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Found 21 papers, 2 papers with code
Risk-Aware Real-Time Task Allocation for Stochastic Multi-Agent Systems under STL Specifications
This paper addresses the control synthesis of heterogeneous stochastic linear multi-agent systems with real-time allocation of signal temporal logic (STL) specifications.
Risk-Aware MPC for Stochastic Systems with Runtime Temporal Logics
This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime.
A Linear Parameter-Varying Approach to Data Predictive Control
By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems.
Verifying the Unknown: Correct-by-Design Control Synthesis for Networks of Stochastic Uncertain Systems
We address two limitations of existing approaches for formal synthesis of controllers for networks of uncertain systems satisfying complex temporal specifications.
Risk-Aware Reward Shaping of Reinforcement Learning Agents for Autonomous Driving
Reinforcement learning (RL) is an effective approach to motion planning in autonomous driving, where an optimal driving policy can be automatically learned using the interaction data with the environment.
Stochastic Model Predictive Control with Dynamic Chance Constraints
The first problem concerns finding the maximal feasible probabilities of the dynamic chance constraints.
Bayesian Formal Synthesis of Unknown Systems via Robust Simulation Relations
With a focus on continuous-space stochastic systems with parametric uncertainty, we propose a two-stage approach that decomposes the problem into a learning stage and a robust formal controller synthesis stage.
Abstracting Linear Stochastic Systems via Knowledge Filtering
In this paper, we propose a new model reduction technique for linear stochastic systems that builds upon knowledge filtering and utilizes optimal Kalman filtering techniques.
Direct data-driven control with signal temporal logic specifications
Most control synthesis methods under temporal logic properties require a model of the system, however, identifying such a model can be a challenging task.
Automated Formation Control Synthesis from Temporal Logic Specifications
In many practical scenarios, multi-robot systems are envisioned to support humans in executing complicated tasks within structured environments, such as search-and-rescue tasks.
Modularized Control Synthesis for Complex Signal Temporal Logic Specifications
In this paper, we propose a framework to transform a long and complex specification into separate forms in time, to be more specific, the logical combination of a series of short and simple subformulas with non-overlapping timing intervals.
Direct data-driven state-feedback control of general nonlinear systems
Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems.
Data-driven Dissipativity Analysis of Linear Parameter-Varying Systems
We derive direct data-driven dissipativity analysis methods for Linear Parameter-Varying (LPV) systems using a single sequence of input-scheduling-output data.
SySCoRe: Synthesis via Stochastic Coupling Relations
We present SySCoRe, a MATLAB toolbox that synthesizes controllers for stochastic continuous-state systems to satisfy temporal logic specifications.
Correct-by-Design Control of Parametric Stochastic Systems
We develop new methods for models of systems subject to both stochastic and parametric uncertainties.
Deep-Learning-Based Identification of LPV Models for Nonlinear Systems
The Linear Parameter-Varying (LPV) framework provides a modeling and control design toolchain to address nonlinear (NL) system behavior via linear surrogate models.
Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems
Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems'.
Data-Driven Predictive Control for Linear Parameter-Varying Systems
Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available.
Convex Incremental Dissipativity Analysis of Nonlinear Systems - Extended version
We investigate how stability and performance characterizations of nonlinear systems in the incremental framework are linked to dissipativity, and how general performance characterization beyond the $\mathcal{L}_2$-gain concept can be understood in this framework.
Automated Experiment Design for Data-Efficient Verification of Parametric Markov Decision Processes
We present a new method for statistical verification of quantitative properties over a partially unknown system with actions, utilising a parameterised model (in this work, a parametric Markov decision process) and data collected from experiments performed on the underlying system.
Sampling-based Approximations with Quantitative Performance for the Probabilistic Reach-Avoid Problem over General Markov Processes
This article deals with stochastic processes endowed with the Markov (memoryless) property and evolving over general (uncountable) state spaces.
