Filter-Based Adaptive Fuzzy Control for an Uncertain Robotic Manipulator with Intermittent Input and Output Triggering

This paper investigates the adaptive output feedback control issue for an uncertain robotic manipulator (RM) under input and output triggering. Due to the influence of the output-triggering mechanism, the output state becomes discrete, thereby leading to the non-differentiability problem of the virtual controller. Therefore, a first-order low-pass filter is constructed to generate a filtered version of the sampled output with continuous differentiability for controller design. By utilizing the fuzzy logic systems (FLSs), the unknown robotic dynamics are effectively approximated without requiring the global Lipschitz condition. Subsequently, an adaptive fuzzy state observer is developed based on the filtered output signal to estimate the joint velocity of the RM. Based on the Lyapunov stability analysis method of the hybrid system, it is rigorously proved that all signals of the RM are bounded, and Zeno behavior is precluded. Furthermore, the designed event-triggered control protocol can ensure the desirable system performance and reduce communication resources even under output discretization. Finally, a two-link RM is employed to verify the effectiveness of the control protocol.