Adaptive finite-time synergetic control for flexible-joint robot manipulator with disturbance inputs
DOI:
https://doi.org/10.20998/2074-272X.2025.3.07Keywords:
flexible-joint manipulator, synergetic control theory, finite-time control, Lyapunov function, adaptive controlAbstract
Introduction. In this paper, the adaptive finite time controller is designed for flexible-joint manipulator (FJM) to stabilize oscillations and track the desired trajectory based on synergetic control theory (SCT) under disturbance inputs. The problem of the proposed work consists in the development of a mathematical model of the flexible joint while ignoring the nonlinear components of the actuator and synthesizing the control law that ensures the system stability within a settling time. The aim of this study is to use finite-time synergetic controller to ensure the reduction of system tracking error, avoid vibration and achieve steady state in a certain time period. An adaptive synergetic law is developed to solve the problem of uncertainty in the mathematical model of the actuator of FJM and input disturbances. Methodology. First, based on SCT the finite-time controller is constructed via the functional equation of the first manifold. The control law is designed to ensure the movement of the closed-loop system from an arbitrary initial state into the vicinity of the desired attractive invariant manifold, that is, the target attracting manifold. Secondly, to adjust the control law online, an adaptive law is developed to estimate the disturbance acting on the input. Then, the Lyapunov function is used to prove that the system can be stabilized in a sufficiently small neighborhood of the origin within finite time under input disturbances. Novelty. The implemented controller is effective in ensuring stability over a given time, minimizing the jitter problem while maintaining tracking accuracy and system robustness in the presence of input noise. Results. Numerical simulation and experimental results are presented to illustrate the effectiveness of the proposed method. The research directions of the model were determined for the subsequent implementation of the results in experimental samples. References 25, table 1, figures 7.
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