87 citations · 156 across the 14 of their papers we have counts for
18 papers
Optimal Data-Driven Prediction and Predictive Control using Signal Matrix Models
Roy S. Smith, Mohamed Abdalmoaty, Mingzhou Yin
Data-driven control uses a past signal trajectory to characterise the input-output behaviour of a system. Willems' lemma provides a data-based prediction model allowing a control d…
Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization
Mohamed Abdalmoaty, Jared Miller, Mingzhou Yin +1
We propose a computationally tractable method for the identification of stable canonical discrete-time rational transfer function models, using frequency domain data. The problem i…
Stochastic Data-Driven Predictive Control: Regularization, Estimation, and Constraint Tightening
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Data-driven predictive control methods based on the Willems' fundamental lemma have shown great success in recent years. These approaches use receding horizon predictive control wi…
Closed-Loop Identification of Stabilized Models Using Dual Input-Output Parameterization
Ran Chen, Amber Srivastava, Mingzhou Yin +1
This paper introduces a dual input-output parameterization (dual IOP) for the identification of linear time-invariant systems from closed-loop data. It draws inspiration from the r…
A Dual System-Level Parameterization for Identification from Closed-Loop Data
Amber Srivastava, Mingzhou Yin, Andrea Iannelli +1
This work presents a dual system-level parameterization (D-SLP) method for closed-loop system identification. The recent system-level synthesis framework parameterizes all stabiliz…
Error Bounds for Kernel-Based Linear System Identification with Unknown Hyperparameters
Mingzhou Yin, Roy S. Smith
The kernel-based method has been successfully applied in linear system identification using stable kernel designs. From a Gaussian process perspective, it automatically provides pr…
Kernel-Based Identification of Local Limit Cycle Dynamics with Linear Periodically Parameter-Varying Models
Defne E. Ozan, Mingzhou Yin, Andrea Iannelli +1
Limit cycle oscillations are phenomena arising in nonlinear dynamical systems and characterized by periodic, locally-stable, and self-sustained state trajectories. Systems controll…
Infinite-Dimensional Sparse Learning in Linear System Identification
Mingzhou Yin, Mehmet Tolga Akan, Andrea Iannelli +1
Regularized methods have been widely applied to system identification problems without known model structures. This paper proposes an infinite-dimensional sparse learning algorithm…
Data-Driven Prediction with Stochastic Data: Confidence Regions and Minimum Mean-Squared Error Estimates
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Recently, direct data-driven prediction has found important applications for controlling unknown systems, particularly in predictive control. Such an approach provides exact predic…
Design of input for data-driven simulation with Hankel and Page matrices
Andrea Iannelli, Mingzhou Yin, Roy S. Smith
The paper deals with the problem of designing informative input trajectories for data-driven simulation. First, the excitation requirements in the case of noise-free data are discu…
On Low-Rank Hankel Matrix Denoising
Mingzhou Yin, Roy S. Smith
The low-complexity assumption in linear systems can often be expressed as rank deficiency in data matrices with generalized Hankel structure. This makes it possible to denoise the…
Experiment design for impulse response identification with signal matrix models
Andrea Iannelli, Mingzhou Yin, Roy S. Smith
This paper formulates an input design approach for truncated infinite impulse response identification in the context of implicit model representations recently used as basis for da…
Maximum Likelihood Signal Matrix Model for Data-Driven Predictive Control
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
The paper presents a data-driven predictive control framework based on an implicit input-output mapping derived directly from the signal matrix of collected data. This signal matri…
Maximum Likelihood Estimation in Data-Driven Modeling and Control
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
Recently, various algorithms for data-driven simulation and control have been proposed based on the Willems' fundamental lemma. However, when collected data are noisy, these method…
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
Mingzhou Yin, Andrea Iannelli, Roy S. Smith
This paper proposes a new methodology for subspace identification of linear time-periodic (LTP) systems with periodic inputs. This method overcomes the issues related to the comput…
Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
Mingzhou Yin, Andrea Iannelli, Mohammad Khosravi +2
This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for…
Probabilistic Flight Envelope Estimation with Application to Unstable Overactuated Aircraft
Mingzhou Yin, Q. P. Chu, Y. Zhang +2
This paper proposes a novel and practical framework for safe flight envelope estimation and protection, in order to prevent loss-of-control-related accidents. Conventional analytic…
Robust Adaptive Model Predictive Control with Worst-Case Cost
Anilkumar Parsi, Andrea Iannelli, Mingzhou Yin +2
A robust adaptive model predictive control (MPC) algorithm is presented for linear, time invariant systems with unknown dynamics and subject to bounded measurement noise. The syste…