Kernel methods and Model predictive approaches for Learning and Control
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In Model Predictive Control, the enforcement of hard state constraints can be overly conservative or even infeasible, especially in the presence of disturbances. This work presents a soft constrained MPC approach that provides closed- loop stability even f ...
In model reference control, the objective is to design a controller such that the closed-loop system resembles a reference model. In the standard model-based solution, a plant model replaces the unknown plant in the design phase. The norm of the error betw ...
In this paper, the Multirate Integral Sliding Mode (MRISM) control strategy for nonlinear discrete-time systems is proposed. The MRISM controller acts at a faster sampling time than a high level controller, and reduces the effect of model uncertainties and ...
This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ...
We address the problem of steering the output of a nonlinear system along a given parametrized reference path, taking input and state constraints into account. Such problems are known as output path-following problems, which typically arise in vehicle and ...
The presented work concerns the modelling and stability analysis of 155 mm spin-stabilised projectiles equipped with steering fins. While a large roll rate provides the projectile with interesting stability properties, it also renders the steering control ...
This paper introduces and studies a class of optimal control problems based on the Clebsch approach to Euler-Poincare dynamics. This approach unifies and generalizes a wide range of examples appearing in the literature: the symmetric formulation of N-dimen ...
In this paper an algorithm for nonlinear explicit model predictive control is introduced based on multiresolution function approximation that returns a low complexity approximate receding horizon control law built on a hierarchy of second order interpolets ...
A model predictive control law (MPC) is given by the solution to a parametric optimization problem that can be pre-computed offline, which provides an explicit map from state to input that can be rapidly evaluated online. However, the primary limitations o ...
Meeting the temperature constraints and reducing the hot-spots are critical for achieving reliable and efficient operation of complex multi-core systems. The goal of thermal management is to meet maximum operating temperature constraints, while tracking ti ...
