Lyapunov-based nonlinear model predictive control for the path following of bevel-tip flexible needles in 3D environment

Jiang, Kai; Qi, Zhi; Zhu, Chaojie; Sun, Hengkai; Zhang, Hui

doi:10.1007/s11760-024-03184-3

Cited by 3 publications

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many of these devices, which are based on linear motor platforms, require high-precision trajectory tracking control [4][5][6]. To enhance tracking accuracy, applicability, and robustness, various feedback methods such as sliding mode control [7], adaptive robust control(ARC) [8], adaptive control [9], fuzzy control [10], model predictive control [11,12] have been studied and employed. It is noteworthy that most of the aforementioned methods are aimed at improving the performance of the system under steady-state conditions [13].…”

Section: Introductionmentioning

confidence: 99%

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Yu,

Ma,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper aims to investigate a chattering-free adaptive iterative learning strategy for trajectory tracking tasks on linear-motor-driven-gantry-stages with initial state errors. The reduction mechanism of the proposed parameter learning law effectively addresses initial state tracking errors, unmodeled system dynamics, and external disturbances during the iterative process. Furthermore, to mitigate the oscillation issues caused by traditional iterative learning control signals, this paper adopts the approximation for the sign function. However, this approximation introduces the non-negative definite problems. Therefore, this paper introduces a novel analysis method based on contraction mapping and composite energy functions in the Lyapunov-like theory. This method rigorously proves the boundedness and convergence during the entire iteration under non-negative definite problems with initial state errors. The effectiveness of the proposed approach is validated through experiments on a linear motor platform.

show abstract

Section: Introductionmentioning

confidence: 99%

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Yu,

Ma,

Wang

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

Stochastic LPV MPC-based path following control for bevel-tip flexible needle with probabilistic constraints

Chen,

Qi,

Zhang

et al. 2024

Int J Adv Manuf Technol

View full text Add to dashboard Cite

This paper addresses the path-tracking problem for flexible needle control systems using a stochastic linear parameter varying (LPV) and model predictive control (MPC) strategy. Flexible needles operating in dynamic environments with non-uniform tissue density often deviate from ideal assumptions, resulting in non-standard models. The bicycle kinematics model for flexible needle motion control is transformed into an LPV model, improving accuracy and enabling more efficient control. The proposed stochastic LPV MPC approach aims to mitigate uncertainties arising from modelling errors and dynamic environmental factors, ensuring accurate trajectory tracking for the flexible needle. The sample and removal method is utilized to reformulate the probabilistic-constrained optimization problem for implementation. The contributions of this work lie in the application of stochastic LPV MPC to address the trajectory tracking problem in the presence of uncertainties. The simulation results illustrate the superior robustness of the stochastic LPV MPC approach, as evidenced by significantly smaller tracking errors across various scenarios.

show abstract

Chattering-free adaptive iterative learning control for linear-motor-driven gantry stage with initial state errors

Yu,

Ma,

Wang

et al. 2024

SIViP

View full text Add to dashboard Cite

Lyapunov-based nonlinear model predictive control for the path following of bevel-tip flexible needles in 3D environment

Cited by 3 publications

References 28 publications

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Chattering-free Adaptive Iterative Learning Control for Linear-Motor-Driven Gantry Stage With Initial State Errors

Stochastic LPV MPC-based path following control for bevel-tip flexible needle with probabilistic constraints

Chattering-free adaptive iterative learning control for linear-motor-driven gantry stage with initial state errors

Contact Info

Product

Resources

About