Constraint-based adaptive robust tracking control of uncertain articulating crane guaranteeing desired dynamic control performance

Abstract Articulating crane (AC), a widely used crane, plays an essential role in various industrial activities. Owing to its strong nonlinearity and uncertainty, tracking control remains challenging, particularly for precise dynamic control. This paper proposes adaptive diffeomorphism-constraint-based (ADCBC) nonlinear AC robustly achieve trajectory while guaranteeing desired performance (DDCP), considering (possibly rapid irregular) time-variant uncertainty with unknown bounds. A user-definable hard-limiting function was guarantee the DDCP, including requirement steady-state error convergence speed. The trajectories DDCP were formulated as equality inequality servo constraints, respectively. diffeomorphism approach adopted incorporate constraints into yielding new constraints. Thus, task converted enable transformed follow completed by constraint-based (CBC) scheme, where law established estimation of online bounds compensate uncertainty. No approximations or linearizations invoked. effectiveness robustness proposed ADCBC confirmed through rigorous proofs simulation results. To best our knowledge, this is first endeavor uncertain AC-like systems.