling function μ(t) update law and using mathematical induction, we can obtain:

[0041]

[0042] Further V j (x(t k+1 ))≤λ min (P j M 2 μ 2 (t k+1 Similarly, it also guarantees

[0043] Therefore, at any sampling time, we have |x(t) k )|≤Mμ(t k This ensures that the dynamic quantizer is not saturated at any sampling time.

[0044] Furthermore, Zeno behavior is avoided in event triggering control, i.e., there is a strictly positive lower bound on the time interval between two adjacent triggering moments.

[0045] Furthermore, the switching law process includes:

[0046] Let Γ = Φ(T) k Given that (i,j)≥1, initial parameter r=1, parameter Λ=1, when the interval [t k+r ,t k+r+1 If there is no switch within ) , execute Λ=Λ·Ω(T) k+r ,i), determine whether the condition Λ·Γ≤δ is satisfied. If not, let r=r+1, and then repeat the process Λ=Λ·Ω(T) k+r i), until the judgment condition is met, the next sampling interval can be switched arbitrarily again; where The threshold parameter δ < 1.

[0047] A dynamic quantization control device for a switching system based on event-triggered communication, the device includes a switching system, which includes several controlled subsystems, several controllers, a zero-order hold, and an event trigger.

[0048] The state variables and control inputs of the switching system are collected, a continuous-time switching linear system model is established for the switching system, and a system stability guarantee mechanism is given; a dynamic quantizer is designed for the continuous-time switching linear system model, dynamic quantization parameters and dynamic quantization update strategy are given, and an event triggering mechanism of event triggers is designed in conjunction with the system's state variables and dynamic quantization parameters; based on the event triggering mechanism, a switching law is designed, and the dynamic quantization parameters are adjusted to achieve stable control of the switching system.

[0049] Beneficial effects: This invention overcomes the rigid constraints of fixed sampling and static quantization in traditional control by synergistic optimization of event triggering mechanism, dynamic quantization strategy and switching model. It effectively solves the problems of communication resource waste, limited control accuracy and multimodal mismatch, and significantly improves the system response speed and adaptability under complex working conditions. At the same time, by combining multiple Lyapunov functions and switching stability guarantee mechanism, it ensures global stability in the dynamic switching process while reducing communication load and computational overhead. It provides a new control solution with high efficiency, robustness and flexibility for fields such as industrial IoT and smart grid. Attached Figure Description

[0050] Figure 1 This is a structural diagram of the closed-loop control system under event-triggered communication according to the present invention;

[0051] Figure 2 This is a schematic diagram of the switching law of the present invention;

[0052] Figure 3 This is a state trajectory diagram of the present invention;

[0053] Figure 4 This is a trajectory diagram of the dynamic quantization scaling function of the present invention;

[0054] Figure 5 This is a schematic diagram of the switching signal of the present invention;

[0055] Figure 6 This is a timeline of event triggering for the present invention. Detailed Implementation

[0056] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings and simulation examples.

[0057] A dynamic quantization control method for a switching system based on event-triggered communication includes the following steps:

[0058] (1) Collect the state variables and control inputs of the switching system, establish a continuous-time switching linear system model for the switching system, and provide a system stability guarantee mechanism;

[0059] (2) Design a dynamic quantizer for a class of continuous-time switching linear system models, give dynamic quantization parameters and dynamic quantization update strategy, and design an event triggering mechanism based on the system's state variables and dynamic quantization parameters.

[0060] (3) Based on the event triggering mechanism, design a switching law and adjust the dynamic quantization parameters of the system to achieve stable control of the system.

[0061] Step (1) Establish a class of continuous-time switched linear system models based on the corresponding actual switching systems:

[0062]

[0063] in and These are the system's state and control inputs, respectively. and It is a constant matrix, n x and n u These are the system's state variable dimension and control input dimension, respectively. This is a segmented constant right continuous switching signal. To switch the number of subsystems, for any and t∈[t s ,t s+1 ), σ(t)=i represents the state of the switching interval [t] s ,t s+1 The i-th subsystem running on ) This is a time switching sequence. For any two switching time intervals τ... d , satisfying t s+1 -t s ≥τ d , τ d This is the minimum stay time.

[0064] Furthermore, the aforementioned switching linear system model must satisfy a stability guarantee mechanism. It is assumed that all subsystems are continuously state-feedback stabilizable, i.e., for each... There exists a corresponding control gain matrix. Make A i +B i K i Let P be a Herwitz matrix. For a given scalar v ≥ 1, there exists a matrix P. i >0, Q i >0 and Make:

[0065] (A i +B i K i ) T P i +P i(A i +B i K i )<-Q i

[0066]

[0067] The controller gain matrix K is determined by solving the above system of inequalities. i Sum of matrix P i Q i and By ensuring that the above conditions are met, the energy decreases continuously during the working phase of each subsystem, and the energy jump at the moment of switching is limited by a factor of v, thus avoiding energy accumulation caused by switching and ensuring the global stability of the closed-loop system under any switching sequence.

[0068] Step (2) Design a dynamic quantizer, provide dynamic quantization parameters and dynamic quantization update strategy, and design an event triggering mechanism based on the system's state variables and dynamic quantization parameters. First, design the dynamic quantizer as follows:

[0069]

[0070] in Let q be a piecewise constant function, with positive real numbers M and Δ satisfying: when |x(t)|≤Δμ(t), μ(t) (x(t))=0; when △μ(t)<|x(t)|≤Mμ(t), |q μ(t) (x(t))-x(t)|≤△μ(t); when |x(t)|>Mμ(t), q μ(t) (x(t))>(M-△)μ(t). M and △ represent the quantization range and quantization error of the quantizer, respectively, and μ(t)>0 is a dynamic quantization scaling function designed in this invention.

[0071] Furthermore, this invention utilizes system state variables and dynamic quantization parameters to collaboratively design an event triggering mechanism as follows:

[0072] t k+1 =inf{t>t k :|x(t k )-x(t)|≥θμ(t)-△μ(t k )}

[0073] in This represents a sampled time series generated by an event-triggered mechanism, with a sampling interval T. k =t k+1 -t k θ>0 is a given threshold parameter.

[0074] The system status is output after passing through event triggers and quantizers. Control input is Once the above event triggering mechanism meets the conditions, the dynamic quantization parameter μ(t) of the system is updated and the control input u(t) is updated.

[0075] Furthermore, this invention designs the corresponding quantization parameters, which include a quantization range M, a quantization error Δ, an event trigger threshold parameter θ, and a dynamic quantization scaling function μ(t). The quantization range M, quantization error Δ, and event trigger threshold parameter θ are selected to satisfy the following:

[0076]

[0077] Furthermore, the update law for the dynamic quantization scaling function μ(t) is designed as follows:

[0078]

[0079] in β i Both β and N(t,t) are positive design parameters. k ) is the interval (t) k The number of switching operations within [t], where sgn represents the symbolic function.

[0080] Furthermore, to avoid quantizer saturation, a Lyapunov function with respect to the system must be selected:

[0081]

[0082] The level set is further defined as follows:

[0083]

[0084] The initial state satisfies In the case of sampling time There are two possible scenarios:

[0085] Consider the first case, in the two sampling intervals [t] k ,t k+1 No switching occurred within N(t,t) k ) = 0, at this time subsystem i is running, i.e., σ(t) k ) = i, which can be obtained from the properties of the quantizer and the event triggering conditions:

[0086]

[0087] Differentiating the Lyapunov function of the system yields:

[0088]

[0089] When |x(t)|≤θρ i When μ(t), then we have When |x(t)|>θρ i When μ(t), it can be further derived that Combining the established update law for the dynamic quantization scaling function μ(t), we can obtain:

[0090]

[0091] Further V i (x(t k+1 ))≤λ min (P i M 2 μ 2 (t k+1 This clearly guarantees

[0092] Next, consider the second case, in the two sampling intervals [t] k ,t k+1 Any number of switching occurs within N(t,t) k If )>0, then σ(t) k )=i,σ(t k+1 If ) = j, similar to the first case, we can obtain the following:

[0093] when At that time, there is when Then, it can be deduced that Combining the established dynamic quantization scaling function μ(t) update law and using mathematical induction, we can obtain:

[0094]

[0095] Further V j (x(t k+1 ))≤λ min (P j M 2 μ 2 (t k+1 Similarly, this also ensures Therefore, at any sampling time, we have |x(t) k )|≤Mμ(t k This ensures that the quantizer is not saturated at any sampling time.

[0096] Step (3) first avoids Zeno behavior in event triggering control, that is, there is a strictly positive lower bound of the time interval between two adjacent triggering times. The process is as follows:

[0097] First, define the sampling error:

[0098]

[0099] In the synchronization interval (t) k ,t k+1 Within this interval, there is no switching, i.e., σ(t) = σ(t). k )=i, taking the derivative of e(t) with respect to time t, we get:

[0100]

[0101] The event trigger condition is |x(t)|≤|x(t) k )|+θμ(t)-△μ(t k )≤(M+θ-△)μ(t k Substituting, we get:

[0102]

[0103] From e(t) k Since ) = 0, we can deduce that:

[0104]

[0105] in

[0106] Furthermore, at the triggering time there is By combining the design of the dynamic quantization scaling function μ(t), there must exist a constant ε. k ∈(0,1) makes Therefore, we can conclude that:

[0107]

[0108] Obviously for all All have T k >0.

[0109] Secondly, considering the asynchronous interval, since there are any number of switching operations, it is also easy to prove that t k+2 -t k ≥τ d or

[0110] Therefore, based on the above discussion, regardless of whether it is asynchronous or synchronous, the triggering interval always has a minimum lower bound. Thus, it can be concluded that the event triggering strategy of this invention is Zeno-free.

[0111] Furthermore, this method designs a suitable switching law that allows the system to switch arbitrarily multiple times within the trigger interval, and performs stability analysis on the closed-loop system, as follows:

[0112] As can be seen from the event triggering mechanism, at the moment the event is triggered, there is Combining this with a dynamic quantization update strategy, we have: Then solve the equation We can obtain:

[0113]

[0114] Within the asynchronous interval, due to T k ≤T * Then Φ(T) k (i,j) will be a finite positive number if Φ(T) k ,i,j)<1, combined with Ω(T k If i) < 1, then we can obtain μ(t) k+1 )<μ(t k This means that as k→∞, μ(t) k → 0. However, in more general cases, such as Figure 2 As shown, in the designed switching law, Γ=Φ(T) k Given that (i,j)≥1, let r=1 and Λ=1 initially. When the interval [t k+r ,t k+r+1 If there is no switch within ) , execute Λ=Λ·Ω(T) k+r ,i), determine whether the condition Λ·Γ≤δ is satisfied. If not, let r=r+1, and then repeat the process Λ=Λ·Ω(T) k+r ,i), until the judgment condition is met, then the next sampling interval can be switched arbitrarily again. The threshold parameter δ < 1. Using this switching law, we can still obtain μ(t) as k → ∞. k → 0. From |x(t) k )|≤Mμ(t k As we can see, when k→∞, x(t) k → 0. Clearly, within the sampling interval [t]... k ,t k+1 If the system state x(t) is continuous and bounded within a given range, then there exists a constant c > 0 such that |x(t)| ≤ c|x(t) k )|≤cMμ(t k ), then as t→∞, k→∞, then x(t) k If x(t) → 0, then x(t) → 0, and the system state is convergent.

[0115] In addition, only the initial conditions need to be met. If a sufficiently small initial x(t0) is chosen, x(t) can converge to the origin, and the system is asymptotically stable.

[0116] To verify the feasibility of the proposed method, this invention presents simulation results of the control method on the MATLAB platform: The parameters are given as follows: Consider a switching system consisting of two subsystems. K1=[-1.5 0], K2=[0 -1], v=1.01, α1=0.2705, α2=0.1431, β1 = 2, β2 = 2, M = 2, Δ = 0.05, θ = 0.2, δ = 0.5; the initial conditions are selected as follows: μ(t0) = 1. Simulation results verify the effectiveness of the designed scheme, such as... Figure 3 As shown, the system's state is stable and converges rapidly; as Figure 4 As shown, the dynamic quantization scaling function μ(t) is asymptotically convergent; as Figure 5 As shown, the system's switching signal is random, and the system is allowed to switch multiple times in a short period of time; as Figure 6 As shown, the system has a significantly wider event triggering interval and a lower triggering frequency, thus saving communication resources.

[0117] The present invention also provides a dynamic quantization control device for a switching system based on event-triggered communication. The device includes a switching system, which includes several controlled subsystems, several controllers, a zero-order hold, and an event trigger.

[0118] The state variables and control inputs of the switching system are collected, a continuous-time switching linear system model is established for the switching system, and a system stability guarantee mechanism is given; a dynamic quantizer is designed for the continuous-time switching linear system model, dynamic quantization parameters and dynamic quantization update strategy are given, and an event triggering mechanism of event triggers is designed in conjunction with the system's state variables and dynamic quantization parameters; based on the event triggering mechanism, a switching law is designed, and the dynamic quantization parameters are adjusted to achieve stable control of the switching system.

[0119] Linear switching systems are widely used in industrial automation, smart grids, intelligent transportation, and other fields. This invention first establishes a continuous-time switching linear system model for these systems, providing a system stability guarantee mechanism. Then, considering both event-triggered control and dynamic quantization control, an event-triggered mechanism is designed collaboratively based on the system state and dynamic quantization parameters. A Lyapunov function is selected that has a given lower bound on the decay rate in the synchronous interval and an upper bound on the growth rate in the asynchronous interval. For the synchronous and asynchronous intervals of the switching system, different dynamic quantization parameter update laws are designed accordingly to ensure exponential stabilization of the switching system while avoiding quantizer saturation. This invention effectively solves the problems of communication resource waste, accuracy-bandwidth contradictions, and multimodal mismatch caused by fixed sampling and static quantization in networked control systems. Through the collaborative control of event triggering and dynamic quantization, it achieves efficient resource utilization and high-precision control under complex operating conditions, significantly improving the system response speed and adaptability under complex conditions.

Claims

1. A dynamic quantization control method for a switching system based on event-triggered communication, characterized in that, Includes the following steps: (1) Collect the state variables and control inputs of the switching system, establish a continuous-time switching linear system model for the switching system, and provide a system stability guarantee mechanism; (2) Design a dynamic quantizer for a class of continuous-time switching linear system models, give dynamic quantization parameters and dynamic quantization update strategy, and design an event triggering mechanism based on the system's state variables and dynamic quantization parameters. (3) Based on the event triggering mechanism, design a switching law and adjust the dynamic quantization parameters of the system to achieve stable control of the system.

2. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, The following establishes a class of continuous-time switched linear system models: in and These are the system's state variables and control inputs, respectively. and It is a constant matrix, n x and n u These are the system's state variable dimension and control input dimension, respectively. This is a segmented constant right continuous switching signal. To switch the number of subsystems, for any and t∈[t s ,t s+1 ), σ(t)=i represents the state of the switching interval [t] s ,t s+1 The i-th subsystem running on ) Let τ be the time switching sequence; for any two switching intervals τ d , satisfying t s+1 -t s ≥τ d , τ d This is the minimum stay time.

3. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, The system stability guarantee mechanism assumes that all subsystems are continuously stable by state feedback, that is, for each subsystem... There exists a corresponding controller gain matrix. Make A i +B i K i Let P be a Herwitz matrix. For a given scalar v ≥ 1, there exists a matrix P. i >0, Q i >0 and Make: (A i +B i K i ) T P i +P i (A i +B i K i )<-Q i P j ≤νP i , The controller gain matrix K is determined by solving the above system of inequalities. i Sum of matrix P i Q i and 4. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, The dynamic quantizer is designed as follows: in It is a piecewise constant function. and Let q represent the quantization range and quantization error of the dynamic quantizer, respectively, satisfying: when |x(t)|≤△μ(t), μ(t) (x(t))=0; when △μ(t)<|x(t)|≤Mμ(t), |q μ(t) (x(t))-x(t)|≤△μ(t); when |x(t)|>Mμ(t), q μ(t) (x(t))>(M-△)μ(t); μ(t)>0 is the dynamic quantization scaling function.

5. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, The following event triggering mechanism is designed by combining the system's state variables and dynamic quantization parameters: t k+1 =inf{t>t k :|x(t k )-x(t)|≥θμ(t)-△μ(t k )} in This represents the sampled time series generated by the event-triggered mechanism, where θ>0 is a given event-triggered threshold parameter, and inf is the infimum function; The system state variables are output after passing through event triggers and dynamic quantizers. Control input is When the formula setting conditions of the event triggering mechanism are met, the dynamic quantization scaling function μ(t) of the system is updated and the control input u(t) is updated.

6. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, The dynamic quantization parameters include quantization range M, quantization error Δ, event trigger threshold parameter θ, and dynamic quantization scaling function μ(t). M, Δ, and θ are selected to satisfy the following: The dynamic quantization update strategy includes the update law of the dynamic quantization scaling function μ(t): in sgn represents the sign function, β i Both β and N(t,t) are positive design parameters. k ) is the interval (t) k The number of switching times within [t], λ min (·) represents the smallest eigenvalue of the matrix, λ max (·) represents the largest eigenvalue of the matrix, T k =t k+1 -t k The sampling interval is denoted as .

7. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, Select the Lyapunov function for the system: Define the level set as follows: The initial state satisfies In the case of sampling time Based on the sampling interval [t] k ,t k+1 Whether or not there is a switch depends on two situations: In the first case, within the two sampling intervals [t] k ,t k+1 No switching occurred within N(t,t) k ) = 0, which can be derived by combining the dynamic quantizer and the event triggering mechanism: Combining the established update law for the dynamic quantization scaling function μ(t), we can obtain: Get V i (x(t k+1 ))≤λ min (P i M 2 μ 2 (t k+1 ),ensure In the second case, within the two sampling intervals [t] k ,t k+1 Any number of switching occurs within N(t,t) k If ) > 0, then we get Combining the established dynamic quantization scaling function μ(t) update law and using mathematical induction, we can obtain: Get V j (x(t k+1 ))≤λ min (P j M 2 μ 2 (t k+1 ), and also guarantee At any sampling time, we have |x(t) k )|≤Mμ(t k This ensures that the dynamic quantizer is not saturated at any sampling time.

8. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, In event-triggered control, there is a strictly positive lower bound on the time interval between two adjacent trigger times.

9. The dynamic quantization control method for a switching system based on event-triggered communication according to claim 1, characterized in that, The switching law process includes: Let Γ = Φ(T) k Given that (i,j)≥1, initial parameter r=1, parameter Λ=1, when the interval [t k+r ,t k+r+1 If there is no switch within ) , execute Λ=Λ·Ω(T) k+r ,i), determine whether the condition Λ·Γ≤δ is satisfied. If not, let r=r+1, and then repeat the process Λ=Λ·Ω(T) k+r i), until the judgment condition is met, the next sampling interval can be switched arbitrarily again; where The threshold parameter δ < 1.

10. A dynamic quantization control device for a switching system based on event-triggered communication, characterized in that, The device includes a switching system, which includes several controlled subsystems, several controllers, zero-order hold circuits, and event triggers. The state variables and control inputs of the switching system are collected, a continuous-time switching linear system model is established for the switching system, and a system stability guarantee mechanism is given; a dynamic quantizer is designed for the continuous-time switching linear system model, dynamic quantization parameters and dynamic quantization update strategy are given, and an event triggering mechanism of event triggers is designed in conjunction with the system's state variables and dynamic quantization parameters; based on the event triggering mechanism, a switching law is designed, and the dynamic quantization parameters are adjusted to achieve stable control of the switching system.

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