Tracking control of a quad-rotor UAV based on T — S fuzzy model

Sun, Miaoping; Liu, Jingjing

doi:10.23919/chicc.2017.8028019

Cited by 4 publications

(4 citation statements)

References 14 publications

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“…The robustness of sliding mode control is good, but the selection of sliding mode surfaces and future planning are still lacking. There are also many studies on MPC control strategies [8][9][10][11][12]. Yang and Zheng designed a double loop MPC controller for the UAV [8].…”

Section: Introductionmentioning

confidence: 99%

“…Yang and Zheng designed a double loop MPC controller for the UAV [8]. From the results, the tracking performance is good, Peng, Cao, Zhang and Gao each studied an improved MPC control strategy, in which Gaussian process regression was used [9][10][11][12]. The results of the article also show that Gaussian process prediction has a strong prediction ability for UAV discretized data.…”

Section: Introductionmentioning

confidence: 99%

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Trajectory tracking control of quadrotor using MPC and Gaussian process regression compensation

Xiong

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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Nowadays, the market for UAV is huge, and they are widely used in civilian, commercial and even military. In order to achieve so many functions, the drone's track tracking strategy is fundamental. Nowadays the PID control technology is quite mature, but this strategy is less robust and easily affected by external factors such as wind disturbance. On the other hand, since the UAV dynamic model is not a black box and most parts can be mathematically modeled, simply using PID cannot make good use of the UAV dynamic model. In order to achieve precise and robust flight control and management and improve the performance and safety of UAV systems, we design a MPC controller by utilizing the UAV's dynamic model and target constraints enables it to perform adaptive control in changing environments and provide more reliable and accurate flight performance. At the same time, Gaussian Process Regression is used to learn the historic data and predict the error, which can be used to compensate the MPC controller, thus enhancing the robustness and adaptability of the system. In the simulation by using SIMULINK and MATLAB, UAV trajectory tracking curves have higher accuracy and higher robustness compared with the results of PID control and ordinary MPC control. This paper uses MPC for the position loop of the UAV with the auxiliary compensation strategy of Gaussian process prediction, optimizes the UAV's more advanced MPC strategy which is also suitable for multi-UAV control, which has certain promoting significance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control of quadrotor using MPC and Gaussian process regression compensation

Xiong

2024

Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024)

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“…In the area of quadcopter literature, there is a variety of applications as aerial manipulation [1,2], quadcopter pendulum [3], navigation and localization [4,5], obstacle avoidance [6], altitude control [7], and cooperative and formation control [8,9]. Moreover, several control schemes have been proposed including adaptive control [10][11][12][13], fuzzy control [14], neural network control [15], linear parameter varying (LPV) control [16], predictive control [17,18], nonlinear control methods [19][20][21][22][23], and sliding mode control [24,25]. In [4], researchers propose localization, navigation, and mapping methods based on the characteristic map; feature map is selected to localize and navigate the UAV under investigation, while drawing up navigation strategy and avoidance strategy.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a tracking control system for the quadrotor UAV based on Takagi-Sugeno (T-S) fuzzy control has been presented in [14]. At first, T-S fuzzy error model has been presented as three independent subsystems for altitude, attitude, and position.…”

Section: Introductionmentioning

confidence: 99%

Advanced UAVs Nonlinear Control Systems and Applications

Joukhadar¹,

Alchehabi²,

Jejeh³

2020

Unmanned Robotic Systems and Applications

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Recent development of different control systems for UAVs has caught the attention of academic and industry, due to the wide range of their applications such as in surveillance, delivery, work assistant, and photography. In addition, arms, grippers, or tethers could be installed to UAVs so that they can assist in constructing, transporting, and carrying payloads. In this book chapter, the control laws of the attitude and position of a quadcopter UAV have been derived basically utilizing three methods including backstepping, sliding mode control, and feedback linearization incorporated with LQI optimal controller. The main contribution of this book chapter would be concluded in the strategy of deriving the control laws of the translational positions of a quadcopter UAV. The control laws for trajectory tracking using the proposed strategies have been validated by simulation using MATLAB ® /Simulink and experimental results obtained from a quadcopter test bench. Simulation results show a comparison between the performances of each of the proposed techniques depending on the nonlinear model of the quadcopter system under investigation; the trajectory tracking has been achieved properly for different types of trajectories, i.e., spiral trajectory, in the presence of unknown disturbances. Moreover, the practical results coincided with the results of the simulation results.

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