Event-triggered reinforcement learning control for the quadrotor UAV with actuator saturation

Lin, Xiaobo; Liu, Jian; Yao, Yu; Sun, Changyin

doi:10.1016/j.neucom.2020.07.042

Cited by 37 publications

(12 citation statements)

References 28 publications

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“…Omid et al [16] and Lin et al [17] utilized the reinforcement learning method to design the controller. In order to ensure that the controller output did not exceed the limit, an output threshold was set during the learning process.…”

Section: Introductionmentioning

confidence: 99%

Quadrotor Trajectory-Tracking Control with Actuator Saturation

2023

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As a very serious problem for control systems, actuator saturation often leads to an unstable system. The maneuvering flight of quadrotors may lead to actuator saturation, which commonly affects the flight quality, including trajectory-tracking accuracy. Quadrotors usually adopt double-closed-loop control, of which the outer loop is the position control used to calculate the desired attitude angle and the inner loop is the attitude control used to generate control actions for the vehicle. To address actuator saturation during the maneuvering flight of quadrotors, an attitude controller based on the conditioned super-twisting algorithm (CSTA) was designed. The sign functions in CSTA were replaced by a hyperbolic tangent function, which suppresses the chattering of CSTA. In order to improve the rapidity of the improved controller, the tanh-CSTA (TCSTA), a gain adjustment factor was utilized. The rapidity and smoothness of the controller can be satisfied by adjusting the gain factor. Additionally, a sliding surface was designed according to the characteristics of the quadrotor control structure, so that the TCSTA controller can be applied to the second-order system. The performances of the designed improved controller were simulated using MATLAB, and the results showed that the designed controller is more robust than the PID controller. Moreover, compared with the CSTA controller, the chattering of the designed controller was obviously suppressed without reducing the control accuracy.

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Section: Introductionmentioning

confidence: 99%

Quadrotor Trajectory-Tracking Control with Actuator Saturation

2023

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“…However, the system design of this technique should be considered, including sensor installation, sensor detection range, and parameter tuning, to ensure the effectiveness of the control system. For automatic parameter tuning or optimization, various machine learning methods have been applied including deep learning (Palossi et al, 2019 , 2022 ; Varshney et al, 2019 ), reinforcement learning (RL) (Shin et al, 2019 ; Wang et al, 2019 ; Lin et al, 2020 ), and an evolutionary algorithm (EA) (Fu et al, 2018 ; Yazid et al, 2019 ). Although they have become more popular in recent years, their learning processes are typically time-consuming and computationally expensive, requiring a large amount of data and multiple learning trials or iterations.…”

Section: Introductionmentioning

confidence: 99%

Neural Control and Online Learning for Speed Adaptation of Unmanned Aerial Vehicles

Jaiton

Rothomphiwat

Ebeid

et al. 2022

Front. Neural Circuits

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Unmanned aerial vehicles (UAVs) are involved in critical tasks such as inspection and exploration. Thus, they have to perform several intelligent functions. Various control approaches have been proposed to implement these functions. Most classical UAV control approaches, such as model predictive control, require a dynamic model to determine the optimal control parameters. Other control approaches use machine learning techniques that require multiple learning trials to obtain the proper control parameters. All these approaches are computationally expensive. Our goal is to develop an efficient control system for UAVs that does not require a dynamic model and allows them to learn control parameters online with only a few trials and inexpensive computations. To achieve this, we developed a neural control method with fast online learning. Neural control is based on a three-neuron network, whereas the online learning algorithm is derived from a neural correlation-based learning principle with predictive and reflexive sensory information. This neural control technique is used here for the speed adaptation of the UAV. The control technique relies on a simple input signal from a compact optical distance measurement sensor that can be converted into predictive and reflexive sensory information for the learning algorithm. Such speed adaptation is a fundamental function that can be used as part of other complex control functions, such as obstacle avoidance. The proposed technique was implemented on a real UAV system. Consequently, the UAV can quickly learn within 3–4 trials to proactively adapt its flying speed to brake at a safe distance from the obstacle or target in the horizontal and vertical planes. This speed adaptation is also robust against wind perturbation. We also demonstrated a combination of speed adaptation and obstacle avoidance for UAV navigations, which is an important intelligent function toward inspection and exploration.

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“…For the uncertainties and external disturbances in a nonlinear system, a neural network‐based tracking controller is designed in Reference 21, which can achieve a good tracking performance with input quantization and time delay. A neural network‐based event‐triggered control strategy in Reference 22 that effectively learns the complex dynamic properties of UAV since the neural networks have a significant application in dealing with modeling uncertainties and external disturbances of nonlinear systems. In Reference 23, the neural networks are adopted to estimate the lumped effect of unknown actuator faults, disturbances and input saturation.…”

Section: Introductionmentioning

confidence: 99%

Finite time fault tolerant tracking control for multiple unmanned aerial vehicles with actuator faults

Qian

Zhang

et al. 2022

Adaptive Control & Signal

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An adaptive fast nonsingular integral terminal sliding mode control scheme is proposed for the distributed formation control problem of multiple unmanned aerial vehicles (UAVs) affected by actuator faults and external disturbances.In order to achieve a good cooperative tracking performance of UAVs during mission execution, the adverse effect of the lumped disturbances composed of actuator failures, wind and vortex disturbances is estimated by the cerebellar model articulation neural network, meanwhile the tangent function is introduced to eliminate the chattering of the sliding mode. Then the analysis of superior convergence performance of the fast nonsingular integral terminal sliding manifold is implemented, and the finite time stability of the closed-loop formation flight control system is proved. Finally, the simulation experiments verify that the proposed approach is superior to the nonsingular integral terminal sliding mode control for the formation control system of faulty multiple UAVs.

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Event-triggered reinforcement learning control for the quadrotor UAV with actuator saturation

Cited by 37 publications

References 28 publications

Quadrotor Trajectory-Tracking Control with Actuator Saturation

Quadrotor Trajectory-Tracking Control with Actuator Saturation

Neural Control and Online Learning for Speed Adaptation of Unmanned Aerial Vehicles

Finite time fault tolerant tracking control for multiple unmanned aerial vehicles with actuator faults

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