Nonlinear curve fitting-based fast robust MPC algorithm for nonlinear system

Tian, Xiaoying; Peng, Hui; Xu, Wenquan; Kang, Tiao; Zhang, Haifeng; Peng, Xiaoyan

doi:10.1016/j.jfranklin.2022.05.047

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…19,20 By approximating the functional coefficients in an SD-ARX model with radial basis function (RBF) neural networks, the RBF-ARX model can be obtained. [21][22][23] The SD-ARX model-based RMPC for systems without disturbance was first studied in the work of Peng et al 24 Based on this type of model, an RMPC with unknown steady-state information was proposed in Zhou et al 25 To decrease the conservativeness of the LPV models in studies such as Peng et al, 24 Zhou et al 25 Zhou et al 26 proposed an RMPC synthesis method using the model's parameter boundary information, and based on this model, they 27 further proposed a one-stage scheduling RMPC. So far, many meaningful researches of SD-ARX model-based RMPC have been reported, [24][25][26][27] whereas the research has mainly focused on studying single-constant feedback RMPC algorithms.…”

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confidence: 99%

SD-ARX modeling and robust MPC with variable feedback gain for nonlinear systems

Zhou,

Xi,

Zhu

2023

Measurement and Control

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As a generalized input-output model, the state-dependent exogenous variable autoregressive (SD-ARX) model has been intensively utilized to model complex nonlinear systems. Considering that more freedom can be provided by the state feedback control with variable feedback gain for constructing robust controllers, we propose a robust model predictive control (RMPC) algorithm with variable feedback gain on the basis of the SD-ARX model. First, the polytopic state space models (SSMs) of the system are constructed and the prediction accuracy of the SSMs is further improved by using the parameter variation rate information of the SD-ARX model. Then, an RMPC algorithm with variable feedback gain is synthesized for increasing the design freedom and enhancing the control performance. Two simulation examples, that is, the modeling and control of a continuous stirred tank reactor (CSTR) and a water tank system, are provided to demonstrate the feasibility and effectiveness of the proposed RMPC algorithm.

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