Xiaofei Chen, Han Zhao, Shengchao Zhen, Xiaoxiao Liu, Jinsi Zhang
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Fixed-Time Adaptive Neural Network-Based Trajectory Tracking Control for Workspace Manipulators
This paper proposes a novel neural network-based control algorithm with fixed-time performance constraints for manipulator systems in workspaces. The algorithm efficiently controls the manipulator’s trajectory tracking by tuning a preset performance function, thereby optimizing both speed and accuracy within a fixed timeframe. Initially, a tangent-type error transformation, applied through homogeneous embryonic transformation, ensures rapid convergence of tracking errors to a specific region. Subsequently, integrating a predetermined control strategy into the fixed-time stability framework ensures the system’s state reaches a defined boundary within a finite period. Lastly, neural networks are employed to approximate dynamic parameters and adjust the controller, achieving optimal parameter approximation and significantly enhancing trajectory tracking robustness. Simulation analyses and comparisons confirm the controller’s effectiveness and superiority in enhancing both the transient and steady-state performance of the control system.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.