Mingzhou Yin

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Yin

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Mingzhou

ORCID

0000-0001-7583-5318

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Publications1 - 10 of 25

Closed-loop identification of stabilized models using dual input–output parameterization
Chen, Ran; Srivastava, Amber; Yin, Mingzhou; et al. (2024)
European Journal of Control
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 recent input-output parameterization developed to synthesize a stabilizing controller. The controller is parameterized in terms of closed-loop transfer functions, from the external disturbances to the input and output of the system, constrained to lie in a given subspace. Analogously, the dual IOP method parameterizes the unknown plant with analogous closed-loop transfer functions, also referred to as dual parameters. In this case, these closed-loop transfer functions are constrained to lie in an affine subspace guaranteeing that the identified plant is stabilized by the known controller. Compared with existing closed-loop identification techniques guaranteeing closed-loop stability, such as the dual Youla parameterization, the dual IOP neither requires a doubly-coprime factorization of the controller nor a nominal plant that is stabilized by the controller. The dual IOP does not depend on the order and the state-space realization of the controller either, as in the dual system-level parameterization. Simulation shows that the dual IOP outperforms the existing benchmark methods.
Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
Yin, Mingzhou; Iannelli, Andrea; Khosravi, Mohammad; et al. (2020)
IFAC-PapersOnLine ~ 21st IFAC World Congress
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 identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.
Error Bounds for Kernel-Based Linear System Identification With Unknown Hyperparameters
Yin, Mingzhou; Smith, Roy (2023)
IEEE Control Systems Letters
Applying regularization in reproducing kernel Hilbert spaces has been successful in linear system identification using stable kernel designs. From a Gaussian process perspective, it automatically provides probabilistic error bounds for the identified models from the posterior covariance, which are useful in robust and stochastic control. However, the error bounds require knowledge of the true hyperparameters in the kernel design. They can be inaccurate with estimated hyperparameters for lightly damped systems or in the presence of high noise. In this letter, we provide reliable quantification of the estimation error when the hyperparameters are unknown. The bounds are obtained by first constructing a high-probability set for the true hyperparameters from the marginal likelihood function. Then the worst-case posterior covariance is found within the set. The proposed bound is proven to contain the true model with a high probability and its validity is demonstrated in numerical simulation.
Frequency-Domain Identification of Discrete-Time Systems using Sum-of-Rational Optimization
Abdalmoaty, Mohamed; Miller, Jared Franklin; Yin, Mingzhou; et al. (2024)
IFAC-PapersOnLine ~ 20th IFAC Symposium on System Identification SYSID 2024 Proceedings
This paper proposes a new computationally tractable method to fit coefficients of a fixed-order discrete-time transfer function to a measured frequency response, with stability guaranteed. The problem is formulated as a non-convex global sum-of-rational optimization problem whose objective function is the sum of weighted squared residuals at each observed frequency datapoint. Stability is enforced using a polynomial matrix inequality constraint. The problem is solved by a moment-sum-of-squares hierarchy of semidefinite programs through a framework for sum-of-rational-functions optimization. Convergence of the moment-sum-of-squares program is guaranteed as the bound on the degree of the sum-of-squares polynomials approaches infinity. The performance of the proposed method is demonstrated using numerical simulation examples.
Subspace Identification of Linear Time-Periodic Systems with Periodic Inputs
Yin, Mingzhou; Iannelli, Andrea; Smith, Roy (2021)
IEEE Control Systems Letters
This letter proposes a new methodology for subspace identification of linear time-periodic (LTP) systems with periodic inputs. This method overcomes the issues related to the computation of frequency response of LTP systems by utilizing the frequency response of the time-lifted system with linear time-invariant structure instead. The response is estimated with an ensemble of input-output data with periodic inputs. This allows the frequency-domain subspace identification technique to be extended to LTP systems. The time-aliased periodic impulse response can then be estimated and the order-revealing decomposition of the block-Hankel matrix is formulated. The consistency of the proposed method is proved under mild noise assumptions. Numerical simulation shows that the proposed method performs better than multiple widely-used time-domain subspace identification methods when an ensemble of periodic data is available.
Regularized and Nonparametric Approaches in System Identification and Data-Driven Control
Yin, Mingzhou (2024)
This thesis delves into regularized and nonparametric approaches in system identification and data-driven control. Classical model-based control design relies on a compact parametric model structure, which is difficult to obtain for modern complex systems. To address this challenge, regularized approaches adopt general high-dimensional model structures and apply sparse learning and kernel learning theories to identify models by leveraging the sparsity and smoothness properties of the system, respectively. In sparse learning, atomic norm regularization is employed to learn the sparse pole locations of the system within the unit disk. A novel algorithm is presented to solve the associated infinite-dimensional sparse learning problem. Debiasing and stability selection algorithms are applied to enhance the identification performance as well. In kernel learning, a multiple kernel design with optimal first-order kernels is proposed to identify the impulse response of the system. This enforces a low-complexity model structure while maintaining the favorable bias-variance trade-off property of kernel learning. More reliable error bounds, associated with the Gaussian process interpretation of kernel learning, are derived when hyperparameters are unknown, supporting safety-critical applications. An alternative path to circumvent model structure selection is to construct nonparametric predictors that predict output trajectories. This can be done by characterizing possible system behaviors as linear combinations of deterministic trajectory data. Extensions of this approach to stochastic data are investigated. A novel algorithm is developed to denoise the data by solving a low-rank Hankel matrix denoising problem. It achieves a more substantial noise reduction than existing algorithms. A maximum likelihood predictor, dubbed the signal matrix model, is derived to establish a statistical framework that provides accurate prediction in the presence of noise without requiring sophisticated tuning. Prediction error quantification associated with the nominal prediction is also provided. The proposed predictor can be directly applied to receding horizon predictive control, replacing model-based predictors, with the possibility to incorporate online data. It demonstrates superior performance compared to existing data-driven predictors. The algorithm is further extended to the stochastic control framework with initial condition estimation and guaranteed constraint satisfaction. Its effectiveness in practice is validated through high-fidelity simulation of a space heating control case study. Specific identification approaches for periodic systems are also studied. Linear time-periodic systems are identified by reformulating them into switched systems and extending the atomic norm regularization approach with grouped variables. In the frequency domain, a novel subspace identification algorithm is proposed by estimating the time-aliased periodic impulse response from the frequency response of the lifted system. Periodic models can also be utilized to identify local limit cycle dynamics. This is accomplished by linearizing the system along the limit cycle and estimating the periodic dynamics matrix of the linearized system by kernel learning. The approach is tested on an airborne wind energy system.
Design of input for data-driven simulation with Hankel and Page matrices
Iannelli, Andrea; Yin, Mingzhou; Smith, Roy (2021)
2021 60th IEEE Conference on Decision and Control (CDC)
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 discussed and new weaker conditions, which assume the simulated input to be known in advance, are provided. Then, the case of noisy data trajectories is considered and an input design problem based on a recently proposed maximum likelihood estimator is formulated. A Bayesian interpretation is provided, and the implications of using Hankel and Page matrix representations are demonstrated. Numerical examples show the impact of the designed input on the predictive accuracy.
Frequency-Domain System Identification using Sum-of-Rational Optimization
Abdalmoaty, Mohamed; Miller, Jared Franklin; Yin, Mingzhou; et al. (2023)
This work proposes a computationally tractable method for the identification of canonical rational transfer function models, using a finite set of input-output measurements. The problem is formulated in frequency-domain as a global optimization problem whose cost function is the sum of weighted squared residuals at each observed frequency datapoint. It is solved by the moment-sum-of-squares hierarchy of semidefinite programs, through a framework for sum-of-rational-functions optimization from Bugarin, Henrion, Lasserre 2016. The generated program contains decomposable term sparsity Wang et al. (2021) which can be exploited for further computational complexity reductions. Convergence of the moment-sum-of-squares program is guaranteed as the bound on the degree of the sum-of-squares polynomials approaches infinity. We discuss extensions of this rational-program method for identification of stable systems, and closed-loop identification.
On Low-Rank Hankel Matrix Denoising
Yin, Mingzhou; Smith, Roy (2021)
IFAC-PapersOnLine ~ 19th IFAC Symposium on System Identification, SYSID 2021
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 data by estimating the underlying structured low-rank matrix. However, standard low-rank approximation approaches are not guaranteed to perform well in estimating the noise-free matrix. In this paper, recent results in matrix denoising by singular value shrinkage are reviewed. A novel approach is proposed to solve the low-rank Hankel matrix denoising problem by using an iterative algorithm in structured low-rank approximation modified with data-driven singular value shrinkage. It is shown numerically in both the input-output trajectory denoising and the impulse response denoising problems, that the proposed method performs the best in terms of estimating the noise-free matrix among existing algorithms of low-rank matrix approximation and denoising. Copyright (C) 2021 The Authors.
Experiment design for impulse response identification with signal matrix models
Iannelli, Andrea; Yin, Mingzhou; Smith, Roy (2021)
IFAC-PapersOnLine ~ 19th IFAC Symposium on System Identification, SYSID 2021
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 data-driven simulation and control approaches. Precisely, the considered model consists of a linear combination of the columns of a data (or signal) matrix. An optimal combination for the case of noisy data was recently proposed using a maximum likelihood approach, and the objective here is to optimize the input entries of the data matrix such that the mean-square error matrix of the estimate is minimized. A least-norm problem is derived in terms of the optimality criteria typically considered in the experiment design literature. Numerical results showcase the improved estimation fit achieved with the optimized input. Copyright (C) 2021 The Authors.

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Mingzhou Yin

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