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Exact feedback linearization is a well-established method in nonlinear control, where the system is transformed into a linear system by a nonlinear change of coordinates in connection with a nonlinear feedback law. For a controllable single-input system, exact linearizability is equivalent to flatness. Unfortunately, the existence conditions are quite restrictive. Even if the existence conditions...
This article addresses the problem of Linear Active Disturbance Rejection Control for a class of nonlinear mechanical input time delayed systems. To solve the problem, it is necessary to obtain a set of predictive lumped disturbance estimations, which is carried out by a purely linear high gain observer of Extended Luenberger type, denoted as a Generalized Proportional Integral (GPI) Observer. This...
In recent literature, ℓ1-regularised MPC, or ℓasso-MPC, has been recommended for control tasks involving complex requirements on the control signals, for instance, the simultaneous solution of regulation and sharp control allocation for redundantly-actuated systems. This is due to the implicit thresholding ability of LASSO regression. In this paper, a stabilising terminal cost featuring a mixed ℓ1...
A universal control scheme is designed for unknown pure feedback systems, capable of guaranteeing, for any initial system condition, output tracking with prescribed performance and bounded signals in the closed loop. In this paper, by prescribed performance, it is meant that the output error converges to a predefined arbitrarily small residual set, with convergence rate no less than a certain prespecified...
The paper deals with the coordination of dynamical systems by distributed model predictive control.We consider a set-up in which the subsystems dynamics are decoupled, while the subsystems outputs are coupled by some constraint. Starting from a well established non-iterative and non-cooperative architecture, we provide a novel interpretation for this non-cooperative scheme as a simplicial approximation...
Averaging is an effective technique which allows the analysis and control design of nonsmooth switched systems through the use of corresponding simpler smooth averaged systems. Approximation results and stability analysis have been presented in the literature for dynamic systems described by switched ordinary differential equations. In this paper the averaging technique is shown to be useful also...
This paper considers the class of deterministic continuous-time optimal control problems (OCPs) with piecewise-affine (PWA) vector field, polynomial Lagrangian and semialgebraic input and state constraints. The OCP is first relaxed as an infinite-dimensional linear program (LP) over a space of occupation measures. This LP is then approached by an asymptotically converging hierarchy of linear matrix...
The aim of this paper is to provide a short introduction to modern issues in the control of infinite dimensional closed quantum systems, driven by the bilinear Schrödinger equation. The first part is a quick presentation of some of the numerous recent developments in the fields. This short summary is intended to demonstrate the variety of tools and approaches used by various teams in the last decade...
Control for safety specifications of large nonlinear systems is a challenging task. By reducing the system to a discrete abstraction the computational demands of the controller can be greatly reduced. We propose a supervisor for differentially flat systems, based on an approximate abstraction of the flat output. By defining the abstraction on the flat output space, we simplify the design of the abstraction...
We demonstrate nonparaxial Mathieu and Weber accelerating beams, generalizing the concept of previously found accelerating beams. Such beams bend into large angles along elliptical or parabolic trajectories but still retain nondiffracting and self-healing capabilities.
In this paper, we propose an algorithm for learning a reward model from an expert policy in partially observable Markov decision processes (POMDPs). The problem is formulated as inverse reinforcement learning (IRL) in the POMDP framework. The proposed algorithm then uses the expert trajectories to find an unknown reward model-based on the known POMDP model components. Similar to previous IRL work...
In time series classification, the nearest neighbor (NN) method is known to compare well against others over a wide range of benchmark data. However, adapting its instance-based learning format for application-specific goals, such as optimizing alternate performance measures or cost-sensitive learning is not a straightforward problem. In this paper, we attempt to extend the effectiveness of the NN...
A methodology is proposed to approximate large-scale networked systems by a lower dimensional networked system. We first group the nodes into m communities which will form the m vertices of the reduced network. We then associate an appropriate scalar dynamics to each community; in that way, the dimension of the new model is equal to m. The main idea is to approximate each node trajectory by the trajectory...
In this paper, an enhanced event-based scheme for model predictive control (MPC) of constrained discrete-time systems with additive disturbances is investigated. The re/calculation of the MPC control law is triggered whenever an event depending on the error of the measured state with respect to the nominal state of the system occurs. Between the controller updates, the last computed control trajectory...
Most generic approaches to empirical reduction of dynamical systems, controlled or otherwise, are global in nature. Yet interesting systems often exhibit multiscale structure in time or in space, suggesting that localized reduction techniques which take advantage of this multiscale structure might provide better approximations with lower complexity. We introduce a snapshot-based framework for localized...
This work presents a new method of stabilization for unstable periodic orbits of continuous-time dynamical systems. The principle of this method is to use feedback term based on the difference between the actual state value and the future state value computed along the trajectories of the uncontrolled system. To compute the value of the latter, an implicit Runge-Kutta ODE integration method is used,...
Many different numerical methods have been developed to compute trajectories of optimal control problems on the one hand and to approximate Pareto sets of multiobjective optimization problems on the other hand. However, so far only few approaches exist for the numerical treatment of the combination of both problems leading to multiobjective optimal control problems. In this contribution we combine...
Increasing demand for Nonlinear Model Predictive Control with the ability to handle highly noise-corrupted systems has recently given rise to stochastic control approaches. Besides providing high-quality results within a noisy environment, these approaches have one problem in common, namely a high computational demand and, as a consequence, generally a short prediction horizon. In this paper, we propose...
We propose a procedure for the synthesis of control protocols for systems governed by nonlinear differential equations and constrained by temporal logic specifications. This procedure relies on a particular finite-state abstraction of the underlying continuous dynamics and a discrete representation of the external environmental signals. A two-player game formulation provides computationally efficient...
In position control of mechatronic devices, velocity feedback is necessary for injecting additional damping to avoid low-frequency fluctuation around desired trajectories. In practice, velocity signal is often obtained by performing Euler discretization on position signal from an optical encoder. However, due to the limited encoder resolution, the obtained velocity signal is corrupted by high-frequency...
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