Daniel Limon

A novel predictive controller is introduced for rendezvous with non-cooperative tumbling targets in active debris removal applications, based on the model predictive control for tracking (MPCT) framework, which improves the robustness and computational efficiency of conventional MPC by optimizing the system toward artificial equilibrium points rather than fixed references. This paper aims to provide a computationally efficient and theoretically sound control strategy that enables reliable proximity operations around tumbling objects, despite their complex rotational motion. The target’s nonperiodic rotational dynamics and state and control constraints are considered. The approach is based on applying an intermediate coordinate transformation that eliminates the time dependency due to rotations in the constraints. The proposed algorithm leverages feasible trajectories, obtained through the conservation of momentum and energy of rotating bodies, to obtain strong convergence guarantees on arbitrary horizons. A control law is then found as the solution to a quadratic programming problem that provides feasibility and stability guarantees by means of a terminal virtual controller. The main result is an MPCT-based controller for linear time-varying systems induced by rotational dynamics, with provable feasibility and stability guarantees. A near-rendezvous simulation with the Envisat spacecraft confirms the practical relevance and performance of the proposed controller.

This paper presents a novel robust predictive controller for constrained nonlinear systems that is able to track piece-wise constant setpoint signals. The tracking model predictive controller presented in this paper extends the nonlinear MPC for tracking to the more complex case of nonlinear systems subject to bounded and not necessarily additive perturbations. The optimal control problem that is solved at each step penalizes the deviation of the predicted nominal system trajectory from an artificial reference, which is added as a decision variable, as well as the distance between the artificial reference and the setpoint. Robust feasibility is ensured by imposing conservative constraints that take into account the effect of uncertainties and convergence to a neighborhood of any feasible setpoint is guaranteed by means of an appropriate terminal cost and an extended stabilizing terminal constraint. In the case of unreachable setpoints, convergence to a neighborhood of the optimal reachable steady output is also proved.

This paper presents a novel robust predictive controller for constrained nonlinear systems that is able to track piece-wise constant setpoint signals. The tracking model predictive controller presented in this paper extends the nonlinear MPC for tracking to the more complex case of nonlinear systems subject to bounded and not necessarily additive perturbations. The optimal control problem that is solved at each step penalizes the deviation of the predicted nominal system trajectory from an artificial reference, which is added as a decision variable, as well as the distance between the artificial reference and the setpoint. Robust feasibility is ensured by imposing conservative constraints that take into account the effect of uncertainties and convergence to a neighborhood of any feasible setpoint is guaranteed by means of an appropriate terminal cost and an extended stabilizing terminal constraint. In the case of unreachable setpoints, convergence to a neighborhood of the optimal reachable steady output is also proved.

Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations. This article presents an extension of the HMPC formulation to track periodic harmonic/sinusoidal references and discusses its use for tracking arbitrary trajectories. The proposed formulation inherits the benefits of its predecessor, namely its good performance and large domain of attraction when using small prediction horizons, and that the complexity of its optimization problem does not depend on the period of the reference. We show closed-loop results discussing its performance and comparing it to other MPC formulations.

Model Predictive Control (MPC) is a widely known control method that has proved to be particularly effective in multivariable and constrained control. Closed-loop stability and recursive feasibility can be guaranteed by employing accurate models in prediction and suitable terminal ingredients, i.e. the terminal cost function and the terminal constraint. Issues might arise in case of model mismatches or perturbed systems, as the state predictions could be inaccurate, and nonlinear systems for which the computation of the terminal ingredients can result challenging. In this manuscript, we exploit the properties of component-wise uniformly continuous and stabilizable systems to introduce a robust contraction-based MPC for the regulation of nonlinear perturbed systems, that employs an easy-to-design terminal cost function, does not make use of terminal constraints, and selects the shortest prediction horizon that guarantees the stability of the closed-loop system.

En este trabajo, abordamos el problema de la predicción en línea de las trayectorias de voltaje e intensidad nodales en la red de distribución. Para esto, proponemos una formulación basada en datos utilizando la interpolación Kriging, una técnica de aprendizaje automático que ha mostrado aplicaciones prometedoras en el campo del control basado en datos. Producimos un oráculo de predicción no paramétrico que permite inferir trayectorias futuras directamente a partir de medidas de voltaje e intensidad en tiempo real. Además, proporcionamos una implementación algorítmica simple pero efectiva basada en el conocido esquema ISTA. Demostramos la efectividad de nuestra metodología para la predicción rápida (subsegundos) de la dinámica del voltaje mediante simulaciones.

Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. However, in practice, these constraints can result in feasibility issues, either because the system model is not accurate or due to the existence of external disturbances. To mitigate this problem, a solution adopted by the MPC community is the use of soft constraints. In this article, we consider a not-so-typical methodology to encode soft constraints in a particular MPC formulation known as MPC for Tracking (MPCT), which has several advantages when compared to standard MPC formulations. The motivation behind the proposed encoding is to maintain the semi-banded structure of the ingredients of a recently proposed solver for the considered MPCT formulation, thus providing an efficient and fast solver when compared to alternative approaches from the literature. We show numerical results highlighting the benefits of the formulation and the computational efficiency of the solver.

This paper proposes a novel two-layer monitoring and rescheduling approach designed for vessels navigating natural inland waterways, addressing diverse scenarios such as arrival delays, speed reductions, and machinery failures. Firstly, a methodology for incident detection and classification is proposed, which compares the planned trajectory of the vessels with their actual position provided by an automatic identification system. Secondly, in case an incident is detected, the plan for each vessel is rescheduled. To achieve this, various rescheduling strategies are proposed, taking into account the particularities of each incident. The aim is not only to ensure the safety and efficiency of the operation but also to minimize the impact of rescheduling on the original plan, thus averting any negative repercussions on the port’s reputation. Different simulations have been carried out taking the Guadalquivir River as an example, demonstrating the efficiency of the proposed methodology in preventing incidents and ensuring optimality.