A posture adjustment control optimization method for electric standing bed

Abstract

The invention discloses a posture adjustment control optimization method for an electric stand-up bed, which belongs to the technical field of PID control optimization. The specific steps are as follows: step one, constructing a posture adjustment control system for the electric stand-up bed; step two, improving a gazelle optimization algorithm; step three, using the improved gazelle optimization algorithm to perform parameter optimization on a speed outer-loop PID controller in the posture adjustment control system for the electric stand-up bed; step four, inputting three optimal parameters of the PID control obtained by optimizing the improved gazelle optimization algorithm into the speed outer-loop PID controller in the posture adjustment control system for the electric stand-up bed to optimize the posture adjustment control effect; the improved gazelle optimization algorithm is used to optimize the speed outer-loop PID controller of a regulating motor of the electric stand-up bed, thereby improving the dynamic response and control accuracy of the motor, and significantly improving the comfort and safety of the electric stand-up bed in the posture adjustment process.

Description

Technical Field

[0001] The invention belongs to the field of PID control optimization, and in particular relates to a posture adjustment control optimization method for an electric standing bed. Background Art

[0002] The posture adjustment of the electric standing bed is achieved through the built-in motor drive system, combined with multi-axis coordinated control technology, to achieve precise angle adjustment of different parts of the bed (such as the back and legs), and support smooth transition from lying flat, tilted to upright and other postures. The bed position and load distribution are monitored in real time through sensors. Optimized trajectory planning and acceleration limitation algorithms are used during the adjustment process to ensure smooth movements without obvious impact, while ensuring the comfort and safety of the user. The system can also provide a variety of preset posture modes according to user needs, and perform intelligent control through voice, buttons or mobile devices to meet the usage needs of multiple scenarios such as medical rehabilitation and elderly care.

[0003] PID controller is a feedback control algorithm widely used in the field of industrial control. It adjusts the system error through three parts: proportional (P), integral (I) and differential (D). Proportional control generates control quantity according to the current error, responds quickly and reduces the error; integral control accumulates historical errors and eliminates steady-state errors; differential control predicts the error change trend, suppresses overshoot and oscillation, thereby achieving accurate and stable control of the system. Although the PID controller has a simple structure and is easy to implement, traditional PID control also has its shortcomings: first, it has poor adaptability to nonlinear systems and time-varying systems, and parameter adjustment requires manual repeated experiments; second, it is sensitive to external interference and noise, which can easily lead to a decrease in control performance; third, in a multi-variable coupled system, it is difficult to handle the complex interactions between variables.

[0004] The Gazelle Optimization Algorithm (GOA) is a metaheuristic algorithm inspired by the survival behavior of gazelles. The development phase of the algorithm simulates a gazelle grazing peacefully in the absence of a predator or a predator stalking it. Once a predator is found, GOA enters the exploration phase. In the exploration phase, the gazelle has to run faster than the predator and find a safe refuge. These two phases are iteratively repeated, subject to termination criteria, to find the optimal solution to the optimization problem. Summary of the invention

[0005] The purpose of the present invention is to use the improved gazelle optimization algorithm to optimize the speed outer loop PID controller of the electric standing bed's regulating motor. The improved gazelle optimization algorithm has stronger adaptability and better optimization accuracy than the standard gazelle optimization algorithm. By optimizing the PID controller, not only the dynamic performance and steady-state accuracy of the motor can be improved, but also the comfort and safety of the electric standing bed in the posture adjustment process can be significantly improved, providing an intelligent and optimized driven solution for medical rehabilitation equipment and elderly care products.

[0006] To achieve the above-mentioned purpose, the present invention adopts a posture adjustment control optimization method for an electric standing bed, and the specific steps of the method are as follows.

[0007] Step 1: Construct a posture adjustment control system for the electric standing bed, the control system includes: a command parsing module, a speed outer loop PID controller, a current inner loop controller, an improved gazelle optimization algorithm, a back adjustment motor, a leg adjustment motor, and an encoder module.

[0008] Step 2: Improve the Gazelle optimization algorithm. The specific improvement strategies are as follows:

[0009] D1. Use the chaotic dual mapping strategy to initialize the population, use Tent mapping and cubic mapping to generate two chaotic mapping sequences respectively, map the fused chaotic numbers into the search space, and initialize the search population of the algorithm;

[0010] D2. Use an adaptive smooth decay factor to generate the predator cumulative effect factor CF in the gazelle optimization algorithm, adjust the CF decay rate based on the dynamic changes of individual fitness values, and add a smooth function for transition;

[0011] D3. Use a Levy cosine adaptive search strategy to improve the update strategy of the development phase of the gazelle optimization algorithm, adjust the position update of individuals in the population through the jump amount generated by the Levy flight distribution, and dynamically perturb according to the current iteration stage through the cosine function.

[0012] Step 3: Use the improved gazelle optimization algorithm to optimize the parameters of the speed outer loop PID controller in the posture adjustment control system of the electric standing bed, and obtain the optimal set of PID control parameters, Kp, Ki, and Kd, through algorithm optimization.

[0013] Step 4: Input the three optimal parameters of PID control obtained by optimizing the improved gazelle optimization algorithm into the speed outer loop PID controller in the posture adjustment control system of the electric standing bed to optimize the posture adjustment control effect.

[0014] Furthermore, the posture adjustment control system of the electric standing bed described in step 1, the execution process of the system is: inputting the posture command to be achieved into the command parsing module, decomposing the specific command into the target angle command that the motor needs to adjust, the adjustment motor is divided into a back adjustment motor and a leg adjustment motor, the target angle command and the actual angle detected in real time by the encoder module are used for error calculation, the real-time error e(t) is input into the speed outer loop PID controller, the speed outer loop PID controller is optimized by the improved gazelle optimization algorithm to output the control quantity u(t), and the control quantity u(t) is input into the current inner loop controller, the current inner loop controller outputs the corresponding control quantity u2(t) according to the speed outer loop command, and the control quantity u2(t) is input into the back adjustment motor and the leg adjustment motor for posture adjustment, wherein the calculation formula of the control quantity u(t) is as shown in formula (1):

[0015] (1);

[0016] In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error.

[0017] Furthermore, as described in D1, the chaotic dual mapping strategy is used to initialize the population, and the specific mathematical model is shown in formula (2):

[0018] (2);

[0019] In formula (2), X represents the position of the initial population, lb represents the lower limit of the search space, and ub represents the upper limit of the search space. represents the fused chaotic sequence, and the calculation formula is shown in formula (3):

[0020] (3);

[0021] In formula (3), λ represents the weight coefficient, represents the chaotic sequence generated by Tent mapping, Represents a chaotic sequence generated by a cubic map.

[0022] Furthermore, by fusing different chaotic maps, the generated initial population has stronger randomness and distribution uniformity, which can avoid the initial population from being concentrated in certain areas in the search space, improve the global search capability, and reduce the possibility of the algorithm falling into the local optimum.

[0023] Furthermore, as described in D2, an adaptive smooth attenuation factor is used to generate the predator cumulative effect factor CF in the gazelle optimization algorithm. The specific mathematical model is shown in formula (4):

[0024] (4);

[0025] In formula (4), iter represents the current number of iterations, max_iter represents the maximum number of iterations, α represents the adjustment coefficient, β represents the fitness value adjustment weight, ε represents a random number between [0, 1], and sigmoid() represents a smoothing function whose value range is [-1, 1].

[0026] The adaptive smooth decay factor can dynamically adjust the decay rate according to the changes in the individual fitness value during the optimization process. The optimization algorithm can adaptively adjust the strategy at different stages. In the initial stage, the decay rate is slow, which helps to search a wide solution space. In the later stage, the decay rate gradually accelerates, which promotes the rapid convergence of the algorithm. By introducing a smoothing mechanism, the mutation or oscillation caused by the fluctuation of the individual fitness value is reduced. The decay rate will not change dramatically, but will be adjusted gradually.

[0027] Furthermore, as described in D3, a Levy cosine adaptive search strategy is used to improve the update strategy of the development phase of the gazelle optimization algorithm. The specific mathematical model is shown in formula (5):

[0028] (5);

[0029] In formula (5), X(iter+1) represents the updated individual position, X(iter) represents the current individual position, Levy() represents the jump value generated according to Levy distribution, iter represents the current iteration number, max_iter represents the maximum iteration number, S represents the grazing speed of gazelle, R represents a random number between [0, 1], Elite represents the optimal position in the population, and Rb represents the random number of Brownian motion.

[0030] Furthermore, by combining Levy flight and cosine perturbation, the global search capability and the gradual transition local optimization capability in the development stage are improved. Levy flight improves the algorithm's optimization range by continuously adjusting the jump value to avoid falling into the local optimum, while cosine perturbation dynamically adjusts the step size to ensure the quality of population update while improving the randomness of the algorithm search.

[0031] Further, as shown in step 3, the improved gazelle optimization algorithm is used to optimize the parameters of the speed outer loop PID controller in the posture adjustment control system of the electric standing bed, and the specific steps are:

[0032] S1. Simulate the operation of the posture adjustment control system of the electric standing bed and design a transfer function to describe the dynamic characteristics of the control system. The transfer function is shown in formula (6):

[0033] (6);

[0034] In formula (6), s is a complex frequency variable;

[0035] S2, set the initial parameters of the improved gazelle optimization algorithm, including the population size N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub and lower limit lb of the search space, and initialize the population through the chaotic dual mapping strategy. The specific mathematical model is shown in formula (2);

[0036] S3. Establish a mapping relationship between the improved gazelle optimization algorithm and the speed outer loop PID controller parameters in the posture control system of the electric standing bed. Specifically, the problem dimension dim=3, and the corresponding individual position vector in the population is X(iter)=( , , ), establish a mapping relationship with the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller, that is, the individual position vector in the algorithm optimization process represents the control parameters of the PID control;

[0037] S4. Set the fitness value function of the improved gazelle optimization algorithm. The fitness value of each individual represents the index of the individual in the optimization process. The fitness value function is set to minimize the error function of the system. The specific formula is shown in formula (7):

[0038] (7);

[0039] In formula (7), J represents the fitness value, r(t) represents the target value, y(t) represents the actual value, and T represents the total system operation time;

[0040] S5, calculating the fitness value of the initial population, sorting the fitness values ​​of the population, and selecting the optimal fitness value and the corresponding optimal individual position;

[0041] S6. updating the positions of individuals in the population by improving the mathematical model of the gazelle optimization algorithm, and updating the optimal individuals and optimal fitness values ​​in the population;

[0042] S7, determine whether the current number of iterations has reached the maximum number of iterations, if not, continue to execute S6 to search for the best, otherwise, output the best position in the population to complete the search for the best.

[0043] Furthermore, as described in S6, the positions of individuals in the population are updated by improving the mathematical model of the gazelle optimization algorithm, and the specific steps are as follows:

[0044] S61. If the random value r<0.5, enter the development phase of the improved gazelle optimization algorithm, and use a Levy cosine adaptive search strategy to update the position. The specific mathematical model is shown in formula (5);

[0045] S62, if the random value r>=0.5, enter the exploration phase of the improved gazelle optimization algorithm. The mathematical model for updating the population position in the exploration phase is shown in formula (8):

[0046] (8);

[0047] In formula (8), X(iter+1) represents the updated individual position, X(iter) represents the current individual position, S represents the grazing speed of gazelle, R represents a random number between [0, 1], Elite represents the optimal position in the population, Rb represents the random number of Brownian motion, Rl represents the random number generated by Levy distribution, CF represents the predator cumulative effect factor, and the calculation formula is shown in formula (4). μ represents the adjustment coefficient. If the current iteration number is a multiple of 2, it takes a value of -1, otherwise it takes a value of 1. Iter represents the current iteration number, and max_iter represents the maximum iteration number.

[0048] S63, calculating the fitness value of the updated population, sorting, and updating the optimal fitness value and optimal position in the population.

[0049] By adopting the above technical scheme, the beneficial effects of the present invention are as follows: the mathematical model of the gazelle optimization algorithm is improved by generating a predator cumulative effect factor CF in the gazelle optimization algorithm through an adaptive smooth attenuation factor and a Levy cosine adaptive search strategy, and the population is initialized through a chaotic dual mapping strategy, so as to ensure the quality of the search population while increasing the diversity of the population, balance the development and exploration stages of the algorithm, improve the optimization accuracy and global search ability of the algorithm, and use the improved gazelle optimization algorithm to optimize the speed outer loop PID controller of the electric standing bed's regulating motor. By optimizing the PID controller, not only the dynamic response and control accuracy of the motor can be improved, but also the comfort and safety of the electric standing bed during posture adjustment can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 The present invention is a flow chart of a posture adjustment control optimization method for an electric standing bed.

[0051] Figure 2 This is the model diagram of the posture adjustment control system of the electric standing bed.

[0052] Figure 3 Comparison of fitness values ​​during the optimization process between the improved Gazelle optimization algorithm and the original Gazelle optimization algorithm.

[0053] Figure 4 Comparison chart of the improved Gazelle optimization algorithm and the original Gazelle optimization algorithm for optimizing the speed outer loop PID controller. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0055] The present invention provides a technical solution: a posture adjustment control optimization method for an electric standing bed, which specifically includes the following steps: Figure 1 shown.

[0056] Step 1: Construct a posture adjustment control system for an electric standing bed, the control system includes: a command parsing module, a speed outer loop PID controller, a current inner loop controller, an improved gazelle optimization algorithm, a back adjustment motor, a leg adjustment motor, and an encoder module, such as Figure 2 shown.

[0057] Furthermore, the posture adjustment control system of the electric standing bed described in step 1, the execution process of the system is: inputting the posture command to be achieved into the command parsing module, decomposing the specific command into the target angle command that the motor needs to adjust, the adjustment motor is divided into a back adjustment motor and a leg adjustment motor, the target angle command and the actual angle detected in real time by the encoder module are used for error calculation, the real-time error e(t) is input into the speed outer loop PID controller, the speed outer loop PID controller is optimized by the improved gazelle optimization algorithm to output the control quantity u(t), and the control quantity u(t) is input into the current inner loop controller, the current inner loop controller outputs the corresponding control quantity u2(t) according to the speed outer loop command, and the control quantity u2(t) is input into the back adjustment motor and the leg adjustment motor for posture adjustment, wherein the calculation formula of the control quantity u(t) is as shown in formula (1):

[0058] (1);

[0059] In formula (1), Kp represents proportional gain, Ki represents integral gain, Kd represents differential gain, and e(t) represents real-time error.

[0060] Step 2: Improve the Gazelle optimization algorithm. The specific improvement strategies are as follows:

[0061] D1. Use the chaotic dual mapping strategy to initialize the population, use Tent mapping and cubic mapping to generate two chaotic mapping sequences respectively, map the fused chaotic numbers into the search space, and initialize the search population of the algorithm;

[0062] D2. Use an adaptive smooth decay factor to generate the predator cumulative effect factor CF in the gazelle optimization algorithm, adjust the CF decay rate based on the dynamic changes of individual fitness values, and add a smooth function for transition;

[0063] D3. Use a Levy cosine adaptive search strategy to improve the update strategy of the development phase of the gazelle optimization algorithm, adjust the position update of individuals in the population through the jump amount generated by the Levy flight distribution, and dynamically perturb according to the current iteration stage through the cosine function.

[0064] Furthermore, as described in D1, the chaotic dual mapping strategy is used to initialize the population, and the specific mathematical model is shown in formula (2):

[0065] (2);

[0066] In formula (2), X represents the position of the initial population, lb represents the lower limit of the search space, and ub represents the upper limit of the search space. represents the fused chaotic sequence, and the calculation formula is shown in formula (3):

[0067] (3);

[0068] In formula (3), λ represents the weight coefficient, represents the chaotic sequence generated by Tent mapping, Represents a chaotic sequence generated by a cubic map.

[0069] Furthermore, as described in D2, an adaptive smooth attenuation factor is used to generate the predator cumulative effect factor CF in the gazelle optimization algorithm. The specific mathematical model is shown in formula (4):

[0070] (4);

[0071] In formula (4), iter represents the current number of iterations, max_iter represents the maximum number of iterations, α represents the adjustment coefficient, β represents the fitness value adjustment weight, ε represents a random number between [0, 1], and sigmoid() represents a smoothing function whose value range is [-1, 1].

[0072] Furthermore, as described in D3, a Levy cosine adaptive search strategy is used to improve the update strategy of the development phase of the gazelle optimization algorithm. The specific mathematical model is shown in formula (5):

[0073] (5);

[0074] In formula (5), X(iter+1) represents the updated individual position, X(iter) represents the current individual position, Levy() represents the jump value generated according to Levy distribution, iter represents the current iteration number, max_iter represents the maximum iteration number, S represents the grazing speed of gazelle, R represents a random number between [0, 1], Elite represents the optimal position in the population, and Rb represents the random number of Brownian motion.

[0075] Step 3: Use the improved gazelle optimization algorithm to optimize the parameters of the speed outer loop PID controller in the posture adjustment control system of the electric standing bed, and obtain the optimal set of PID control parameters, Kp, Ki, and Kd, through algorithm optimization.

[0076] Further, as shown in step 3, the improved gazelle optimization algorithm is used to optimize the parameters of the speed outer loop PID controller in the posture adjustment control system of the electric standing bed, and the specific steps are:

[0077] S1. Simulate the operation of the posture adjustment control system of the electric standing bed and design a transfer function to describe the dynamic characteristics of the control system. The transfer function is shown in formula (6):

[0078] (6);

[0079] In formula (6), s is a complex frequency variable;

[0080] S2, set the initial parameters of the improved gazelle optimization algorithm, including the population size N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub and lower limit lb of the search space, and initialize the population through the chaotic dual mapping strategy. The specific mathematical model is shown in formula (2);

[0081] S3. Establish a mapping relationship between the improved gazelle optimization algorithm and the speed outer loop PID controller parameters in the posture control system of the electric standing bed. Specifically, the problem dimension dim=3, and the corresponding individual position vector in the population is X(iter)=( , , ), establish a mapping relationship with the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller, that is, the individual position vector in the algorithm optimization process represents the control parameters of the PID control;

[0082] S4. Set the fitness value function of the improved gazelle optimization algorithm. The fitness value of each individual represents the index of the individual in the optimization process. The fitness value function is set to minimize the error function of the system. The specific formula is shown in formula (7):

[0083] (7);

[0084] In formula (7), J represents the fitness value, r(t) represents the target value, y(t) represents the actual value, and T represents the total system operation time;

[0085] S5, calculating the fitness value of the initial population, sorting the fitness values ​​of the population, and selecting the optimal fitness value and the corresponding optimal individual position;

[0086] S6. updating the positions of individuals in the population by improving the mathematical model of the gazelle optimization algorithm, and updating the optimal individuals and optimal fitness values ​​in the population;

[0087] S7, determine whether the current number of iterations has reached the maximum number of iterations, if not, continue to execute S6 to search for the best, otherwise, output the best position in the population to complete the search for the best.

[0088] Furthermore, as described in S6, the positions of individuals in the population are updated by improving the mathematical model of the gazelle optimization algorithm, and the specific steps are as follows:

[0089] S61. If the random value r<0.5, enter the development phase of the improved gazelle optimization algorithm, and use a Levy cosine adaptive search strategy to update the position. The specific mathematical model is shown in formula (5);

[0090] S62, if the random value r>=0.5, enter the exploration phase of the improved gazelle optimization algorithm. The mathematical model for updating the population position in the exploration phase is shown in formula (8):

[0091] (8);

[0092] In formula (8), X(iter+1) represents the updated individual position, X(iter) represents the current individual position, S represents the grazing speed of gazelle, R represents a random number between [0, 1], Elite represents the optimal position in the population, Rb represents the random number of Brownian motion, Rl represents the random number generated by Levy distribution, CF represents the predator cumulative effect factor, and the calculation formula is shown in formula (4). μ represents the adjustment coefficient. If the current iteration number is a multiple of 2, it takes a value of -1, otherwise it takes a value of 1. Iter represents the current iteration number, and max_iter represents the maximum iteration number.

[0093] S63, calculating the fitness value of the updated population, sorting, and updating the optimal fitness value and optimal position in the population.

[0094] Step 4: Input the three optimal parameters of PID control obtained by optimizing the improved gazelle optimization algorithm into the speed outer loop PID controller in the posture adjustment control system of the electric standing bed to optimize the posture adjustment control effect.

[0095] Furthermore, in order to verify that the posture adjustment control optimization method for an electric standing bed proposed in the present invention has stronger performance than other methods, the present invention will perform simulations through Matlab and Simulink, improve the mathematical model of the original gazelle optimization algorithm through Matlab, and use Simulink to construct a posture adjustment control system for an electric standing bed, including a command parsing module, a speed outer loop PID controller, a current inner loop controller, an improved gazelle optimization algorithm, a back adjustment motor, a leg adjustment motor, and an encoder module, and initialize the parameters of the improved gazelle optimization algorithm, where the population size is N=100, the problem dimension dim=3, the maximum number of iterations max_iter=25, the upper limit ub=[100,100,100] of the search space, and the lower limit lb=[0.001, 0.001, 0.001].

[0096] Furthermore, Figure 3 The comparison chart of the fitness values ​​of the improved Gazelle optimization algorithm and the original Gazelle optimization algorithm during the optimization process shows that the improved Gazelle optimization algorithm has a lower fitness value during the optimization process, indicating that the optimization accuracy is higher and the optimization speed is faster during the optimization process. It reaches the optimal position in about 10 iterations. Figure 4 A comparison chart of the speed outer loop PID controller optimized by the improved gazelle optimization algorithm and the original gazelle optimization algorithm is shown. The target value is set to 1 unit. It can be seen from the figure that the control amount overshoot of the speed outer loop PID controller optimized by the improved gazelle optimization algorithm is lower, the stability is better, and the adjustment is completed in a shorter time to reach a stable state.

Claims

1. A posture adjustment control optimization method for an electric standing bed, characterized in that: The specific steps are: Step 1: construct a posture adjustment control system for the electric standing bed, the control system comprising: a command parsing module, a speed outer loop PID controller, a current inner loop controller, an improved gazelle optimization algorithm, a back adjustment motor, a leg adjustment motor, and an encoder module; Step 2: Improve the Gazelle optimization algorithm. The specific improvement strategies are as follows: D1. Use the chaotic dual mapping strategy to initialize the population, use Tent mapping and cubic mapping to generate two chaotic mapping sequences respectively, map the fused chaotic numbers into the search space, and initialize the search population of the algorithm; D2. Use an adaptive smooth decay factor to generate the predator cumulative effect factor CF in the gazelle optimization algorithm. Adjust the CF decay rate based on the dynamic change of the individual fitness value, and add a smooth function for transition. The specific mathematical model is shown in formula (4): In formula (4), iter represents the current number of iterations, max_iter represents the maximum number of iterations, α represents the adjustment coefficient, β represents the fitness value adjustment weight, ε represents a random number between [0, 1], sigmoid() represents a smoothing function, and the function value range is [-1, 1]; D3. Use a Levy cosine adaptive search strategy to improve the update strategy of the development phase of the gazelle optimization algorithm. The position update of individuals in the population is adjusted by the jump amount generated by the Levy flight distribution, and the cosine function is used to perform dynamic perturbations according to the current iteration stage. The specific mathematical model is shown in formula (5): In formula (5), X(iter+1) represents the updated individual position, X(iter) represents the current individual position, Levy() represents the jump value generated according to Levy distribution, iter represents the current iteration number, max_iter represents the maximum iteration number, S represents the grazing speed of gazelle, R represents a random number between [0, 1], Elite represents the optimal position in the population, and Rb represents the random number of Brownian motion; Step 3: Optimize the parameters of the speed outer loop PID controller in the posture control system of the electric standing bed by using the improved gazelle optimization algorithm, and obtain the optimal set of PID control parameters, Kp, Ki, and Kd, through algorithm optimization; Step 4: Input the three optimal parameters of PID control obtained by optimizing the improved gazelle optimization algorithm into the speed outer loop PID controller in the posture adjustment control system of the electric standing bed to optimize the posture adjustment control effect.

2. The posture adjustment control optimization method for an electric standing bed according to claim 1, characterized in that: In the step 1, the posture adjustment control system of the electric standing bed has the following execution process: the posture command to be achieved is input into the command parsing module, the specific command is decomposed into the target angle command that the motor needs to adjust, the adjustment motor is divided into a back adjustment motor and a leg adjustment motor, the target angle command and the actual angle detected in real time by the encoder module are used for error calculation, the real-time error e(t) is input into the speed outer loop PID controller, the speed outer loop PID controller is optimized by the improved gazelle optimization algorithm to output the control quantity u(t), and the control quantity u(t) is input into the current inner loop controller, the current inner loop controller outputs the corresponding control quantity u2(t) according to the command of the speed outer loop, and the control quantity u2(t) is input into the back adjustment motor and the leg adjustment motor for posture adjustment.

3. The posture adjustment control optimization method for an electric standing bed according to claim 2, characterized in that: In D1, the chaotic dual mapping strategy is used to initialize the population. The specific mathematical model is shown in formula (2): X=lb+(ub-lb)·x fused (2); In formula (2), X represents the position of the initial population, lb represents the lower limit of the search space, ub represents the upper limit of the search space, and x fused represents the fused chaotic sequence, and the calculation formula is shown in formula (3): x fused =λ·x tent +(1-λ)·x cube (3); In formula (3), λ represents the weight coefficient, x tent represents the chaotic sequence generated by Tent mapping, x cube Represents a chaotic sequence generated by a cubic map.

4. The posture adjustment control optimization method for an electric standing bed according to claim 3, characterized in that: In the step three, the improved gazelle optimization algorithm is used to optimize the parameters of the speed outer loop PID controller in the posture adjustment control system of the electric standing bed. The specific steps are: S1. Simulate the operation of the posture adjustment control system of the electric standing bed and design a transfer function to describe the dynamic characteristics of the control system. The transfer function is shown in formula (6): In formula (6), s is a complex frequency variable; S2, set the initial parameters of the improved gazelle optimization algorithm, including the population size N, the problem dimension dim, the maximum number of iterations max_iter, the upper limit ub and lower limit lb of the search space, and initialize the population through the chaotic dual mapping strategy. The specific mathematical model is shown in formula (2); S3. Establish a mapping relationship between the improved gazelle optimization algorithm and the speed outer loop PID controller parameters in the posture adjustment control system of the electric standing bed. Specifically, the problem dimension dim=3, and the corresponding individual position vector in the population is X(iter)=(X1(iter), X2(iter), X3(iter)). Establish a mapping relationship with the proportional coefficient Kp, integral coefficient Ki, and differential coefficient Kd of the PID controller, that is, the individual position vector in the algorithm optimization process represents the control parameters of the PID control; S4. Set the fitness value function of the improved gazelle optimization algorithm. The fitness value of each individual represents the index of the individual in the optimization process. The fitness value function is set to minimize the error function of the system. The specific formula is shown in formula (7): In formula (7), J represents the fitness value, r(t) represents the target value, y(t) represents the actual value, and T represents the total system operation time; S5, calculating the fitness value of the initial population, sorting the fitness values ​​of the population, and selecting the optimal fitness value and the corresponding optimal individual position; S6. By improving the mathematical model of the gazelle optimization algorithm, the positions of the individuals in the population are updated, and the optimal individuals and optimal fitness values ​​in the population are updated. The specific updating steps are: S61. If the random value r<0.5, enter the development phase of the improved gazelle optimization algorithm, and use a Levy cosine adaptive search strategy to update the position. The specific mathematical model is shown in formula (5); S62, if the random value r>=0.5, enter the exploration phase of the improved gazelle optimization algorithm. The mathematical model for updating the population position in the exploration phase is shown in formula (8): In formula (8), X(iter+1) represents the updated individual position, X(iter) represents the current individual position, S represents the grazing speed of gazelle, R represents a random number between [0, 1], Elite represents the optimal position in the population, Rb represents the random number of Brownian motion, Rl represents the random number generated by Levy distribution, CF represents the predator cumulative effect factor, and the calculation formula is shown in formula (4). μ represents the adjustment coefficient. If the current iteration number is a multiple of 2, it takes a value of -1, otherwise it takes a value of 1. Iter represents the current iteration number, and max_iter represents the maximum iteration number. S63, calculating the fitness value of the updated population, sorting, and updating the optimal fitness value and optimal position in the population; S7, determine whether the current number of iterations has reached the maximum number of iterations, if not, continue to execute S6 to search for the best, otherwise, output the best position in the population to complete the search for the best.

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