Search Results for author:
Found 48 papers, 0 papers with code
Data-Based System Representation and Synchronization for Multiagent Systems
We consider the cases of homogeneous and heterogeneous systems.
Disturbance feedback-based model predictive control in uncertain dynamic environments
This paper presents a robust MPC scheme for linear systems subject to time-varying, uncertain constraints that arise from uncertain environments.
Moving horizon estimation for nonlinear systems with time-varying parameters
We consider the case where observability of the parameters depends on the excitation of the system and may be absent during operation, with the parameter dynamics fulfilling a weak incremental bounded-energy bounded-state property to ensure boundedness of the estimation error (with respect to the disturbance energy).
Data-Based Control of Continuous-Time Linear Systems with Performance Specifications
First, we formulate and solve a trajectory-reference control problem, on which desired closed-loop trajectories are known and a controller that allows the system to closely follow those trajectories is computed.
Gaussian Process-Based Nonlinear Moving Horizon Estimation
On the other hand, we exploit the posterior variances of the Gaussian processes to design the weighting matrices in the MHE cost function and account for the uncertainty in the learned system dynamics.
Online convex optimization for robust control of constrained dynamical systems
This article investigates the problem of controlling linear time-invariant systems subject to time-varying and a priori unknown cost functions, state and input constraints, and exogenous disturbances.
Sample-based nonlinear detectability for discrete-time systems
This paper introduces two sample-based formulations of incremental input/output-to-state stability (i-IOSS), a suitable detectability notion for general nonlinear systems.
Nonlinear moving horizon estimation for robust state and parameter estimation
We propose a moving horizon estimation scheme to estimate the states and the unknown constant parameters of general nonlinear uncertain discrete time systems.
An efficient data-based off-policy Q-learning algorithm for optimal output feedback control of linear systems
In this paper, we present a Q-learning algorithm to solve the optimal output regulation problem for discrete-time LTI systems.
Notes on data-driven output-feedback control of linear MIMO systems
Recent works have approached the data-driven design of output-feedback controllers for discrete-time LTI systems by constructing non-minimal state vectors composed of past inputs and outputs.
Sample- and computationally efficient data-driven predictive control
Recently proposed data-driven predictive control schemes for LTI systems use non-parametric representations based on the image of a Hankel matrix of previously collected, persistently exciting, input-output data.
Model predictive control for the prescription of antithyroid agents
Although hyperthyroidism is a common disease, the pharmaceutical therapy is based on a trial-and-error approach.
Data-based system representations from irregularly measured data
Non-parametric representations of dynamical systems based on the image of a Hankel matrix of data are extensively used for data-driven control.
Robust stability of moving horizon estimation for continuous-time systems
We consider a moving horizon estimation (MHE) scheme involving a discounted least squares objective for general nonlinear continuous-time systems.
On an integral variant of incremental input/output-to-state stability and its use as a notion of nonlinear detectability
We propose a time-discounted integral variant of incremental input/output-to-state stability (i-iIOSS) together with an equivalent Lyapunov function characterization.
Robust Stability of Gaussian Process Based Moving Horizon Estimation
In this paper, we introduce a Gaussian process based moving horizon estimation (MHE) framework.
Distributed Model Predictive Control for Periodic Cooperation of Multi-Agent Systems
We show that under suitable assumptions, the agents can incrementally move their artificial output trajectories towards the cooperative goal, and, hence, their closed-loop output trajectories asymptotically achieve it.
An Efficient Off-Policy Reinforcement Learning Algorithm for the Continuous-Time LQR Problem
Moreover, we formulate the policy evaluation step as the solution of a Sylvester-transpose equation, which increases the efficiency of its solution.
Treating Hyperthyroidism: Model Predictive Control for the Prescription of Antithyroid Agents
Instead of relying on a trial-and-error approach as it is commonly done in clinical practice, we suggest to determine the dosages by means of a model predictive control (MPC) scheme.
Robust peak-to-peak gain analysis using integral quadratic constraints
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).
Online convex optimization for constrained control of linear systems using a reference governor
In this work, we propose a control scheme for linear systems subject to pointwise in time state and input constraints that aims to minimize time-varying and a priori unknown cost functions.
A moving horizon state and parameter estimation scheme with guaranteed robust convergence
We propose a moving horizon estimation scheme for joint state and parameter estimation for nonlinear uncertain discrete-time systems.
Data-driven Nonlinear Predictive Control for Feedback Linearizable Systems
We present a data-driven nonlinear predictive control approach for the class of discrete-time multi-input multi-output feedback linearizable nonlinear systems.
Distributed MPC for Self-Organized Cooperation of Multi-Agent Systems
We present a sequential distributed model predictive control (MPC) scheme for cooperative control of multi-agent systems with dynamically decoupled heterogeneous nonlinear agents subject to individual constraints.
Robust Data-Driven Moving Horizon Estimation for Linear Discrete-Time Systems
In this paper, a robust data-driven moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems is introduced.
Stability in data-driven MPC: an inherent robustness perspective
Moreover, we discuss how the presented proof technique allows to show closed-loop stability of a variety of DD-MPC schemes with noisy data, as long as the corresponding model-based MPC is inherently robust.
Linearly discounted economic MPC without terminal conditions for periodic optimal operation
Under standard dissipativity and controllability assumptions, we can prove that the resulting linearly discounted economic MPC without terminal conditions achieves optimal asymptotic average performance up to an error that vanishes with growing prediction horizons.
Sample-based observability of linear discrete-time systems
In this work, sample-based observability of linear discrete-time systems is studied.
A simple suboptimal moving horizon estimation scheme with guaranteed robust stability
We propose a suboptimal moving horizon estimation (MHE) scheme for a general class of nonlinear systems.
A novel constraint tightening approach for robust data-driven predictive control
This allows the entire scheme to be set up using only a priori measured data and knowledge of an upper bound on the system order.
On a Continuous-Time Version of Willems' Lemma
In this paper, a method to represent every input-output trajectory of a continuous-time linear system in terms of previously collected data is presented.
Model predictive control for retinal laser treatment at 1 kHz
Laser photocoagulation is a technique applied in the treatment of retinal diseases.
A Lyapunov function for robust stability of moving horizon estimation
We provide a novel robust stability analysis for moving horizon estimation (MHE) using a Lyapunov function.
Data-driven distributed MPC of dynamically coupled linear systems
In this paper, we present a data-driven distributed model predictive control (MPC) scheme to stabilise the origin of dynamically coupled discrete-time linear systems subject to decoupled input constraints.
Parameter estimation and model reduction for retinal laser treatment
Laser photocoagulation is one of the most frequently used treatment approaches for retinal diseases such as diabetic retinopathy and macular edema.
Data-Based Moving Horizon Estimation for Linear Discrete-Time Systems
This paper introduces a data-based moving horizon estimation (MHE) scheme for linear time-invariant discrete-time systems.
Suboptimal nonlinear moving horizon estimation
In this paper, we propose a suboptimal moving horizon estimator for a general class of nonlinear systems.
Efficient Off-Policy Q-Learning for Data-Based Discrete-Time LQR Problems
The proposed method does not require any knowledge of the system dynamics, and it enjoys significant efficiency advantages over other data-based optimal control methods in the literature.
Robust output feedback model predictive control using online estimation bounds
Robust constraint satisfaction is guaranteed by suitably incorporating these online validated bounds on the estimation error in a homothetic tube based MPC formulation.
Data-Based System Analysis and Control of Flat Nonlinear Systems
Willems et al. showed that all input-output trajectories of a discrete-time linear time-invariant system can be obtained using linear combinations of time shifts of a single, persistently exciting, input-output trajectory of that system.
State and parameter estimation for model-based retinal laser treatment
We find that, regarding convergence speed, the moving horizon estimation slightly outperforms the extended Kalman filter on measurement data in terms of parameter and state estimation, however, on simulated data the results are very similar.
Transient Performance of Tube-based Robust Economic Model Predictive Control
In this paper, we provide non-averaged and transient performance guarantees for recently developed, tube-based robust economic model predictive control (MPC) schemes.
On the design of terminal ingredients for data-driven MPC
We present a model predictive control (MPC) scheme to control linear time-invariant systems using only measured input-output data and no model knowledge.
Optimization and Control Systems and Control Systems and Control
Robust Stability of Suboptimal Moving Horizon Estimation using an Observer-Based Candidate Solution
In this paper, we propose a suboptimal moving horizon estimator for nonlinear systems.
Constrained nonlinear output regulation using model predictive control -- extended version
The paper also contains novel results for MPC without terminal constraints with positive semidefinite input/output stage costs that are of independent interest.
Robust Constraint Satisfaction in Data-Driven MPC
We propose a purely data-driven model predictive control (MPC) scheme to control unknown linear time-invariant systems with guarantees on stability and constraint satisfaction in the presence of noisy data.
Data-Driven Tracking MPC for Changing Setpoints
We propose a data-driven tracking model predictive control (MPC) scheme to control unknown discrete-time linear time-invariant systems.
Data-Driven Model Predictive Control with Stability and Robustness Guarantees
First, we prove exponential stability of a nominal data-driven MPC scheme with terminal equality constraints in the case of no measurement noise.
