Dynamic event-triggered adaptive cooperative control for robotic manipulator with output constraints and model uncertainties

This paper introduces an adaptive fuzzy-based dynamic event-triggered adaptive cooperative control to address the challenge of achieving high-performance control for robotic manipulators under model uncertainties and output constraints. This strategy designs a signal-processing-based backstepping control scheme and an energy-transformation-based error port-Hamiltonian (EPH) control scheme, respectively. The uncertain dynamics of the system are approximated through adaptive fuzzy logic systems, while the output of the system is constrained by boundary constraints gain. Furthermore, an adaptive cooperative control mechanism is designed, which dynamically adjusts the weighting between the two control schemes. This mechanism integrates the advantages of the fast response from the signal-processing-based scheme and the minimal energy consumption from the energy-transformation-based scheme. This paper also proposes a dynamic event-triggered control (ETC) mechanism, whose triggering threshold adaptively adjusts according to the system state, achieving dynamic matching between the triggering frequency and the system status. Theoretical analysis shows that all signals in the closed-loop system are bounded, the output does not violate the constraints, and the ETC mechanism avoids Zeno behavior. Experimental results verify that the proposed method can achieve high-precision trajectory tracking under disturbances and output constraints, while effectively reducing the control signal update frequency, demonstrating excellent comprehensive performance.