Distributed adaptive event-triggered consensus control of multiple series robotic manipulators with uncertain dynamics and input deadzone

This paper presents an adaptive distributed consensus control scheme with an event-triggered mechanism (ETM) for multiple manipulator systems (MMS) with model uncertainties and input deadzone nonlinearity, where the directed communication topology is modeled using graph theory. A fixed-time backstepping controller is developed to guarantee that the state errors converge to a small neighborhood of zero, regardless of the initial conditions. To compensate for dynamic uncertainties and deadzone effects, a fuzzy logic system (FLS) and adaptive laws are employed for real-time estimation. Additionally, an ETM is integrated to reduce control signal update frequency, alleviating network congestion while maintaining performance. Simulation and experimental results demonstrate the superior convergence speed, robustness, and communication efficiency of the proposed approach.