Minghao Han

Tianjin University of Science and Technology

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Lixian Zhang

Harbin Institute of Technology

Xunyuan Yin

Nanyang Technological University

Zhenbin Zhang

Shandong University

Wei Pan

Delft University of Technology

Yuan Tian

ETH Zurich

Adrian Wing‐Keung Law

Nanyang Technological University

Jingshi Yao

Nanyang Technological University

Xukun Zhang

Nanchang Hangkong University

Xunyuan Yin

Nanyang Technological University

Zheng Dong

Shandong University

Cooperative Institutions

Harbin Institute of Technology

Fudan University

Chinese Academy of Sciences

Shandong University

Nanyang Technological University

Shanghai Jiao Tong University

Jilin University

University of Chinese Academy of Sciences

Ministry of Education of the People's Republic of China

Sun Yat-sen University

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Machine learning-based input-augmented Koopman modeling and predictive control of nonlinear processes

arXiv (Cornell University) (2024)

Zhaoyang Li Minghao Han Dat-Nguyen Vo Xunyuan Yin

Koopman-based modeling and model predictive control have been a promising alternative for optimal control of nonlinear processes. Good Koopman modeling performance significantly depends on an appropriate nonlinear mapping from the original state-space to a lifted state space. In this work, we propose an input-augmented Koopman modeling and model predictive control approach. Both the states and the known inputs are lifted using two deep neural networks (DNNs), and a Koopman model with nonlinearity in inputs is trained within the higher-dimensional state-space. A Koopman-based model predictive control problem is formulated. To bypass non-convex optimization induced by the nonlinearity in the Koopman model, we further present an iterative implementation algorithm, which approximates the optimal control input via solving a convex optimization problem iteratively. The proposed method is applied to a chemical process and a biological water treatment process via simulations. The efficacy and advantages of the proposed modeling and control approach are demonstrated.

Model Predictive Control

Nonlinear model

10.48550/arxiv.2408.02315

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Reduced-order Koopman modeling and predictive control of nonlinear processes

Computers & Chemical Engineering (2023)

Xuewen Zhang Minghao Han Xunyuan Yin

Model Predictive Control

Benchmark (surveying)

10.1016/j.compchemeng.2023.108440

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Citations (14)

Semi-time-dependent asynchronously switched control of continuous-time switched systems with persistent dwell time

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society (2017)

Tianhe Liu Changhong Wang Zeyang Fan Hui Wang Minghao Han

This paper mainly concerns the issue of asynchronously switched control for a class of continuous-time switched linear systems with persistent dwell time (PDT). Asynchronous switching implies that when a subsystem switches the matched controller remains active for a finite period of time, such that the Lyapunov-like function increases. A semi-time-dependent (STD) multiple Lyapunov-like function with special form suitable to asynchronously switched systems is proposed, upon which a set of STD and mode-dependent stabilizing controllers is designed. The validity and advantage of the proposed results is demonstrated through a numerical example.

Dwell time

10.1109/iecon.2017.8217325

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Model-free Learning Control of Nonlinear Stochastic Systems with Stability Guarantee

Minghao Han Yuan Tian Lixian Zhang Jun Wang Wei Pan

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Self-tuning moving horizon estimation of nonlinear systems via physics-informed machine learning Koopman modeling

arXiv (Cornell University) (2024)

Meng Yan Minghao Han Adrian Wing‐Keung Law Xunyuan Yin

In this paper, we propose a physics-informed learning-based Koopman modeling approach and present a Koopman-based self-tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks - the state lifting network and the noise characterization network - using both data and available physical information. The two neural networks account for the nonlinear lifting functions for Koopman modeling and describing system noise distributions, respectively. Accordingly, a stochastic linear Koopman model is established in the lifted space to forecast the dynamic behavior of the nonlinear system. Based on the Koopman model, a self-tuning linear moving horizon estimation (MHE) scheme is developed. The weighting matrices of the MHE design are updated using the pre-trained noise characterization network at each sampling instant. The proposed estimation scheme is computationally efficient because only convex optimization is involved during online implementation, and updating the weighting matrices of the MHE scheme does not require re-training the neural networks. We verify the effectiveness and evaluate the performance of the proposed method via the application to a simulated chemical process.

10.48550/arxiv.2408.03653

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Research on the effect of LAMP1 in the development and progression of ccRCC and its potential mechanism with LC3C-mediated autophagy

Frontiers in Immunology (2024)

Xiongbao Wang Liang Fang Lixiang Xiao Guangxin Zhong Minghao Han

Lysomembrane-associated protein 1 (LAMP1), known to exhibit differential expression in various tumor types and play a crucial role in the development of tumors. Clear cell Renal Cell Carcinoma (ccRCC) is still the most common pathological type of renal carcinoma with poor prognosis. However, the expression of LAMP1 and its underlying molecular mechanism with ccRCC remain elusive.

10.3389/fimmu.2024.1494005

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Quantized output-feedback control of piecewise-affine systems with reachable regions of quantized measurements

Information Sciences (2024)

Bo Cai Zepeng Ning Wenqiang Ji Yanzheng Zhu Minghao Han

Feedback Control

10.1016/j.ins.2024.120828

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Robust Learning-Based Control for Uncertain Nonlinear Systems With Validation on a Soft Robot

IEEE Transactions on Neural Networks and Learning Systems (2023)

Minghao Han Kiwan Wong Jacob Euler-Rolle Lixian Zhang Robert K. Katzschmann

Existing modeling and control methods for real-world systems typically deal with uncertainty and nonlinearity on a case-by-case basis. We present a universal and robust control framework for the general class of uncertain nonlinear systems. Our data-driven deep stochastic Koopman operator (DeSKO) model and robust learning control framework guarantee robust stability. DeSKO learns the uncertainty of dynamical systems by inferring a distribution of observables. The inferred distribution is used in our robust and stabilizing closed-loop controller for dynamical systems. We also develop a model predictive control framework with integral action to compensate for run-time parametric uncertainty, such as manipulating unknown objects. Modeling and control experiments in simulation show that our presented framework is more robust and scalable for robotic systems than state-of-the-art controllers using deep Koopman operators and reinforcement learning (RL) methods. We demonstrate that our method resists previously unseen uncertainties, such as external disturbances, at a magnitude of up to five times the maximum control input. Furthermore, we test our DeSKO-based control framework on a real-world soft robotic arm. It shows that our framework outperforms model-based controllers that have full knowledge of the model parameters, and the controller can conduct object pick-and-place tasks without further training. Our approach opens up new possibilities in robustly managing internal or external uncertainty while controlling high-dimensional nonlinear systems in a learning framework. This approach serves as a foundation to greatly simplify high-level control and decision-making for robots.

10.1109/tnnls.2023.3328643

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Self‐tuning moving horizon estimation of nonlinear systems via physics‐informed machine learning Koopman modeling

AIChE Journal (2024)

Meng Yan Minghao Han Adrian Wing‐Keung Law Xunyuan Yin

Abstract In this article, we propose a physics‐informed learning‐based Koopman modeling approach and present a Koopman‐based self‐tuning moving horizon estimation design for a class of nonlinear systems. Specifically, we train Koopman operators and two neural networks—the state lifting network and the noise characterization network—using both data and available physical information. The first network accounts for the nonlinear lifting functions for the Koopman model, while the second network characterizes the system noise distributions. Accordingly, a stochastic linear Koopman model is established in the lifted space to forecast the dynamic behaviors of the nonlinear system. Based on the Koopman model, a self‐tuning linear moving horizon estimation (MHE) scheme is developed. The weighting matrices of the MHE design are updated using the pretrained noise characterization network at each sampling instant. The proposed estimation scheme is computationally efficient, as only convex optimization needs to be solved during online implementation, and updating the weighting matrices of the MHE scheme does not require re‐training the neural networks. We verify the effectiveness and evaluate the performance of the proposed method via the application to a simulated chemical process.

10.1002/aic.18649

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Citations (1)

$H_\infty$ Model-free Reinforcement Learning with Robust Stability Guarantee

arXiv (Cornell University) (2019)

Minghao Han Yuan Tian Lixian Zhang Jun Wang Wei Pan

Reinforcement learning is showing great potentials in robotics applications, including autonomous driving, robot manipulation and locomotion. However, with complex uncertainties in the real-world environment, it is difficult to guarantee the successful generalization and sim-to-real transfer of learned policies theoretically. In this paper, we introduce and extend the idea of robust stability and $H_\infty$ control to design policies with both stability and robustness guarantee. Specifically, a sample-based approach for analyzing the Lyapunov stability and performance robustness of a learning-based control system is proposed. Based on the theoretical results, a maximum entropy algorithm is developed for searching Lyapunov function and designing a policy with provable robust stability guarantee. Without any specific domain knowledge, our method can find a policy that is robust to various uncertainties and generalizes well to different test environments. In our experiments, we show that our method achieves better robustness to both large impulsive disturbances and parametric variations in the environment than the state-of-art results in both robust and generic RL, as well as classic control. Anonymous code is available to reproduce the experimental results at https://github.com/RobustStabilityGuaranteeRL/RobustStabilityGuaranteeRL.

Robustness

Lyapunov stability

10.48550/arxiv.1911.02875

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Citations (8)