Search Results for author:
Found 31 papers, 4 papers with code
Adaptive Economic Model Predictive Control for linear systems with performance guarantees
We present a model predictive control (MPC) formulation to directly optimize economic criteria for linear constrained systems subject to disturbances and uncertain model parameters.
Safe Guaranteed Exploration for Non-linear Systems
Based on this framework we propose an efficient algorithm, SageMPC, SAfe Guaranteed Exploration using Model Predictive Control.
MHE under parametric uncertainty -- Robust state estimation without informative data
In this paper, we study state estimation for general nonlinear systems with unknown parameters and persistent process and measurement noise.
Nonlinear Functional Estimation: Functional Detectability and Full Information Estimation
Together, we present a unified framework to study functional estimation with a detectability condition, which is necessary and sufficient for the existence of a stable functional estimator, and a corresponding functional estimator design.
Automatic nonlinear MPC approximation with closed-loop guarantees
We address this problem by presenting a novel algorithm that automatically computes an explicit approximation to nonlinear MPC schemes while retaining closed-loop guarantees.
Towards non-stochastic targeted exploration
We present a novel targeted exploration strategy for linear time-invariant systems without stochastic assumptions on the noise, i. e., without requiring independence or zero mean, allowing for deterministic model misspecifications.
Robust Nonlinear Reduced-Order Model Predictive Control
Real-world systems are often characterized by high-dimensional nonlinear dynamics, making them challenging to control in real time.
Analysis and design of model predictive control frameworks for dynamic operation -- An overview
This article provides an overview of model predictive control (MPC) frameworks for dynamic operation of nonlinear constrained systems.
Approximate non-linear model predictive control with safety-augmented neural networks
Our method requires a single evaluation of the NN and forward integration of the input sequence online, which is fast to compute on resource-constrained systems.
On stochastic MPC formulations with closed-loop guarantees: Analysis and a unifying framework
We investigate two particular MPC formulations representative for these two frameworks called robust-stochastic MPC and indirect feedback stochastic MPC.
Active Learning-based Model Predictive Coverage Control
In addition, we derive a control framework that avoids the hierarchical structure by integrating the reference optimization in the MPC formulation.
Sequential learning and control: Targeted exploration for robust performance
This provides an a priori upper bound on the remaining model uncertainty after exploration, which can further be leveraged in a gain-scheduling controller design that guarantees robust performance.
Predictive safety filter using system level synthesis
In this paper, we present an improved model predictive safety filter (MPSF) formulation, which incorporates system level synthesis techniques in the design.
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.
Robust adaptive MPC using control contraction metrics
As a result, the proposed MPC formulation is applicable to a large class of nonlinear systems, reduces conservatism during online operation, and guarantees robust constraint satisfaction and convergence to a neighborhood of the desired setpoint.
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.
Recursively feasible stochastic predictive control using an interpolating initial state constraint -- extended version
We present a stochastic model predictive control (SMPC) framework for linear systems subject to possibly unbounded disturbances.
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.
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.
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
Stability and performance in MPC using a finite-tail cost
In this paper, we provide a stability and performance analysis of model predictive control (MPC) schemes based on finite-tail costs.
Offset-free setpoint tracking using neural network controllers
In this paper, we present a method to analyze local and global stability in offset-free setpoint tracking using neural network controllers and we provide ellipsoidal inner approximations of the corresponding region of attraction.
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 and optimal predictive control of the COVID-19 outbreak
Our theoretical findings support various recent studies by showing that 1) adaptive feedback strategies are required to reliably contain the COVID-19 outbreak, 2) well-designed policies can significantly reduce the number of fatalities compared to simpler ones while keeping the amount of social distancing measures on the same level, and 3) imposing stronger social distancing measures early on is more effective and cheaper in the long run than opening up too soon and restoring stricter measures at a later time.
Robust Dual Control based on Gain Scheduling
We present a novel strategy for robust dual control of linear time-invariant systems based on gain scheduling with performance guarantees.
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.
Safe and Fast Tracking on a Robot Manipulator: Robust MPC and Neural Network Control
Fast feedback control and safety guarantees are essential in modern robotics.
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.
Learning an Approximate Model Predictive Controller with Guarantees
A supervised learning framework is proposed to approximate a model predictive controller (MPC) with reduced computational complexity and guarantees on stability and constraint satisfaction.
