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Found 58 papers, 8 papers with code
Orthogonal projection-based regularization for efficient model augmentation
Deep-learning-based nonlinear system identification has shown the ability to produce reliable and highly accurate models in practice.
The behavioral approach for LPV data-driven representations
In this paper, we present data-driven representations of linear parameter-varying (LPV) systems that can be used for direct data-driven analysis and control of LPV systems.
SRG Analysis of Lur'e Systems and the Generalized Circle Criterion
Scaled Relative Graphs (SRGs) provide a novel graphical frequency-domain method for the analysis of nonlinear systems.
Kernel-based multi-step predictors for data-driven analysis and control of nonlinear systems through the velocity form
We propose kernel-based approaches for the construction of a single-step and multi-step predictor of the velocity form of nonlinear (NL) systems, which describes the time-difference dynamics of the corresponding NL system and admits a highly structured representation.
Space-Filling Input Design for Nonlinear State-Space Identification
Hence, this paper focuses on designing space-filling experiments i. e., experiments that cover the full operation range of interest, for nonlinear dynamical systems that can be represented in a state-space form using a broad set of input signals.
Computationally Efficient Sampling-Based Algorithm for Stability Analysis of Nonlinear Systems
For complex nonlinear systems, it is challenging to design algorithms that are fast, scalable, and give an accurate approximation of the stability region.
Baseline Results for Selected Nonlinear System Identification Benchmarks
Nonlinear system identification remains an important open challenge across research and academia.
Physics-Guided State-Space Model Augmentation Using Weighted Regularized Neural Networks
A new weighted regularization term is added to the cost function to penalize the difference between the state and output function of the baseline physics-based and final identified model.
On the reduction of Linear Parameter-Varying State-Space models
This paper presents an overview and comparative study of the state of the art in State-Order Reduction (SOR) and Scheduling Dimension Reduction (SDR) for Linear Parameter-Varying (LPV) State-Space (SS) models, comparing and benchmarking their capabilities, limitations and performance.
Learning-based model augmentation with LFRs
The performance and generalization capabilities of the proposed method are demonstrated on a hardening mass-spring-damper simulation.
Decoupling parameter variation from noise: Biquadratic Lyapunov forms in data-driven LPV control
A promising step from linear towards nonlinear data-driven control is via the design of controllers for linear parameter-varying (LPV) systems, which are linear systems whose parameters are varying along a measurable scheduling signal.
Frequency Domain Auto-tuning of Structured LPV Controllers for High-Precision Motion Control
Motion systems are a vital part of many industrial processes.
Convex Equilibrium-Free Stability and Performance Analysis of Discrete-Time Nonlinear Systems
Namely, the universal shifted concept, which considers stability and performance w. r. t.
Unconstrained Parameterization of Stable LPV Input-Output Models: with Application to System Identification
Ensuring stability of discrete-time (DT) linear parameter-varying (LPV) input-output (IO) models estimated via system identification methods is a challenging problem as known stability constraints can only be numerically verified, e. g., through solving Linear Matrix Inequalities.
State Derivative Normalization for Continuous-Time Deep Neural Networks
However, in continuous-time state-space model estimation, it has been observed that improper normalization of either the hidden state or hidden state derivative of the model estimate, or even of the time interval can lead to numerical and optimization challenges with deep learning based methods.
Learning Reduced-Order Linear Parameter-Varying Models of Nonlinear Systems
In this paper, we consider the learning of a Reduced-Order Linear Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data.
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.
Attitude Takeover Control for Noncooperative Space Targets Based on Gaussian Processes with Online Model Learning
One major challenge for autonomous attitude takeover control for on-orbit servicing of spacecraft is that an accurate dynamic motion model of the combined vehicles is highly nonlinear, complex and often costly to identify online, which makes traditional model-based control impractical for this task.
Direct Learning for Parameter-Varying Feedforward Control: A Neural-Network Approach
The aim of this paper is to develop an input-output linear parameter-varying (LPV) feedforward parameterization and a corresponding data-driven estimation method in which the dependency of the coefficients on the scheduling signal are learned by a neural network.
Equilibrium-Independent Control of Continuous-Time Nonlinear Systems via the LPV Framework -- Extended Version
Additionally, we compare the proposed method to a standard LPV control design, demonstrating the improved stability and performance guarantees of the new approach.
Meta-State-Space Learning: An Identification Approach for Stochastic Dynamical Systems
Available methods for identification of stochastic dynamical systems from input-output data generally impose restricting structural assumptions on either the noise structure in the data-generating system or the possible state probability distributions.
Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models
Hamiltonian Neural Networks (HNNs) implement Hamiltonian theory in deep learning and form a comprehensive framework for modeling autonomous energy-conservative systems.
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.
Learning Stable and Robust Linear Parameter-Varying State-Space Models
This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models.
Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach
The SUBNET neural network architecture has been developed to identify nonlinear state-space models from input-output data.
Computationally efficient predictive control based on ANN state-space models
Artificial neural networks (ANN) have been shown to be flexible and effective function estimators for identification of nonlinear state-space models.
On Improved Commutation for Moving-Magnet Planar Actuators
The demand for high-precision and high-throughput motion control systems has increased significantly in recent years.
Exploring the use of deep learning in task-flexible ILC
To benchmark the performance of the proposed approach, a simulation study is presented which compares the TAIL-ILC to classical model-based feedforward strategies and existing learning-based approaches, such as neural network based feedforward learning.
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.
Finite Dimensional Koopman Form of Polynomial Nonlinear Systems
The Koopman framework is a popular approach to transform a finite dimensional nonlinear system into an infinite dimensional, but linear model through a lifting process, using so-called observable functions.
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 only a single sequence of data and guarantee stability and performance of the closed-loop system.
Learning For Predictive Control: A Dual Gaussian Process Approach
Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data.
Deep Subspace Encoders for Nonlinear System Identification
In this paper, we aim to overcome these issues by proposing a method which uses a truncated prediction loss and a subspace encoder for state estimation.
Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning
The paper proposes two control methods for performing a backflip maneuver with miniature quadcopters.
On modal observers for beyond rigid body $H_\infty$ control in high-precision mechatronics
The ever increasing need for performance results in increasingly rigorous demands on throughput and positioning accuracy of high-precision motion systems, which often suffer from position dependent effects that originate from relative actuation and sensing of the moving-body.
Koopman Form of Nonlinear Systems with Inputs
In applications for systems with inputs, generally a linear time invariant (LTI) form of the Koopman model is assumed, as it facilitates the use of control techniques such as linear quadratic regulation and model predictive control.
Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs
In the lifted space, the dynamics are linear and represented by a so-called Koopman operator.
LPV Modeling of the Atmospheric Flight Dynamics of a Generic Parafoil Return Vehicle
Obtaining models that can be used for control is of utmost importance to ensure the guidance and navigation of spacecraft, like a Generic Parafoil Return Vehicle (GPRV).
Continuous-time identification of dynamic state-space models by deep subspace encoding
Continuous-time (CT) modeling has proven to provide improved sample efficiency and interpretability in learning the dynamical behavior of physical systems compared to discrete-time (DT) models.
LPV sequential loop closing for high-precision motion systems
Increasingly stringent throughput requirements in the industry necessitate the need for lightweight design of high-precision motion systems to allow for high accelerations, while still achieving accurate positioning of the moving-body.
On discretization of continuous-time LPV control solutions
However, the main complication of CT synthesis approaches is the successive implementation of the resulting CT control solutions on physical hardware.
Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process
In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control strategy that improves the performance of a quadcopter during trajectory tracking.
Deep Identification of Nonlinear Systems in Koopman Form
The present paper treats the identification of nonlinear dynamical systems using Koopman-based deep state-space encoders.
Frequency-Domain Data-Driven Controller Synthesis for Unstable LPV Systems
Synthesizing controllers directly from frequency-domain measurement data is a powerful tool in the linear time-invariant framework.
Learning-based feedforward augmentation for steady state rejection of residual dynamics on a nanometer-accurate planar actuator system
Growing demands in the semiconductor industry result in the need for enhanced performance of lithographic equipment.
Nonlinear Tracking and Rejection using Linear Parameter-Varying Control
The Linear Parameter-Varying (LPV) framework has been introduced with the intention to provide stability and performance guarantees for analysis and controller synthesis for Nonlinear (NL) systems via convex methods.
Nonlinear parameter-varying state-feedback design for a gyroscope using virtual control contraction metrics
In this paper, we present a virtual control contraction metric (VCCM) based nonlinear parameter-varying (NPV) approach to design a state-feedback controller for a control moment gyroscope (CMG) to track a user-defined trajectory set.
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.
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.
Incremental Stability and Performance Analysis of Discrete-Time Nonlinear Systems using the LPV Framework
By embedding nonlinear systems in an LPV representation, the convex tools of the LPV framework can be applied to nonlinear systems for convex dissipativity based analysis and controller synthesis.
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.
Virtual Control Contraction Metrics: Convex Nonlinear Feedback Design via Behavioral Embedding
This paper presents a systematic approach to nonlinear state-feedback control design that has three main advantages: (i) it ensures exponential stability and $ \mathcal{L}_2 $-gain performance with respect to a user-defined set of reference trajectories, and (ii) it provides constructive conditions based on convex optimization and a path-integral-based control realization, and (iii) it is less restrictive than previous similar approaches.
SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation
Estimating 3D orientation and translation of objects is essential for infrastructure-less autonomous navigation and driving.
Ranked #24 on
Monocular 3D Object Detection
on KITTI Cars Moderate
A Tree Adjoining Grammar Representation for Models Of Stochastic Dynamical Systems
Model structure and complexity selection remains a challenging problem in system identification, especially for parametric non-linear models.
Data-driven Modelling of Dynamical Systems Using Tree Adjoining Grammar and Genetic Programming
Based on the results achieved for the case studies, we critically analyse the performance of the proposed method.
