Q-Learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control

Chen, Zengqiang; Qin, Beibei; Sun, Mingwei; Sun, Qinglin

doi:10.1016/j.neucom.2019.10.060

Cited by 63 publications

(23 citation statements)

References 19 publications

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“…Wei et al [25] (2019) proposed a time-varying ADRC method to adjust its parameters, but this method did not realize the active adjustment of parameters to obtain a satisfactory dynamic performance. Chen et al [26] (2019) proposed an adaptive method of ADRC parameters based on Q-learning.The result showed the proposed algorithm had the advantages of robustness and higher tracking precision. But there is no performance indicator to evaluate the result.…”

Section: Introductionmentioning

confidence: 99%

Adaptive LADRC Parameter Optimization in Magnetic Levitation

et al. 2021

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In this study, an adaptive linear active disturbance rejection control is proposed to achieve steady levitation of the magnetic levitation ball.The proposed algorithm was designed and its convergence was proven via derivation. It can address the difficulty in parameter tuning of the controller and realize the real-time self-adaptive optimization of parameters. Besides,to verify the effectiveness of the proposed parameter tuning strategy,we analysed the anti-interference ability and tracking effects of SMC, LADRC and A-LADRC in different cases. Finally, the results showed that the anti-interference ability of A-LADRC is stronger than SMC and LADRC. In addition,the anti-interference effect intensifies when the interference increases. The designed controller also performs the best tracking performance among these three controllers. Therefore, A-LADRC exhibits better robustness and dynamic performance.

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Section: Introductionmentioning

confidence: 99%

Adaptive LADRC Parameter Optimization in Magnetic Levitation

et al. 2021

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“…Guo et al [22,23] proved that the active disturbance rejection control is suitable for the SISO system and the MIMO system. Chen et al [24,25] obtained the stable region of LADRC and reduced-order LADRC based on the Lyapunov function and the Markus-Yamabe theorem, they also get mathematical proofs of global stability and asymptotic regulation; they also proposed an adaptive method of ADRC parameters based on Q-learning. From the above study, we can find that the ADRC technology is relatively mature and can effectively overcome internal and external disturbance, so ADRC is suitable for the unmanned helicopter.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Tracking Active Disturbance Rejection Control of the Unmanned Helicopter and Its Parameters Tuning

Shen

2021

IEEE Access

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To solve the problem that the trajectory tracking control system of the unmanned helicopter is limited by the accuracy of the physical model (parameters and dynamic characteristics) and external disturbance of the unmanned helicopter, this paper designs a trajectory tracking control system of the unmanned helicopter based on the linear/nonlinear hybrid Active Disturbance Rejection Control (ADRC). At the same time, this paper proposes a method for tuning the controller parameters based on the Bacterial Foraging Optimization-Flower Pollination Algorithm (BFO-FPA). Finally, the simulation test of spiral climb and "8"-figure climb is applied to verify the controller's performance. The results show that the controller proposed in this paper can effectively overcome the influence of the unmanned helicopter's internal and external disturbance, and the controller has the advantages of strong anti-disturbance ability and strong robustness. Simultaneously, the optimization algorithm can obtain the optimal global solution, and can improve the controller's performance.

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“…Considering the nonlinear and uncertain dynamic characteristics, a fuzzy neural network backstepping controller is designed to deal with the uncertainties, including estimation and compensation [22]. Brouwer et al [23] presented an estimator to quantify the random uncertainty of the mean (RUM) from a stationary single time series, without performing actual repeat tests.…”

Section: Introductionmentioning

confidence: 99%

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

Liu

Yao

et al. 2020

Complexity

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The dynamic positioning system of unmanned underwater vehicles (UUVs) is a complex and large-scale system mainly due to the nonlinear dynamics, uncertainty in model parameters, and external disturbances. With the aid of the bio-inspired computing (BIC) method, the designed three-dimensional (3D) spatial positioning system is used for enlarging communication constraints and increasing signal coordination processing. With the growing of measurement scales, the issue of the networked high-precision positioning has been developed rapidly. Then, an information fusion estimation approach is presented for the distributed networked systems with data random transmission time delays and lost and disordered packets. To reduce the communication burden, an adaptive signal selection scheme is employed to reorganize the measurement sequence, and the parameter uncertainties as well as cross-correlated noise are used to describe the uncertain disturbances. Moreover, a reoptimal weighted fusion state estimation is designed to alleviate the information redundancy and maintain higher measurement accuracy. An illustrative example obtained from the 3D spatial positioning system is given to validate the effectiveness of the proposed method.

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Q-Learning-based parameters adaptive algorithm for active disturbance rejection control and its application to ship course control

Cited by 63 publications

References 19 publications

Adaptive LADRC Parameter Optimization in Magnetic Levitation

Adaptive LADRC Parameter Optimization in Magnetic Levitation

Trajectory Tracking Active Disturbance Rejection Control of the Unmanned Helicopter and Its Parameters Tuning

Distributed State Estimation for Dynamic Positioning Systems with Uncertain Disturbances and Transmission Time Delays

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