AMPC-ASO Strategy for Nonlinear Systems with Uncertainty

This paper proposes a control strategy for solving the trajectory tracking control problem with uncertainty in nonlinear systems. Considering the uncertainties and state estimation of the system, an adaptive state observer (ASO) is proposed to solve the estimated problem and ensure the model is adaptive. ASO allows nonlinear systems to be theoretically modelled as linear systems with estimated parameters. Moreover, to satisfy the constraint of adaptive MPC (AMPC) for trajectory tracking control, a parameter identification method for chance-constrained optimization is proposed and uses prediction parameters to compensate for the system's uncertainty by interval estimation and parameter optimization with chance constraints. Finally, the proposed scheme is verified by a quadrotor attitude control simulation. The simulation results show that the AMPC-ASO strategy has perfect tracking control performance.