Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation |
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Authors: | Lai Wei Ryan McCloy Jie Bao Jesse Cranney |
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Affiliation: | 1. School of Chemical Engineering, University of New South Wales UNSW, Sydney, New South Wales, Australia;2. Research School of Astronomy and Astrophysics, The Australian National University, Canberra, Australian Capital Territory, Australia |
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Abstract: | Modern chemical processes need to operate around time-varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time-varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem. |
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Keywords: | contraction theory discrete-time nonlinear systems graph theory nonlinear model predictive control stability design |
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