**Reference:**

J. Xu,
L. Busoniu, and
B. De Schutter,
"Near-optimal control with adaptive receding horizon for discrete-time
piecewise affine systems," *Proceedings of the 20th IFAC World
Congress*, Toulouse, France, pp. 4168-4173, July 2017.

**Abstract:**

We consider the infinite-horizon optimal control of discrete-time,
Lipschitz continuous piecewise affine systems with a single input.
Stage costs are discounted, bounded, and use a 1 or ∞-norm.
Rather than using the usual fixed-horizon approach from
model-predictive control, we tailor an adaptive-horizon method called
optimistic planning for continuous actions (OPC) to solve the
piecewise affine control problem in receding horizon. The main
advantage is the ability to solve problems requiring arbitrarily long
horizons. Furthermore, we introduce a novel extension that provides
guarantees on the closed-loop performance, by reusing data
("learning") across different steps. This extension is general and
works for a large class of nonlinear dynamics. In experiments with
piecewise affine systems, OPC improves performance compared to a
fixed-horizon approach, while the data-reuse approach yields further
improvements.

Online version of the paper

@inproceedings{XuBus:17-005,

author={J. Xu and L. Bu{\c{s}}oniu and B. {D}e Schutter},

title={Near-optimal control with adaptive receding horizon for discrete-time piecewise affine systems},

booktitle={Proceedings of the 20th IFAC World Congress},

address={Toulouse, France},

pages={4168--4173},

month=jul,

year={2017},

doi={10.1016/j.ifacol.2017.08.806}

}

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