Four-axis unmanned aerial vehicle PID control system parameter setting method based on Harlisia eagle optimization algorithm

Abstract

The invention relates to a four-axis unmanned aerial vehicle PID control system parameter setting method based on a Harris eagle optimization algorithm, and belongs to the field of embedded systems. According to the method, the limitation that traditional PID parameter setting depends on artificial experience, efficiency is low and global optimum is difficult to approach is broken through, and autonomous and efficient optimization of the parameters of the PID controller of the unmanned aerial vehicle is achieved by introducing the Harris eagle optimization algorithm and utilizing the collaborative hunting strategy and the self-adaptive escape energy mechanism of the Harris eagle optimization algorithm. The method comprises the following steps: firstly, establishing a six-degree-of-freedom kinetic model of the four-axis unmanned aerial vehicle based on a Newton-Euler equation, taking parameters of an inner ring PID controller and an outer ring PID controller as optimization variables, and designing a multi-target fitness function fusing ITAE indexes, overshoot and adjustment time; then, executing intelligent search in a parameter space by utilizing a Harris eagle algorithm; dynamically switching global exploration and local development through escape energy, and gradually approaching an optimal parameter; and finally, the optimized parameters are deployed to a flight control system, and high-precision robust control over the attitude and the position in the complex disturbance environment is achieved.

Description

Technical Field

[0001] This invention belongs to the field of embedded systems, specifically involving parameter optimization of proportional-integral-derivative (PID) controllers and quadcopter drone control technology. Background Technology

[0002] In recent years, drone technology has developed rapidly and has become a core tool in agriculture, logistics, film and television production, infrastructure inspection and other fields, significantly improving operational efficiency. In the flight control system of a quadcopter drone, the PID controller, as the core algorithm, achieves precise control of attitude, altitude and position by adjusting the rotor speed in real time. Its parameter performance directly determines the flight stability and dynamic response quality.

[0003] Traditional PID parameter tuning has long relied on manual adjustments by engineers, resulting in drawbacks such as strong reliance on experience, long debugging cycles, and difficulty in adapting to sudden wind disturbances and load changes. To address this bottleneck, this invention proposes a self-tuning method based on an improved Harris Eagle Optimization (HHO) algorithm. By simulating the adaptive strategy of Harris Eagles' cooperative hunting, a mapping model between PID parameters and biological behavior is constructed, enabling efficient adjustment of PID controller parameters, thereby improving the flight stability and control accuracy of quadcopter UAVs. Summary of the Invention

[0004] This invention proposes a parameter tuning method for the PID control system of a quadcopter UAV based on the Harris Eagle optimization algorithm, aiming to improve the flight stability and control accuracy of the quadcopter UAV in complex flight environments. This method optimizes the PID controller parameters by simulating the cooperative tracking, encirclement, and ambush behavior of a Harris Eagle flock during hunting, thereby achieving optimal UAV flight performance. The technical principle is explained below:

[0005] The PID controller, a classic control algorithm, consists of three parts: proportional, integral, and derivative. It balances system response speed, stability, and anti-interference capability by adjusting the proportional, integral, and derivative gains. Proportional control generates control action based on the current error, integral control eliminates steady-state error, and derivative control suppresses system oscillations.

[0006] The Harris Eagle Optimization Algorithm simulates the intelligent hunting behavior of raptor flocks, dynamically switching between four strategies through an adaptive escape energy mechanism: when the energy factor ≥ 1, it performs global exploration; when the energy factor < 1, it updates the parameter position by selecting a soft encirclement, hard encirclement, or a progressive dive strategy involving levy flight based on prey behavior. The algorithm first establishes a UAV dynamics model and designs a fitness function, randomly initializing the eagle population within the PID parameter search space. During iterative optimization, the current PID parameters are injected into the UAV simulation model each time to evaluate dynamic performance. The optimal solution is retained based on the fitness value. By controlling the balance between exploration and development through escape energy, and combining a multi-strategy collaborative mechanism and random jump characteristics, it efficiently escapes local optima, ultimately outputting the PID parameter combination that minimizes control error and deploying it to the flight control system. The Harris Eagle Optimization Algorithm enhances the search capability in complex spaces through levy flight, and, combined with the escape energy mechanism, ensures extensive exploration in the early stages and precise convergence in the later stages, significantly improving the control accuracy of UAVs in disturbed environments.

[0007] The PID self-tuning method of the present invention includes the following steps:

[0008] 1. Establish UAV model and fitness function: Construct a dynamic mathematical model of a quadcopter UAV and design a comprehensive performance evaluation function.

[0009] 2. Set the core parameters of the Harris Eagle optimization algorithm: population size, maximum number of iterations, PID parameter search boundary, and initial escape energy.

[0010] 3. Initialize the eagle population by randomly generating N initial solutions in the three-dimensional search space of the PID parameters. Each set of parameters corresponds to the position vector Xi of an individual Harris eagle.

[0011] 4. Assess population fitness. Perform the following operations for each eagle: Inject the current PID parameters into the UAV simulation model, run flight simulation under strong disturbance environment, collect data such as attitude angle tracking error and wind disturbance response, and calculate the current parameter performance score according to the fitness function.

[0012] 5. Update eagle flock positions: Calculate escape energy and dynamically select strategies based on energy factors.

[0013] 6. Iterative optimization: Repeat steps 4 and 5 until the termination condition is met.

[0014] 7. Output optimal PID parameters: Apply the optimized PID parameters to the flight control system of the quadcopter UAV to achieve stable and precise flight control.

[0015] The Harris Eagle algorithm automatically balances global exploration and local exploitation through an escape energy mechanism, and its four hunting strategies can finely adjust PID parameters. The introduced Levy flight strategy enhances the search capability in complex parameter spaces, and together with the adaptive strategy switching mechanism, it effectively solves the problem of traditional optimization algorithms easily getting trapped in local optima, enabling the UAV to maintain control accuracy in complex scenarios such as gusts of wind and load changes. In practical applications, multiple experiments are still needed to determine the optimal parameter combination and optimization results.

[0016] Beneficial effects

[0017] Compared with traditional PID parameter tuning, the present invention has the following advantages:

[0018] 1) Automated parameter tuning: By simulating the intelligent hunting behavior of Harris eagles, the system automatically searches for the optimal combination of PID parameters, eliminating the tedious process of traditional manual trial and error parameter tuning.

[0019] 2) Performance optimization: The PID parameters tuned based on the dynamic switching exploration and development strategy of the escape energy mechanism can effectively improve the dynamic response speed and accuracy of the UAV attitude control system.

[0020] 3) Global Optimality Guarantee: Manually adjusting PID parameters may lead to local optima, preventing the system from achieving optimal performance. The Harris Eagle algorithm avoids local optima through a four-strategy collaborative mechanism.

[0021] 4) Adaptability: No precise dynamic modeling is required. It is robust to nonlinear / time-varying systems. The Harris Eagle algorithm can adjust parameters based on actual feedback without requiring precise system modeling, thus reducing the dependence on system modeling. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the overall structure of the present invention; Detailed Implementation

[0023] This invention provides an embodiment: a parameter tuning method for a quadcopter UAV PID control system based on the Harris Eagle optimization algorithm, which uses the Harris Eagle algorithm to find the optimal PID parameters for the quadcopter UAV to achieve stable and accurate flight control.

[0024] The above-mentioned method for tuning parameters of a quadcopter UAV PID control system based on the Harris Eagle optimization algorithm includes the following steps:

[0025] Step 1: Construct a six-degree-of-freedom dynamic model based on the Newton-Euler equations:

[0026]

[0027] in θ and ψ are the roll / pitch / yaw angles, respectively, and p, q, and r are the corresponding angular velocities.

[0028] Step 2: Construct a dual-loop PID controller architecture:

[0029] Outer loop angle PID controller:

[0030]

[0031] Inner loop angular velocity PID controller

[0032]

[0033] Step 3: Design the multi-constraint fitness function:

[0034] Improve the ITAE metric:

[0035]

[0036] Where δ% is the overshoot, t s To adjust the time, T = 5s is the simulation duration.

[0037] Step 4: Apply the Harris Eagle optimization algorithm:

[0038] (a) Initialize eagle flock parameters:

[0039] Set the flock size, define the search space, set the escape energy base, and the maximum iteration.

[0040] (b) Dynamic fitness assessment:

[0041] For each eagle (PID parameter set): inject a dual-loop PID controller, run a step response simulation with wind disturbance, and calculate the triaxial overall fitness value.

[0042] (c) Escape energy renewal mechanism:

[0043] E = 2E0(1-t / T) max )

[0044] (d) Four-stage position update strategy:

[0045] Global search:

[0046]

[0047] Soft enclosure:

[0048] X new =ΔX-E|JX rabbit -X∣

[0049] J = 2(1 - r⁵)

[0050] Hard surround:

[0051] X new =X rabbit -E|ΔX|

[0052] Gradual dive:

[0053]

[0054] Levy flight:

[0055]

[0056] (e) Elite retention mechanism: The top 10% of individuals with the best fitness are retained in each generation.

[0057] (f) Iteration termination condition: Stop when the termination condition is met.

[0058] Step 5: Parameter deployment and verification. Verify the optimal output parameters.

Claims

1. A four-axis unmanned aerial vehicle PID parameter intelligent setting method based on a Harris hawk optimization algorithm, characterized in that, The method comprises the following steps: A dynamic model of the quadrotor is established, and a control target of a double-loop PID controller is determined. A proportional coefficient Kp, an integral coefficient Ki, and a differential coefficient Kd of the PID controller are taken as optimization variables. A multi-objective fitness function of fusion ITAE index, overshoot, and regulation time is defined. A Harris hawk optimization algorithm is used to perform a global search in a parameter space, and a search strategy is dynamically switched through an escape energy mechanism. The optimized PID parameters are deployed to a flight control system to achieve stable control against interference.

2. The method of claim 1, wherein, The fitness function is defined as: Wherein, δ% is an overshoot, ts is a regulation time, λ1=100, and λ2=10.

3. The method of claim 1, wherein, The Harris hawk optimization algorithm comprises: a) initializing a hawk group size, setting an escape energy base number E0, and setting a maximum iteration number. b) calculating a time-varying escape energy: E = 2E0(1 - t / T max ) c) when |E|≥1, a global exploration strategy is executed: d) when |E|<1, a development strategy is executed: Soft enveloping: X new = ΔX - E | JX rabbit - X | J＝2(1-r5) Hard enveloping: X new = X rabbit - |ΔX| Progressive dive:

4. The method of claim 3, wherein, Levy flight in the progressive dive:

5. The method of claim 3, wherein, An elite reservation mechanism is set: the top 10% individuals with the best fitness in each generation are reserved to prevent loss of high-quality parameters.

6. A quadcopter control system, comprising: The PID parameter tuning is performed by using the method according to any one of claims 1 to 5 to achieve stable flight control of the unmanned aerial vehicle.

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