Search Results for author:
Found 9 papers, 0 papers with code
Obstacle Avoidance of Autonomous Vehicles: An LPVMPC with Scheduling Trust Region
Reference tracking and obstacle avoidance rank among the foremost challenging aspects of autonomous driving.
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.
On the Design of Nonlinear MPC and LPVMPC for Obstacle Avoidance in Autonomous Driving
In this study, we are concerned with autonomous driving missions when a static obstacle blocks a given reference trajectory.
Direct data-driven LPV control of nonlinear systems: An experimental result
The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior.
Direct Data-Driven State-Feedback Control of Linear Parameter-Varying Systems
In this work, we derive novel methods that allow to synthesize LPV state-feedback controllers directly from a single sequence of data and guarantee stability and performance of the closed-loop system, without knowing the model of the plant.
A real-time GP based MPC for quadcopters with unknown disturbances
Unfortunately, the computational complexity of inference and learning on classical GPs scales cubically, which is intractable for real-time applications.
A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees
This paper presents a learning- and scenario-based model predictive control (MPC) design approach for systems modeled in linear parameter-varying (LPV) framework.
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.
LPV Modeling of Nonlinear Systems: A Multi-Path Feedback Linearization Approach
In the SISO case, all nonlinearities of the original system are embedded into one NL function, which is factorized, based on a proposed algorithm, to construct an LPV representation of the original NL system.
