Industrial personal computer control method based on Internet of Things

Abstract

The invention relates to the technical field of intelligent control, and discloses an industrial personal computer control method based on Internet of Things, which comprises the following steps of: 1, establishing a dynamic characteristic state space model of an industrial personal computer, and obtaining an operation state, control input and external disturbance data of the industrial personal computer through measurement; a clock and a network monitoring tool are used for measuring control input delay and parameter uncertainty; and 2, performing off-line simulation by using the state space model and the parameter data obtained in the step 1, obtaining a delay prediction model and a robust feedback control law through feedback control gain design, and verifying that the closed-loop system meets the stability requirement in an off-line simulation environment. A full-process closed-loop control technical scheme is adopted, the effects of real-time monitoring of the state of the industrial personal computer and delay self-adaptive compensation are achieved, and compared with the scheme that data collection is lagged and control response is not timely in the prior art, the problems that system response is delayed and stability is insufficient are solved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and particularly to an industrial computer control method based on the Internet of Things. Background Art

[0002] In traditional industrial control systems, the control methods of industrial computers face technical challenges, especially in data acquisition and delay compensation. Traditional control methods usually rely on offline models and manual adjustment, resulting in long system response times and the inability to process changing control inputs and external disturbances in real time. In addition, due to the instability of the network environment, control signals are often affected by delays, seriously affecting the real-time performance and stability of industrial computers. Therefore, the control methods of industrial computers in the prior art have the following deficiencies:

[0003] There are lag problems in data acquisition and control response in the prior art, and it cannot meet the requirements of rapidly changing industrial control. Traditional industrial computer systems mainly rely on timed data acquisition and offline calculation, resulting in the inability of the control system to respond to changing states in real time. Due to the influence of control inputs and delays, the system cannot be adjusted precisely, resulting in response lag and poor stability.

[0004] In existing technical solutions, many industrial computer control methods rely on fixed control algorithms and parameters and lack the ability of dynamic adaptation. Traditional methods adopt fixed control strategies, making the system unable to make effective adjustments when facing complex and sudden working conditions, ignoring the integration of delay compensation and dynamic regulation, resulting in weak anti-interference ability of the system in practical applications.

[0005] Most current industrial computer control systems lack an efficient online optimization mechanism. In traditional control strategies, control adjustment usually relies on offline simulation and preset parameters and cannot dynamically optimize the system according to real-time data. In a complex environment, the control strategy will fail due to fluctuations in input signals and external disturbances, resulting in insufficient system stability and even the risk of system collapse.

[0006] The delay compensation and robust control methods in the prior art have not been effectively integrated, resulting in a decrease in the response accuracy of the system to delays. Traditional methods consider delay compensation, but only focus on a single control strategy and lack robustness, resulting in a decrease in the response stability of industrial computers and even affecting the overall operation effect of industrial control systems.

[0007] Through the above analysis, it can be seen that the prior art fails to meet the requirements of modern industrial control systems for high real-time performance, dynamic regulation, and robustness in many aspects.

[0008] Therefore, those skilled in the art provide an industrial computer control method based on the Internet of Things to solve the above-mentioned problems. Summary of the Invention

[0009] In view of the deficiencies of the prior art, the present invention provides an industrial control computer control method based on the Internet of Things to solve the problems raised in the above background art.

[0010] To achieve the above objectives, the present invention is realized through the following technical solutions: An industrial control computer control method based on the Internet of Things includes the following steps:

[0011] In the first step, establish a state space model of the dynamic characteristics of the industrial control computer. Obtain the operating state, control input, and external disturbance data of the industrial control computer through measurement, and use clock and network monitoring tools to determine the control input delay and parameter uncertainty;

[0012] In the second step, carry out offline simulation using the state space model and parameter data obtained in the first step. Obtain a delay prediction model and a robust feedback control law through feedback control gain design, and verify that the closed-loop system meets the stability requirements in the offline simulation environment;

[0013] In the third step, based on the feedback control law and delay prediction model obtained in the second step, dynamically solve the control input using an optimization method under the online model predictive control framework;

[0014] In the fourth step, based on the solution of the control input in the third step, collect the actual operating state, control input, and control delay data of the industrial control computer, and perform real-time monitoring and dynamic update of the network transmission delay;

[0015] In the fifth step, embed the online model predictive control algorithm obtained through real-time data collection and monitoring in the fourth step into the industrial control computer and the edge computing platform to realize the integration of the state collection, delay monitoring, and online optimization control modules;

[0016] In the sixth step, according to the control method deployed in the fifth step, verify through on-site testing that the response, overshoot, and steady-state error of the industrial control computer meet the design requirements, and set up a self-diagnostic safety monitoring module to automatically trigger the safety control mode when abnormal delays and external disturbances are detected, so as to ensure the stable operation of the industrial control computer under abnormal conditions.

[0017] Preferably, in the step of establishing the state space model, the dynamic characteristics of the industrial control computer are realized through a mathematical model that describes the control input, state vector, and external disturbance vector. The model combines the control signal transmission delay and parameter uncertainty, and uses the local linearization method near the system operating point to reflect the dynamic response of the industrial control computer.

[0018] Preferably, in the step of offline simulation and feedback control gain design, the delay prediction model uses exponential matrix transformation and integral operation to describe the effect of delay compensation. The model is realized through the following formula:

[0019]

[0020] Among them, A D is the system dynamic matrix, B E is the input matrix, x A (t) is the system state,

[0021] is the control input, τ C is the input delay, θ O is the prediction time step, x A (t + θ O ) represents the state vector of the industrial control computer at the prediction moment t + θ O , s represents the integral variable,

[0022] represents the control input vector actually applied to the industrial control computer after delay compensation at the moment t + s - τ C , e is the exponent.

[0023] Preferably, the design steps of the robust feedback control law are to obtain the state feedback gain and delay compensation gain by solving the Riccati inequality, and the solution formula of the Riccati inequality is as follows:

[0024]

[0025] Among them, P R is a positive definite matrix, is the state feedback matrix, R L is the input weight matrix, Q K is the state penalty matrix, γ S is the perturbation coefficient, is the identity matrix,

[0026] represents the transpose of the industrial control computer dynamic matrix A D , B E represents the input matrix, represents the inverse matrix of the input weight matrix R L .

[0027] Preferably, in the design of the feedback control law, the delay compensation gain is calculated by the following formula:

[0028]

[0029] Among them, L Q (τ C ) is the delay compensation matrix, τ C is the control input delay, is the state feedback matrix, Denote the input weight matrix as R L , the inverse matrix of Denote the input matrix as B E , the transpose of P R Denote the positive definite matrix Denote the matrix exponential operation as A D Denote the dynamic matrix of the industrial control computer as τ C Denote the control input delay

[0030] Preferably, in the online model predictive control framework, the optimization performance index function includes the following parts

[0031]

[0032] where J represents the performance index function, t 0 represents the starting time of control optimization, t f represents the ending time of control optimization, s represents the integral variable, x A (s) represents the state vector of the industrial control computer at time s, reflecting the dynamic response of the industrial control computer

[0033] u B (s) represents the control input vector applied by the industrial control computer at time s, reflecting the external input action, α M represents the delay penalty coefficient, τ C is the control input delay, R L is the input weight matrix, Q K is the state penalty matrix

[0034] Preferably, in the online model predictive control step, the optimization method dynamically solves the optimal control input according to the current state and delay information of the system. The optimal control input is calculated by the following formula

[0035]

[0036] where represents the optimal control input vector obtained by solving through the online optimization method at time t, reflecting the actual control signal applied by the industrial control computer

[0037] K P is the state feedback matrix, L Q (τ C ) is the delay compensation matrix, τ C is the control input delay, x A (t represents the state vector of the industrial control computer at the prediction time t

[0038] x A )t - τ C ) represents the state vector of the industrial control computer at the prediction time t - τ C ​

[0039] Preferably, in the real-time data acquisition and delay monitoring step, by deploying sensors and network monitoring modules on the industrial control computer, the status information, control inputs, and delay data of the system are monitored in real time, and the parameters of the delay prediction model are dynamically adjusted through the edge computing platform, so that the online controller can adapt to changes in network delays.

[0040] Preferably, in the edge node deployment and field test step, the embedding and testing of the control algorithm are carried out in the following manner:

[0041] The control algorithm verified by offline simulation is transplanted onto the industrial control computer and the edge computing platform, and long-term experimental data is recorded and tested to verify the response time, overshoot, steady-state error, and stability of the control method in the actual industrial control environment.

[0042] Preferably, in the safety monitoring and exception handling step, a self-diagnosis module is integrated for real-time monitoring. When it is detected that the control input delay and external disturbances exceed the preset thresholds, the safety control mode and alarm mechanism are automatically triggered to ensure that the industrial control system can maintain stable operation under abnormal conditions and prevent the expansion of system failures.

[0043] The present invention provides an industrial control computer control method based on the Internet of Things. It has the following beneficial effects:

[0044] 1. The present invention adopts a full-process closed-loop control technical solution to achieve real-time monitoring of the industrial control computer status and delay adaptive compensation effects. Compared with the prior art solutions with lagging data acquisition and untimely control responses, it solves the problems of system response delay and insufficient stability.

[0045] 2. The present invention adopts a data acquisition and edge deployment technical solution based on the Internet of Things to achieve real-time optimization and dynamic adjustment of control inputs. Compared with the prior art solutions with slow offline controller update speeds and poor adaptability, it solves the deficiencies of poor real-time performance and dynamic response.

[0046] 3. The present invention adopts a robust feedback control and delay compensation fusion technical solution to achieve dual correction effects of state feedback and delay compensation. Compared with the prior art solutions that focus on single control strategies, it solves the problems of unbalanced dynamic response and insufficient disturbance rejection ability of the industrial control computer.

[0047] 4. The present invention adopts an online model predictive control algorithm technical solution to achieve dynamic solution and real-time optimization of optimal control inputs. Compared with the prior art solutions where fixed control strategies are prone to failure, it solves the deficiencies of lagging control adjustment and poor system stability under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 is a flowchart of the present invention. Detailed implementation manners

[0049] To enable those skilled in the art to understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0050] The present invention will be described in detail below with reference to the accompanying drawings:

[0051] Embodiment:

[0052] Please refer to the attached Figure 1 , the embodiment of the present invention provides an industrial control computer control method based on the Internet of Things, including the following steps:

[0053] In the first step, establish a state space model of the dynamic characteristics of the industrial control computer. Obtain the operating state, control input, and external disturbance data of the industrial control computer through measurement, and use clock and network monitoring tools to measure the control input delay and parameter uncertainty. In the step of establishing the state space model, the dynamic characteristics of the industrial control computer are realized through a mathematical model describing the control input, state vector, and external disturbance vector. The model combines the control signal transmission delay and parameter uncertainty, and adopts a local linearization method near the system operating point to reflect the dynamic response of the industrial control computer;

[0054] In the second step, use the state space model and parameter data obtained in the first step to carry out offline simulation. Obtain a delay prediction model and a robust feedback control law through feedback control gain design, and verify that the closed-loop system meets the stability requirements in the offline simulation environment;

[0055] In the third step, based on the feedback control law and delay prediction model obtained in the second step, adopt an optimization method to dynamically solve the control input in the online model predictive control framework;

[0056] In the fourth step, based on the control input solved in the third step, collect the actual operating state, control input, and control delay data of the industrial control computer, and perform real-time monitoring and dynamic update of the network transmission delay;

[0057] Step 5: Embed the online model predictive control algorithm obtained through real-time data collection and monitoring in Step 4 into the industrial control computer and the edge computing platform to achieve the integration of the state collection, delay monitoring, and online optimization control modules; in the real-time data collection and delay monitoring step, deploy sensors and network monitoring modules on the industrial control computer to monitor the state information, control inputs, and delay data of the system in real time, and dynamically adjust the parameters of the delay prediction model through the edge computing platform to enable the online controller to adapt to changes in network delays; in the edge node deployment and field test step, the embedding and testing of the control algorithm are carried out in the following manner:

[0058] Transfer the control algorithm verified by offline simulation to the industrial control computer and the edge computing platform, and record and test the experimental data for a long time to verify the response time, overshoot, steady-state error, and stability of the control method in the actual industrial control environment;

[0059] Step 6: According to the control method deployed in Step 5, verify through field tests that the response, overshoot, and steady-state error of the industrial control computer meet the design requirements, and set up a self-diagnostic safety monitoring module to automatically trigger the safety control mode when detecting abnormal delays and external disturbances to enable the industrial control computer to maintain stable operation under abnormal conditions; in the safety monitoring and abnormal handling step, integrate the self-diagnostic module for real-time monitoring. If it detects that the control input delay and external disturbances exceed the preset thresholds, it will automatically trigger the safety control mode and the alarm mechanism to ensure that the industrial control system can maintain stable operation under abnormal conditions and prevent the expansion of system failures.

[0060] This embodiment adopts a technical solution that combines real-time acquisition of the industrial control computer status, offline simulation, and online model predictive control, uses the edge computing platform to process the collected data, dynamically calculates the optimal control input, and monitors delays and disturbances through the self-diagnostic safety module. This solution breaks through the dilemmas of traditional offline parameter setting, lagging control response, and insufficient delay compensation, and realizes real-time dynamic control and automatic compensation.

[0061] During the implementation process, first conduct a comprehensive parameter measurement of the industrial control computer. Deploy clocks, sensors, and network monitoring tools to collect the operating status, control inputs, and external disturbance data of the industrial control computer, and at the same time detect signal transmission delays and parameter uncertainties. Through data analysis, establish a state-space model that describes the relationship between control inputs, state vectors, and disturbance vectors. This model uses the local linearization method to ensure high-precision description near the operating point, laying a foundation for the subsequent steps.

[0062] Based on the established state - space model, the system is comprehensively simulated by means of offline simulation. During this process, a delay prediction model and a robust feedback control law are obtained through the design of feedback control gains. The exponential matrix transformation and integral operation are used to achieve the delay compensation effect, and the positive definite matrix, state - feedback gain, and delay compensation gain are obtained by solving the Riccati inequality. This process fully considers the influence of control signal transmission delay and external disturbance on the state of the industrial control computer, ensuring that the stability and response performance of the closed - loop system in the simulation meet the design requirements.

[0063] After the successful verification of the offline simulation, the feedback control law and the delay prediction model are introduced into the online model predictive control framework. The edge computing platform is used for optimization calculation to solve the optimal control input in real - time. This solution process adjusts the optimal input according to the currently collected state data and detected network delay information to achieve dynamic compensation of the control signal. The controller adopts an optimized performance index function and balances the state error, control input, and delay penalty through integration to achieve global optimal regulation.

[0064] During the online control process, the system collects the actual operating state, control input, and delay data of the industrial control computer, and the deployed network monitoring module monitors the delay in real - time. The edge platform dynamically updates the parameters of the delay prediction model according to the latest data, ensuring that the online controller always uses accurate data for calculation. The control algorithm is embedded in the industrial control computer and the edge computing platform to form an integrated module of state acquisition, delay monitoring, and online optimization control, enabling the control process to maintain real - time response and stability in a complex network environment.

[0065] To ensure the safe operation of the industrial control system in abnormal situations, a self - diagnostic safety monitoring module is set up in this embodiment. This module monitors the control input delay and external disturbance in real - time. Once an abnormal situation exceeding the preset threshold is detected, it immediately triggers the safety control mode and alarm mechanism. The safety module uses simple and direct logical judgment to ensure that the system can quickly switch to a safe state in an emergency, avoid the spread of faults, and maintain the safety and stability of the overall system.

[0066] In this embodiment, by combining real - time state acquisition with offline simulation and adopting online model predictive control, the response speed of the industrial control computer is greatly improved. The edge platform is used to dynamically optimize the control input to achieve delay adaptive compensation, ensuring the stable operation of the system under network delay conditions. The integrated self - diagnostic safety monitoring module realizes automatic protection in abnormal states and improves the robustness of the system. The overall solution has a compact structure and is easy to implement, which can significantly reduce the problems of data acquisition lag and untimely control response in traditional industrial control systems.

[0067] In the steps of offline simulation and feedback control gain design, the delay prediction model uses exponential matrix transformation and integral operation to describe the effect of delay compensation, and the model is realized through the following formula:

[0068]

[0069] Among them, A D is the system dynamic matrix, B E is the input matrix, x A (t) is the system state,

[0070] is the control input, τ C is the input delay, θ O is the prediction time step, x A (t + θ O ) represents the state vector of the industrial control computer at the prediction moment t + θ O The integral variable is represented by s,

[0071] represents the actual control input vector applied to the industrial control computer after delay compensation at the moment t + s - τ C and e is the exponent.

[0072] Through exponential matrix transformation and integral operation, effective compensation for the control input delay is achieved, enabling the system to operate stably in different delay environments and reducing oscillation and overshoot phenomena.

[0073] By introducing a delay prediction model, the system can accurately calculate the optimal control input, ensure that the change of the state vector conforms to the set trajectory, and improve the overall control accuracy.

[0074] The prediction model can dynamically adjust the compensation parameters according to time, enabling the controller to adapt to different working conditions and delay changes, and improving the robustness and adaptive ability of the system.

[0075] Adopting the mathematical method of exponential matrix and integral operation makes the delay compensation calculation process efficient, avoids complex numerical solution processes, and improves the real-time performance of the control algorithm.

[0076] In summary, through exponential matrix transformation and integral operation, precise compensation for the control input delay is achieved, effectively enhancing the real-time response ability and stability of the industrial control computer. Compared with traditional methods, it reduces the control calculation complexity, improves the dynamic adaptation ability, and enables the industrial control system to operate efficiently and stably in complex environments.

[0077] The design steps of the robust feedback control law obtain the state feedback gain and delay compensation gain of the system by solving the Riccati inequality. The solution formula of the Riccati inequality is as follows:

[0078]

[0079] Among them, P R is a positive definite matrix, is the state feedback matrix, R L is the input weight matrix, Q K is the state penalty matrix, γ S is the disturbance coefficient is the identity matrix

[0080] represents the dynamic matrix A of the industrial control computer D transpose of, B E represents the input matrix represents the input weight matrix R L inverse matrix of

[0081] By solving the Riccati inequality, the optimal state feedback gain can be obtained, enabling the system to remain stable under the influence of external disturbances and delays, and avoiding system divergence and unstable oscillations

[0082] The solution of the Riccati inequality provides the optimal state penalty and input weight allocation, ensuring that the system can achieve precise state regulation under limited control inputs, and reducing overshoot and steady-state error

[0083] The calculation of the state feedback gain and delay compensation gain can optimize the control input while ensuring system performance, minimize energy consumption, and improve the operating efficiency of the system

[0084] It can automatically adjust the control input through the robust feedback control law in the presence of uncertain disturbances and model errors, enabling the system to have good anti-interference ability in complex environments

[0085] In summary, the present invention designs the optimal state feedback gain and delay compensation gain by solving the Riccati inequality to achieve precise control and enhanced robustness of the industrial control computer. Compared with traditional control methods, this solution can keep the system stable under different working conditions, optimize energy consumption, and improve the anti-interference ability, making the industrial control computer adaptable and reliable in complex industrial environments

[0086] In the design of the feedback control law, the delay compensation gain is calculated by the following formula

[0087]

[0088] where, L Q (τ C ) is the delay compensation matrix, τ C is the control input delay is the state feedback matrix represents the inverse matrix of the input weight matrix R L of represents the transpose of the input matrix B E transpose of, PR represents a positive definite matrix, represents matrix exponential operation, A D represents the dynamic matrix of the industrial control computer, τ C represents the control input delay.

[0089] By using matrix exponential operation, the delay compensation gain can be accurately calculated, enabling the control input to effectively hedge the impact caused by network delay and improving the control accuracy.

[0090] This method can dynamically adjust the compensation gain, enabling the system to adapt to different degrees of delay changes, optimizing the dynamic response of the industrial control computer, and avoiding control errors caused by lag.

[0091] By adopting an optimization method that combines the state feedback matrix and the input weight matrix, the control system can remain stable under the influence of external disturbances and delay uncertainties, improving the anti-interference ability.

[0092] Through the mathematical optimization method of matrix exponential operation, the complex iterative solution process is avoided, the computational complexity is reduced, and the real-time performance and substantiality of the control algorithm are improved.

[0093] In summary, by calculating the delay compensation gain through matrix exponential operation, precise compensation for network delay is achieved, improving the dynamic response ability and stability of the system. Compared with traditional fixed compensation strategies, this method can dynamically adjust the compensation parameters according to the actual delay situation, enabling the industrial control computer to operate efficiently and stably in complex network environments.

[0094] In the online model predictive control framework, the optimized performance index function includes the following parts:

[0095]

[0096] Among them, J represents the performance index function, t 0 represents the starting time of control optimization, t f represents the termination time of control optimization, s represents the integration variable, x A (s) represents the state vector of the industrial control computer at time s, reflecting the dynamic response of the industrial control computer,

[0097] u B (s) represents the control input vector applied to the industrial control computer at time s, reflecting the external input action, α M represents the delay penalty coefficient, τ C is the control input delay, R L is the input weight matrix, Q K is the state penalty matrix.

[0098] The optimized performance index function ensures that the control strategy of the industrial control computer reduces unnecessary energy consumption while meeting the target performance by balancing the state error, control input, and delay penalty.

[0099] Through integration, the control process is globally optimized, enabling the control system to remain stable during long-term operation, reducing overshoot and steady-state error, and improving overall stability.

[0100] This method can dynamically adjust the control optimization target according to the real-time collected data, enabling the industrial control computer to adapt to different working conditions and external disturbances and improving robustness.

[0101] The optimized performance index function uses mathematical modeling to improve calculation efficiency, enabling the rapid and accurate solution of the optimal control input and ensuring the real-time performance of the system.

[0102] In summary, by optimizing the performance index function and comprehensively considering the state error, control input, and delay penalty, precise control and dynamic optimization of the industrial control computer are achieved. Compared with traditional control methods, this solution can maintain stable operation under different working conditions and complex network environments, improve the response speed, and optimize the system energy consumption, making the industrial control system intelligent and flexible.

[0103] In the online model predictive control step, the optimization method dynamically solves the optimal control input according to the current state and delay information of the system. The optimal control input is calculated by the following formula:

[0104]

[0105] Where represents the optimal control input vector obtained by the online optimization method at time t, reflecting the actual control signal applied by the industrial control computer,

[0106] K P is the state feedback matrix, L Q (τ C ) is the delay compensation matrix, τ C is the control input delay, x A (t represents the state vector of the industrial control computer at the prediction time t,

[0107] x A (t - τ C ) represents the state vector of the industrial control computer at the prediction time t - τ C .

[0108] The optimization method calculates the optimal control input in real time according to the current state and network delay of the system, ensuring that the control strategy always meets the best performance and improving the response speed and accuracy of the system.

[0109] By adopting a delay compensation matrix, the control input can be dynamically adjusted according to the actual network delay, reducing the impact of delay on the system stability and enhancing the robustness of the system.

[0110] This method can analyze the state information at the prediction moment in real time, enabling the industrial control computer to adapt to different working conditions and environmental changes, and ensuring the control accuracy and system stability.

[0111] The optimization solution ensures that the control input can meet the system performance requirements, and can reduce unnecessary energy consumption, improving the overall efficiency and economy of the control system.

[0112] In summary, by adopting the online optimization method, the optimal control input is dynamically calculated based on the current state and delay information of the system, realizing the precise control and intelligent adjustment of the industrial control computer. Compared with the traditional fixed control strategy, this method can make adaptive adjustments according to real-time data, optimize the control accuracy, improve the system response speed, and enhance the anti-interference ability, enabling the industrial control system to operate efficiently and stably in a complex environment.

[0113] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An industrial computer control method based on the Internet of Things, characterized in that: The steps include: The first step is to establish a state space model of the dynamic characteristics of the IPC, obtain the IPC operating status, control input and external disturbance data through measurement, and use clock and network monitoring tools to determine the control input delay and parameter uncertainty; In the second step, the state space model and parameter data obtained in the first step are used to carry out offline simulation, and the delay prediction model and robust feedback control law are obtained through feedback control gain design, and the closed-loop system is verified to meet the stability requirements in the offline simulation environment; In the third step, based on the feedback control law and delay prediction model obtained in the second step, the control input is dynamically solved using an optimization method in the online model predictive control framework; The fourth step is to collect the actual operation status, control input and control delay data of the industrial computer based on the control input solved in the third step, and to monitor and dynamically update the network transmission delay in real time; The fifth step is to embed the online model predictive control algorithm obtained through real-time data collection and monitoring in the fourth step into the industrial computer and edge computing platform to realize the integration of state collection, delay monitoring and online optimization control modules; In the sixth step, according to the control method deployed in the fifth step, field tests are conducted to verify that the response, overshoot, and steady-state error of the industrial computer meet the design requirements, and a self-diagnostic safety monitoring module is established to automatically trigger the safety control mode when abnormal delays and external disturbances are detected, so as to enable the industrial computer to maintain stable operation under abnormal conditions.

2. The method for controlling an industrial computer based on the Internet of Things according to claim 1, characterized in that: In the step of establishing the state space model, the dynamic characteristics of the industrial computer are realized by describing a mathematical model of control input, state vector and external disturbance vector. The model combines control signal transmission delay and parameter uncertainty, and adopts a local linearization method near the system operating point to reflect the dynamic response of the industrial computer.

3. The method for controlling an industrial computer based on the Internet of Things according to claim 1, characterized in that: In the offline simulation and feedback control gain design step, the delay prediction model uses exponential matrix transformation and integral operation to describe the effect of delay compensation, and the model is implemented by the following formula: Among them, A D is the system dynamic matrix, B E is the input matrix, x A (t) is the system status, is the control input, τ C is the input delay, θ O is the prediction time step, x A (t+θ O ) represents the predicted time t+θ O The state vector of the industrial computer is s, which represents the integral variable. Indicates that at time t+s-τ C The control input vector actually applied to the industrial computer after delay compensation is shown below, where e is the exponent.

4. The method for controlling an industrial computer based on the Internet of Things according to claim 3 is characterized in that: The robust feedback control law design step obtains the state feedback gain and delay compensation gain of the system by solving the Riccati inequality. The solution formula of the Riccati inequality is as follows: Among them, P R is a positive definite matrix, is the state feedback matrix, R L is the input weight matrix, Q K is the state penalty matrix, γ S is the disturbance coefficient, I nA is the identity matrix, Represents the dynamic matrix A of the industrial computer D The transpose of B E represents the input matrix, Represents the input weight matrix R L The inverse matrix of .

5. The method for controlling an industrial computer based on the Internet of Things according to claim 4, characterized in that: In the feedback control law design, the delay compensation gain is calculated by the following formula: Among them, L Q (τ C ) is the delay compensation matrix, τ C To control input delay, is the state feedback matrix, Represents the input weight matrix R L The inverse matrix of Represents the input matrix B E The transpose of R represents a positive definite matrix, represents the matrix exponential operation, A D represents the dynamic matrix of the industrial computer, τ C Indicates control input delay.

6. The method for controlling an industrial computer based on the Internet of Things according to claim 1, characterized in that: In the online model predictive control framework, the optimization performance index function includes the following parts: Where J represents the performance index function, t0 represents the start time of control optimization, and t f represents the control optimization termination time, s represents the integral variable, x A (s) represents the state vector of the industrial computer at time s, reflecting the dynamic response of the industrial computer. u B (s) represents the control input vector applied by the industrial computer at time s to reflect the external input effect, α M represents the delay penalty coefficient, τ C To control input delay, R L is the input weight matrix, Q K is the state penalty matrix.

7. The method for controlling an industrial computer based on the Internet of Things according to claim 6, characterized in that: In the online model predictive control step, the optimization method dynamically solves the optimal control input according to the current state of the system and the delay information. The optimal control input is calculated by the following formula: in, It means that the optimal control input vector obtained by online optimization method at time t reflects the actual control signal applied by the industrial computer. K P is the state feedback matrix, L Q (τ C ) is the delay compensation matrix, τ C To control the input delay, x A (t represents the state vector of the industrial computer at the prediction time t, x A (t-τ C ) represents the prediction time t-τ C The state vector of the industrial computer.

8. The method for controlling an industrial computer based on the Internet of Things according to claim 1, characterized in that: In the real-time data collection and delay monitoring step, sensors and network monitoring modules are deployed on the industrial computer to monitor the system status information, control input and delay data in real time, and the parameters of the delay prediction model are dynamically adjusted through the edge computing platform to enable the online controller to adapt to changes in network delay.

9. The method for controlling an industrial computer based on the Internet of Things according to claim 1, characterized in that: In the edge node deployment and field testing steps, the control algorithm is embedded and tested in the following manner: The control algorithm verified by offline simulation is transplanted to the industrial computer and edge computing platform, and long-term experimental data recording and testing are carried out to verify the response time, overshoot, steady-state error and stability of the control method in the actual industrial control environment.

10. The method for controlling an industrial computer based on the Internet of Things according to claim 1, characterized in that: In the safety monitoring and exception handling steps, an integrated self-diagnosis module performs real-time monitoring. If it is detected that the control input delay and external disturbance exceed the preset threshold, the safety control mode and alarm mechanism are automatically triggered to ensure that the industrial control system can maintain stable operation under abnormal conditions and prevent the expansion of system faults.

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