Robust implicit Lyapunov control for rigid chain lifter with real-time disturbance compensation

In this paper, an output feedback implicit Lyapunov control (OFILC) method is proposed to address the positioning challenges of rigid chain lifters under load variations and unmeasurable velocity conditions. OFILC represents a novel integration of a linear extended state observer (LESO) and implicit Lyapunov control (IL), a combination that has not been previously explored. Firstly, OFILC introduces friction and gravity moment compensation to reduce the uncertainty of the dynamics model. Secondly, a LESO-based velocity observer is developed to overcome the measurement limitation, providing clean state estimation without the noise amplification issues inherent in signal differentiation. Finally, to address the disturbance problem caused by parameter uncertainty and load differences, LESO is introduced to estimate and compensate for model disturbances online, thereby enhancing robustness and control accuracy compared to the IL approach. Theoretical analyses demonstrate that this controller is uniformly ultimately bounded convergence. Experimental results demonstrate that OFILC offers advantages in terms of high precision, robustness, and low power consumption with different load conditions.