Model-free adaptive control method with variable forgetting factor for unmanned surface vehicle control

Liao, Yulei; Du, Tingpeng; Jiang, Quanquan

doi:10.1016/j.apor.2019.101945

Cited by 32 publications

(13 citation statements)

References 13 publications

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“…Search for the negative gradient direction of the coefficient according to Equations ( 18)-( 25), and result is as shown in Equation (26).…”

Section: Rbfnn-enhanced Controller Designmentioning

confidence: 99%

“…In addition, MFAC was widely used in many fields as well, such as underwater vehicle manipulators, torque control of asynchronous motors, unmanned surface vehicles, cable-driven robots, and multi-agent systems [23][24][25][26][27][28]. The feature of independence of mathematical models also leads to the application intelligent transportation field.…”

Section: Introductionmentioning

confidence: 99%

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Data-Driven RBFNN-Enhanced Model-Free Adaptive Traffic Symmetrical Signal Control for a Multi-Phase Intersection with Fast-Changing Traffic Flow

et al. 2023

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Fast-changing demand in real traffic systems always leads to asymmetrical traffic flow and queues, which aggravates congestion and energy waste. In this paper, the traffic signal control problem of multi-phase intersections was studied with fast-changing traffic flows. First, a novel model-free adaptive control-based symmetrical queuing balancing method was designed by using the full-format dynamic linearization (FFDL) technique. Second, in order to deal with the fast-changing traffic flow, a radial basis function neural network (RBFNN) was added to adjust parameters in a two-layer structure. Moreover, a variable cycle tuning algorithm was introduced to further reduce the time loss. Using the simulation, the proposed algorithm was compared with three other control strategies under low and high traffic demand, respectively, and the results showed the capability of the proposed algorithm.

show abstract

“…Search for the negative gradient direction of the coefficient according to Equations ( 18)-( 25), and result is as shown in Equation (26).…”

Section: Rbfnn-enhanced Controller Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data-Driven RBFNN-Enhanced Model-Free Adaptive Traffic Symmetrical Signal Control for a Multi-Phase Intersection with Fast-Changing Traffic Flow

et al. 2023

View full text Add to dashboard Cite

show abstract

“…In [15], the bounded input-output stability and tracking performance of full format dynamic linearized MFAC were strictly proved by using the compression mapping principle. In [16], aiming at the problem of driverless vehicle ground heading control, an MFAC method based on the adaptive forgetting factor was proposed. When the control task is repeated, the system shows the same behavior.…”

Section: Introductionmentioning

confidence: 99%

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Zhou

Che

et al. 2022

Actuators

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This paper mainly studies the data-based security fault tolerant iterative learning control (SFTILC) problem of nonlinear networked control systems (NCSs) under sensor failures and denial-of-service (DoS) attacks. Firstly, the radial basis function neural network (RBFNN) is used to approximate the sensor failure function and a DoS attack compensation mechanism is proposed in the iterative domain to lessen the impact of DoS attacks. Then, using the dynamic linearization technology, the nonlinear system considering failures and network attacks is transformed into a linear data model. Further, based on the designed linearization model, a new data-based SFTILC algorithm is designed to ensure the satisfactory tracking performance of the system. This process only uses the input and output data of the system, and the stability of the system is proved by using the compression mapping principle. Finally, a digital simulation is used to demonstrate the effectiveness of the proposed SFTILC algorithm.

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“…Liao et al (2020) proposed a redefined output model‐free adaptive heading control method, which solves the limitation of MFAC method in the application of the USV heading control, however, this method is more sensitive to the change of the redefinition output gain, and the robustness of the algorithm is weak. Liao et al (2019) introduced an adaptive variable forgetting factor redefinition output model‐free adaptive heading control method, which combines forgetting factor adjustment mechanism and expert dynamic control behavior. Jiang et al (2019) proposed a redefined output model‐free adaptive heading control method based on the output constraints of the controlled system, which fully considers the mechanical saturation characteristics of the actuator.…”

Section: Introductionmentioning

confidence: 99%

Research on disturbance rejection motion control method of USV for UUV recovery

Liao

Chen

et al. 2022

Journal of Field Robotics

Self Cite

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The recovery of unmanned underwater vehicle (UUV) by unmanned surface vehicle (USV) has the characteristics of autonomy, safety, and efficiency. Taking the recovery of UUV by USV as the engineering background, this paper studies the guidance and anti-interference motion control of USV in the recovery process.Aiming at the problem of dynamic guidance when recovering UUV, the USV guidance strategy for UUV recovery is studied. Fuzzy guidance is introduced as the dynamic terminal guidance method, and a layered guidance strategy combining classical guidance and fuzzy guidance is proposed. On the basis of the theory of compact form dynamic linearization-based model-free adaptive control (CFDL-MFAC), the motion control of USV in the process of recovering UUV under the influence of model perturbation, external interference, and other uncertainties is studied. Theoretical analysis and experimental results show that there is a contradiction in the matching of dynamic change speed between the USV heading control subsystem and CFDL-MFAC. By introducing the difference item into the standard control criterion to weaken the integral effect in the heading control subsystem of USV, a difference-type compact format model-free adaptive control method (DCFDL-MFAC) is proposed, and the stability of DCFDL-MFAC method is proved theoretically. The effectiveness and practicability of the proposed method are verified by simulation tests and field tests of "Dolphin IB" small USV.

show abstract

Model-free adaptive control method with variable forgetting factor for unmanned surface vehicle control

Cited by 32 publications

References 13 publications

Data-Driven RBFNN-Enhanced Model-Free Adaptive Traffic Symmetrical Signal Control for a Multi-Phase Intersection with Fast-Changing Traffic Flow

Data-Driven RBFNN-Enhanced Model-Free Adaptive Traffic Symmetrical Signal Control for a Multi-Phase Intersection with Fast-Changing Traffic Flow

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Research on disturbance rejection motion control method of USV for UUV recovery

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