Disturbance observer-based control with adaptive neural network for unknown nonlinear system

Hu, Jia; Zheng, Meirong

doi:10.1177/00202940221106123

Cited by 2 publications

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most common control strategy for brake systems is slip rate control, which obtains the maximum tire-road friction by regulating the slip rate. Several methods have been proposed, such as dynamic surface control [2], adaptive control [3], and extreme value search control [4]. These methods focus on adjusting the slip rate in real time to track the expected slip rate but only partially consider an excellent dynamic performance and the system's robustness.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Optimal Control of UAV Brake Based on Parameter Estimation

Lv,

Liu,

Wan

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

A new parameter estimation method for a UAV braking system with unknown friction parameters is suggested. The unknown part containing friction is separated from the coupling system. The law of parameter updating is driven by the estimation error extracted by the auxiliary filter. A controller is developed to guarantee the convergence of the parameter estimation and tracking errors simultaneously. Furthermore, a designed performance index function enables the system to track the desired slip rate optimally. The optimal value of the performance function is updated by a single critic neural network (NN) to obtain comprehensive optimization of the control energy consumption, dynamic tracking error, and filtering error during braking. In addition, the unknown bounded interference and neural network approximation errors are compensated by robust integral terms. Simulation results are presented to confirm the effectiveness of the proposed control scheme.

show abstract

Section: Introductionmentioning

confidence: 99%

Adaptive Optimal Control of UAV Brake Based on Parameter Estimation

Lv,

Liu,

Wan

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

show abstract

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Liu,

Zhang,

et al. 2024

Measurement and Control

View full text Add to dashboard Cite

Research on initial errors and constraint restrictions is one of the main research directions in the field of control of uncertain robotic systems. An adaptive iterative learning control (AILC) method based on radial basis function (RBF) neural network is proposed to address the trajectory tracking problem of the long-stroke hybrid robot system with random initial errors and full state constraints. The RBF neural network is used to approximate the unknown nonlinear terms, and the network weights are updated using an iterative learning law that incorporates a projection mechanism. Additionally, a robust learning strategy is used to compensate for both the approximation error of the neural network and the external disturbances that vary with each iteration. To relax the requirement of traditional iterative learning control (ILC) for identical initial condition, an equivalent error function is constructed based on the time-varying boundary layer. The tangent-type barrier Lyapunov function (BLF) is designed to ensure that the joint position and speed of the robot system are bounded within a predetermined range. Through stability analysis based on barrier composite energy function (BCEF), it can be proved that the boundedness of all signals in the closed-loop system and the tracking error of the robot system will converge to an adjustable residual set asymptotically. Finally, through simulation experiments conducted on the MATLAB platform, the results demonstrate that the method overcomes the random initial errors of the system effectively, ensures that the system satisfies the full-state constraints, and realizes high-precision trajectory tracking.

show abstract

Disturbance observer-based control with adaptive neural network for unknown nonlinear system

Cited by 2 publications

References 22 publications

Adaptive Optimal Control of UAV Brake Based on Parameter Estimation

Adaptive Optimal Control of UAV Brake Based on Parameter Estimation

Adaptive neural network iterative learning control of long-stroke hybrid robots with initial errors and full state constraints

Contact Info

Product

Resources

About