期刊
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
卷 53, 期 9, 页码 2010-2031出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2022.2034067
关键词
Nonlinear disturbance observer-based adaptive nonlinear model predictive control (NDO-ANMPC); multi-input-multi-output (MIMO); Twin Rotor MIMO system (TRMS); ensemble kalman filter (EnKF); second level adaptation (SLA); model predictive control (MPC)
This paper presents a disturbance observer-based adaptive nonlinear model predictive control scheme for dealing with external disturbance and parameter uncertainties in a class of nonlinear MIMO systems. The effectiveness of the proposed control algorithm has been verified using a twin-rotor MIMO system, showing better performance than existing control algorithms in the presence of unknown disturbances and parameter uncertainties.
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.
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