Neuroadaptive Cooperative Control Without Velocity Measurement for Multiple Humanoid Robots Under Full-State Constraints

This paper studies the cooperative control problem of multiple humanoid robots handling a common payload in the presence of position and velocity constraints, unmeasurable velocity, as well as nonparametric uncertainties. By using a state observer to estimate the unmeasured velocity, a neuroadaptive output-feedback control scheme is developed, which by blending an error transformation with barrier Lyapunov function ensures that the fullstate tracking error converges to a prescribed compact set around origin within a given finite time at a preassignable convergence rate. Furthermore, it is shown that all the signals in the closed-loop system are ultimately semiglobally uniformly bounded. Simulation results are verified to show the effectiveness and benefits of the proposed scheme.