A RBF-ARX model-based robust MPC for tracking control without steady state knowledge

Feng, Zhou; Peng, Hui; Qin, Yuwen; Zeng, Xiaoyong; Tian, Xiaoying; Xu, Wenquan

doi:10.1016/j.jprocont.2016.12.008

Cited by 20 publications

(32 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[5], the RBF-ARX modelbased quasi-min-max MPC algorithm (MPC-2) proposed in Ref. [28], the RBF-ARX model-based RPC algorithm with considering the system's external disturbance (RPC-1) proposed in Ref. [29] and the LPV model-based RPC algorithm (RPC-2) proposed in Ref.…”

Section: B Simulation Results and Analysismentioning

confidence: 99%

“…Remark 1: Considering the conservativeness of the system's state space model, which is built using only the boundary information of the RBF-ARX model parameters [27], [28], in this paper, the model's parameter variation rate information is further considered to compress the variation range of the two-step-ahead prediction X (t + 2|t)'s state matrices A t+1|t , B t+1|t .…”

Section: Construction Process Of System State Space Modelmentioning

confidence: 99%

“…V (j, t) = X (t + j|t) T P(j, t)X (t + j|t) (28) where {P(j, t) > 0|t ≥ 0; j ≥ 1} are positive definite matrices and are determined by subsequent optimization. For all A t+1|t ∈ A , B t+1|t ∈ B , A t+j|t ∈ C , B t+j|t ∈ D | j ≥ 2}, assume the following inequalities are met:…”

Section: Synthesis Approach Of Rbf-arx Model-based Output Trackimentioning

confidence: 99%

“…Substituting (27)(28) and models (10)(11) into (29), the following relationship can be satisfied for all j ≥ 1:…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…But, the stability and robustness of such type MPC strategies [19]- [23] still need to be further resolved [26]. Peng et al [27] proposed an RBF-ARX model-based RPC algorithm, in which the system's future nonlinear behavior is represented by the constructed polytopic LPV model [28]. For the system with external bounded disturbance, Zhou et al [29] proposed an RBF-ARX model-based output tracking RPC algo-rithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Robust Controller Design Method Based on Parameter Variation Rate of RBF-ARX Model

et al. 2019

Self Cite

View full text Add to dashboard Cite

As an extension of the exponential autoregressive model and radial basis function (RBF) network, the RBF-ARX model has been widely used in nonlinear system modeling and control. Considering conservativeness of the previous method, which only uses the upper and lower limits of the RBF-ARX model parameters to construct a system's polytopic state space model, in this paper, the model's parameter variation rate information is also utilized to compress variation range of the coefficient matrices in the system's state space model. And then, a robust predictive control (RPC) strategy for output tracking without using system's steady state information is designed. The method of constructing the system's polytopic state space model takes advantage of the fact that the RBF-ARX model itself is a special quasi-LPV model, and there is no need to assume the time varying parameters and/or the variation rate of the parameters in the system model are known or measurable. The effectiveness of the proposed control strategy is verified on a continuous stirred tank reactor (CSTR) process. INDEX TERMS Robust predictive control, robustness and stability, nonlinear model, parameter variation rate.

show abstract

Section: B Simulation Results and Analysismentioning

confidence: 99%

Section: Construction Process Of System State Space Modelmentioning

confidence: 99%

Section: Synthesis Approach Of Rbf-arx Model-based Output Trackimentioning

confidence: 99%

“…Substituting (27)(28) and models (10)(11) into (29), the following relationship can be satisfied for all j ≥ 1:…”

Section: Proof Of Theoremmentioning

confidence: 99%