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Neural Computing and Applications

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15-01-2018 | Original Article

RBF-ARX model-based two-stage scheduling RPC for dynamic systems with bounded disturbance

Authors: Feng Zhou, Hui Peng, Xiaoyong Zeng, Xiaoying Tian

Published in: Neural Computing and Applications | Issue 8/2019

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Abstract

With directly considering the unknown and bounded disturbance, a RBF-ARX model-based two-stage scheduling quasi-min–max robust predictive control (RBF-ARX-TRPC) algorithm for output-tracking control is proposed for a class of smooth nonlinear systems with unknown steady-state knowledge. Firstly, from the RBF-ARX model that is identified using input/output data of the system, the two local linearization state-space models that consider the bounded disturbance and a polytopic uncertain LPV state-space model are built to approximate the present and future system’s nonlinear dynamics, respectively. Based on the state-space models, the RBF-ARX-TRPC algorithm is designed without relying on the system steady-state knowledge. In the RBF-ARX-TRPC algorithm, the future nonlinear behavior of the system is forced to vary within the region constructed by the polytopic uncertain LPV state-space model. Closed-loop stability is guaranteed when the algorithm is implemented in a receding horizon fashion by including a Lyapunov constraint in the formulation. The comparative experiments demonstrate the effectiveness of the proposed strategy on a continuously stirred tank reactor (CSTR) simulator.

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Title: RBF-ARX model-based two-stage scheduling RPC for dynamic systems with bounded disturbance
Authors: Feng Zhou
Hui Peng
Xiaoyong Zeng
Xiaoying Tian
Publication date: 15-01-2018
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 8/2019
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3347-y

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