Direct current motor dynamic event trigger iteration interval generation method based on ellipsoid set membership estimation

Abstract

The invention discloses a DC motor dynamic event trigger iteration interval generation method based on ellipsoid set membership estimation. According to the method, firstly, a coding-decoding scheme based on an event dynamic event trigger mechanism is designed to meet the limited bit rate limitation during network transmission, and then, an iteration interval generation method based on a trigger sampling signal is designed to estimate the state of the direct current motor and external interference and generate an interval. When the method is applied to the direct current motor, the interference estimation effect is good, the state interval generation is tight, and the limited bit rate limitation during transmission is met.

Description

Technical Field

[0001] This invention relates to an iterative interval generation method, specifically to a DC motor dynamic event-triggered iterative interval generation method based on ellipsoidal set member estimation. Background Technology

[0002] In recent years, governments and enterprises have intensified their efforts to promote the widespread application of renewable energy and actively advocate for the use of clean energy in power generation. Wind power, with its higher cost-effectiveness compared to traditional fossil fuels and nuclear energy, is widely considered a clean and sustainable energy solution. This advantage has driven in-depth research into the stability of wind turbine systems. In the field of wind power generation, DC generators are widely used, mainly due to their direct drive capability, low speed, and low maintenance costs. However, industrial systems are inevitably affected by adverse factors such as external interference, environmental noise, and component failures, which may lead to abnormal operation or even shutdown of wind turbine systems. Therefore, developing efficient fault estimation methods and timely detection and compensation for the impact of faults has become a key research focus and challenge.

[0003] Furthermore, with the rapid development of communication technology and its deep integration with industrial systems, networked control has gradually become an important means of promoting the development of industrial systems. In a networked control framework, data exchange is typically conducted through time-triggered (periodic) network channels. However, with the expansion of system scale and the frequent transmission of redundant data packets, network congestion problems are becoming increasingly prominent, severely impacting system performance. Therefore, it is urgent to introduce efficient communication strategies into networked control systems to reduce data transmission frequency and conserve limited network resources. Existing research largely integrates static event-triggered strategies, which have limited effectiveness in reducing network load. Therefore, improving existing event-triggered strategies to further optimize bandwidth utilization has become one of the key research directions. Summary of the Invention

[0004] The purpose of this invention is to propose a dynamic event-triggered iterative interval generation method for DC motors based on ellipsoidal set member estimation, which can effectively improve the state interval generation effect of DC motors in industrial network environments.

[0005] The specific technical solution of the present invention is as follows: A method for generating iterative intervals for dynamic events triggered by DC motors based on ellipsoidal set membership estimation, comprising the following steps:

[0006] Design an encoding-decoding scheme based on an event-driven dynamic event-triggered mechanism to meet the limited bit rate constraints during network transmission:

[0007] The dynamic model of a DC motor is as follows:

[0008]

[0009] Among them, F M J represents the coefficient of friction, and C represents the rotor inertia. M R represents the motor constant. M L represents armature resistance. M ω represents armature inductance, i represents current, d represents load torque disturbance, and V represents voltage.

[0010] Next, the sampling interval is set to T, and the forward Euler method is used to transform the above continuous-time DC motor model into the following discrete system model:

[0011]

[0012] Where, x(k)=[ω i] T v(k) represents the sensor noise. C1 = [0.8 0.6], C2 = [1.6 -0.4], D1 = 0.02, D2 = 0.02, I represents the identity matrix of appropriate dimensions;

[0013] To address the finite bit rate limitation of the discrete DC motor model described above, an encoding-decoding scheme based on a dynamic event-triggered mechanism is designed. First, the sampled signal is verified by trigger adjustment, and then encoded and transmitted to the decoder over the network. The dynamic event-triggered mechanism is constructed as follows:

[0014]

[0015] Where m is the sensor number, e m，y (k)=y m (k m，l )-y m (k) represents the triggering error output by the m-th sensor, k m，l Let k represent the last trigger time of the m-th sensor. m,l+1 This represents the next trigger time of the m-th sensor, and the dynamic threshold h of the m-th sensor. m (k) is updated by the following formula:

[0016]

[0017] Where, α m ,β m h is the preset trigger parameter for the m-th sensor. min h represents the lower bound of the threshold. max The upper bound of the threshold is represented by argmin(), which represents the value of the independent variable that makes a function reach its minimum value.

[0018] When the signal from the m-th sensor is determined to be trigger-sensitive, it will be encoded using an encoder; the encoded sensor sample signal is as follows:

[0019]

[0020] Among them, y m，c (k) represents the encoded trigger output of the m-th sensor. Indicates sensor tag, r m (k) Payload Index; After receiving the signal, the decoder first analyzes... To determine if the signal is sent by sensor m, then via payload index r. m (k) Retrieve the cipher table to reconstruct the signal; the reconstructed signal is as follows:

[0021]

[0022] Among them, y m，d (k) represents the decoded signal from the m-th sensor, s m w represents the quantization interval. m Indicates the level of uniform quantization.

[0023] Next, an iterative interval generation method based on trigger sampling signals is designed to estimate the state of the DC motor and external disturbances and generate intervals;

[0024] First, construct the following iterative estimator:

[0025]

[0026] Where r represents the iteration number label; This represents the state estimate for the r-th iteration; This represents the disturbance estimate in the r-th iteration; L a (k), L b (k), L c (k) is the gain of the estimator to be designed;

[0027] Select The following error system can be obtained:

[0028]

[0029] in, e m，q (k)=y m，d (k)-y m (k) represents the quantization error.

[0030] This iterative estimator can simultaneously estimate the state and disturbances of a DC motor, as demonstrated below:

[0031] D001: Select And let g [r] (k) satisfies g [r]T (k)g [r] (k)≤1, then It can be represented as:

[0032]

[0033] in, From the equation get, Generate the ellipsoid shape at time k;

[0034] D002: Next, select It can be rewritten as:

[0035]

[0036] In the formula,

[0037] D003: Based on the target From D002, we can obtain

[0038]

[0039] In the formula, Generate the ellipsoid shape matrix at time k+1, Λ0=diag(-1,0,0,0,0,0,0,0,0), where diag() represents a diagonal matrix;

[0040] D004: Next, assuming We can obtain:

[0041]

[0042] D005: Next, using the S-process technique, D003 can be scaled down to:

[0043]

[0044] in, It is a positive number;

[0045] D006: Then, applying the Schul complement lemma to D005, we can obtain the following stability condition:

[0046] Attached Figure Description

[0047] Figure 1 This is a system structure diagram for an embodiment;

[0048] Figure 2 The following is an example of an estimation diagram and range of motor speed ω using the method proposed in this invention;

[0049] Figure 3 The following is an example of the estimation diagram and range of motor current i using the method proposed in this invention;

[0050] Figure 4 This is an example of an estimation diagram of interference d using the method proposed in this invention;

[0051] Figure 5 This is a trigger diagram for event triggering using the method proposed in this invention, as shown in the embodiment. Detailed Implementation

[0052] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. After reading the present invention, any modifications of the present invention in various equivalent forms by those skilled in the art will fall within the scope defined by the appended claims.

[0053] like Figure 1 As shown, a method for generating iterative intervals for dynamic events triggered by DC motors based on ellipsoidal set membership estimation includes the following steps:

[0054] Step 1: Set initial values ​​for each parameter;

[0055] Step 2: Sample the output y of the two sensors m (k);

[0056] Step 3: Update the threshold parameter h m (k);

[0057] Step 4: Use the threshold parameter h m (k) and output y m (k) Verify the event triggering conditions and update the trigger output y. m (k m，l );

[0058] Step 5: Use the encoder to trigger the output y. m (k m,l ) to get y m，c (k) is transmitted to the decoder via the network;

[0059] Step 6: Decode y using a decoder m，c (k) Obtain the quantized output y m，d (k);

[0060] Step 7: Solve for the stability conditions to obtain the shape matrix. With estimator gain L a (k), L b (k), L c (k);

[0061] Step 8: Use quantization to output y m，d (k) Update the iterative estimator and output the system state estimate of the DC motor. Estimated values ​​of interference And using shape matrix Generate state intervals;

[0062] Step 9: Repeat steps 2, 3, 4, 5, 6, 7, and 8 until the simulation time ends.

[0063] An embodiment of the present invention is described below:

[0064] Consider the system parameters shown in the table below: symbol value symbol value symbol value symbol value <![CDATA[F M ]]> 0.1 J 0.01 <![CDATA[C M ]]> 0.01 <![CDATA[R M ]]> 1 <![CDATA[L M ]]> 0.05 T 0.01

[0065] Figure 1 This is a system structure diagram of an embodiment of the present invention; Figure 2 and Figure 3 This invention provides a DC motor state estimation diagram and state interval using the method proposed in this invention. Figure 4 This is an interference estimation map using the method proposed in this invention; Figure 5 The diagram illustrates the triggering effect of the dynamic event triggering mechanism designed in this invention; from Figures 2-4 As can be seen, the method proposed in this invention successfully estimates the system state and disturbances simultaneously, and successfully generates a reliable interval for the DC motor state; from Figure 5 It can be seen that the proposed event triggering mechanism, while meeting the limited bit rate, further reduces the number of signal transmissions and lowers the network bandwidth usage.

[0066] References

[0067] [1] Yan, S., Gu, Z., Park, JH, & Xie, X. (2021). Adaptive memory-event-triggered static output control of TS fuzzy wind turbine systems. IEEE Transactions on Fuzzy Systems, 30(9), 3894-3904.

[0068] [2]Peng,C.,&Yang,T.C.(2013).Event-triggered communication and H∞control co-design for networked control systems.Automatica,49(5),1326-1332。

Claims

1. A method for generating iterative intervals for dynamic events triggered by DC motors based on ellipsoidal set membership estimation, characterized in that, Includes the following steps: Design an encoding-decoding scheme based on an event-driven dynamic event triggering mechanism to meet the limited bit rate constraints during network transmission; Design an iterative interval generation method based on trigger sampling signals to estimate the state of a DC motor and external disturbances and generate intervals.

2. According to claim 1, a method for generating iterative intervals for dynamic events triggered by DC motors based on ellipsoidal set membership estimation is used to design an encoding-decoding scheme based on an event-driven dynamic event triggering mechanism to meet the finite bit rate limitation during network transmission. The specific steps are as follows: The dynamic model of a DC motor is as follows: in, F M J represents the coefficient of friction, and C represents the rotor inertia. M R represents the motor constant. M L represents armature resistance. M ω represents armature inductance, i represents current, d represents load torque interference, and V represents voltage. Next, setting the sampling interval to T, and using the forward Euler method, the above continuous-time DC motor model is transformed into the following discrete system model: Where, x(k)=[ω i] T v(k) represents the sensor noise. C1 = [0.8 0.6], C2 = [1.6 -0.4], D1 = 0.02, D2 = 0.02, I represents the identity matrix of appropriate dimensions; To address the finite bit rate limitation of the discrete DC motor model described above, an encoding-decoding scheme based on a dynamic event-triggered mechanism is designed. First, the sampled signal is verified by trigger adjustment, and then encoded and transmitted to the decoder over the network. The dynamic event-triggered mechanism is constructed as follows: Where m is the sensor number, e m，y (k)=y m (k m，l )-y m (k) represents the triggering error output by the m-th sensor, k m，l Let k represent the last trigger time of the m-th sensor. m,l+1 This represents the next trigger time of the m-th sensor, and the dynamic threshold h of the m-th sensor. m (k) is updated by the following formula: Where, α m ,β m h is the preset trigger parameter for the m-th sensor. min h represents the lower bound of the threshold. max The upper bound of the threshold is indicated, and arg min() represents the value of the independent variable that enables a function to reach its minimum value. When the signal from the m-th sensor is determined to be trigger-sensitive, it will be encoded using an encoder; the encoded sensor sample signal is as follows: Among them, y m，c (k) represents the encoded trigger output of the m-th sensor. Indicates sensor tag, r m (k) Payload Index; After receiving the signal, the decoder first analyzes... To determine if the signal is sent by sensor m, then via payload index r. m (k) Retrieve the cipher table to reconstruct the signal; the reconstructed signal is as follows: Among them, y m，d (k) represents the decoded signal from the m-th sensor, s m w represents the quantization interval. m Indicates the level of uniform quantization.

3. According to claim 1, a method for generating iterative intervals for dynamic events triggered by ellipsoidal set membership estimation of a DC motor is proposed. An iterative interval generation method based on trigger sampling signals is designed to estimate the state of the DC motor and external disturbances and generate intervals. The specific steps are as follows: First, construct the following iterative estimator: in, r represents the iteration number label; This represents the state estimate for the r-th iteration; This represents the disturbance estimate in the r-th iteration; L a (k), L b (k), L c (k) is the gain of the estimator to be designed; Select The following error system can be obtained: in, e m，q (k)=y m，d (k)-y m (k) represents the quantization error. This iterative estimator can simultaneously estimate the state and disturbances of a DC motor, as demonstrated below: B001: Select And let g [r] (k) satisfies g [r]T (k)g [r] (k)≤1, then It can be represented as: in, From the equation get, Generate the ellipsoid shape at time k; B002: Next, select It can be rewritten as: In the formula, B003: Based on the objective From B002, we can obtain In the formula, Generate the ellipsoid shape matrix at time k+1, Λ0=diag(-1,0,0,0,0,0,0,0,0), where diag() represents a diagonal matrix; B004: Next, assuming We can obtain: B005: Next, using the S-process technique, B003 can be scaled down to: in, It is a positive number; B006: Then, applying the Schul complement lemma to B005, we can obtain the following stability condition:

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