Approximate infinite-horizon predictive control

In this paper, we present a tube-based framework for robust adaptive model predictive control (RAMPC) for nonlinear systems subject to parametric uncertainty and additive disturbances. Set-membership estimation is used to provide accurate bounds on the parametric uncertainty, which are employed for the construction of the tube in a robust MPC scheme. The resulting RAMPC framework ensures robust recursive feasibility and robust constraint satisfaction, while allowing for less conservative operation compared to robust MPC schemes without model/parameter adaptation. Furthermore, by using an additional mean-squared point estimate in the objective function the framework ensures finite-gain L2 stability w.r.t. additive disturbances.As a first contribution we derive suitable monotonicity and non-increasing properties on general parameter estimation algorithms and tube/set based RAMPC schemes that ensure robust recursive feasibility and robust constraint satisfaction under recursive model updates. Then, as the main contribution of this paper, we provide similar conditions for a tube based formulation that is parametrized using an incremental Lyapunov function, a scalar contraction rate and a function bounding the uncertainty. With this result, we can provide simple constructive designs for different RAMPC schemes with varying computational complexity and conservatism. As a corollary, we can demonstrate that state of the art formulations for nonlinear RAMPC are a special case of the proposed framework. We provide a numerical example that demonstrates the flexibility of the proposed framework and showcase improvements compared to state of the art approaches.

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“…• terminal weights (Tuna et al, 2006;Grüne et al, 2010;Beckenbach and Streif, 2022;Moreno-Mora et al, 2023;Köhler et al, 2023);…”

Section: Stability Conditions -Historical Developmentsunclassified

“…2): For practical application, we require a simple way to obtain a terminal cost V f . Beckenbach and Streif (2022) and Moreno-Mora et al (2023) compute V f using approximate dynamic programming and relate ǫ f to the stopping condition. Grüne et al (2010, Thm.…”

Section: From Analysis To Designmentioning

confidence: 99%