Nonlinear disturbance observer-based adaptive nonlinear model predictive control design for a class of nonlinear MIMO system

Abstract

Model predictive control with less prior knowledge of system uncertainty and external disturbance is a long-standing theoretical and practical problem. In this paper, a solution is presented by proposing a disturbance observer-based adaptive nonlinear model predictive control scheme for a class of nonlinear MIMO systems. Our scheme requires the state and parameterdependent state-space model to linearise the nonlinear system along the prediction horizon. To cope with the unknown system uncertainty, the multiple estimation model and the concept of second-level adaptation [Pandey, V. K., Kar, I., & Mahanta, C. (2014). Multiple models and second level adaptation for a class of nonlinear systems with nonlinear parameterisation. In Industrial and Information Systems (ICIIS), 2014 9th International Conference on (pp. 1–6). IEEE.] technique for the nonlinear system is used. To ensure the boundedness of the estimated parameter, a projection-based adaptive law [Hovakimyan, N., & Cao, C. (2010). L1 adaptive control theory: Guaranteed robustness with fast adaptation. SIAM.] is used. Using a twin-rotor MIMO system (TRMS), the effectiveness of the proposed control algorithm has been verified. Simulation and real-time results show that the proposed control algorithm performs better than the existing control algorithm in the presence of unknown external disturbance and parameter uncertainties.