Abstract
A two-stage scheduling robust predictive control (RPC) algorithm, which is based on the time-varying coefficient information of the state-dependent ARX (SD-ARX) model, is designed for the output tracking control of a class of nonlinear systems. First, by using the parameter variation range information of the SD-ARX, a strategy for constructing the system’s polytopic model is designed. To further reduce the conservativeness of the convex polytopic sets which are designed to wrap the system’s future dynamics, the variation range information of the SD-ARX model’s parameters is also considered and compressed. In this method, the polytopic state-space model of the system is constructed directly based on the special structure of the SD-ARX model itself, and there is no need to make such assumption that the bounds on the parameter’s variation range in the system model are known or measurable. And then, a two-stage scheduling RPC algorithm is designed for the output tracking control. A numerical example is presented to demonstrate the effectiveness of the proposed RPC strategy.
Highlights
Model predictive control (MPC), which directly uses the mathematical model to predict the future behavior of the system, has been widely studied in the past decade [1]
Considering that models with polytopic description can be effectively used to include the uncertainties of the nonlinear systems, the MPC strategies based on these types of models have been widely studied
Many achievements have been achieved in the research of robust predictive control (RPC) algorithms for systems with polytopic description [4,5,6,7,8,9,10], they mainly focused on the state adjustment or output tracking problems based on certain restrictive assumptions; for example, the steady-state information of the system is accurately known or measured [11]
Summary
Model predictive control (MPC), which directly uses the mathematical model to predict the future behavior of the system, has been widely studied in the past decade [1]. Based on the RBF-ARX model, Peng et al [28] designed a min-max MPC algorithm for nonlinear systems without external disturbance.
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