Intelligent algorithm optimization gate adaptive steady-state adjustment method

Abstract

The invention provides an intelligent algorithm optimization gate adaptive steady-state adjustment method, and relates to the field of water conservancy projects. The intelligent algorithm optimization gate self-adaptive steady-state adjustment method comprises gate adjustment system model building, gate feedback adjustment algorithm definition, complex system transfer function fitting, MATLAB / Simulink simulation verification analysis and one-dimensional hydraulic model verification, and specifically comprises the steps of firstly building a gate self-adaptive steady-state adjustment model, defining a gate feedback adjustment algorithm, and performing one-dimensional hydraulic model verification; considering that a gate adjusting system is a large-time-delay and strong-coupling system, fitting is conducted on a transfer function according to actually-measured water level and flow data, then an IPSO-Fuzzy PID control model is established according to the principle and characteristics of an IPSO optimization algorithm, three simulation models of PID control, Fuzzy PID control and PSO-Fuzzy PID control are established based on MATLAB / Simulink, and a simulation model is established for verifying the self-adaptability of the optimization model. Step signals are applied to simulate external interference, simulation comparison is carried out, the built model is utilized, actual measurement data are input, and optimal control over the gate self-adaptive steady-state adjusting system through an intelligent algorithm is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects, and specifically to an intelligent algorithm optimized gate adaptive steady-state regulation method. Background Technique

[0002] As a key control node for the water supply flow and water level of the channel, the fast response and stability of its dynamic regulation are the key prerequisites for ensuring the safe operation of the water conveyance system and achieving the water transfer project's water conveyance goal. At the same time, it is also an important link to improve the automation level of the channel. Although existing gates mostly adopt mechanical or hydraulic control, when the water storage volume and flow rate change greatly, it often takes a long time to reach the expected regulation goal. During the regulation process, how to effectively reduce the overshoot and response time is directly related to the dynamic performance and stability accuracy of the gate regulation model.

[0003] In recent years, with the continuous development of automatic control, many scholars have tried to introduce the PID algorithm in the regulation of water conservancy facilities. However, the traditional PID control has poor anti-interference ability and cannot adjust parameters in real time, and its effect is not good when dealing with nonlinear and time-varying uncertain systems. Although fuzzy control performs well in anti-interference ability, the performance of the fuzzy controller highly depends on the design and selection of empirical rules. Inappropriate control rules and membership functions will lead to a decline in control performance and control accuracy. Therefore, relevant scholars use an intelligent optimization algorithm, which is an efficient optimization algorithm. Compared with the traditional particle swarm algorithm, it can improve the local search ability, dynamically adjust the search strategy, and improve the tracking accuracy of the optimization process. Therefore, the technical personnel in this field provide an intelligent algorithm optimized gate adaptive steady-state regulation method to solve the problems raised in the above background technique. Summary of the Invention

[0004] (1) Technical Problems to be Solved

[0005] In view of the deficiencies of the prior art, the present invention provides an intelligent algorithm optimized gate adaptive steady-state regulation method, which solves the problems that the traditional PID control has poor anti-interference ability, cannot adjust parameters in real time, and has poor effect when dealing with nonlinear and time-varying uncertain systems.

[0006] (2) Technical Solutions

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent algorithm optimized gate adaptive steady-state regulation method includes building a gate system model, defining a gate feedback regulation algorithm, fitting a complex system transfer function, MATLAB / Simulink simulation verification and analysis, and one-dimensional hydraulic model verification. The specific steps are as follows:

[0008] S1. Build a gate adaptive steady-state regulation model;

[0009] After the model is built, define the gate feedback regulation algorithm according to the working principle of the gate;

[0010] S3. Considering that the gate regulation system is a large time-delay and strong coupling system, fit the transfer function according to the measured water level and flow data;

[0011] S4. Establish an IPSO-Fuzzy PID control model according to the principle and characteristics of the IPSO optimization algorithm;

[0012] S5. Based on MATLAB / Simulink, build three simulation models of PID control, Fuzzy PID control and PSO-Fuzzy PID control respectively. To verify the adaptability of the optimized model, apply a step signal to simulate external interference and conduct a simulation comparison;

[0013] S6. Build a one-dimensional hydraulic model to further verify the obtained conclusions;

[0014] S7. Use the built model to input the measured data, and finally realize the optimal control of the intelligent algorithm for the gate adaptive steady-state regulation system.

[0015] Preferably, when building the gate regulation model of the open channel water diversion project, take the canal pool water level as the controlled variable, the water level deviation as the controlled variable deviation, and the flow change of the upstream gate of the canal pool as the control variable.

[0016] Preferably, build a closed-loop control block diagram according to the working principle of the gate.

[0017] Preferably, make corresponding adjustments to the gate opening according to the water level deviation. This process has a certain time delay, and there are influencing factors that cannot be accurately measured at present, making it difficult to establish an accurate mathematical model; according to engineering experience, the gate regulation system can be approximately regarded as consisting of two first-order inertia links and one pure delay link. The specific calculation formula is as follows:

[0018]

[0019] Among them, G(s) is the transfer function of the system, representing the ratio of the Laplace transform of the output to the input. C(s) is the Laplace transform of the system output, R(s) is the Laplace transform of the system input, K is the gain of the system, reflecting the static amplification ability of the system. T1 and T2 are time constants, describing the dynamic characteristics of the system inertia link. The larger the value, the slower the response. s is the complex frequency variable, τ is the pure delay time, representing the delay time of the system output relative to the input, and e -τs is the Laplace transform of the time-delay link, representing that the output is delayed by τ time.

[0020] Preferably, the secondary regulation loading system based on the secondary regulation principle has non-linearity and time-variation, so it is difficult to obtain an accurate mathematical model of the controlled object. According to the limitations and advantages of fuzzy control and PID control respectively, the two are combined to form a fuzzy PID control, which not only gives play to the characteristics of flexible, strong adaptability and high robustness of fuzzy control, but also retains the good dynamic tracking quality and steady-state accuracy of PID control. Based on this, the complex system can achieve an ideal control effect.

[0021] The fuzzy PID control system mainly consists of two parts: a PID controller with variable parameters and a fuzzy controller. The position deviation e and the deviation change rate ec are used as input parameters and sent into the fuzzy controller. After fuzzification, fuzzy inference and defuzzification, the correction amounts of Δk p , Δk i and Δk d are obtained. According to the fuzzy rules, each parameter of the PID is modified online to make the system reach the best, so as to realize the optimal control of the gate system.

[0022] The particle swarm optimization algorithm is a global optimization algorithm inspired by the activities of birds. During the iteration process, the particles update their velocities and positions by tracking P best and G best to achieve the global optimum. The process of the particle swarm optimization algorithm can be summarized by the following formula:

[0023]

[0024] However, since the inertia weight and learning factor of the traditional particle swarm algorithm are both fixed values, it is easy for the particles to fall into the local optimum during the optimization process. Therefore, this paper adopts an improved particle swarm algorithm with dynamically adjusted inertia weight and learning factor. The inertia weight ω is adjusted in a linearly decreasing manner, and the maximum values of c1 and c2 are set to be equal, so that c1 increases with the increase of the search times, and c2 decreases with the increase of the search times. The improved particle swarm algorithm has a strong global optimization ability in the initial stage of optimization and a strong local search ability in the later stage of optimization. Compared with the particle swarm algorithm, it can effectively avoid the particles from falling into "prematurity" during the optimization process, thus improving the global search ability of the particles while enhancing the search efficiency and accuracy of the algorithm. The change formula of the improved particle swarm algorithm is as follows:

[0025]

[0026] c′1 = 1.2 - c′2;

[0027] In the optimization algorithm, fitness is an important index to evaluate the quality of individual solutions and also the driving force in the algorithm optimization process. This paper selects the ITAE index as the fitness function, which reflects the influence of error and time on the control performance. The fitness function is:

[0028]

[0029] Preferably, an IPSO-Fuzzy PID gate adaptive steady-state regulation simulation model is built based on the MATLAB / Simulink platform. To compare the control effects of different control methods, traditional PID and Fuzzy-PID controllers widely used in the engineering field are used for comparative verification;

[0030] After setting the simulation time step and regulation target, observe the response curves under the control of different controllers in the oscilloscope. At the same time, to verify the adaptability of the model optimized by the IPSO algorithm, after the response curve reaches the desired stable position, a step signal is applied to simulate the influence of external disturbances on the model. The simulation results show that in the dynamic response stage of the system, compared with traditional PID control and Fuzzy PID control, IPSO-Fuzzy PID control can significantly reduce the oscillation amplitude and oscillation frequency of the curve, effectively improve the dynamic characteristics of the system, and make the response curve meet the control requirements of fast response, small overshoot and high robustness. In the adaptive regulation stage after applying disturbances, IPSO-Fuzzy PID control has less response time and reaches stability again at the fastest speed.

[0031] Preferably, to further verify the optimization of control parameters by IPSO, a one-dimensional hydraulic model based on the research area is built; the hydrodynamic module HD is the core module of the one-dimensional hydraulic model. This module is based on the one-dimensional unsteady Saint-Venant equations and can describe the hydrological movement laws of rivers or estuaries from a physical mechanism. Its equations mainly include the continuity equation and the momentum equation:

[0032]

[0033] (III) Beneficial effects

[0034] The present invention provides a method for optimizing the adaptive steady-state regulation of a gate by an intelligent algorithm, which has the following beneficial effects:

[0035] 1. In the present invention, to achieve the adaptive steady-state regulation of the channel gate, an improved particle swarm algorithm is introduced on the basis of the Fuzzy-PID model, and a gate adaptive steady-state regulation model based on IPSO-Fuzzy PID is constructed to optimize the control parameters and dynamically adjust the control strategy to reduce the overshoot and response time in the control process; the transfer function is fitted according to the water level before the gate and the data of the flow rate through the gate, an IPSO Fuzzy-PID controller is built based on MATLAB / Simulink, and a step disturbance signal is applied to verify the anti-interference ability of the model.

[0036] 2. In the present invention, an optimization algorithm is proposed to analyze the influence framework of the gate regulation model, establish a mathematical model of gate regulation, fit the transfer function of the regulation model, and on this basis, construct a gate adaptive steady-state regulation model based on IPSO-Fuzzy PID to seek the optimal control parameters. Then, MATLAB / Simulink is used to simulate and verify the research example to prove the feasibility of the optimization scheme, and a MIKE11 one-dimensional hydraulic model is established to further verify the conclusion. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the system principle of the gate adaptive steady-state regulation method of the present invention;

[0038] Figure 2 It is a schematic diagram of the fuzzy PID control system in the present invention;

[0039] Figure 3 It is a schematic diagram of the fuzzy rule elements in the present invention;

[0040] Figure 4 It is a schematic diagram of the comparison of fitness values before and after the improvement of the particle swarm optimization algorithm in the present invention;

[0041] Figure 5 It is a schematic diagram of the simulation model of the IPSO-Fuzzy PID control system in the present invention;

[0042] Figure 6 It is a schematic diagram of the comparison of dynamic response curves before and after the application of disturbance in the present invention;

[0043] Figure 7 It is a schematic diagram of the channel water level deviation after the application of disturbance in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0045] Embodiment 1:

[0046] As Figure 1-7 shown, the embodiment of the present invention provides an intelligent algorithm to optimize the gate adaptive steady-state regulation method, including gate system model construction, gate feedback regulation algorithm definition, complex system transfer function fitting, MATLAB / Simulink simulation verification analysis, and one-dimensional hydraulic model verification. The specific steps are as follows:

[0047] S1. Build a gate adaptive steady-state regulation model;

[0048] S2. After the model is built, define the gate feedback regulation algorithm according to the working principle of the gate;

[0049] S3. Considering that the gate regulation system is a large time-delay and strong coupling system, fit the transfer function according to the measured water level and flow data;

[0050] S4. According to the principle and characteristics of the IPSO optimization algorithm, establish an IPSO-Fuzzy PID control model;

[0051] S5. Based on MATLAB / Simulink, build three simulation models of PID control, Fuzzy PID control and PSO-Fuzzy PID control respectively. To verify the adaptability of the optimized model, apply a step signal to simulate external interference and conduct a simulation comparison;

[0052] S6. Build a one-dimensional hydraulic model to further verify the obtained conclusions;

[0053] S7. Use the built model to input the measured data, and finally realize the optimal control of the intelligent algorithm for the gate adaptive steady-state regulation system.

[0054] When building the gate regulation model of the open channel water diversion project, the water level of the canal pond is used as the controlled variable, the water level deviation is the deviation of the controlled variable, and the flow change of the upstream gate of the canal pond is used as the control variable.

[0055] Build a closed-loop control block diagram according to the working principle of the gate.

[0056] Make corresponding adjustments to the gate opening according to the water level deviation. This process has a certain time delay, and there are influencing factors that cannot be accurately measured at present, making it difficult to establish an accurate mathematical model; according to engineering experience, the gate regulation system can be approximately regarded as composed of 2 first-order inertia links and 1 pure delay link. The specific calculation formula is as follows:

[0057]

[0058] Among them, G(s) is the transfer function of the system, representing the ratio of the Laplace transform of the output to the input, C(s) is the Laplace transform of the system output, R(s) is the Laplace transform of the system input, K is the gain of the system, reflecting the static amplification ability of the system, T1 and T2 are time constants, describing the dynamic characteristics of the system inertia link. The larger the value, the slower the response. s is the complex frequency variable, τ is the pure delay time, representing the delay time of the system output relative to the input, and e -τs is the Laplace transform of the time-delay link, representing the output delay of τ time.

[0059] The secondary regulation loading system based on the secondary regulation principle has nonlinearity and time-variation, so it is difficult to obtain an accurate mathematical model of the controlled object. According to the limitations and advantages of fuzzy control and PID control respectively, the two are combined to form fuzzy PID control, which not only gives play to the characteristics of flexible, strong adaptability and high robustness of fuzzy control, but also retains the good dynamic tracking quality and steady-state accuracy of PID control. Based on this, ideal control effects can be achieved for complex systems.

[0060] The fuzzy PID control system mainly consists of two parts: a PID controller with variable parameters and a fuzzy controller. The position deviation e and the deviation change rate ec are taken as input parameters and sent into the fuzzy controller. After fuzzification, fuzzy inference and defuzzification, the correction amounts of Δk p , Δk i and Δk d are obtained. According to the fuzzy rules, each parameter of PID is modified online to make the system reach the best, so as to realize the optimal control of the gate system.

[0061] The particle swarm optimization algorithm is a global optimization algorithm inspired by the activities of birds. In the iterative process, the particles update their velocities and positions by tracking P best and G best to achieve the global optimum. The process of the particle swarm optimization algorithm can be summarized by the following formula:

[0062]

[0063] However, since the inertia weight and learning factors of the traditional particle swarm algorithm are both fixed values, it is easy for the particles to fall into the local optimum during the optimization process. Therefore, this paper adopts an improved particle swarm algorithm with dynamically adjusted inertia weight and learning factors. The inertia weight ω is adjusted in a linearly decreasing manner, and the maximum values of c1 and c2 are set to be equal, so that c1 increases with the increase of the search times, and c2 decreases with the increase of the search times. The improved particle swarm algorithm has strong global optimization ability in the initial stage of optimization and strong local search ability in the later stage of optimization. Compared with the particle swarm algorithm, it can effectively avoid the particles from falling into "prematurity" during the optimization process, thus improving the global search ability of the particles while enhancing the search efficiency and accuracy of the algorithm. The change formula of the improved particle swarm algorithm is as follows:

[0064]

[0065] c′1 = 1.2 - c′2;

[0066] In the optimization algorithm, fitness is an important index to evaluate the quality of individual solutions and also the driving force in the algorithm optimization process. This paper selects the ITAE index as the fitness function, which reflects the influence of error and time on the control performance. The fitness function is:

[0067]

[0068] Based on the MATLAB / Simulink platform, an IPSO-Fuzzy PID gate adaptive steady-state regulation simulation model is built. To compare the control effects of different control methods, traditional PID and Fuzzy-PID controllers widely used in the engineering field are used for comparative verification;

[0069] After setting the simulation time step and regulation target, observe the response curves under the control of different controllers in the oscilloscope. At the same time, to verify the adaptability of the model optimized by the IPSO algorithm, after the response curve reaches the desired stable position, a step signal is applied to simulate the influence of external disturbances on the model. The simulation results show that in the dynamic response stage of the system, compared with traditional PID control and Fuzzy PID control, IPSO-Fuzzy PID control can significantly reduce the oscillation amplitude and oscillation frequency of the curve, effectively improve the dynamic characteristics of the system, and make the response curve meet the control requirements of fast response, small overshoot and high robustness. In the adaptive regulation stage after applying the disturbance, IPSO-Fuzzy PID control has less response time and reaches stability again at the fastest speed.

[0070] To further verify the optimization of control parameters by IPSO, a one-dimensional hydraulic model based on the research area is built; the hydrodynamic module HD is the core module of the one-dimensional hydraulic model. This module is based on the one-dimensional unsteady flow Saint-Venant equations and can describe the hydrological movement laws of rivers or estuaries from a physical mechanism. Its equations mainly include the continuity equation and the momentum equation:

[0071]

[0072] Example 2:

[0073] The embodiment of the present invention provides an intelligent algorithm optimized gate adaptive steady-state regulation method, including gate system model building, gate feedback regulation algorithm definition, complex system transfer function fitting, MATLAB / Simulink simulation verification analysis, and one-dimensional hydraulic model verification. The specific steps are as follows:

[0074] S1. In the gate regulation of the open channel water diversion project, the canal pool water level is used as the controlled variable, the water level deviation is the controlled variable deviation, and the flow rate change of the upstream gate of the canal pool is used as the control variable;

[0075] S2. Build a closed-loop control block diagram according to the gate working principle;

[0076] S3. Adjust the opening of the gate according to the water level deviation. This process has a certain time lag, and there are influencing factors that cannot be accurately measured at present, making it difficult to establish an accurate mathematical model. According to engineering experience, the gate control system can be approximately regarded as consisting of two first-order inertia links and one pure time-delay link:

[0077]

[0078] S4. The secondary regulation loading system based on the secondary regulation principle has nonlinearity and time-variation, so it is difficult to obtain an accurate mathematical model of the controlled object. According to the limitations and advantages of fuzzy control and PID control respectively, the two are combined to form a fuzzy PID control, which not only gives play to the characteristics of flexible, strong adaptability and high robustness of fuzzy control, but also retains the good dynamic tracking quality and steady-state accuracy of PID control. Based on this, an ideal control effect can be achieved for complex systems;

[0079] The fuzzy PID control system is mainly composed of a PID controller with variable parameters and a fuzzy controller. The position deviation e and the deviation change rate ec are used as input parameters and sent into the fuzzy controller. After fuzzification, fuzzy inference and defuzzification, the correction amounts of Δk p 、Δk i and Δk d are obtained. According to the fuzzy rules, each parameter of the PID is modified online to make the system reach the best, so as to realize the optimal control of the gate system;

[0080] According to the dynamic model of the gate, combined with its control objectives and system requirements, the control parameters of the fuzzy controller are designed. Among them, e represents the position deviation of the gate opening, and ec is used as the change rate of the deviation. The input and output are divided into 7 fuzzy partitions, and the fuzzy subsets within the defined range of each variable are NB, NM, NS, ZO, PS, PM and PB. In practical applications, the upper and lower limit values of the input and output value domains usually select finite integers. Therefore, in this paper, the domain ranges are all set to -3, 3. Correspondingly, the triangular function and S-shaped function with higher sensitivity are selected as the membership degrees of the input and output. According to the control law of the gate opening, 49 fuzzy rules are designed to form a fuzzy control rule diagram, and at the same time, the control rule surface observation diagram is obtained;

[0081] The particle swarm optimization algorithm is a global optimization algorithm inspired by the activities of birds. In the iterative process, the particles update their velocities and positions by tracking P best and G best to achieve the global optimum. The process of the particle swarm optimization algorithm can be summarized by the following formula:

[0082]

[0083]

[0083] However, since the inertia weight and learning factors of the traditional particle swarm optimization algorithm are both fixed values, it is easy for particles to fall into local optima during the optimization process. Therefore, this paper adopts an improved particle swarm optimization algorithm with dynamically adjusted inertia weight and learning factors. The inertia weight ω is adjusted in a linearly decreasing manner, and the maximum values of c1 and c2 are set to be equal, so that c1 increases with the increase of the search times and c2 decreases with the increase of the search times. The improved particle swarm optimization algorithm has strong global optimization ability in the initial stage of optimization and strong local search ability in the later stage of optimization. Compared with the particle swarm optimization algorithm, it can effectively prevent particles from falling into "prematurity" during the optimization process, thereby improving the global search ability of particles while enhancing the search efficiency and accuracy of the algorithm. The change formula of the improved particle swarm optimization algorithm is as follows:

[0084]

[0085] c′1 = 1.2 - c′2;

[0086] In the optimization algorithm, fitness is an important index to evaluate the quality of individual solutions and also the driving force in the algorithm optimization process. This paper selects the ITAE index as the fitness function, which reflects the influence of error and time on the control performance. The fitness function is:

[0087]

[0088] S5. Respectively take the measured flow rate and water level data as the control quantity and the controlled quantity, and use the System Identification toolbox in Matlab to identify the system for the transfer function to determine the parameters of the gate adjustment model;

[0089] S6. Build an IPSO-Fuzzy PID gate adaptive steady-state regulation simulation model based on the MATLAB / Simulink platform. To compare the control effects of different control methods, traditional PID and Fuzzy-PID controllers widely used in the engineering field are used for comparative verification;

[0090] After setting the simulation time step and adjustment target, observe the response curves under the control of different controllers in the oscilloscope. At the same time, to verify the self-adaptability of the model optimized by the IPSO algorithm, after the response curve reaches the desired stable position, a step signal is applied to simulate the influence of external disturbances on the model. The simulation results show that in the dynamic response stage of the system, compared with traditional PID control and Fuzzy PID control, IPSO-Fuzzy PID control can significantly reduce the oscillation amplitude and oscillation frequency of the curve, effectively improve the dynamic characteristics of the system, and make the response curve meet the control requirements of fast response, small overshoot, and high robustness. In the adaptive regulation stage after applying disturbances, IPSO-Fuzzy PID control has less response time and the fastest speed to reach stability again;

[0091] S7. To further verify the optimization of the control parameters by IPSO, a one-dimensional hydraulic model based on the research area is built;

[0092] The hydrodynamic module HD is the core module of the one-dimensional hydraulic model. This module is based on the one-dimensional unsteady Saint-Venant equations and can describe the hydrological movement laws of rivers or estuaries from a physical mechanism. Its equations mainly include the continuity equation and the momentum equation:

[0093]

[0094] To further simulate the effectiveness of the method of the present invention, the experimental data of the present invention are the water level and flow rate collected by the sensors at the gate measuring station in a certain place, and corresponding standards are set;

[0095] Taking the measured flow rate and water level data as the control quantity and the controlled quantity respectively, the System Identification toolbox in Matlab is used to identify the system of the transfer function, and the parameters of the gate regulation model are determined: K = 0.54, T1 = 0.1048, T2 = -0.3108, τ = -3.76.

[0096] According to the simulation results, the transfer function is:

[0097]

[0098] In this IPSO-Fuzzy PID gate adaptive steady-state regulation model, the final prediction error and the mean square error are used as indicators to evaluate the performance of the transfer function.

[0099]

[0100] Among them, f i is the predicted value, y i is the measured value, m is the number of samples, and n is the order of the fitted model; E FP and E MS The smaller the value, the more accurate the prediction of the model samples based on this transfer function.

[0101] The hardware environment used in this experiment is an 1ntol(R) Core(TM) i5-10500 CPU@3.10GHz processor, an Nvidia GeForce GTX 1050Ti graphics card, and 16G of memory;

[0102] The software environment is: the operating system is Windows11, the acceleration environment is CUDA11.1, the programming language is MATLAB, and the relevant parameters for initializing the particle swarm algorithm are as follows: the initial number of particles is 100, k p = 2.87, ki = 0.03, k d = 5.41, set the particle swarm dimension as D = 5, acceleration constants c1 = c2 = 0.2, inertia factor w = 0.8, particle velocity v max = 0.01, v min = -0.01, maximum number of iterations I max = 100, minimum fitness value F min = 1e - 50; as shown in the following table:

[0103] Evaluation index <![CDATA[E FP > <![CDATA[E MS > Numerical value 0.0396 0.0365

[0104] Table 1 shows the evaluation indexes of the transfer function. It can be seen that after fitting, the prediction accuracy of the transfer function meets the requirements of the model and the dynamic characteristics of the actual system, and can be used for performance optimization and precise control of the gate regulation model;

[0105] Control system Adjustment time / s Overshoot / % IPSO-Fuzzy PID control system 460 3.34 Fuzzy PID control system 665 4.72 Traditional PID control system 780 14.30

[0106] Table 2 shows the performance indexes of the IPSO - Fuzzy PID control system;

[0107] Control system Adjustment time / s Overshoot / % IPSO-Fuzzy PID control system 89 19.61 Fuzzy PID control system 114 19.71 Traditional PID control system 136 19.93

[0108] Table 3 shows the simulation comparison of the control effects after applying disturbances;

[0109] It can be seen from Table 2 and Table 3 that compared with the traditional PID, the adjustment time of the response curve of the IPSO - Fuzzy PID control system is reduced by 41.03%, the overshoot is reduced by 76.64%, and after applying disturbances, the response time is reduced by 34.56%; compared with Fuzzy - PID, the adjustment time of the response curve of the IPSO - Fuzzy PID control system is reduced by 30.83%, the overshoot is reduced by 29.24%, and after applying disturbances, the response time is reduced by 21.93%; thus, it can be seen that the IPSO - Fuzzy PID control system has better control effects compared with FuzzyPID and the traditional PID, and can meet the requirements of small overshoot and fast response time during the gate regulation process.

[0110] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent algorithm for optimizing the adaptive steady-state regulation method of a sluice, characterized in that: It includes the construction of the gate system model, the definition of the gate feedback regulation algorithm, the fitting of the transfer function of the complex system, the MATLAB / Simulink simulation verification and analysis, and the verification of the one-dimensional hydraulic model. The specific steps are as follows: S1. Construct the gate adaptive steady-state regulation model; S2. After the model is constructed, define the gate feedback regulation algorithm according to the working principle of the gate; S3. Considering that the gate regulation system is a large time-delay and strong coupling system, fit the transfer function according to the measured water level and flow data; S4. Establish the IPSO-Fuzzy PID control model according to the principle and characteristics of the IPSO optimization algorithm; S5. Based on MATLAB / Simulink, respectively construct three simulation models of PID control, Fuzzy PID control and PSO-Fuzzy PID control. To verify the adaptability of the optimized model, apply a step signal to simulate external interference and conduct simulation comparison; S6. Construct a one-dimensional hydraulic model to further verify the obtained conclusions; S7. Use the constructed model to input the measured data, and finally realize the optimal control of the intelligent algorithm for the gate adaptive steady-state regulation system.

2. The intelligent algorithm optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: When constructing the gate regulation model of the open-channel water diversion project, the water level of the canal pond is used as the controlled variable, the water level deviation is used as the deviation of the controlled variable, and the flow rate change of the upstream gate of the canal pond is used as the control variable.

3. An intelligent algorithm optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: Construct a closed-loop control block diagram according to the working principle of the gate.

4. An intelligent algorithm optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: Make corresponding adjustments to the gate opening according to the water level deviation. This process has a certain time delay, and there are influencing factors that cannot be accurately measured at present, making it difficult to establish an accurate mathematical model; according to engineering experience, the gate regulation system can be approximately regarded as composed of two first-order inertial links and one pure delay link. The specific calculation formula is as follows: Among them, G(s) is the transfer function of the system, representing the ratio of the Laplace transform of the output to the input. C(s) is the Laplace transform of the system output, R(s) is the Laplace transform of the system input, K is the gain of the system, reflecting the static amplification ability of the system. T1 and T2 are time constants, describing the dynamic characteristics of the inertial link of the system. The larger the value, the slower the response. s is the complex frequency variable, τ is the pure time delay, representing the delay time of the system output relative to the input, and e -τs is the Laplace transform of the time-delay link, indicating that the output is delayed by τ time.

5. An intelligent algorithm optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: The secondary regulation loading system based on the secondary regulation principle has nonlinearity and time-variation, so it is difficult to obtain an accurate mathematical model of the controlled object; according to the limitations and advantages of fuzzy control and PID control, combine the two to form fuzzy PID control, which not only gives play to the characteristics of flexible, strong adaptability and high robustness of fuzzy control, but also retains the good dynamic tracking quality and steady-state accuracy of PID control; based on this, the complex system can achieve an ideal control effect; The fuzzy PID control system mainly consists of two parts: a PID controller with variable parameters and a fuzzy controller. The position deviation e and the deviation change rate ec are taken as input parameters and sent into the fuzzy controller. After fuzzification, fuzzy inference, and defuzzification, the correction amounts of Δk p , Δk i and Δk d are obtained. According to the fuzzy rules, each parameter of the PID is modified online to make the system reach the best state, so as to realize the optimal control of the gate system; Particle Swarm Optimization (PSO) is a global optimization algorithm inspired by the activities of birds. During the iteration process, particles update their velocities and positions by tracking P best and G best to achieve the global optimum. The process of the Particle Swarm Optimization algorithm can be summarized by the following formula: However, since the inertia weight and learning factor of the traditional particle swarm algorithm are both fixed values, it is easy for particles to fall into local optimum during the optimization process. Therefore, this paper adopts an improved particle swarm algorithm with dynamically adjusted inertia weight and learning factor; the inertia weight ω is adjusted in a linearly decreasing manner, and the maximum values of c1 and c2 are set to be equal, so that c1 increases with the increase of the search times, and c2 decreases with the increase of the search times; the improved particle swarm algorithm has strong global optimization ability in the initial stage of optimization and strong local search ability in the later stage of optimization. Compared with the particle swarm algorithm, it can effectively avoid particles from falling into "premature" during the optimization process, thereby improving the global search ability of particles while enhancing the search efficiency and accuracy of the algorithm. The change formula of the improved particle swarm algorithm is as follows: c1 ′ = 1.2 - c2 ′ ; In the optimization algorithm, fitness is an important indicator for evaluating the quality of individual solutions and also the driving force in the algorithm optimization process. In this paper, the ITAE index is selected as the fitness function, which reflects the influence of error and time on the control performance. The fitness function is as follows:

6. The intelligent algorithm-optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: Taking the measured flow rate and water level data as the control quantity and the controlled quantity respectively, use the System Identification toolbox in Matlab to identify the system of the transfer function and determine the parameters of the gate adjustment model.

7. An intelligent algorithm optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: Based on the MATLAB / Simulink platform, build an IPSO-Fuzzy PID gate adaptive steady-state regulation simulation model. To compare the control effects of different control methods, traditional PID and Fuzzy-PID controllers widely used in the engineering field are used for comparative verification. After setting the simulation time step and adjustment target, observe the response curves under the control of different controllers in the oscilloscope. At the same time, to verify the self-adaptability of the model optimized by the IPSO algorithm, after the response curve reaches the expected stable position, a step signal is applied to simulate the influence of external disturbances on the model. The simulation results show that in the dynamic response stage of the system, compared with traditional PID control and Fuzzy PID control, IPSO-Fuzzy PID control can significantly reduce the oscillation amplitude and oscillation frequency of the curve, effectively improve the dynamic characteristics of the system, and make the response curve meet the control requirements of fast response, small overshoot and high robustness. In the adaptive regulation stage after applying the disturbance, IPSO-Fuzzy PID control has less response time and reaches stability again at the fastest speed.

8. An intelligent algorithm optimized gate adaptive steady-state regulation method according to claim 1, characterized in that: To further verify the optimization of the control parameters by IPSO, build a one-dimensional hydraulic model based on the research area. The hydrodynamic module HD is the core module of the one-dimensional hydraulic model. This module is based on the one-dimensional unsteady flow Saint-Venant equations and can describe the hydrological movement laws of rivers or estuaries from a physical mechanism. Its equations mainly include the continuity equation and the momentum equation:

Images