A method of optimizing fuzzy PID controller to control helicopter attitude

Abstract

The present invention relates to the field of helicopter control technology, and specifically to a method for optimizing a fuzzy PID controller to control helicopter attitude. To address the problem that traditional fuzzy PID controller parameter optimization methods primarily rely on expert experience, resulting in low control accuracy for helicopter attitude, the present invention provides a new method for optimizing a fuzzy PID controller to control helicopter attitude, comprising the following steps: 1) establishing a helicopter dynamics model and a kinematic model; 2) optimizing the quantization factor and proportional factor in the fuzzy PID controller using a hybrid optimization method of a simulated plant growth algorithm and a genetic algorithm to obtain globally optimal parameters; and 3) assigning the globally optimal parameters to the quantization factor and proportional factor in the fuzzy PID controller to form a complete control closed loop for the fuzzy PID controller, thereby completing helicopter attitude control. The control method of the present invention significantly improves the accuracy of helicopter attitude control.

Description

Technical Field

[0001] The present invention relates to the technical field of helicopter control, in particular to a method for controlling the attitude of a helicopter by optimizing a fuzzy PID controller. Background Art

[0002] When a helicopter is performing maneuvering flight missions, fuzzy PID controllers are often used for tasks such as attitude adjustment, altitude maintenance, and trajectory tracking. They quickly correct flight deviations through a combination of proportional, integral, and differential terms, and have the advantages of simple structure and strong real-time performance. However, due to the problems of helicopters having multiple degrees of freedom, strong coupling, nonlinear aerodynamic characteristics, and external disturbances, in order to achieve control of the helicopter's attitude (such as pitch angle, roll angle, and yaw angle), it is necessary to quantize the factors in the fuzzy PID controller. 、 and proportional factors Five parameters are optimized, among which the quantization factor 、 The deviations corresponding to the desired posture and the rate of change of deviation The parameter optimization in the traditional fuzzy PID controller mainly relies on expert experience, resulting in low control accuracy of the helicopter attitude. Summary of the Invention

[0003] In order to solve the problem that the traditional parameter optimization method of fuzzy PID controller mainly relies on expert experience, resulting in low control accuracy of helicopter attitude, the present invention provides a new method for optimizing fuzzy PID controller to control helicopter attitude.

[0004] The present invention is achieved by adopting the following technical solutions:

[0005] A method for controlling a helicopter's attitude by optimizing a fuzzy PID controller comprises the following steps:

[0006] 1) Establish helicopter dynamics model and kinematics model, and design fuzzy PID controller;

[0007] 2) A hybrid optimization method of simulated plant growth algorithm and genetic algorithm is used to optimize the parameters of the quantization factor and proportional factor in the fuzzy PID controller. The optimization process specifically includes the following steps:

[0008] a. Initialization parameters: Use the genetic algorithm to initialize a population of growing points corresponding to the quantization factor and proportional factor in the fuzzy PID controller for the simulated plant growth algorithm;

[0009] b. Assigning the growth point population initialized in step a to the quantization factor and the proportional factor in the fuzzy PID controller, and running the fuzzy PID controller to control the attitude of the helicopter, constructing the objective function and the corresponding fitness function;

[0010] Among them, the objective function is divided into the objective function of no overshoot phenomenon and the objective function of overshoot phenomenon.

[0011] Objective function for no overshoot:

[0012]

[0013] Where, The deviation of the expected posture between the given value and the actual output value At each moment, the given value is the expected attitude angle, and the actual output value is the current attitude angle. is the actual output posture change value, Adjust the time for the system, for The absolute value weight coefficient, for The weight coefficient of the square term, for The weight coefficient of

[0014] Objective function of overshoot phenomenon:

[0015]

[0016] Where, For helicopters The actual posture output value at the moment, For helicopters The actual posture output value at the moment, The weight coefficient for the absolute value of the helicopter output change;

[0017] Fitness function:

[0018] c. Calculate the fitness value corresponding to each growth point in the initialized growth point population according to the fitness function, and find the worst growth point based on the calculated fitness value , and use the intelligent variable step method to update the growth point ;

[0019] When the new growth point The corresponding fitness value Less than the worst growth point The corresponding fitness value When the new growth point is directly removed; when the new growth point is The corresponding fitness value Greater than the worst growth point The corresponding fitness value When the growth point of its renewal Sort by the corresponding fitness value; the first P% updated growth points are added to the growth point set , select the growth point set at this time The worst growth point And record the worst growth point The corresponding fitness value is , where P≤100, and then (100-P)% of the updated growth points are again subjected to genetic algorithm, and global search is performed through crossover mutation to select the ones whose corresponding fitness value is greater than Growth points and add the selected growth points to the growth point set middle;

[0020] d. Calculate the growth point set Middle Morphogen concentration at each growing point , and its calculation formula is

[0021]

[0022] Where, is the fitness value corresponding to the initial growth point, A collection of growth points Middle The fitness value of each growth point, A collection of growth points The number of growth points in the equation is known. From this formula, we can see that the concentration of morphogens at each growth point is between [0,1], and the sum of the concentrations of morphogens at all growth points is 1, that is, , when the computer generates an arbitrary random number λ between [0,1], λ falls on the probability space composed of the morphogen concentrations of each growth point, and the corresponding growth point can grow first; at this time, it is judged whether there is a new growth point. When there is no new growth point, the growth point corresponding to the maximum fitness value at this time is the global optimal parameter; otherwise, return to step c until there is no new growth point, and the growth point corresponding to the maximum fitness value at this time is the global optimal parameter;

[0023] 3) Assign the global optimal parameters obtained in step 2) to the quantization factor and proportional factor in the fuzzy PID controller to form a complete control closed loop of the fuzzy PID controller and complete the helicopter attitude control.

[0024] The beneficial effects of the present invention are as follows: (1) The present invention combines simulated plant growth with a genetic algorithm and combines it with a fuzzy PID control method for optimization, thereby reducing dependence on expert experience, achieving adaptive parameter adjustment optimization, and significantly improving the accuracy and dynamic performance of helicopter attitude control; (2) By integrating the genetic algorithm with the simulated plant growth algorithm, the problem of initial growth point selection of the simulated plant growth algorithm is solved, the overall efficiency of the algorithm is improved, the global search capability is enhanced, and the global search capability of the genetic algorithm and the local search capability of the simulated plant growth algorithm are fully utilized, thereby improving the overall efficiency and preventing the algorithm from falling into a local optimal solution. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0027] Figure 1 A flow chart of the method of the present invention;

[0028] Figure 2 Schematic diagram of dynamic response curves after attitude control of the helicopter's pitch angle using the traditional fuzzy PID control method and the control method of the present invention;

[0029] Figure 3 Schematic diagram of dynamic response curves after attitude control of the helicopter's yaw angle using the traditional fuzzy PID control method and the control method of the present invention;

[0030] Figure 4 The figure is a schematic diagram of the dynamic response curve after the roll angle of the helicopter is controlled by the traditional fuzzy PID control method and the control method of the present invention. DETAILED DESCRIPTION

[0031] In order to more clearly understand the above-mentioned objectives, features and advantages of the present invention, the scheme of the present invention will be further described below. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features therein can be combined with each other.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein; it is obvious that the embodiments in the specification are only part of the embodiments of the present invention, rather than all the embodiments.

[0033] The specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0034] like Figure 1 As shown, a method for optimizing a fuzzy PID controller to control a helicopter attitude includes the following steps:

[0035] 1) Establish helicopter dynamics model and kinematics model, and design fuzzy PID controller;

[0036] 2) A hybrid optimization method of simulated plant growth algorithm and genetic algorithm is used to optimize the parameters of the quantization factor and proportional factor in the fuzzy PID controller. The optimization process specifically includes the following steps:

[0037] a. Initialization parameters: Use the genetic algorithm to initialize a population of growing points corresponding to the quantization factor and proportional factor in the fuzzy PID controller for the simulated plant growth algorithm;

[0038] b. Assigning the growth point population initialized in step a to the quantization factor and the proportional factor in the fuzzy PID controller, and running the fuzzy PID controller to control the attitude of the helicopter, constructing the objective function and the corresponding fitness function;

[0039] Among them, the objective function is divided into the objective function of no overshoot phenomenon and the objective function of overshoot phenomenon.

[0040] Objective function for no overshoot:

[0041]

[0042] Where, The deviation of the expected posture between the given value and the actual output value At each moment, the given value is the expected attitude angle, and the actual output value is the current attitude angle. is the actual output posture change value, Adjust the time for the system, for The absolute value weight coefficient, for The weight coefficient of the square term, for The weight coefficient of

[0043] Objective function of overshoot phenomenon:

[0044]

[0045] Where, For helicopters The actual posture output value at the moment, For helicopters The actual posture output value at the moment, The weight coefficient for the absolute value of the helicopter output change;

[0046] Fitness function:

[0047] c. Calculate the fitness value corresponding to each growth point in the initialized growth point population according to the fitness function, and find the worst growth point based on the calculated fitness value , and use the intelligent variable step method to update the growth point ;

[0048] When the new growth point The corresponding fitness value Less than the worst growth point The corresponding fitness value When the new growth point is directly removed; when the new growth point is The corresponding fitness value Greater than the worst growth point The corresponding fitness value When the growth point of its renewal Sort by the corresponding fitness value; the first P% updated growth points are added to the growth point set , select the growth point set at this time The worst growth point And record the worst growth point The corresponding fitness value is , where P≤100, and then (100-P)% of the updated growth points are again subjected to genetic algorithm, and global search is performed through crossover mutation to select the ones whose corresponding fitness value is greater than Growth points and add the selected growth points to the growth point set middle;

[0049] d. Calculate the growth point set Middle Morphogen concentration at each growing point , and its calculation formula is

[0050]

[0051] Where, is the fitness value corresponding to the initial growth point, A collection of growth points Middle The fitness value of a growth point, A collection of growth points The number of growth points in the equation is known. From this formula, we can see that the concentration of morphogens at each growth point is between [0,1], and the sum of the concentrations of morphogens at all growth points is 1, that is, , when the computer generates an arbitrary random number λ between [0,1], λ falls on the probability space composed of the morphogen concentrations of each growth point, and the corresponding growth point can grow first; at this time, it is judged whether there is a new growth point. When there is no new growth point, the growth point corresponding to the maximum fitness value at this time is the global optimal parameter; otherwise, return to step c until there is no new growth point, and the growth point corresponding to the maximum fitness value at this time is the global optimal parameter;

[0052] 3) Assign the global optimal parameters obtained in step 2) to the quantization factor and proportional factor in the fuzzy PID controller to form a complete control closed loop of the fuzzy PID controller and complete the helicopter attitude control.

[0053] In order to verify the effectiveness of the control method described in the present invention, a certain type of helicopter was selected as an experimental platform to carry out digital simulation research. In the helicopter attitude adjustment test, the traditional fuzzy PID control method and the control method described in the present invention were used to compare and analyze the pitch angle, yaw angle and roll angle of the helicopter. Figure 2 As shown in FIG, when the target pitch angle is set to 15°, the overshoot of the pitch angle controlled by the method of the present invention is about 3.8%, which is about 10% lower than that of the traditional fuzzy PID control method, and the response speed is significantly faster, and the convergence speed is about 2.5s faster than that of the traditional fuzzy PID control method; Figure 3 As described above, when the target roll angle is set to 15°, the overshoot controlled by the method of the present invention is greatly reduced, and there is no obvious oscillation in the dynamic process, which meets the engineering requirements of helicopter roll control; Figure 4 As shown, for yaw angle, the control method described in the present invention achieves an overshoot of approximately 4%, which is approximately 5% lower than the traditional fuzzy PID control method, and exhibits faster response and convergence speeds. The above simulation data demonstrates that the control method described in the present invention exhibits significant performance improvements, including shortened dynamic response time, reduced overshoot, and a smaller angular fluctuation range during steady-state control. This fully demonstrates the technical advantages of the control method described in the present invention in improving the dynamic response characteristics and steady-state control accuracy of the control system, effectively enhancing the control accuracy of the helicopter's attitude.

[0054] The above description is merely a specific embodiment of the present invention, which enables those skilled in the art to understand or implement the present invention. Although detailed descriptions have been made with reference to the aforementioned embodiments, those skilled in the art should understand that they may still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions within the scope of the technical solutions of the embodiments, and they should all be included in the scope of protection of the claims.

Claims

1. A method for optimizing a fuzzy PID controller to control a helicopter's attitude, characterized in that: The steps include: 1) Establish helicopter dynamics model and kinematics model, and design fuzzy PID controller; 2) A hybrid optimization method of simulated plant growth algorithm and genetic algorithm is used to optimize the parameters of the quantization factor and proportional factor in the fuzzy PID controller. The optimization process specifically includes the following steps: a. Initialization parameters: Use the genetic algorithm to initialize a population of growing points corresponding to the quantization factor and proportional factor in the fuzzy PID controller for the simulated plant growth algorithm; b. Assigning the growth point population initialized in step a to the quantization factor and the proportional factor in the fuzzy PID controller, and running the fuzzy PID controller to control the attitude of the helicopter, constructing the objective function and the corresponding fitness function; Among them, the objective function is divided into the objective function of no overshoot phenomenon and the objective function of overshoot phenomenon. Objective function for no overshoot: ， Where, The deviation of the expected posture between the given value and the actual output value At each moment, the given value is the expected attitude angle, and the actual output value is the current attitude angle. is the actual output attitude change value, Adjust the time for the system, for The absolute value weight coefficient, for The weight coefficient of the square term, for The weight coefficient of Objective function of overshoot phenomenon: ， Where, For helicopters The actual posture output value at the moment, For helicopters The actual posture output value at the moment, The weight coefficient for the absolute value of the helicopter output change; Fitness function: c. Calculate the fitness value corresponding to each growth point in the initialized growth point population according to the fitness function, and find the worst growth point based on the calculated fitness value , and use the intelligent variable step method to update the growth point ; When the new growth point The corresponding fitness value Less than the worst growth point The corresponding fitness value When the new growth point is directly removed; when the new growth point is The corresponding fitness value Greater than the worst growth point The corresponding fitness value When the growth point of its renewal Sort by the corresponding fitness value; the first P% updated growth points are added to the growth point set , select the growth point set at this time The worst growth point And record the worst growth point The corresponding fitness value is , where P≤100, and then (100-P)% of the updated growth points are again subjected to genetic algorithm, and global search is performed through crossover mutation to select the ones whose corresponding fitness value is greater than Growth points and add the selected growth points to the growth point set middle; d. Calculate the growth point set Middle Morphogen concentration at each growing point , and its calculation formula is Where, is the fitness value corresponding to the initial growth point, A collection of growth points Middle The fitness value of each growth point, A collection of growth points The number of growth points in the equation is known. From this formula, we can see that the concentration of morphogens at each growth point is between [0,1], and the sum of the concentrations of morphogens at all growth points is 1, that is, , when the computer generates an arbitrary random number λ between [0,1], λ falls on the probability space composed of the morphogen concentrations of each growth point, and the corresponding growth point can grow first; at this time, it is judged whether there is a new growth point. When there is no new growth point, the growth point corresponding to the maximum fitness value at this time is the global optimal parameter; otherwise, return to step c until there is no new growth point, and the growth point corresponding to the maximum fitness value at this time is the global optimal parameter; 3) Assign the global optimal parameters obtained in step 2) to the quantization factor and proportional factor in the fuzzy PID controller to form a complete control closed loop of the fuzzy PID controller and complete the helicopter attitude control.

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