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CN-121978950-A - Fuzzy H for implanted wearable medical device∞Closed loop control method

CN121978950ACN 121978950 ACN121978950 ACN 121978950ACN-121978950-A

Abstract

Compared with the prior art, the fuzzy H ∞ closed-loop control method for the implanted wearable medical equipment is creatively coupled with multi-class network induction phenomenon, and the method for the fuzzy closed-loop control for the implanted wearable medical equipment breaks through the limitation of researching multi-independent or partial network induction phenomenon by considering dynamic quantization and random communication protocol (SCP) scheduling in a T-S fuzzy system communication network with parameter uncertainty and disturbance noise for the first time, is more suitable for multi-class interference coupling scenes in an actual networked control system, and expands the application range of the T-S fuzzy system in a complex network environment.

Inventors

  • WU BO
  • HU JUNWEI
  • WAN LIGUANG
  • XIAO FAN

Assignees

  • 湖北师范大学

Dates

Publication Date
20260505
Application Date
20260206

Claims (10)

  1. 1. The fuzzy H ∞ closed-loop control method for the implanted wearable medical equipment is characterized by comprising the following steps of: A. Establishing a T-S fuzzy model of nonlinear NCSs, namely describing system dynamics by adopting a T-S fuzzy rule aiming at a discrete time nonlinear system, wherein the T-S fuzzy model is the first one The bar rule is expressed as if Equal to ,., And Equal to The output equation is: (1); Wherein, the In the form of a discrete time period, , In order to blur the number of rules, The number of preconditions is represented by the number of preconditions, The representation comprises Is used to determine the fuzzy set of (a), In order to be in the state of the system, In order to measure the output of the device, In order to control the output of the device, In order to control the input of the device, Is an external disturbance and belongs to ; 、 、 、 、 、 Is a known dimensionality constant matrix; 、 For the norm bounded parameter uncertainty, satisfy: (2); Wherein the method comprises the steps of To meet the requirements of Is used to determine the uncertainty matrix of (1), 、 、 Defining normalized membership functions for a matrix of known constants: (3); Wherein the method comprises the steps of And (2) and , And then the T-S fuzzy system is rewritten as: (4); Wherein: And in the subsequent derivation Is simply described as ; B. design of communication network related mechanism by dynamic quantization strategy Quantization, basic quantization function The method meets the following conditions: (5); Wherein the method comprises the steps of 0 Is the quantization range and, 0 Is the quantization error boundary, and the dynamic quantizer output is: (6); Wherein the method comprises the steps of Quantization error for dynamic quantization parameters Satisfy the following requirements (When ) When adopting random communication protocol (SCP) to dispatch Transmission, SCP through Markov chain In the description of the present invention, Indicating time of day The sensor accessing to the network has the transition probability of: (7); Post-schedule quantized measurement output Satisfy the following requirements (8); Further rewritten as (9); Wherein the method comprises the steps of , Is a kronecker delta function; C. building an augmentation System defining an augmentation State Combining equations (4), (6), (9), the T-S blur system is reconstructed as: (10); Wherein, the , , 、 、 、 Is a corresponding dimension matrix; D. Designing a dynamic output feedback controller by adopting a mode-dependent and a fuzzy-dependent dynamic output feedback controller, the first of which The rule of the bar is that if Equal to ,., And Equal to The output equation is: (11); Wherein the method comprises the steps of In order to be in the state of the controller, 、 、 、 Defining a gain matrix for the controller Combining equations (10), (11) yields a closed loop system: (12); In the middle of 、 、 、 。
  2. 2. The method for closed loop control of fuzzy H ∞ for an implanted wearable medical device of claim 1, further comprising E, defining and quotients: definition 1 closed loop system (12) randomly stabilizes, i.e. when When for any initial conditions And And if the corresponding stability judging condition is met,: (13); Definition 2 closed loop system (12) meets H ∞ performance, i.e. for a given scalar gamma >0, under zero initial conditions, meets the corresponding Performance determination conditions, then: (14); Lemma 1 matrix 、 And For a proper dimension matrix, then for any one of the following Is a matrix of (a) , Hold true if and only if there is a scalar 0 Such that: ; 2, lemma 2, proper dimension matrix 、 、 And Scalar quantity If (if) The corresponding inequality holds.
  3. 3. The method for closed loop control of fuzzy H ∞ for implanted wearable medical device of claim 2, further comprising F, providing sufficient conditions for random stabilization and H ∞ performance analysis (theorem 1) for T-S fuzzy system (10), given controller (11) and its gain matrix, quantizer range And error bound If a matrix is present And , For any one , The method meets the following conditions: (15); Wherein, the , , And parameters of The on-line adjustment strategy of (a) is: (16); 1. Gtoreq., then in the Markov chain defined by transition probabilities (7) Under the controlled SCP scheduling, the controller (11) and the quantizer (6) can ensure the random stability of the closed-loop system (12) and the performance gamma of H ∞ to be more than 0; constructing a Lyapunov function dependent on mode and blur: (17) Wherein, the method comprises the steps of, Quantization error according to the formulas (5) and (16) The method meets the following conditions: (19) And in combination with the system (4), the formula (19) is rewritable as well Related inequalities, and there are: (21); (22); ; (24); ; ; (25) Wherein, the method comprises the steps of, , Differential correlation matrix for Lyapunov function; For non-zero Recording There are: (27); (30); (31); (32)。
  4. 4. The method for closed loop control of fuzzy H ∞ for an implantable wearable medical device of claim 3, further comprising G, performing a dynamic output feedback controller design: theorem 2 for T-S fuzzy system (10), given controller (11) and its gain parameters, quantizer range And error bound If a matrix is present 、 Scalar quantity And (For arbitrary) , ) The method meets the following conditions: (33); Wherein, the 、 、 Under SCP scheduling, the controller and quantizer can ensure that the closed-loop system (12) is randomly stable and satisfies H ∞ performance gamma >0, and the proof of theorem 2 depends on rewriting the formula (15) to be contained 、 The form of (2) is: (34); And according to the quotation mark 1: (35); Theorem 3 given scalar quantity Probability of transition Quantizer range And error bound If a matrix is present 、 、 Scalar quantity 、 And (To arbitrary , ) The method comprises the following steps: (36); Wherein, the 、 、 、 、 、 The dimension adjustment matrix is ; ; In the time-course of which the first and second contact surfaces, ) The closed loop system (12) is randomly stable and meets Performance gamma >0; derivation of theorem 3 relies on definition And combining the primer 2 to obtain: (37); Wherein, the 。
  5. 5. The method of claim 1, wherein the T-S fuzzy model approximates the nonlinear object with a set of linear models by local linearization, and the nonlinear object is smoothly connected in combination with fuzzy membership functions, approximating the nonlinear object with arbitrary precision, and the norm-bounded uncertainty in equation (2) exists in both the state and measurement outputs.
  6. 6. The method of claim 1, wherein the dynamic quantizer in step B is more generic than the static quantization strategy by The dynamic adjustment of (equation (16)) increases the system attraction domain and decreases the steady state limit cycle, the SCP schedule avoids data collisions by markov chain transition probabilities and signal update rules, and the T-S fuzzy system is reconfigured to equation (10) to handle the time lag term introduced by the SCP.
  7. 7. The method of closed loop control of fuzzy H ∞ for implanted wearable medical devices of claim 1, wherein in step C the state dimension of the feedback controller is dynamically output And (3) with The dimensions are consistent, increasing the numerical complexity but simplifying the decoupling of variables in the controller design, and the controller is synchronized with the markov mode of the SCP schedule.
  8. 8. The method of closed loop control of ambiguous H ∞ for an implantable wearable medical device of claim 2 wherein in step E a matrix is introduced by reducing conservatism by constructing a mode and ambiguous Lyapunov function dependent Treatment with relaxation method (formula (22)) The nonlinear terms, in combination with the S-Procedure and Schur complement derivatives (21), (25), (27), (30), (32), ensure the closed loop system random stability and H ∞ performance.
  9. 9. The method for closed-loop control of fuzzy H ∞ for implanted wearable medical devices of claim 3, wherein theorem 2 in step F separates the uncertainty in equation (2) by way of a lemma 1 、 Converting formula (15) into a compound containing Matrix inequality (33), theorem 3 by definition 、 The parameters, in combination with the quotients 2, convert the equation (37) into a solvable linear matrix inequality (36), resulting in the controller gain.
  10. 10. The method for closed-loop control of fuzzy H ∞ for an implantable wearable medical device of claim 1, further comprising a step of simulation verification by numerical example and mass-bullet-damper mechanical system example, setting system parameters, and interference Uncertainty of Solving the linear matrix inequality (36) of theorem 3 to obtain the controller gain, wherein the simulation result shows that the state of the closed-loop system is converged stably, and the H ∞ performance is satisfied.

Description

Fuzzy H ∞ closed-loop control method for implanted wearable medical equipment Technical Field The invention belongs to the technical field of communication, and particularly relates to a fuzzy H ∞ closed-loop control method for implanted wearable medical equipment. Background With the rapid development of communication technology, networked Control Systems (NCSs) are focused in engineering application by virtue of low cost, simple installation, hard wire reduction, high reliability and the like, and related control and filtering problem researches also emerge a great deal of literature; In the traditional NCSs research, it is often assumed that all sensors can be simultaneously connected with a communication network to transmit signals, but the limited bandwidth is realized, the simultaneous connection of multiple sensors is extremely easy to cause data conflict, the assumption is not true in an actual scene, in order to solve the problem, the sequence of introducing a communication protocol to schedule the sensors to be connected with the network becomes an effective means, common protocols comprise a Round-Robin protocol (Round-Robin), a one-time Discard (Try-Once-Discard) protocol, a random communication protocol (SCP) and the like, while the traditional research aims at comprehensive development and exploration of the performance analysis and control of NCSs under protocol scheduling, such as modeling a closed loop system under the SCP as a multivariable time-varying delay random system, researching output feedback control, or adopting a Markov process to model the SCP and obtaining sufficient conditions of system stability and performance through a Li Kadi differential equation (RDE) method, the researches mostly do not fully consider the coupling influence of various network induction phenomena, and have limitations in the design of control strategies, such as adopting only static output feedback or observer-based output feedback control; The method comprises the steps of carrying out quantitative processing on signals before the signals are transmitted through a communication network, wherein the influence of the quantitative effect on NCSs (non-linear systems) is unavoidable, the current mainstream quantitative modeling method is divided into a static quantitative strategy and a dynamic quantitative strategy, wherein the dynamic quantitative strategy has a memory function, the quantitative level can be dynamically adjusted, an attraction domain is enlarged, a steady-state limit cycle is reduced, compared with the static quantitative strategy, the dynamic quantitative strategy is more universal, meanwhile, fuzzy control is widely applied in solving the actual engineering problem, a Takagi-Sugeno (T-S) fuzzy model approximates a nonlinear system to a group of linear models through a local linearization method, smooth connection is realized by utilizing a fuzzy membership function, and the linear models become important tools for researching the nonlinear system, in recent years, the analysis and comprehensive research on the T-S fuzzy system with quantification are carried out, such as the research on the non-fragile filtering of the T-S fuzzy system, the feedback control of the fuzzy Markov jump system and the like, but most researches are based on state feedback, the assumption that the state feedback is directly used for the controller design, the state of the current research is difficult to directly acquire the state of the system, the feedback control is more practical, in addition, the consideration of the fact, the fact that the fuzzy control is very good and the influence on the dynamic control of the T-S fuzzy control is needed to be fully solved on the condition under the condition of the proper design of the comprehensive performance of the fuzzy control of the system. Disclosure of Invention The invention aims to solve the defects of the prior art and provides a fuzzy H ∞ closed-loop control method for implantation of wearable medical equipment; in order to achieve the above purpose, the technical scheme of the invention is realized as follows: The invention provides a fuzzy H ∞ closed-loop control method for implanted wearable medical equipment, which comprises the following steps: A. Establishing a T-S fuzzy model of nonlinear NCSs, namely describing system dynamics by adopting a T-S fuzzy rule aiming at a discrete time nonlinear system, wherein the T-S fuzzy model is the first one The bar rule is expressed as ifEqual to,., AndEqual toThe output equation is: (1); Wherein, the In the form of a discrete time period,,In order to blur the number of rules,The number of preconditions is represented by the number of preconditions,The representation comprisesIs used to determine the fuzzy set of (a),In order to be in the state of the system,In order to measure the output of the device,In order to control the output of the device,In order to control the input of the device,Is an external distur