High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control
In view of the difficulties encountered when tuning parameters and the lack of anti-interference capabilities exhibited by high-precision trajectory-tracking control of quadrotor UAVs in complex dynamic environments, this paper proposes a fusion control framework based on an improved crowned pig opt...
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MDPI AG
2025-06-01
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| author | Junhao Li Junchi Bai Jihong Wang |
| author_facet | Junhao Li Junchi Bai Jihong Wang |
| author_sort | Junhao Li |
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| description | In view of the difficulties encountered when tuning parameters and the lack of anti-interference capabilities exhibited by high-precision trajectory-tracking control of quadrotor UAVs in complex dynamic environments, this paper proposes a fusion control framework based on an improved crowned pig optimisation algorithm (ICPO) and preset performance anti-disturbance control (PPC-ADRC). Initially, this paper addresses the limited convergence efficiency of the traditional crowned pig algorithm (CPO) by introducing a dynamic time threshold mechanism and an adaptability-based directed elimination strategy to balance the algorithm’s global exploration and local development capabilities. This results in a significant improvement in the convergence speed and optimisation accuracy. Secondly, a hierarchical control architecture is designed, with the outer loop using a PPC-ADRC controller to dynamically constrain the tracking error boundary using an exponential performance funnel function and a combined state observer (ESO) to estimate the compound disturbance in real time. The inner-loop attitude control uses ADRC, and the 24-dimensional parameters of the ADRC (including the ESO bandwidth and non-linear feedback gain) are optimised autonomously using the ICPO to achieve efficient parameter tuning. The simulation experiments demonstrate that, in comparison with the original CPO, the ICPO attains an average fitness ranking that is superior in the CEC2014–2022 benchmark test, thereby substantiating its global optimisation capability. In the PPC-ADRC controller parameter optimisation, the preset performance of the ICPO-tuned PPC-ADRC controller (PPC-ADRC) is superior to that of the particle swarm optimisation (PSO), genetic algorithm (GA) and original CPO. The ICPO-based PPC-ADRC controller is shown to reduce the total error by more than 45.6% compared to the ordinary ADRC controller in the task of tracking a spiral trajectory, and it effectively reduces the overshoot. Its capacity to withstand complex wind disturbances is notably superior to that of the traditional PID and ADRC architectures. Stability analysis further proves that the system satisfies the Lyapunov convergence condition in a finite time. This research provides a theoretical foundation for the high-precision control of UAVs in complex dynamic environments. |
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| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
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| series | Drones |
| spelling | doaj-art-bbc436afad7346bb88c254dfced716b02025-08-20T03:27:28ZengMDPI AGDrones2504-446X2025-06-019642010.3390/drones9060420High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance ControlJunhao Li0Junchi Bai1Jihong Wang2College of Future Technology for Intelligent Ocean, Harbin Engineering University, Harbin 150001, ChinaCollege of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, ChinaFaculty of Engineering, The University of Hong Kong, Hong Kong 999077, ChinaIn view of the difficulties encountered when tuning parameters and the lack of anti-interference capabilities exhibited by high-precision trajectory-tracking control of quadrotor UAVs in complex dynamic environments, this paper proposes a fusion control framework based on an improved crowned pig optimisation algorithm (ICPO) and preset performance anti-disturbance control (PPC-ADRC). Initially, this paper addresses the limited convergence efficiency of the traditional crowned pig algorithm (CPO) by introducing a dynamic time threshold mechanism and an adaptability-based directed elimination strategy to balance the algorithm’s global exploration and local development capabilities. This results in a significant improvement in the convergence speed and optimisation accuracy. Secondly, a hierarchical control architecture is designed, with the outer loop using a PPC-ADRC controller to dynamically constrain the tracking error boundary using an exponential performance funnel function and a combined state observer (ESO) to estimate the compound disturbance in real time. The inner-loop attitude control uses ADRC, and the 24-dimensional parameters of the ADRC (including the ESO bandwidth and non-linear feedback gain) are optimised autonomously using the ICPO to achieve efficient parameter tuning. The simulation experiments demonstrate that, in comparison with the original CPO, the ICPO attains an average fitness ranking that is superior in the CEC2014–2022 benchmark test, thereby substantiating its global optimisation capability. In the PPC-ADRC controller parameter optimisation, the preset performance of the ICPO-tuned PPC-ADRC controller (PPC-ADRC) is superior to that of the particle swarm optimisation (PSO), genetic algorithm (GA) and original CPO. The ICPO-based PPC-ADRC controller is shown to reduce the total error by more than 45.6% compared to the ordinary ADRC controller in the task of tracking a spiral trajectory, and it effectively reduces the overshoot. Its capacity to withstand complex wind disturbances is notably superior to that of the traditional PID and ADRC architectures. Stability analysis further proves that the system satisfies the Lyapunov convergence condition in a finite time. This research provides a theoretical foundation for the high-precision control of UAVs in complex dynamic environments.https://www.mdpi.com/2504-446X/9/6/420quadrotor UAVtrajectory-tracking controlcrowned pig optimisation algorithmself-disturbance rejection controlpreset performance functionparameter optimisation |
| spellingShingle | Junhao Li Junchi Bai Jihong Wang High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control Drones quadrotor UAV trajectory-tracking control crowned pig optimisation algorithm self-disturbance rejection control preset performance function parameter optimisation |
| title | High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control |
| title_full | High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control |
| title_fullStr | High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control |
| title_full_unstemmed | High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control |
| title_short | High-Precision Trajectory-Tracking Control of Quadrotor UAVs Based on an Improved Crested Porcupine Optimiser Algorithm and Preset Performance Self-Disturbance Control |
| title_sort | high precision trajectory tracking control of quadrotor uavs based on an improved crested porcupine optimiser algorithm and preset performance self disturbance control |
| topic | quadrotor UAV trajectory-tracking control crowned pig optimisation algorithm self-disturbance rejection control preset performance function parameter optimisation |
| url | https://www.mdpi.com/2504-446X/9/6/420 |
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