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• W2099656731 abstract "Abstract A class of parameter-dependent dynamic control policies is explored for its use in a model predictive control (MPC) algorithm for a nonlinear system modeled with a feedforward neural network (NN). The NN-modeled system is expressed as a polytopic quasi-linear-parameter-varying (quasi-LPV) system over a region of the state-input space for a range of operating points, and the dynamics of the proposed policy, which are optimized off-line to enlarge the region of attraction, are allowed to depend on a time-varying parameter of the polytopic quasi-LPV system model such that the resulting control involves a continuous gain-scheduling that leads to reduced conservativeness. A complete MPC algorithm using the dynamic policy as the terminal policy ensures stabilization and improved control performance over a larger domain of attraction without a larger horizon length. Simulation examples with tank and tubular reactor systems illustrate the effective performance of the proposed approach in practical applications." @default.
• W2099656731 created "2016-06-24" @default.
• W2099656731 creator A5000524330 @default.
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• W2099656731 date "2015-12-01" @default.
• W2099656731 modified "2023-09-24" @default.
• W2099656731 title "Stabilizing model predictive control using parameter-dependent dynamic policy for nonlinear systems modeled with neural networks" @default.
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• W2099656731 doi "https://doi.org/10.1016/j.jprocont.2015.09.003" @default.
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