Intermittent sampling differential privacy protection method for intelligent networked automobile system

Abstract

The invention relates to the field of intelligent networked automobiles, in particular to an intermittent sampling differential privacy protection method for an intelligent networked automobile system. The method comprises the following steps: firstly, simplifying a vehicle dynamics system into a heterogeneous linear system coupled through a communication graph; secondly, a distributed hybrid controller and a dynamic noise generator are designed, the distributed hybrid controller is applied to intermittent sampling data information and assisted by the dynamic noise generator, information exchange is limited to vehicles in a specified neighborhood set, and then a preset differential privacy series is designed according to a differential privacy algorithm and Laplacian random noise; and finally, performing design solution on the controller by using a pole placement method in combination with a generalized reverse concept to realize networked control. According to the invention, differential privacy protection is realized, and at the same time, the average output consensus controller is utilized to realize intermittent data information sampling of adjacent intelligent network connection automobile systems.

Description

Technical Field

[0001] The present invention relates to the field of intelligent networked vehicle systems, and more specifically, to an intermittent sampling differential privacy protection method for intelligent networked vehicle systems. Background Art

[0002] With the development of network technology, network technology has greatly changed people's lifestyles. Modern communication networks provide fast and reliable communication between multiple intelligent agents located in different locations. Therefore, communication networks are widely used to connect control components within control loops, resulting in network control systems that have applications in various fields. Since self-driving cars have significant advantages over traditional manually driven cars, research on intelligent vehicle systems has become increasingly popular in recent years. Platoon control is a type of networked vehicle control that has attracted the interest of many scholars in recent years and is widely used to solve many practical problems. However, these studies have limitations. For example, it is unrealistic to assume that vehicles have ideal information exchange in a vehicle self-organizing network. Therefore, this paper aims to analyze the problem of information exchange between vehicles, especially when dealing with various topological structures of platoon vehicles.

[0003] Sampling control has many advantages, including precise adjustment of sampling rate and time resolution, thereby minimizing signal distortion and noise interference, improving signal quality and system stability. This approach also enhances the scalability and flexibility of the system while reducing system complexity and associated costs]. Therefore, this paper studies a platoon vehicle system with intermittent sampling data. Due to the problems of predictable interference control, random data loss, etc. in the existing vehicle communication mechanism. The role of sampled data control in network control systems is becoming increasingly important.

[0004] It is equally important to ensure the confidentiality of driving information in a platoon vehicle driving system. In the past decade, many privacy protection methods have been proposed for data privacy protection, such as homomorphic encoding, differential privacy, and state decomposition. Eavesdroppers illegally intercept and monitor data transmission between platoon vehicles to obtain information about team actions and activities, which may disrupt communication channels and hinder information exchange between vehicles. Therefore, it is crucial for platoon vehicles to adopt advanced security measures to prevent eavesdropping. The initial information between vehicles may contain a lot of private information, and differential privacy methods are effective in protecting privacy. Differential privacy is one of the strongest mathematical guarantees and there is still a lot of room for development in the future. To ensure privacy protection, noise should be applied during the sampling process, and the communication between cars must also be affected by noise, so appropriate noise design must be carried out. Based on these ideas, this paper applies the noise mechanism of differential privacy with preset indexes to the vehicle communication controlled by sampled data related to the differential privacy algorithm, thereby protecting the exchange of intermittent data. Summary of the invention

[0005] The technical problem to be solved by the present invention is to protect the privacy of uniform intermittent information of the average output consistency of the platoon vehicle system. At the same time, a hybrid controller with a decay index and noise dynamics are adopted to achieve intermittent information improvement of vehicles with preset accuracy and privacy index, relax the restrictions of the noise addition mechanism, and achieve more precise protection.

[0006] In order to solve the above technical problems, the present invention provides an intermittent sampling differential privacy protection method for an intelligent connected vehicle system, comprising the following steps:

[0007] Step 1, simplify the vehicle dynamics system into a heterogeneous linear system coupled through a communication graph;

[0008] Step 2: Design a distributed hybrid controller and a dynamic noise generator, apply the distributed hybrid controller to the intermittently sampled data information, and use the dynamic noise generator to limit the information exchange to vehicles within a specified neighborhood set;

[0009] Step 3: Design a predetermined differential privacy level based on the differential privacy algorithm and Laplace random noise

[0010] Step 4: Use the pole placement method combined with the generalized inverse concept to design and solve the distributed controller to achieve networked control;

[0011] Furthermore, the specific process of step 1 is to simplify the vehicle dynamics system into a heterogeneous linear system coupled by a communication graph, including the following steps:

[0012] The simplified vehicle dynamics model is as follows:

[0013]

[0014] Among them, z i is the position, t is the time, v i is the speed, m i For quality, is the mechanical efficiency of the transmission system, T i is the actual driving torque, R i is the tire radius, C A,i is the comprehensive aerodynamic resistance system, g is the acceleration of gravity, f is the rolling resistance coefficient, T i,des To achieve the desired driving torque, For inertia delay, the specific form of the driving torque is expected to be:

[0015]

[0016] According to the basic formula of physics, the vehicle dynamics model is simplified and redesigned as follows: where u i For the new input of the design, a i is the acceleration, so the simpler vehicle dynamics formula is,

[0017]

[0018] in,

[0019]

[0020] The output vector of the position tracking error is

[0021] y(t)=Cx i (t) (4)

[0022] in C' =

[100] .

[0023] Furthermore, the specific process of step 2 is to design a distributed hybrid controller and a dynamic noise generator, and the distributed hybrid controller is applied to intermittent sampling data information, and the dynamic noise generator includes the following steps:

[0024] Design a distributed hybrid distributor, whose distributed hybrid controller is

[0025]

[0026] The noise generator is

[0027]

[0028] where η i,l (t k )~Lap(0,b),l∈{1,2,…,p} is Laplace noise, ζ i is the reference state, and the control gain γ k+1 is a time-varying positive value, is the gain matrix,

[0029] K 2i =U i +K 1i LΞ i And U i With Ξ i Satisfy A i Ξ i +B i U i =0,C i Ξ i =I p×p ;ζ i (t) represents the reference state ζ at the initial time i (0) = yi (0) and is a random vector, each of which is independently Laplace distributed. i The covariance of (t) is 2b 2 I P , where L is the Laplace matrix, Ξ i , U i is the parameter matrix, b is the noise parameter, I P is the p-dimensional identity matrix.

[0030] Limit information exchange to vehicles within a specified neighborhood set, taking Through the distributed hybrid controller (5), the collective dynamics of the i-th vehicle is finally obtained as

[0031]

[0032] in is a node set, Υ∈{1,2,…,N}, an edge set and weighted adjacency matrix Α=(a ij ) N×N , the Laplacian matrix of graph G ι=(l ij ) N×N , if i≠j then l ij =-a ij otherwise

[0033]

[0034] Let A=[A 1 ,A 2 ,…,A N ],B=[B 1 ,B 2 ,…,B N ], in Assume μ c (t) = μ(t) - Ξζ(t), Therefore, the following formula holds true:

[0035]

[0036] The collective dynamics of the reference state is finally obtained as,

[0037]

[0038] When J=I N -11 T / N

[0039]

[0040] also

[0041]

[0042] From formula (9) and formula (10), we can get and

[0043]

[0044] And the resulting closed-loop system The accumulation state satisfies the mixing equation.

[0045] Furthermore, the specific process of step three is to design a predetermined differential privacy level based on the differential privacy algorithm and Laplace random noise.

[0046] The heterogeneous system is ε-differentially private and has distributed controllers and noise generators on T, where

[0047]

[0048] in

[0049]

[0050] Then the control gain {γ k} design experiment predetermined range of privacy indicators under the same noise generator and system, given a parameter ε* and take Among them 1 >0,o 2 >0,α∈(0.5,1], control gain γ k Designed to ensure differential privacy protection for a given ε* within a time range where ε* satisfies

[0051]

[0052] Where β = 1-α.

[0053] Applying the distributed hybrid controller with asynchronous time sampling to the hybrid system can ensure the asymptotic output average consistency of the system under given (s*,r*) precision and ε* privacy index. Applying the noise generated by the algorithm to the hybrid system studied in this paper can solve the consistency problem of intermittent time information exchange in the intelligent connected vehicle system.

[0054] Furthermore, the specific process of step 4 is to design and solve the controller for the intelligent connected vehicle system using the pole placement method combined with the generalized inverse concept, and the realization of networked control includes the following steps:

[0055] Through the pole placement method, adjust the pole position of the controller to achieve the system response required by the design. Determine the required system performance indicators; calculate or select the pole position of the controller based on the system model and performance indicators. Then determine the appropriate pole position through root locus design.

[0056] Generalized inverse design reversely derives the appropriate controller structure and parameters based on the system's response characteristics and control requirements; first analyze the system's nonlinear characteristics or complex dynamics to determine the problem that needs to be solved, and then select the appropriate control strategy or controller structure based on experience or experimental data. Then adjust the controller parameters to optimize the system's response and performance;

[0057] Controller implementation is to implement the designed controller structure into actual code or configuration of control hardware. First, write the code of the control algorithm or configure the controller hardware according to the designed controller structure and parameters. Ensure that the interface and communication protocol of the controller are compatible with the system to achieve network control requirements.

[0058] System simulation testing and debugging: verify the effectiveness and stability of the controller design through simulation to ensure the good performance of the system under different working conditions; use Matlab to test the designed controller, and evaluate the closed-loop response, stability and performance indicators of the control system by analyzing the simulation results;

[0059] Deployment and optimization: deploy the optimized controller to the actual networked control system; regularly monitor and optimize the controller to ensure that the system always maintains good control performance; deploy the verified controller to the actual networked control system; monitor and record system operation data, and regularly adjust and optimize the controller to adapt to system changes and performance requirements.

[0060] The beneficial effects of the present invention are: using a hybrid controller with a decay index and noise dynamics to achieve intermittent information improvement of vehicles with preset accuracy and privacy index, relaxing the restrictions of the noise addition mechanism, and achieving more precise protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 Schematic diagram of the sampling data queue control framework of autonomous driving vehicles on a section of highway, where (a) is a schematic diagram of a one-way closed-loop transmission smart car, (b) is a schematic diagram of a two-way transmission closed-loop smart connected car, and (c) is a schematic diagram of a one-way interval transmission.

[0062] Figure 2 for Figure 1 Output state trajectory diagram, where (a) is the output state trajectory diagram of a unidirectional closed-loop transmission intelligent vehicle, (b) is the output state trajectory diagram of a bidirectional closed-loop transmission intelligent networked vehicle, and (c) is the output state trajectory diagram of a unidirectional spaced-interval transmission intelligent networked vehicle.

[0063] Figure 3 for Figure 1 Trajectory diagram of reference state, where (a) is the reference state trajectory diagram of a one-way closed-loop transmission intelligent vehicle, (b) is the reference state trajectory diagram of a two-way closed-loop transmission intelligent connected vehicle, and (c) is the reference state trajectory diagram of a one-way spaced transmission intelligent connected vehicle.

[0064] Figure 4 It is the topological structure diagram of the communication graph.

[0065] Figure 5 It is the trajectory diagram of the topological reference state.

[0066] Figure 6 It is the trajectory diagram of the topology transmission state.

[0067] Figure 7 Output state trajectory for the topology.

[0068] Figure 8 To compare different α and value.

[0069] Fig. 9 To compare ε with different o 1 With o 2 .

[0070] Fig.10 This is a framework diagram of the method of the present invention. DETAILED DESCRIPTION

[0071] The specific implementation of the present invention is described below in conjunction with the accompanying drawings so that those skilled in the art can better understand the present invention. It should be noted that in the following description, when the detailed description of known functions and designs may dilute the main content of the present invention, these descriptions will be omitted here.

[0072] The present invention realizes differential privacy protection and uses the average output consensus controller to realize the information exchange of intermittent sampling data between adjacent intelligent networked vehicles. Figure 1 As shown, the sampling data queue control framework of autonomous driving vehicles on the highway includes one-way closed-loop transmission, two-way transmission closure and one-way interval transmission.

[0073] like Fig.10 As shown, the specific implementation process includes the following steps.

[0074] Step 1: Simplify the vehicle dynamics system into a heterogeneous linear system coupled by a communication graph; this includes the following steps:

[0075] The simplified vehicle dynamics model is as follows:

[0076]

[0077] Among them, z i is the position, t is the time, v i is the speed, m i For quality, is the mechanical efficiency of the transmission system, T i is the actual driving torque, R i is the tire radius, C A,i is the comprehensive aerodynamic resistance system, g is the acceleration of gravity, f is the rolling resistance coefficient, T i,des To achieve the desired driving torque, For inertia delay, the specific form of the driving torque is expected to be:

[0078]

[0079] According to the basic formula of physics, the vehicle dynamics model is simplified and redesigned as follows: where u i For the new input of the design, a i is the acceleration, so the simpler vehicle dynamics formula is,

[0080]

[0081] in,

[0082]

[0083] The output vector of the position tracking error is

[0084] y(t)=Cx i (t) (4)

[0085] in C'=[1 0 0].

[0086] Step 2, designing a distributed hybrid controller and a dynamic noise generator, and applying the distributed hybrid controller to intermittent sampling data information and supplemented by a dynamic noise generator includes the following steps:

[0087] Design a distributed hybrid distributor, whose distributed hybrid controller is

[0088]

[0089] The noise generator is

[0090]

[0091] where η i,l (t k)~Lap(0,b),l∈{1,2,…,p} is Laplace noise, ζ i is the reference state, and the control gain γ k+1 is a time-varying positive value, is the gain matrix, K 2i =U i +K 1i LΞ i AndU i With Ξ i Satisfy A i Ξ i +B i U i =0,C i Ξ i =I p×p ;ζ i (t) represents the reference state ζ at the initial time i (0) = y i (0) and is a random vector, each of which is independently Laplace distributed. i The covariance of (t) is 2b 2 I P , where L is the Laplace matrix, Ξ i , U i is the parameter matrix, b is the noise parameter, I P is the p-dimensional identity matrix.

[0092] Limit information exchange to vehicles within a specified neighborhood set, taking Through the distributed hybrid controller (5), the collective dynamics of the i-th vehicle is finally obtained as

[0093]

[0094] in is a node set, Υ∈{1,2,…,N}, an edge set and weighted adjacency matrix Α=(a ij ) N×N , the Laplacian matrix of graph G ι=(l ij ) N×N , if i≠j then l ij =-a ij otherwise

[0095] Let A=[A 1 ,A 2 ,…,A N ],B=[B 1 ,B 2 ,…,B N ], in Assume μ c (t) = μ(t) - Ξζ(t), Therefore, the following formula holds true:

[0096]

[0097] The collective dynamics of the reference state is finally obtained as,

[0098]

[0099] When J=I N -11 T / N

[0100]

[0101] also

[0102]

[0103] From formula (9) and formula (10), we can get and

[0104]

[0105] And the resulting closed-loop system The accumulation state satisfies the mixing equation.

[0106] Step 3: Design a predetermined differential privacy level based on the differential privacy algorithm and Laplace random noise. The specific process is as follows:

[0107] The heterogeneous system is ε-differentially private and has distributed controllers and noise generators on T, where

[0108]

[0109] in

[0110]

[0111] Then the control gain {γ k} design experiment predetermined range of privacy indicators under the same noise generator and system, given a parameter ε* and take Among them 1 >0,o 2 >0,α∈(0.5,1], control gain γ k Designed to ensure differential privacy protection for a given ε* within a time range where ε* satisfies

[0112]

[0113] Where β = 1-α

[0114] Applying the distributed hybrid controller with asynchronous time sampling to the hybrid system can ensure the asymptotic output average consistency of the system under given (s*,r*) precision and ε* privacy index. Applying the noise generated by the algorithm to the hybrid system studied in this paper can solve the consistency problem of intermittent time information exchange in the intelligent connected vehicle system.

[0115] Step 4: Design and solve the controller for the intelligent connected vehicle system using the pole placement method combined with the generalized inverse concept. The realization of networked control includes the following steps:

[0116] Through the pole placement method, adjust the pole position of the controller to achieve the system response required by the design. Determine the required system performance indicators; calculate or select the pole position of the controller based on the system model and performance indicators. Then determine the appropriate pole position through root locus design.

[0117] Generalized inverse design reversely derives the appropriate controller structure and parameters based on the system's response characteristics and control requirements; first analyze the system's nonlinear characteristics or complex dynamics to determine the problem that needs to be solved, and then select the appropriate control strategy or controller structure based on experience or experimental data. Then adjust the controller parameters to optimize the system's response and performance;

[0118] Controller implementation is to implement the designed controller structure into actual code or configuration of control hardware. First, write the code of the control algorithm or configure the controller hardware according to the designed controller structure and parameters. Ensure that the interface and communication protocol of the controller are compatible with the system to achieve network control requirements.

[0119] System simulation testing and debugging: verify the effectiveness and stability of the controller design through simulation to ensure the good performance of the system under different working conditions; use Matlab to test the designed controller, and evaluate the closed-loop response, stability and performance indicators of the control system by analyzing the simulation results;

[0120] Deployment and optimization: deploy the optimized controller to the actual networked control system; regularly monitor and optimize the controller to ensure that the system always maintains good control performance; deploy the verified controller to the actual networked control system; monitor and record system operation data, and regularly adjust and optimize the controller to adapt to system changes and performance requirements.

[0121] In order to verify the validity of the theoretical results, the specific values ​​of the vehicle design parameters used in the simulation are shown in the following table.

[0122] Table 1, Vehicle design parameters

[0123]

[0124] Assume s*=0.56,r*=4,ε*=1 to satisfy consistency while ensuring ε* differential privacy with (s*,r*). The proposed controller (1) and the controller of the above topology use parameters Here, the dynamics of each vehicle is characterized according to (5) and (6), the initial state is chosen in [30,90], and the average value of the output state is y ave =48.5000. For the noise in this example, the control gain can be set to Then the pole arrangement method is used to make all poles less than -5, and the controller is designed as K = [83.66030.38693.0241], and the sampling data interval h k =0.1s. The result is as follows Figure 2 and Figure 3 , where the trajectories of the output state and the reference state are shown. Figure 2 and Figure 3 The system (1) can converge to the average value of the initial value, and the added noise (2) can achieve differential privacy protection. When the topology changes, the exchange of intermittent information may become easier or more difficult, resulting in inconsistent implementation time. It can be seen from Figure (3) that in the topology (b), the communication distance of non-adjacent vehicles is longer and the convergence time is also longer. By implementing privacy algorithms for different topologies, it is not only demonstrated that the method protects the communication between adjacent nodes, but also shows that for vehicle systems with longer average communication distances, it takes longer to achieve consistency. This paper considers an intelligent connected vehicle system with a more complex topology to verify that the impact of noise on the system is limited in actual complex applications, and designs different control gains to meet privacy requirements. Considering the intermittent information exchange between vehicles with n=18 follows the following Figure 4 The random topology shown is a balanced topology, each edge weight is independent and identically distributed, and each edge weight is equal to the sum of two independent and identically distributed Bernoulli random variables. According to s*=0.56,r*=4,ε*=1, φ=0.7 and b=4 are used to obtain the control gain setting, and noise can be generated. The initial state is randomly selected in [0,80] and y ave =55.4444. The dynamics of each vehicle conforms to equations (5) and (6), where and Then, the pole arrangement method is used to make all poles less than -2. According to the above steps, the controller of each vehicle is consistent. According to the theoretical conclusion, the sampling data interval is set to h k =0.1s, the control gain is The simulation results are as follows Figure 5-7As shown. In addition to the trajectory of the transferred state, it also gives the problem of describing the convergence of the reference state and the output state, from which it can be concluded that the system defined in (1) can achieve unbiased asymptotic consistency while ensuring (s*, r*)-precision and ε* differential privacy. By verification, even in more complex topologies, the privacy noise addition dynamics mentioned in this paper can be realized under the preset s*, r*, ε*. Therefore, the protection of communication information between vehicles can be achieved in more vehicles and more complex topologies. Now, the impact of multiple designs of control gains and preset parameters on the effectiveness of privacy protection is discussed. Assume b = 80,o 1 =1,o 2 =3, the differential privacy index varies with α and φ, such as Figure 8 As shown, the closer α is to 1, the better the privacy protection is. The closer it is to 0.5, the higher the ε value. Next, fix the values ​​of φ and α to α=0.5, o 1 and 2 Adjust as Fig. 9 The object privacy results shown in Figure 2. When α is small, the value of the control gain constant has little effect on the differential privacy effect, but as α increases, larger o 1 and 2 The value is conducive to the work of the protector. By adjusting the parameters, the intelligent connected vehicle system can obtain appropriate noise to meet the protection requirements of the differential privacy index, thereby protecting the communication information between vehicles and obtaining a safer driving experience.

[0125] The above description is only a preferred feasible embodiment of the present invention, and does not limit the scope of rights of the present invention. All equivalent structural changes made using the contents of the present invention description and drawings are included in the scope of rights of the present invention.

Claims

1. A method for protecting differential privacy by intermittent sampling in an intelligent connected vehicle system, characterized in that: The following steps are included: Step 1, simplify the vehicle dynamics system into a heterogeneous linear system coupled through a communication graph; Step 2: Design a distributed hybrid controller and a dynamic noise generator, apply the distributed hybrid controller to the intermittently sampled data information, and use the dynamic noise generator to limit the information exchange to vehicles within a specified neighborhood set; Step 3: Design a predetermined differential privacy level based on the differential privacy algorithm and Laplace random noise; Step 4: Use the pole placement method combined with the generalized inverse concept to design and solve the distributed hybrid controller to achieve networked control.

2. The method for protecting differential privacy by intermittent sampling of an intelligent connected vehicle system according to claim 1, characterized in that: The specific process of step one is: The simplified vehicle dynamics model is as follows: Among them, z i is the position, t is the time, v i is the speed, m i For quality, is the mechanical efficiency of the transmission system, T i is the actual driving torque, R i is the tire radius, C A,i is the comprehensive aerodynamic resistance system, g is the acceleration of gravity, f is the rolling resistance coefficient, T i,des To achieve the desired driving torque, For inertia delay, the specific form of the driving torque is expected to be: According to the basic formula of physics, the vehicle dynamics model is simplified and redesigned as follows: where u i For the new input of the design, a i is the acceleration, so the simpler vehicle dynamics formula is, in, The output vector of the position tracking error is y(t)=Cx i (t) (4) in C' = [100].

3. The method for protecting differential privacy by intermittent sampling of an intelligent connected vehicle system according to claim 2, characterized in that: The specific process of step 2 is: Design a distributed hybrid distributor, whose distributed hybrid controller is The noise generator is where η i,l (t k )~Lap(0,b),l∈{1,2,…,p} is Laplace noise, ζ i is the reference state, and the control gain γ k+1 is a time-varying positive value, is the gain matrix, K 2i =U i +K 1i LΞ i And U i With Ξ i Satisfy A i Ξ i +B i U i =0,C i Ξ i =I p×p ;ζ i (t) represents the reference state ζ at the initial time i (0) = y i (0) and is a random vector, each of which is independently Laplace distributed; η i The covariance of (t) is 2b 2 I P ; L is the Laplace matrix; Ξ i , U i is the parameter matrix; b is the noise parameter; I P is the p-dimensional identity matrix; Limit information exchange to vehicles within a specified neighborhood set, taking Through the distributed hybrid controller (5), the collective dynamics of the i-th vehicle is finally obtained as where G = (Υ, θ, Α) is the node set, Υ ∈ {1, 2, ..., N}, the edge set θ ∈ Υ × Υ and the weighted adjacency matrix Α = (a ij ) N×N , the Laplacian matrix of graph G ι=(l ij ) N×N , if i≠j then l ij =-a ij otherwise Let A=[A1,A2,…,A N ],B=[B1,B2,…,B N ], in Assume μ c (t) = μ(t) - Ξζ(t), Therefore, the following formula holds true: The collective dynamics of the reference state is finally obtained as, When J=I N -11 T / N also From formula (9) and formula (10), we can get and And the resulting closed-loop system The accumulation state satisfies the mixing equation.

4. The method for protecting differential privacy by intermittent sampling of an intelligent connected vehicle system according to claim 3, characterized in that: The specific process of step three is: The heterogeneous system is ε-differentially private and has distributed controllers and noise generators on T, where in Then the control gain {γ k } design experiment to determine the privacy index within a predetermined range; under the same noise generator and system, a parameter ε* is given and the Where o1>0, o2>0, α∈(0.5,1], control gain γ k Designed to ensure differential privacy protection for a given ε* within a time range where ε* satisfies Where β = 1-α.

5. The method for protecting differential privacy by intermittent sampling in an intelligent connected vehicle system according to claim 1, characterized in that: The specific process of step 4 is: Adjust the pole position of the controller by pole placement method to achieve the system response required by the design; determine the required system performance indicators; calculate or select the pole position of the controller based on the system model and performance indicators; then determine the appropriate pole position by root locus design; Generalized inverse design reversely derives the appropriate controller structure and parameters based on the system's response characteristics and control requirements; first analyze the system's nonlinear characteristics or complex dynamics to determine the problem to be solved, and then select the appropriate control strategy or controller structure based on experience or experimental data; then adjust the controller parameters to optimize the system's response and performance; Controller implementation, which implements the designed controller structure into actual code or configuration of control hardware. First, write the code of the control algorithm or configure the controller hardware according to the designed controller structure and parameters. Ensure that the interface and communication protocol of the controller are compatible with the system to achieve network control requirements. System simulation testing and debugging: verify the effectiveness and stability of the controller design through simulation to ensure the good performance of the system under different working conditions; use Matlab to test the designed controller, and evaluate the closed-loop response, stability and performance indicators of the control system by analyzing the simulation results; Deployment and optimization: deploy the optimized controller to the actual networked control system; regularly monitor and optimize the controller to ensure that the system always maintains good control performance; deploy the verified controller to the actual networked control system; monitor and record system operation data, and regularly adjust and optimize the controller to adapt to system changes and performance requirements.

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