Adaptive Fuzzy Control for Uncertain Mechatronic Systems With State Estimation and Input Nonlinearities

In the field of practical engineering, the performance of mechatronic systems is influenced by model uncertainties, velocity unavailability, input nonlinearities (e.g., actuator deadzones/ faults), etc. Moreover, some complex nonlinear dynamics do not satisfy the linear parameterization condition. Hence, due to intractable approximation errors, some existing controllers may obtain only uniformly ultimately bounded results and require velocity feedback to accomplish online estimation. To overcome the above obstacles, this paper designs a new output feedback controller to fulfill accurate trajectory tracking and obtain state estimates for a class of Euler-Lagrange mechatronic systems. Specifically, we first construct a group of auxiliary variables to accurately recover velocities without numerical differential operations. Then, by employing only the available output information, unknown model knowledge and actuator deadzones/faults are simultaneously approximated online; more importantly, the asymptotic stability of the system equilibrium point is guaranteed by strict theoretical analysis. Another merit of the proposed controller is that the approximation errors are addressed in a new way, where no discontinuous robust terms are required; hence, the chattering problem is effectively alleviated. To the best of our knowledge, for uncertain Euler-Lagrange mechatronic systems with actuator deadzones/faults, this paper proposes the first solution to eliminate tracking errors and accurately recover unmeasurable states by continuous control signals. The asymptotic convergence of closed-loop signals is proven based on Lyapunov methods, and the performance of the proposed controller is validated by hardware experiments.