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Model predictive control for nonlinear system with feedback linearization and extended state observer
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DOI：10.1080/00207721.2025.2563104.png)
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Abstract
En 中文
In this paper, Model Predictive Control based on Feedback Linearization-Extended State Observer (FL-ESO based MPC) has been investigated for the nonlinear system with unmodelled dynamics and disturbances, which can enhance the robustness and reduce. First, a FL-ESO based linearisation method is designed to dynamically compensate for system nonlinearities, thus constructing a linear nominal model with a cascaded integrator structure. Next, based on this model, a linear MPC (LMPC) is designed, where the disturbance estimates from FL-ESO based linearisation method are incorporated into the constraints during receding horizon optimisation for online correction. This effectively mitigates the control input's sensitivity to abrupt state changes. Furthermore, by constructing a Lyapunov-based terminal cost function and designing the corresponding terminal constraint set, the stability of the closed-loop system is proven. In the end, simulation and experimental studies are performed to illustrate the effectiveness of the proposed method.
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