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Found 47 papers, 10 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.
State Space Models as Foundation Models: A Control Theoretic Overview
In recent years, there has been a growing interest in integrating linear state-space models (SSM) in deep neural network architectures of foundation models.
Safe Guaranteed Exploration for Non-linear Systems
Based on this framework we propose an efficient algorithm, SageMPC, SAfe Guaranteed Exploration using Model Predictive Control.
Predictive stability filters for nonlinear dynamical systems affected by disturbances
Predictive safety filters provide a way of projecting potentially unsafe inputs onto the set of inputs that guarantee recursive state and input constraint satisfaction.
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.
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.
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.
Inverse Optimal Control as an Errors-in-Variables Problem
Inverse optimal control (IOC) is about estimating an unknown objective of interest given its optimal control sequence.
A Stiffness-Oriented Model Order Reduction Method for Low-Inertia Power Systems
The fast modes are then truncated in the rotated coordinate system to obtain a lower-order model with reduced stiffness.
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.
Submodular Reinforcement Learning
In many important applications, such as coverage control, experiment design and informative path planning, rewards naturally have diminishing returns, i. e., their value decreases in light of similar states visited previously.
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
Time Dependent Inverse Optimal Control using Trigonometric Basis Functions
The choice of objective is critical for the performance of an optimal controller.
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.
Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met.
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
The proposed approach jointly optimizes a nominal nonlinear trajectory and an error feedback, requiring minimal offline design effort and offering low conservatism.
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.
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.
LQG for Constrained Linear Systems: Indirect Feedback Stochastic MPC with Kalman Filtering
We present an output feedback stochastic model predictive control (SMPC) approach for linear systems subject to Gaussian disturbances and measurement noise and probabilistic constraints on system states and inputs.
Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter
A predictive control barrier function (PCBF) based safety filter is a modular framework to verify safety of a control input by predicting a future trajectory.
Asynchronous Computation of Tube-based Model Predictive Control
Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction.
Generalised Regret Optimal Controller Synthesis for Constrained Systems
This paper presents a synthesis method for the generalised dynamic regret problem, comparing the performance of a strictly causal controller to the optimal non-causal controller under a weighted disturbance.
Near-Optimal Multi-Agent Learning for Safe Coverage Control
In this paper, we aim to efficiently learn the density to approximately solve the coverage problem while preserving the agents' safety.
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.
Stochastic MPC with robustness to bounded parametric uncertainty
assumption on the noise distribution, we also provide an average asymptotic performance bound for the l2-norm of the closed-loop state.
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 System Level Approach to Regret Optimal Control
We present an optimisation-based method for synthesising a dynamic regret optimal controller for linear systems with potentially adversarial disturbances and known or adversarial initial conditions.
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.
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction.
On-Policy Model Errors in Reinforcement Learning
In contrast, model-based methods can use the learned model to generate new data, but model errors and bias can render learning unstable or suboptimal.
Adaptive Model Predictive Safety Certification for Learning-based Control -- Extended Version
By continuously connecting the current system state with a safe terminal set using a robust tube, safety can be ensured.
Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers
In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.
Predictive control barrier functions: Enhanced safety mechanisms for learning-based control
While learning-based control techniques often outperform classical controller designs, safety requirements limit the acceptance of such methods in many applications.
A System Level Approach to Tube-based Model Predictive Control
Robust tube-based model predictive control (MPC) methods address constraint satisfaction by leveraging an a priori determined tube controller in the prediction to tighten the constraints.
Meta Learning MPC using Finite-Dimensional Gaussian Process Approximations
Data availability has dramatically increased in recent years, driving model-based control methods to exploit learning techniques for improving the system description, and thus control performance.
Cautious Bayesian MPC: Regret Analysis and Bounds on the Number of Unsafe Learning Episodes
Furthermore, it is shown that the proposed constraint tightening implies a bound on the expected number of unsafe learning episodes in the linear and nonlinear case using a soft-constrained MPC formulation.
Maximum Likelihood Methods for Inverse Learning of Optimal Controllers
This paper discusses theoretic properties of the learning methods and presents simulation results that highlight the advantages of using the maximum likelihood formulation for learning objective functions.
Data-Driven Distributed Stochastic Model Predictive Control with Closed-Loop Chance Constraint Satisfaction
Distributed model predictive control methods for uncertain systems often suffer from considerable conservatism and can tolerate only small uncertainties due to the use of robust formulations that are amenable to distributed design and optimization methods.
Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization
We consider the problem of robust optimization within the well-established Bayesian optimization (BO) framework.
Bayesian Optimization for Policy Search in High-Dimensional Systems via Automatic Domain Selection
Bayesian Optimization (BO) is an effective method for optimizing expensive-to-evaluate black-box functions with a wide range of applications for example in robotics, system design and parameter optimization.
On Simulation and Trajectory Prediction with Gaussian Process Dynamics
Second, we propose a linearization-based technique that directly provides approximations of the trajectory distribution, taking correlations explicitly into account.
Distributed Model Predictive Safety Certification for Learning-based Control
While distributed algorithms provide advantages for the control of complex large-scale systems by requiring a lower local computational load and less local memory, it is a challenging task to design high-performance distributed control policies.
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems
In this paper, we address this problem for nonlinear systems with continuous state and input spaces by introducing a predictive safety filter, which is able to turn a constrained dynamical system into an unconstrained safe system and to which any RL algorithm can be applied `out-of-the-box'.
Linear model predictive safety certification for learning-based control
The MPSC scheme can be used in order to expand any potentially conservative set of safe states for learning and we prove an iterative technique for enlarging the safe set.
Scalable synthesis of safety certificates from data with application to learning-based control
The control of complex systems faces a trade-off between high performance and safety guarantees, which in particular restricts the application of learning-based methods to safety-critical systems.
Cautious NMPC with Gaussian Process Dynamics for Autonomous Miniature Race Cars
This paper presents an adaptive high performance control method for autonomous miniature race cars.
