Active disturbance rejection control for hydraulic systems with full‐state constraints and input saturation

Xu, Zhangbao; Sun, Chuanbin; Yang, Mingxing; Liu, Qingyun

doi:10.1049/cth2.12292

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…The use of flight control strategies with disturbance resistance and higher efficiency has also received attention. At present, the flight control methods for drones mainly include linear control methods [8][9][10], nonlinear control methods [11][12][13], and intelligent control methods [14][15][16][17]. The learning-based robot control method has received widespread attention in the field of automatic control [18][19][20], as it ignores the dynamic model of the robot and learns control methods through a large amount of motion data.…”

Section: Introductionmentioning

confidence: 99%

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

Wu,

Yang,

Zhuo

et al. 2024

Drones

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The application of drones carrying different devices for aerial hovering operations is becoming increasingly widespread, but currently there is very little research relying on reinforcement learning methods for hovering control, and it has not been implemented on physical machines. Drone’s behavior space regarding hover control is continuous and large-scale, making it difficult for basic algorithms and value-based reinforcement learning (RL) algorithms to have good results. In response to this issue, this article applies a watcher-actor-critic (WAC) algorithm to the drone’s hover control, which can quickly lock the exploration direction and achieve high robustness of the drone’s hover control while improving learning efficiency and reducing learning costs. This article first utilizes the actor-critic algorithm based on behavioral value Q (QAC) and the deep deterministic policy gradient algorithm (DDPG) for drone hover control learning. Subsequently, an actor-critic algorithm with an added watcher is proposed, in which the watcher uses a PID controller with parameters provided by a neural network as the dynamic monitor, transforming the learning process into supervised learning. Finally, this article uses a classic reinforcement learning environment library, Gym, and a current mainstream reinforcement learning framework, PARL, for simulation, and deploys the algorithm to a practical environment. A multi-sensor fusion strategy-based autonomous localization method for unmanned aerial vehicles is used for practical exercises. The simulation and experimental results show that the training episodes of WAC are reduced by 20% compared to the DDPG and 55% compared to the QAC, and the proposed algorithm has a higher learning efficiency, faster convergence speed, and smoother hovering effect compared to the QAC and DDPG.

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Section: Introductionmentioning

confidence: 99%

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

Wu,

Yang,

Zhuo

et al. 2024

Drones

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show abstract

“…In the actual operation, robot manipulators are affected by the uncertainties of the internal dynamic modeling parameters and external disturbances, which decrease the control performance and are not conducive to the accurate and efficient tracking of the desired trajectory. With the development of research, various advanced control strategies such as feedback linearization control [5], PID control [6], model predictive control [7], fuzzy control [8], robust control [9,10], neural network control [11][12][13], active disturbance rejection control [14,15], and sliding mode controller (SMC) [16][17][18] have been widely used in the tracking tasks of robot manipulators.…”

Section: Introductionmentioning

confidence: 99%

Adaptive non‐singular fast terminal sliding mode based on prescribed performance for robot manipulators

Zhang

Fang

Song

et al. 2023

Asian Journal of Control

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A fast convergent non-singular terminal sliding mode adaptive control law based on prescribed performance is formulated to solve the uncertainties and external disturbances of robot manipulators. First, the tracking error of robot manipulators is transformed by using the prescribed performance function, which improves the transient behaviors and steady-state accuracy of robot manipulators. Then, a novel fast convergent non-singular terminal sliding mode surface is brought up according to the transformed error, and the control law is derived to meet the stability requirements of robot manipulators. In practice, the upper boundary of the lumped disturbances cannot be accurately obtained. Therefore, an adaptive prescribed performance control (PPC) controller to lumped disturbances is brought up to ensure the stability and finite-time convergence of robot manipulators. Finally, the system stability of robot manipulators is proved by the Lyapunov theorem. Simulation results and comparative analysis demonstrate the superiority and robustness of the raised strategy.

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“…Based on the predictive model, the control input was given by calculating the permissible value of future states at every sampling instant. In works [15][16][17][18], barrier Lyapunov function (BLF) for control of nonlinear system with state constraints has been proposed. Combined with the backstepping technique, the BLF-based controller can provide large control action when the state approaches the barriers, pulling the state away from the preset boundary.…”

Section: Introductionmentioning

confidence: 99%

A refined anti‐disturbance control method for gimbal servo systems subject to multiple disturbances under constraints

Cui

Yang

Qiao

et al. 2023

IET Control Theory & Appl

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The performance of the gimbal servo system in control moment gyro (CMG), which includes precision, lifespan etc., is one of the crucial factors of spacecraft attitude control. The various practical disturbances, however, will not only deteriorate the velocity‐tracking accuracy but will also cause the abnormal gimbal velocity problem (especially peak phenomenon). To this end, this paper proposes a refined anti‐disturbance control method to deal with velocity output constraints and multiple disturbances. Starting with fully understanding the prior information of multiple disturbances, a refined disturbance observer with a low conservativeness is designed to accurately estimate disturbances. The disturbance‐estimation error is analyzed in detail to ensure convergence to a bounded region. Subsequently, a novel barrier Lyapunov function‐based backstepping controller is proposed that considers the residuals of disturbance estimation to simultaneously achieve multiple disturbances attenuation and compensation, and handle velocity output constraints. Notably, the gimbal's maximum velocity is precisely limited to a pre‐specified low range, which benefits the CMG's lifespan and performance. Finally, both simulation and experimental results show that the proposed method performs better in disturbance estimation, velocity tracking, and robustness.

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Active disturbance rejection control for hydraulic systems with full‐state constraints and input saturation

Cited by 8 publications

References 38 publications

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

A Supervised Reinforcement Learning Algorithm for Controlling Drone Hovering

Adaptive non‐singular fast terminal sliding mode based on prescribed performance for robot manipulators

A refined anti‐disturbance control method for gimbal servo systems subject to multiple disturbances under constraints

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