Predictor based Data-Driven Anti-Disturbance Control Scheme for Unknown Nonlinear Systems

This paper proposes a predictor based data-driven anti-disturbance control scheme for nonlinear nonaffine systems with unknown dynamics, by only using the measured system input and output data. A partial-form dynamic linearization (PFDL) data model with disturbance predictor is firstly established for the data-driven control system design. Then, an anti-disturbance control law is developed by solving a constrained optimization problem, where the unknown pseudo-gradient (PG) vector at the current and future time instants are, respectively, estimated by a projection algorithm and another autoregressive prediction algorithm. Meanwhile, the unknown residual term at the current and future time instants are approximated by a high-order extended state observer (HOESO). The bounded convergence of the PG estimation, HOESO, and output tracking error is analyzed with proof. Finally, a case study is adopted to validate the effectiveness and advantage of the proposed method over the recently developed ones.