In this work, we propose a tube-based model predictive control (MPC) scheme for state- and input-constrained linear systems subject to dynamic uncertainties characterized by integral quadratic constraints (IQCs). particular, extend the framework of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\rho$</tex-math></inline-formula> -hard IQCs exponential stability analysis external inputs. This result yields...

In this paper, a tube-based economic Model Predictive Control (MPC) scheme for systems subject to bounded disturbances is investigated that uses neither terminal costs nor constraints. We provide robust guarantees on the closed-loop performance under suitable dissipativity and controllability conditions. Furthermore, we prove practical convergence an optimal control invariant set, as well its stability slightly stronger assumptions. Hence, work extends results from nominal MPC without...

In this work, we propose a tube-based model predictive control (MPC) scheme for state and input constrained linear systems that are subject to dynamic uncertainties de-scribed by integral quadratic constraints (IQCs). We extend the framework of verifying exponential decay rates with IQCs in order derive an exponentially stable scalar system bounds error between nominal prediction actual unknown system. proposed MPC scheme, bounding is predicted together define size tube. prove achieves...

In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes. particular, consider both MPC schemes with without terminal conditions. We show that the closed-loop obtained by applying such is approximately optimal when evaluated on finite infinite time horizons. These bounds are similar to those derived previously nominal MPC. The theoretical results discussed in a numerical example.

Predictive safety filters enable the integration of potentially unsafe learning-based control approaches and humans into safety-critical systems. In addition to simple constraint satisfaction, many problems involve additional stability requirements that may vary depending on specific use case or environmental context. this work, we address problem by augmenting predictive with guarantees, ranging from bounded convergence uniform asymptotic stability. The proposed framework extends well-known...

In this paper, we investigate discounted economic model predictive control (E-MPC) schemes without terminal conditions in scenarios where the optimal operating behavior is a periodic orbit. For such setting, it known that linearly stage cost guarantees asymptotic stability of any arbitrarily small neighborhood orbit if prediction horizon sufficiently long. However, some examples very long horizons are needed to achieve desired performance. work, extend these results by providing same...

This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs). Such bounds are particular interest in computation reachable sets and l1-norm, as well when safety-critical need be satisfied pointwise time. The use p-hard IQCs with terminal cost enables us deal wide variety uncertainty classes, for example, we provide class parametric uncertainties. approach unifies, generalizes,...

In this paper, a novel tube-based economic Model Predictive Control (MPC) scheme for uncertain systems that uses neither terminal costs nor constraints is investigated. We show the results from undisturbed case can be extended to with bounded disturbances by using similar turnpike arguments and properly modified stage cost. prove robust guarantees on closed-loop performance, convergence, stability under suitable dissipativity controllability conditions discuss them in numerical example.

In this work, we study economic model predictive control (MPC) in situations where the optimal operating behavior is periodic. such a setting, performance of standard MPC scheme without terminal conditions can generally be far from even with arbitrarily long prediction horizons. Whereas there are modified schemes that guarantee performance, all them based on prior knowledge period length or periodic orbit itself. contrast to these approaches, propose achieve optimality by multiplying stage...

This work provides a framework to compute an upper bound on the robust peak-to-peak gain of discrete-time uncertain linear systems using integral quadratic constraints (IQCs). Such bounds are particular interest in computation reachable sets and $\ell_1$-norm, as well when safety-critical need be satisfied pointwise time. The use $\rho$-hard IQCs with terminal cost enables us deal wide variety uncertainty classes, for example, we provide class parametric uncertainties. approach unifies,...