Water tiger foraging optimization algorithm for setting EHA actuator position synovial membrane controller parameters

Abstract

The invention discloses a water tiger foraging optimization algorithm for setting EHA actuator position synovial membrane controller parameters, and the algorithm comprises the following steps: S1, building an EHA system mathematical model, and dividing subsystems; s2, a sliding mode PID controller is designed, PID is used for a motor, and sliding mode control is used for hydraulic pressure; s3, initializing a population, generating individuals and randomly initializing parameters; s4, the fitness is calculated, and evaluation is carried out based on performance indexes; s5, updating the position based on a foraging and attack strategy; s6, judging the blood concentration and updating the optimal solution; and S7, judging a stop condition, and outputting an optimal parameter. According to the method, the parameters of the position sliding mode controller are set based on the water tiger foraging optimization algorithm, the control precision, the response speed and the robustness of the EHA system are remarkably improved, and the method adapts to complex dynamic working conditions.

Description

Technical Field

[0001] The invention relates to the technical field of control technology, and in particular to a piranha foraging optimization algorithm for adjusting parameters of an EHA actuator position diaphragm controller. Background Art

[0002] Electro-hydrostatic Actuator (EHA) is a hybrid actuator that combines electric drive with hydraulic system. In recent years, it has been widely used in aerospace, military, industrial automation and high-end manufacturing. The EHA system has the advantages of high thrust output, high-precision control and fast response speed. Its working principle is to drive the hydraulic pump through the motor to deliver oil to the hydraulic actuator to generate stable and efficient thrust. However, since the EHA system usually needs to operate in a complex dynamic environment, such as temperature changes, load fluctuations and external disturbances, the structural parameters in the system (such as motor resistance, oil elastic modulus, hydraulic cylinder leakage, etc.) will change significantly with external conditions, which greatly challenges the stability and accuracy of the system. Therefore, how to design a controller for the EHA system that can adapt to complex working conditions, with high precision and high robustness is one of the core issues of current research.

[0003] At present, the control methods for EHA systems mainly include traditional PID control, fuzzy control, model predictive control and sliding mode control. Traditional PID control is widely used in various engineering practices because of its simple implementation and fast response. However, due to the significant nonlinear coupling characteristics of EHA systems, traditional PID control methods require manual adjustment of parameters to adapt to changing working conditions. This method is not only inefficient, but also easily affected by external disturbances and difficult to meet the needs of high-performance control. Fuzzy control is a control method based on a rule base. The system is controlled by designing fuzzy logic rules, but the design complexity of the rule base is closely related to the nonlinearity of the EHA system. For complex EHA systems, it is extremely difficult to design a fuzzy rule base suitable for actual working conditions, and the size of the rule base may expand rapidly as the complexity of the system increases, resulting in a decrease in computational efficiency. Model predictive control (MPC) predicts future states and generates control strategies through the mathematical model of the system. Although it can achieve good control performance in theory, it is highly dependent on the accuracy of the mathematical model. Once the model has simplified assumptions or inaccuracies, it may lead to system performance degradation or even instability.

[0004] In contrast, sliding mode control has become an alternative for EHA system control due to its outstanding robustness in dealing with nonlinear systems and external disturbances. By constructing a sliding mode surface and designing a reaching law, sliding mode control enables the system state to quickly converge to the sliding mode surface, thereby achieving robust control of nonlinear systems. However, the traditional sliding mode control method has chattering phenomenon in practical applications. This phenomenon may cause mechanical damage to the system and reduce the control accuracy of the system. In addition, the parameter adjustment of sliding mode control needs to be optimized according to the specific working conditions of the system, but the initial design and optimization of its parameters lack effective automation means. To address the above problems, the sliding mode PID controller came into being. It combines the robustness of sliding mode control and the precision of PID control, and can better adapt to the complex dynamic environment of the EHA system. However, the performance of the sliding mode PID controller still depends on the precise adjustment of the controller parameters, and the adjustment of these parameters usually relies on manual experience or trial-and-error methods, lacking systematic and intelligent optimization strategies.

[0005] Regarding the parameter optimization problem of the sliding mode PID controller, the current mainstream methods include genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization (ACO), and sparrow search algorithm (SSA), etc. These optimization algorithms realize the automatic optimization of the controller parameters by simulating processes such as evolution and biological behaviors in nature. However, these methods still have significant defects when dealing with complex high-dimensional optimization problems. Taking the genetic algorithm as an example, there is a risk of loss of population diversity in its optimization process and it is easy to fall into local optimal solutions; although the particle swarm optimization algorithm has a relatively fast convergence speed, its global search ability in the later stage is insufficient and it is also easy to converge to suboptimal solutions prematurely; the ant colony optimization algorithm may cause excessive consumption of computing resources when the solution space is large; although the sparrow search algorithm improves the global search ability by imitating the foraging behavior of sparrows, there is still a problem of unstable convergence speed. These defects lead to problems such as limited optimization effect, insufficient computing efficiency, and poor robustness in the practical application of existing optimization methods, and it is difficult to fully meet the requirements of the parameter optimization of the position sliding mode controller.

[0006] Therefore, it is an urgent problem for those skilled in the art to provide a piranha foraging optimization algorithm for how to tune the parameters of the EHA actuator position sliding mode controller. Summary of the Invention

[0007] One object of the present invention is to propose a piranha foraging optimization algorithm for adjusting the parameters of the sliding mode PID controller of an EHA actuator. The present invention makes full use of the global search capability of the piranha foraging optimization algorithm and the robustness of the sliding mode PID controller, and describes in detail the steps for achieving high-precision parameter optimization in the control system of an electric hydrostatic actuator (EHA). The method dynamically optimizes the sliding surface and reaching law parameters of the sliding mode controller by simulating the scavenging foraging behavior, local group attack and bloodthirsty group attack of piranhas, significantly improving the adaptability of the controller to complex nonlinear dynamic environments, and having the advantages of high optimization efficiency, strong anti-interference ability and high control accuracy.

[0008] The piranha foraging optimization algorithm for adjusting the parameters of the EHA actuator position sliding membrane controller according to an embodiment of the present invention comprises the following steps:

[0009] S1. Establish a mathematical model of the EHA actuator system, divide the EHA system into a motor regulation subsystem and a hydraulic pump and hydraulic cylinder subsystem, and obtain a mathematical model of the EHA system;

[0010] S2. According to the mathematical model of the EHA system, a sliding mode PID controller is designed. The motor regulation subsystem uses a traditional PID controller to control the current and speed, and the hydraulic pump and hydraulic cylinder subsystem uses a sliding mode controller for position control;

[0011] S3, initializing the population of piranha foraging optimization algorithm, generating multiple piranha individuals, each piranha individual corresponds to a set of sliding mode controller parameters, and randomly initializing the position of the piranha individual;

[0012] S4, according to the sliding mode controller parameters corresponding to each piranha individual, the simulation model in Simulink is called in real time during the algorithm operation process, and a set of dynamic performance indicators are obtained to evaluate the fitness of each piranha individual;

[0013] S5. According to the fitness evaluation results of individual piranhas, three foraging modes, namely, scavenging foraging, local group attack and bloodthirsty group attack, are adopted to simulate the foraging behavior of piranhas, and the fitness values ​​of individual piranhas are updated through algorithm iteration;

[0014] S6. Recall the simulation model to obtain the corresponding EHA system performance index according to the updated individual fitness value of the piranha;

[0015] S7. According to the fitness value iterated by the algorithm, determine whether the stopping condition is met. When the stopping condition is met, output the controller parameters corresponding to the optimal piranha individual as the final adjusted synovial controller parameters.

[0016] Optionally, the S1 specifically includes:

[0017] S11. Determine the overall structure of the EHA actuator system, divide it into the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem, and clarify the functions and interrelationships of each subsystem;

[0018] S12. Establish a mathematical model of the motor regulation subsystem, consider the electrical and mechanical equations of the motor, describe the relationship between voltage, current, torque and speed, and form the dynamic equation of the motor part:

[0019]

[0020] Among them, U d , U q are the voltages of the motor d and q axes respectively, R is the motor equivalent resistance, i d 、i q are the currents of the motor d and q axes, respectively, f is the magnetic flux of the motor, W e and W q is the motor angle, L d and L q is the direct-axis inductance;

[0021] S13. Establish a mathematical model of the hydraulic pump and hydraulic cylinder subsystem, including the flow-pressure relationship of the hydraulic pump and the relationship between the displacement of the hydraulic cylinder piston and the oil flow rate:

[0022]

[0023] Among them, D p is the displacement of the plunger pump, ω m is the mechanical angular velocity of the motor rotor, L a is the overall leakage coefficient of the system, A is the effective area of ​​the hydraulic cylinder piston, Δp is the pressure difference between the inlet and outlet of the hydraulic cylinder, V a is the effective volume of the hydraulic cylinder, E y is the elastic modulus of the oil, x is the displacement of the hydraulic cylinder piston, x' and x' ‘ is the updated displacement of the hydraulic cylinder, M is the load mass, K t is the elastic load factor, F L is the load force;

[0024] S14, describe the coupling relationship between the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem, establish the interaction equation between the two, and reflect the interaction between the motor output and the hydraulic system response;

[0025] S15, combining the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem models to obtain a mathematical model of the EHA system, which includes the speed and torque of the motor and the pressure and flow of the hydraulic system;

[0026] S16. Verify the accuracy of the mathematical model of the EHA system by comparing it with the actual EHA system experimental data.

[0027] Optionally, the S3 specifically includes:

[0028] S31, determining the population size of the piranha foraging optimization algorithm, and selecting an appropriate population size according to the complexity of the setting problem and the limitation of computing resources;

[0029] S32, allocating a position sliding mode controller parameter set to each piranha individual, including a sliding mode surface and a convergence rate parameter;

[0030] S33, setting the search range of each controller parameter, dynamically adjusting the search range of each parameter by analyzing system characteristics and historical performance, and optimizing the initial search space;

[0031] S34. Randomly initialize the position of each piranha individual, use Gaussian distribution to generate initial parameter values, adjust the distribution strategy, and enhance the diversity of the population:

[0032]

[0033] Where f(x) is the probability density function of the Gaussian distribution, μ is the mean of the controller parameter, σ is the standard deviation of the controller parameter, x is the value of the controller parameter, and exp is the exponential function;

[0034] S35, introduce local optimization factors, adjust the position of individuals, improve local search efficiency, and promote convergence:

[0035] x i new =x i +α·(x best -x i );

[0036] Among them, x i new is the new position of the ith piranha individual, x i is the current position of the ith piranha individual, x best is the global optimal position, α is the local optimization factor;

[0037] S36. Perform cluster analysis on the population, group individuals according to similarities, optimize the population structure through clustering, and identify individuals with greater potential.

[0038] Optionally, the S4 specifically includes:

[0039] S41. The position of each piranha individual is expressed as a position sliding mode controller parameter, including a sliding surface parameter and a convergence rate parameter:

[0040] xi =[C 1 ,C 2 ,ξ,k];

[0041] Among them, C 1 , C 2 are the structural parameters of the sliding surface ξ and k, which represent the speed at which the moving point of the system approaches the switching surface s = 0, and ξ and k are both greater than zero;

[0042] S42, applying the position sliding mode controller parameters to the EHA system, simulating the dynamic response of the system, and obtaining the control performance index of the system;

[0043] S43. According to the response performance of the EHA system, the control effect of each piranha individual is evaluated to obtain the fitness value of each piranha individual:

[0044] F i =w 1 ·e steady +w 2 ·t r +w 3 ·t s +w 4 ·M p ;

[0045] Among them, F i is the fitness value of the i-th piranha individual, e steady is the steady-state error, t r is the rise time, t s To adjust the time, M p is the overshoot, w 1 ,w 2 ,w 3 ,w 4 is the weight coefficient;

[0046] S44, sorting the piranha individuals according to the calculated fitness values ​​of the piranha individuals, wherein the piranha individuals with lower fitness values ​​indicate that the controller has better performance and are given priority to participate in the next round of optimization iteration;

[0047] S45. According to the fitness values, select the piranha individuals with the best fitness, retain these piranha individuals, and update the structure of the population according to the fitness evaluation results of all piranha individuals.

[0048] Optionally, the S5 specifically includes:

[0049] S51, according to the fitness evaluation results of the piranha individuals, determine the fitness value of each piranha individual, and calculate the hunger level H according to the fitness value of the piranha individual i ;

[0050] S52, if the hunger level is H i When the value is less than 0.5, the piranha individuals adopt scavenging foraging behavior, simulating global search to expand the search range:

[0051] x i (t+1)=x i (t)+r·(x c -x i (t));

[0052] Among them, x i (t) is the current position of the i-th piranha individual, x i (t+1) is the updated new position of the i-th piranha individual, x c is the position of a randomly selected piranha individual, and r is a random factor;

[0053] S53, if the hunger level is H i When the value is greater than 0.5, the piranha individuals adopt local group attack behavior, simulating local search to approach the optimal solution:

[0054]

[0055] Among them, x best is the position of the piranha individual with the best current fitness, x j (t) is the position of a randomly selected piranha individual in the local neighborhood, γ is the local search step factor, is the neighborhood impact factor;

[0056] S54, in the process of position update, the two strategies are combined according to the hunger level H i Choose appropriate behavior for different situations;

[0057] S55, storing the updated individual positions of the piranha in the population data structure.

[0058] Optionally, the S6 specifically includes:

[0059] S61, according to the updated individual positions of the piranha, calling the EHA model in Simulink to obtain corresponding EHA system performance indicators, including steady-state error, rise time, adjustment time and overshoot;

[0060] S62. Calculate the blood concentration factor of the individual piranha based on the re-acquired performance index and determine the size:

[0061]

[0062] Among them, F i is the blood concentration factor, w 1 ,w 2 ,w3 ,w 4 is the weight factor, e steady is the steady-state error, t r is the rise time, t s To adjust the time, M p is the overshoot;

[0063] S63. When the blood concentration is greater than 0.7, the individual piranhas will conduct concentrated attacks on prey, improving the global search capability:

[0064] x i (t+1)=x i (t)+G·F i ·S·β 4 +y 2 ;

[0065] Among them, x i (t+1) is the updated position after the concentrated attack, x i (t) is the position of the i-th piranha individual in the current generation, G is the piranha foraging ability coefficient, S is the nonlinear cosine factor, β 4 is a random factor, y 2 is the random disturbance term;

[0066] S64. When the blood concentration is less than 0.7, the piranha individuals maintain local optimization behavior, simulating the exploration of the optimal solution of the current population and the neighboring individuals to improve the local search efficiency:

[0067] x i (t+1)=x i (t)+τ·(x best -x i (t))+θ·(x center -x i (t))+δ;

[0068] Among them, x i (t+1) is the position of the i-th piranha individual in the next generation, x i (t) is the position of the i-th piranha individual in the current generation, x best is the position of the individual with the best fitness value in the current population, x center is the population center position, τ is the optimal solution influencing factor, θ is the population center influencing factor, and δ is the random disturbance term;

[0069] S65. Based on the results of global search and local search, the optimal solution of the population is updated to the individual with the lowest fitness value in the current population.

[0070] The beneficial effects of the present invention are:

[0071] The present invention combines the piranha foraging optimization algorithm with the sliding mode PID controller to achieve efficient optimization of the controller parameters of the electric hydrostatic actuator (EHA) system under complex working conditions, and solves the problems that the optimization method in the prior art is prone to fall into local optimality, low optimization efficiency and insufficient control accuracy. Compared with the traditional optimization algorithm, the present invention makes full use of the global search ability and dynamic adaptation characteristics of the piranha foraging optimization algorithm, and ensures the balance between exploration and development in the parameter optimization process by introducing behavior modes such as scavenging foraging, local group attack and concentrated pursuit, so that the sliding mode PID controller can maintain high stability and accuracy in systems with strong nonlinear coupling and large environmental disturbances. At the same time, the efficiency of the optimization algorithm is further improved by dynamically adjusting the search behavior and the control of nonlinear parameters through the blood concentration factor, so that the EHA system shows excellent response speed and robustness in a complex dynamic environment. The present invention also effectively suppresses the chattering phenomenon through the strong robustness of the sliding mode PID controller, and realizes precise control of the system steady-state error, adjustment time and overshoot under various working conditions. In summary, the present invention not only significantly improves the efficiency and accuracy of EHA system controller parameter optimization, but also enhances the system's anti-disturbance capability and ability to adapt to complex environments, providing an efficient, stable and scalable solution for the control of complex nonlinear dynamic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0073] Figure 1 A flow chart of the piranha foraging optimization algorithm for adjusting the parameters of the EHA actuator position diaphragm controller proposed by the present invention;

[0074] Figure 2 Schematic diagram of the behavioral simulation of the piranha foraging optimization algorithm for adjusting the parameters of the EHA actuator position diaphragm controller proposed in the present invention. DETAILED DESCRIPTION

[0075] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, which only illustrate the basic structure of the present invention in a schematic manner, and therefore only show the components related to the present invention.

[0076] refer to Figure 1-2 The Piranha foraging optimization algorithm for adjusting the parameters of the EHA actuator position diaphragm controller includes the following steps:

[0077] S1. Establish a mathematical model of the EHA actuator system, divide the EHA system into a motor regulation subsystem and a hydraulic pump and hydraulic cylinder subsystem, and obtain a mathematical model of the EHA system;

[0078] S2. According to the mathematical model of the EHA system, a sliding mode PID controller is designed. The motor regulation subsystem uses a traditional PID controller to control the current and speed, and the hydraulic pump and hydraulic cylinder subsystem uses a sliding mode controller for position control;

[0079] S3, initializing the population of piranha foraging optimization algorithm, generating multiple piranha individuals, each piranha individual corresponds to a set of sliding mode controller parameters, and randomly initializing the position of the piranha individual;

[0080] S4, according to the sliding mode controller parameters corresponding to each piranha individual, the simulation model in Simulink is called in real time during the algorithm operation process, and a set of dynamic performance indicators are obtained to evaluate the fitness of each piranha individual;

[0081] S5. According to the fitness evaluation results of individual piranhas, three foraging modes, namely, scavenging foraging, local group attack and bloodthirsty group attack, are adopted to simulate the foraging behavior of piranhas, and the fitness values ​​of individual piranhas are updated through algorithm iteration;

[0082] S6. Recall the simulation model to obtain the corresponding EHA system performance index according to the updated individual fitness value of the piranha;

[0083] S7. According to the fitness value iterated by the algorithm, determine whether the stopping condition is met. When the stopping condition is met, output the controller parameters corresponding to the optimal piranha individual as the final adjusted synovial controller parameters.

[0084] In this implementation, S1 specifically includes:

[0085] S11. Determine the overall structure of the EHA actuator system, divide it into the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem, and clarify the functions and interrelationships of each subsystem;

[0086] S12. Establish a mathematical model of the motor regulation subsystem, consider the electrical and mechanical equations of the motor, describe the relationship between voltage, current, torque and speed, and form the dynamic equation of the motor part:

[0087]

[0088] Among them, U d , U q are the voltages of the motor d and q axes respectively, R is the motor equivalent resistance, i d 、i q are the currents of the motor d and q axes, respectively, f is the magnetic flux of the motor, W e and W q is the motor angle, Ld and L q is the direct-axis inductance;

[0089] S13. Establish a mathematical model of the hydraulic pump and hydraulic cylinder subsystem, including the flow-pressure relationship of the hydraulic pump and the relationship between the displacement of the hydraulic cylinder piston and the oil flow rate:

[0090]

[0091] Among them, D p is the displacement of the plunger pump, ω m is the mechanical angular velocity of the motor rotor, L a is the overall leakage coefficient of the system, A is the effective area of ​​the hydraulic cylinder piston, Δp is the pressure difference between the inlet and outlet of the hydraulic cylinder, V a is the effective volume of the hydraulic cylinder, E y is the elastic modulus of the oil, x is the displacement of the hydraulic cylinder piston, x' and x' ‘ is the updated displacement of the hydraulic cylinder, M is the load mass, K t is the elastic load factor, F L is the load force;

[0092] S14, describe the coupling relationship between the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem, establish the interaction equation between the two, and reflect the interaction between the motor output and the hydraulic system response;

[0093] S15, combining the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem models to obtain a mathematical model of the EHA system, which includes the speed and torque of the motor and the pressure and flow of the hydraulic system;

[0094] S16. Verify the accuracy of the mathematical model of the EHA system by comparing it with the actual EHA system experimental data.

[0095] In this implementation, S3 specifically includes:

[0096] S31, determining the population size of the piranha foraging optimization algorithm, and selecting an appropriate population size according to the complexity of the setting problem and the limitation of computing resources;

[0097] S32, allocating a position sliding mode controller parameter set to each piranha individual, including a sliding mode surface and a convergence rate parameter;

[0098] S33, setting the search range of each controller parameter, dynamically adjusting the search range of each parameter by analyzing system characteristics and historical performance, and optimizing the initial search space;

[0099] S34. Randomly initialize the position of each piranha individual, use Gaussian distribution to generate initial parameter values, adjust the distribution strategy, and enhance the diversity of the population:

[0100]

[0101] Where f(x) is the probability density function of the Gaussian distribution, μ is the mean of the controller parameter, σ is the standard deviation of the controller parameter, x is the value of the controller parameter, and exp is the exponential function;

[0102] S35, introduce local optimization factors, adjust the position of individuals, improve local search efficiency, and promote convergence:

[0103] x i new =x i +α·(x best -x i );

[0104] Among them, x i new is the new position of the ith piranha individual, x i is the current position of the ith piranha individual, x best is the global optimal position, α is the local optimization factor;

[0105] S36. Perform cluster analysis on the population, group individuals according to similarities, optimize the population structure through clustering, and identify individuals with greater potential.

[0106] In this implementation, S4 specifically includes:

[0107] S41. The position of each piranha individual is expressed as a position sliding mode controller parameter, including a sliding surface parameter and a convergence rate parameter:

[0108] x i =[C 1 ,C 2 ,ξ,k];

[0109] Among them, C 1 , C 2 are the structural parameters of the sliding surface ξ and k, which represent the speed at which the moving point of the system approaches the switching surface s = 0, and ξ and k are both greater than zero;

[0110] S42, applying the position sliding mode controller parameters to the EHA system, simulating the dynamic response of the system, and obtaining the control performance index of the system;

[0111] S43. According to the response performance of the EHA system, the control effect of each piranha individual is evaluated to obtain the fitness value of each piranha individual:

[0112] F i =w 1 ·e steady +w2 ·t r +w 3 ·t s +w 4 ·M p ;

[0113] Among them, F i is the fitness value of the i-th piranha individual, e steady is the steady-state error, t r is the rise time, t s To adjust the time, M p is the overshoot, w 1 ,w 2 ,w 3 ,w 4 is the weight coefficient;

[0114] S44, sorting the piranha individuals according to the calculated fitness values ​​of the piranha individuals, wherein the piranha individuals with lower fitness values ​​indicate that the controller has better performance and are given priority to participate in the next round of optimization iteration;

[0115] S45. According to the fitness values, select the piranha individuals with the best fitness, retain these piranha individuals, and update the structure of the population according to the fitness evaluation results of all piranha individuals.

[0116] In this implementation manner, S5 specifically includes:

[0117] S51, according to the fitness evaluation results of the piranha individuals, determine the fitness value of each piranha individual, and calculate the hunger level H according to the fitness value of the piranha individual i ;

[0118] S52, if the hunger level is H i When the value is less than 0.5, the piranha individuals adopt scavenging foraging behavior, simulating global search to expand the search range:

[0119] x i (t+1)=x i (t)+r·(x c -x i (t));

[0120] Among them, x i (t) is the current position of the i-th piranha individual, x i (t+1) is the updated new position of the i-th piranha individual, x c is the position of a randomly selected piranha individual, and r is a random factor;

[0121] S53, if the hunger level is H iWhen the value is greater than 0.5, the piranha individuals adopt local group attack behavior, simulating local search to approach the optimal solution:

[0122]

[0123] Among them, x best is the position of the piranha individual with the best current fitness, x j (t) is the position of a randomly selected piranha individual in the local neighborhood, γ is the local search step factor, is the neighborhood impact factor;

[0124] S54, in the process of position update, the two strategies are combined according to the hunger level H i Choose appropriate behavior for different situations;

[0125] S55, storing the updated individual positions of the piranha in the population data structure.

[0126] In this implementation manner, S6 specifically includes:

[0127] S61, according to the updated individual positions of the piranha, calling the EHA model in Simulink to obtain corresponding EHA system performance indicators, including steady-state error, rise time, adjustment time and overshoot;

[0128] S62. Calculate the blood concentration factor of the individual piranha based on the re-acquired performance index and determine the size:

[0129]

[0130] Among them, F i is the blood concentration factor, w 1 ,w 2 ,w 3 ,w 4 is the weight factor, e steady is the steady-state error, t r is the rise time, t s To adjust the time, M p is the overshoot;

[0131] S63. When the blood concentration is greater than 0.7, the individual piranhas will conduct concentrated attacks on prey, improving the global search capability:

[0132] x i (t+1)=x i (t)+G·F i ·S·β 4 +y 2 ;

[0133] Among them, x i(t+1) is the updated position after the concentrated attack, x i (t) is the position of the i-th piranha individual in the current generation, G is the piranha foraging ability coefficient, S is the nonlinear cosine factor, β 4 is a random factor, y 2 is the random disturbance term;

[0134] S64. When the blood concentration is less than 0.7, the piranha individuals maintain local optimization behavior, simulating the exploration of the optimal solution of the current population and the neighboring individuals to improve the local search efficiency:

[0135] x i (t+1)=x i (t)+τ·(x best -x i (t))+θ·(x center -x i (t))+δ;

[0136] Among them, x i (t+1) is the position of the i-th piranha individual in the next generation, x i (t) is the position of the i-th piranha individual in the current generation, x best is the position of the individual with the best fitness value in the current population, x center is the population center position, τ is the optimal solution influencing factor, θ is the population center influencing factor, and δ is the random disturbance term;

[0137] S65. Based on the results of global search and local search, the optimal solution of the population is updated to the individual with the lowest fitness value in the current population.

[0138] Embodiment 1:

[0139] In order to verify the feasibility of the present invention in implementation, the present invention is applied to the EHA system of a certain type of aircraft, which is used to control the angle of the aircraft's control surface. The control surface is required to respond quickly to the pilot's instructions and maintain high precision and high stability under the influence of load fluctuations and external disturbances. The experimental environment is set up in a flight control laboratory of an airline company. The test platform can simulate different flight conditions, including load changes, temperature fluctuations, and disturbance signals. The experiment sets up three typical ambient temperatures (20°C, 40°C, 60°C), three load conditions (20kN, 40kN, 60kN) and two types of disturbances (sinusoidal disturbance and random noise), and comprehensively tests the key performance indicators of the control system, such as the rise time, adjustment time, steady-state error and overshoot.

[0140] The experiment used three control strategies for comparative tests, including the traditional PID controller, the unoptimized sliding mode PID controller and the sliding mode PID controller tuned based on the piranha foraging optimization algorithm. Under the conditions of 20℃ ambient temperature, 20kN load and sinusoidal disturbance, the rise time of the traditional PID controller was 1.5 seconds, the adjustment time was 3.2 seconds, the steady-state error was 2.4%, and the overshoot was 8.1%. The unoptimized sliding mode PID controller showed some improvement compared with the traditional PID controller, with its rise time shortened to 1.2 seconds, the adjustment time reduced to 2.8 seconds, and the steady-state error reduced to 1.5%, but the overshoot was still high at 6.3%. In contrast, the sliding mode PID controller tuned based on the piranha foraging optimization algorithm achieved more significant performance improvement, with its rise time further shortened to 0.9 seconds, the adjustment time reduced to 2.1 seconds, the steady-state error only 0.7%, and the overshoot significantly reduced to 3.2%. This improvement shows that the piranha foraging optimization algorithm can more efficiently solve the performance problems that traditional methods are difficult to deal with in the sliding mode PID controller parameter tuning.

[0141] In high temperature environments, the performance of traditional control methods is significantly reduced. For example, under 60°C ambient temperature, 60kN load and random disturbance conditions, the steady-state error of the traditional PID controller increases to 4.3%, and the adjustment time is extended to 4.6 seconds. The unoptimized sliding mode PID controller performs slightly better under these conditions, with a steady-state error of 2.7% and an adjustment time of 3.8 seconds, while the steady-state error of the sliding mode PID controller tuned based on the piranha foraging optimization algorithm is significantly reduced to 1.2%, and the adjustment time is 2.6 seconds, showing strong robustness and anti-disturbance capabilities.

[0142] The experiment also evaluated the efficiency of different optimization algorithms in optimizing the parameters of the sliding mode PID controller. The results showed that the optimization time of the genetic algorithm (GA) was 120 seconds and the convergence generation was 80 generations; the optimization time of the particle swarm algorithm (PSO) was 95 seconds and the convergence generation was 70 generations; while the tuning time of the piranha foraging optimization algorithm was only 67 seconds and the convergence generation was 50 generations. This shows that the piranha foraging optimization algorithm has significant advantages in tuning efficiency and convergence speed, and is particularly suitable for control systems with high real-time requirements.

[0143] It can be seen from the above experiments that the method for adjusting the sliding mode PID controller parameters based on the piranha foraging optimization algorithm proposed in the present invention has significant beneficial effects in the EHA system. It not only significantly improves the efficiency and accuracy of controller parameter optimization, but also enhances the robustness of the system, so that the EHA system can maintain high stability and high performance under complex dynamic conditions. In addition, while reducing the optimization time, this method also effectively reduces the steady-state error and overshoot in the control process, thereby greatly improving the overall response performance of the system. The tuning method based on the piranha foraging optimization algorithm provides an efficient and reliable solution to the control problems of the EHA system and other complex nonlinear dynamic systems.

[0144] Table 1 Performance comparison of different control methods at 20℃, 20kN load and sinusoidal disturbance

[0145]

[0146] Table 2 Comparison of the efficiency of different optimization algorithms in sliding mode PID controller parameter optimization

[0147] Optimization Algorithm Optimization time (seconds) Convergent Algebra Genetic Algorithms 120 80 Particle Swarm Optimization 95 70 Piranha foraging optimization algorithm 67 50

[0148] As can be seen from Table 1, the sliding mode PID controller tuned by the Piranha foraging optimization algorithm is superior to the traditional PID controller and the unoptimized sliding mode PID controller in terms of performance indicators. Under the conditions of 20℃, 20kN load and sinusoidal disturbance, the steady-state error of the traditional PID controller is 2.4%, and the overshoot is 8.1%, while the steady-state error of the sliding mode PID controller tuned by the Piranha foraging optimization algorithm is reduced to 0.7%, and the overshoot is reduced to 3.2%. In addition, the rise time is shortened from 1.5 seconds of the traditional PID controller to 0.9 seconds, and the adjustment time is reduced from 3.2 seconds to 2.1 seconds. This improvement shows the significant effect of the Piranha foraging optimization algorithm on the parameter optimization of the sliding mode PID controller, especially in the improvement of control accuracy and dynamic response speed.

[0149] As can be seen from Table 2, the tuning efficiency of the Piranha foraging optimization algorithm is significantly higher than that of the traditional optimization algorithm. The optimization time of the genetic algorithm is 120 seconds, the particle swarm algorithm is 95 seconds, and the Piranha foraging optimization algorithm only takes 67 seconds. At the same time, the convergence generation of the Piranha foraging optimization algorithm is 50 generations, which is 30 and 20 generations less than the genetic algorithm and particle swarm algorithm, respectively. This shows that the Piranha foraging optimization algorithm has achieved a good balance between global search capability and local convergence efficiency, and can complete parameter optimization faster and more accurately, which is suitable for the real-time control requirements of complex dynamic systems.

[0150] In summary, the tuning method based on the Piranha foraging optimization algorithm shows obvious advantages in both control performance and optimization efficiency. The data in Table 1 verify that the Piranha foraging optimization algorithm-tuned sliding mode PID controller can significantly improve the response speed, control accuracy and anti-disturbance capability of the EHA system, while Table 2 further proves the efficiency and reliability of the Piranha foraging optimization algorithm in dealing with complex parameter tuning problems. This method not only solves the problem that traditional PID controllers are difficult to cope with nonlinear complex systems, but also overcomes the technical bottleneck that the sliding mode PID controller parameters are difficult to optimize, providing an innovative solution for control system design in complex dynamic environments.

[0151] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A piranha foraging optimization algorithm for setting the parameters of the EHA actuator position diaphragm controller, characterized in that: The steps include: S1. Establish a mathematical model of the EHA actuator system, divide the EHA system into a motor regulation subsystem and a hydraulic pump and hydraulic cylinder subsystem, and obtain a mathematical model of the EHA system; S2. According to the mathematical model of the EHA system, a sliding mode PID controller is designed. The motor regulation subsystem uses a traditional PID controller to control the current and speed, and the hydraulic pump and hydraulic cylinder subsystem uses a sliding mode controller for position control; S3, initializing the population of piranha foraging optimization algorithm, generating multiple piranha individuals, each piranha individual corresponds to a set of sliding mode controller parameters, and randomly initializing the position of the piranha individual; S4, according to the sliding mode controller parameters corresponding to each piranha individual, the simulation model in Simulink is called in real time during the algorithm operation process, and a set of dynamic performance indicators are obtained to evaluate the fitness of each piranha individual; S5. According to the fitness evaluation results of individual piranhas, three foraging modes, namely, scavenging foraging, local group attack and bloodthirsty group attack, are adopted to simulate the foraging behavior of piranhas, and the fitness values ​​of individual piranhas are updated through algorithm iteration; S6. Recall the simulation model to obtain the corresponding EHA system performance index according to the updated individual fitness value of the piranha; S7. According to the fitness value iterated by the algorithm, determine whether the stopping condition is met. When the stopping condition is met, output the controller parameters corresponding to the optimal piranha individual as the final adjusted synovial controller parameters.

2. The piranha foraging optimization algorithm for setting the parameters of the EHA actuator position diaphragm controller according to claim 1, characterized in that: The S1 specifically includes: S11. Determine the overall structure of the EHA actuator system, divide it into the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem, and clarify the functions and interrelationships of each subsystem; S12. Establish a mathematical model of the motor regulation subsystem, consider the electrical and mechanical equations of the motor, describe the relationship between voltage, current, torque and speed, and form the dynamic equation of the motor part: Among them, U d , U q are the voltages of the motor d and q axes respectively, R is the motor equivalent resistance, i d 、i q are the currents of the motor d and q axes, respectively, f is the magnetic flux of the motor, W e and W q is the motor angle, L d and L q is the direct-axis inductance; S13. Establish a mathematical model of the hydraulic pump and hydraulic cylinder subsystem, including the flow-pressure relationship of the hydraulic pump and the relationship between the displacement of the hydraulic cylinder piston and the oil flow rate: Among them, D p is the displacement of the piston pump, ω m is the mechanical angular velocity of the motor rotor, L a is the overall leakage coefficient of the system, A is the effective area of ​​the hydraulic cylinder piston, Δp is the pressure difference between the inlet and outlet of the hydraulic cylinder, V a is the effective volume of the hydraulic cylinder, E y is the elastic modulus of the oil, x is the displacement of the hydraulic cylinder piston, x' and x' ‘ is the updated displacement of the hydraulic cylinder, M is the load mass, K t is the elastic load factor, F L is the load force; S14, describe the coupling relationship between the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem, establish the interaction equation between the two, and reflect the interaction between the motor output and the hydraulic system response; S15, combining the motor regulation subsystem and the hydraulic pump and hydraulic cylinder subsystem models to obtain a mathematical model of the EHA system, which includes the speed and torque of the motor and the pressure and flow of the hydraulic system; S16. Verify the accuracy of the mathematical model of the EHA system by comparing it with the actual EHA system experimental data.

3. The piranha foraging optimization algorithm for setting the parameters of the EHA actuator position diaphragm controller according to claim 1, characterized in that: The S3 specifically includes: S31, determining the population size of the piranha foraging optimization algorithm, and selecting an appropriate population size according to the complexity of the setting problem and the limitation of computing resources; S32, allocating a position sliding mode controller parameter set to each piranha individual, including a sliding mode surface and a convergence rate parameter; S33, setting the search range of each controller parameter, dynamically adjusting the search range of each parameter by analyzing system characteristics and historical performance, and optimizing the initial search space; S34. Randomly initialize the position of each piranha individual, use Gaussian distribution to generate initial parameter values, adjust the distribution strategy, and enhance the diversity of the population: Where fx is the probability density function of the Gaussian distribution, μ is the mean of the controller parameter, σ is the standard deviation of the controller parameter, x is the value of the controller parameter, and exp is the exponential function; S35, introduce local optimization factors, adjust the position of individuals, improve local search efficiency, and promote convergence: x i new =x i +α·x best -x i ; Among them, x i new is the new position of the ith piranha individual, x i is the current position of the ith piranha individual, x best is the global optimal position, α is the local optimization factor; S36. Perform cluster analysis on the population, group individuals according to similarities, optimize the population structure through clustering, and identify individuals with greater potential.

4. The piranha foraging optimization algorithm for setting the parameters of the EHA actuator position diaphragm controller according to claim 1, characterized in that: The S4 specifically includes: S41. The position of each piranha individual is expressed as a position sliding mode controller parameter, including a sliding surface parameter and a convergence rate parameter: x i =[C1,C2,ξ,k]; Among them, C1 and C2 are the structural parameters of the sliding surface, ξ and k represent the speed at which the moving point of the system approaches the switching surface s = 0, and ξ and k are both greater than zero; S42, applying the position sliding mode controller parameters to the EHA system, simulating the dynamic response of the system, and obtaining the control performance index of the system; S43. According to the response performance of the EHA system, the control effect of each piranha individual is evaluated to obtain the fitness value of each piranha individual: F i =w1·e steady +w2·t r +w3·t s +w4·M p ; Among them, F i is the fitness value of the i-th piranha individual, e steady is the steady-state error, t r is the rise time, t s To adjust the time, M p is the overshoot, w1,w2,w3,w4 are weight coefficients; S44, sorting the piranha individuals according to the calculated fitness values ​​of the piranha individuals, wherein the piranha individuals with lower fitness values ​​indicate that the controller has better performance and are given priority to participate in the next round of optimization iteration; S45. According to the fitness value, select the piranha individuals with the best fitness, retain these piranha individuals, and update the structure of the population according to the fitness evaluation results of all piranha individuals.

5. The piranha foraging optimization algorithm for setting the parameters of the EHA actuator position diaphragm controller according to claim 1, characterized in that: The S5 specifically includes: S51, according to the fitness evaluation results of the piranha individuals, determine the fitness value of each piranha individual, and calculate the hunger level H according to the fitness value of the piranha individual i ; S52, if the hunger level is H i When the value is less than 0.5, the piranha individuals adopt scavenging foraging behavior, simulating global search to expand the search range: x i t+1=x i t+r·x c -x i t; Among them, x i t is the current position of the i-th piranha individual, x i t+1 is the updated new position of the ith piranha individual, x c is the position of a randomly selected piranha individual, and r is a random factor; S53, if the hunger level is H i When the value is greater than 0.5, the piranha individuals adopt local group attack behavior, simulating local search to approach the optimal solution: Among them, x best is the position of the piranha individual with the best current fitness, x j t is the position of a randomly selected piranha individual in the local neighborhood, γ is the local search step factor, is the neighborhood impact factor; S54, in the process of position update, the two strategies are combined according to the hunger level H i Choose appropriate behavior for different situations; S55, storing the updated individual positions of the piranha in the population data structure.

6. The piranha foraging optimization algorithm for setting the parameters of the EHA actuator position diaphragm controller according to claim 1, characterized in that: The S6 specifically includes: S61. According to the updated individual positions of the piranha, the EHA model in Simulink is called to obtain corresponding EHA system performance indicators, including steady-state error, rise time, adjustment time, and overshoot; S62. Calculate the blood concentration factor of the individual piranha based on the re-acquired performance index and determine the size: Among them, F i is the blood concentration factor, w1, w2, w3, w4 are weight factors, e steady is the steady-state error, t r is the rise time, t s To adjust the time, M p is the overshoot; S63. When the blood concentration is greater than 0.7, individual piranhas will conduct concentrated attacks on prey, improving their global search capabilities: x i t+1=x i t+G·F i ·S·β4+y2; Among them, x i t+1 is the updated position after the concentrated attack, x i t is the position of the i-th piranha individual in the current generation, G is the piranha foraging ability coefficient, S is the nonlinear cosine factor, β4 is the random factor, and y2 is the random disturbance term; S64. When the blood concentration is less than 0.7, the piranha individuals maintain local optimization behavior, simulating the exploration of the optimal solution of the current population and the neighboring individuals to improve the local search efficiency: x i t+1=x i t+τ·x best -x i t+θ·x center -x i t+δ; Among them, x i t+1 is the position of the i-th piranha individual in the next generation, x i t is the position of the i-th piranha individual in the current generation, x best is the position of the individual with the best fitness value in the current population, x center is the population center position, τ is the optimal solution influencing factor, θ is the population center influencing factor, and δ is the random disturbance term; S65. Based on the results of global search and local search, the optimal solution of the population is updated to the individual with the lowest fitness value in the current population.

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