arXiv subjects

Chenchen Zhou

Publications and source records attributed to Chenchen Zhou.

8 recordsLinked to original sources

arXiv2026

A Theory-Guided Advanced Regulatory Control Synthesis for Cooling-Limited Exothermic Semi-Batch Reactors

This paper studies theory-guided advanced regulatory control (ARC) synthesis for cooling-limited exothermic semi-batch reactors, whose productivity and thermal safety are governed by changing active constraints. Industrial ARC uses feedback loops, cascades, selectors, feedforward/override logic, and valve-position elements, but signal selection, pairing, interconnection, and tuning remain heuristic. Nonlinear model predictive control (NMPC) gives a systematic constrained-operation workflow, but requires a maintained nonlinear model, state estimator, and online optimizer. We combine finite-horizon minimum-time optimality with local safety analysis to develop a systematic analysis-to-architecture ARC synthesis workflow for cooling-limited semi-batch reactors. Under stated assumptions, the workflow translates boundary-seeking optimality into a cooling-demand valve-position-control (VPC) architecture and translates local safety requirements into near-boundary tuning rules. On a reduced benchmark and an industrial-scale polymerization, ARC is nominally competitive with an implemented nominal-model output-feedback nonlinear model predictive control (OF-NMPC) benchmark using extended Kalman filter (EKF) state estimation. In the studied adverse parameter mismatch and unmodeled fault scenarios, ARC keeps temperature-limit violation at 0%, whereas OF-NMPC either violates the limit or fails to complete the batch.

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arXiv2026

Deployment-Aware Controller and Control Architecture Co-Design via Mixed-Integer Output-Feedback SLS

We study controller and control-architecture co-design for output-feedback systems under a hard budget. The architecture activates sensors and actuators and selects among directed communication-service options with specified delivery bounds and costs. Direct optimization over controller transfer matrices and discrete deployments is mixed-integer nonconvex; convex alternatives fix the architecture, use regularization, or impose a quadratically invariant (QI) controller-information pattern. We instead optimize finite impulse response (FIR) output-feedback system-level synthesis (OF-SLS) responses. Binary variables select devices and service options; cumulative binaries record whether the selected service can deliver by each FIR lag. Indicator constraints zero response coefficients that would require unavailable messages. For fixed device and OF-SLS realization-state locations, this yields an exact mixed-integer convex program (MICP) over finite service menus and deployment constraints. Every feasible response admits the standard OF-SLS implementation using only the selected devices and services. In a three-follower platoon, 2736 of 139,968 stabilizable and detectable deployments are QI-compatible. All three actuators are necessary, whereas intermediate-budget optima retain strict subsets of seven sensor packages. At a common budget, the best QI design has 3.86 times the performance loss of the co-design optimum relative to the dense deployment.

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arXiv2026

Real-time nonlinear model predictive control framework for event-triggered switching in industrial batch polymerization process

Controlling batch polymerization is challenging because the absence of a steady operating point prevents standard linearization; the dynamics are intrinsically nonlinear; and multi-phase operation induces state-triggered switching. This study systematically combines four established real-time NMPC ingredients, smooth mode blending, advanced-step warm starts, variable scaling, and a capped iteration budget, to attain real-time feasibility without ad hoc switching heuristics. We provide practice-oriented guidance for selecting smoothing gains and locating switching surfaces, and we make explicit the approximations introduced by smoothing such that, with appropriate tuning, the smoothed and original switching logic are numerically indistinguishable at solver-tolerance levels. All results are obtained in closed-loop simulation using an industrial gas-liquid polymerization benchmark with estimator-in-the-loop, compared against PID and conventional NMPC baselines. Results show improved constraint satisfaction and shorter batch duration under bounded computation, while an ablation study quantifies the specific contributions of each component individually

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arXiv2026

Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach

Self-optimizing control (SOC) aims to maintain near-optimal process operation by judiciously selecting controlled variables (CVs). In this series of work, the generalized global SOC (g2SOC) approach is proposed, which extends the concept of SOC to the whole operation space and uses general nonlinear functions to design CVs instead of linear combinations. In the first part of this series work, two numerical approaches for g2SOC are proposed: the optimization-based approach and the regression-based approach, based on a theoretical analysis of the existence of perfect self-optimizing CVs. The CVs designed by the former perform better, but are usually infeasible for large-scale problems. In this paper, we propose an algorithm called objective-guided controlled variable learning (OGCVL) that combines the advantages of both and has a better scalability. OGCVL is proposed for efficient CV design that seamlessly integrates symbolic and numerical computation techniques. Finally, the effectiveness of the OGCVL method is verified in two numerical examples. Both examples illustrate show that the OGCVL method is able to achieve good results while maintaining computational efficiency and is also feasible in large-scale problems.

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arXiv2026

Global self-optimizing control of batch processes

This work considers to achieve near-optimal operation for a class of batch processes by employing self-optimizing control (SOC). Comparing with a continuous one, a batch process exhibits stronger nonlinearity with dynamics because of the non-steady operation condition. This necessitates a global version of SOC to achieve satisfactory performance. Meanwhile, it also makes the existing global SOC (gSOC) not directly applicable to batch processes due to the causality amongst variables. Therefore, it is necessary to extend the original gSOC to batch processes. In addition to the nonconvexity challenge of the original gSOC problem, the new extension for batch processes has to face even more challenges. Particularly, the causality due to dynamics of batch processes brings in structural constraints on controlled variables (CVs), making a CV selection problem even more difficult. To address these challenges, the gSOC problem is recast in a vectorized formulation and it is proved that the structural constraints considered are linear in the vectorized formulation. Moreover, a novel shortcut method is proposed to efficiently find sub-optimal but more transparent solutions for this problem. The effectiveness of the new approach is validated through a case study of a fed-batch reactor, where CVs are constructed through a combination matrix with a repetitive structure, resulting in a simple SOC scheme. This simplicity facilitates the implementation of the SOC approach and enhances its practical applicability and robustness.

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arXiv2026

Dynamic Controlled Variables Based Dynamic Self-Optimizing Control

Self-optimizing control is a strategy for selecting controlled variables, where the economic objective guides the selection and design of controlled variables, with the expectation that maintaining the controlled variables at constant values can achieve optimization effects, translating the process optimization problem into a process control problem. Currently, self-optimizing control is widely applied to steady-state optimization problems. However, the development of process systems exhibits a trend towards refinement, highlighting the importance of optimizing dynamic processes such as batch processes and grade transitions. This paper formally introduces the self-optimizing control problem for dynamic optimization, termed the dynamic self-optimizing control problem, extending the original definition of self-optimizing control. A novel concept, "dynamic controlled variables" (DCVs), is proposed, and an implicit control policy is presented based on this concept. The paper theoretically analyzes the advantages and generality of DCVs compared to explicit control strategies and elucidates the relationship between DCVs and traditional controllers. Moreover, this paper puts forth a data-driven approach to designing self-optimizing DCVs, which considers DCV design as a mapping identification problem and employs deep neural networks to parameterize the variables. Three case studies validate the efficacy and superiority of DCVs in approximating multi-valued and discontinuous functions, as well as their application to dynamic optimization problems with non-fixed horizons, which traditional self-optimizing control methods are unable to address.

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arXiv2026

Performance guaranteed MPC Policy Approximation via Cost Guided Learning

Model predictive control (MPC) is widely used in industries but implementing it poses challenges due to hardware or time constraints. A promising solution is to approximate the MPC policy using function approximators like neural networks. Existing methods focus on minimizing the error between the approximators outputs and the MPC optimal control actions on training data, which is called error guided learning approach in this paper. However, the goals of control law design is not to minimize the fitting error but to minimize the operation cost. This paper proposes a novel cost-guided learning approach that utilizes the cost sensitivity information from the MPC problem to directly minimize the loss in closed-loop performance. A theoretical analysis shows cost-guided learning provides tighter guarantees on optimality loss compared to traditional error-guided learning. Experiments on a continuous stirred tank reactor (CSTR) benchmark demonstrate that the proposed technique results in approximate MPC policies that achieve substantially better closed-loop performance. This work makes an important contribution by connecting the fitting errors with operational objectives, overcoming key limitations of existing approximation methods. The core idea could be applied more broadly for data-driven control.

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arXiv2022

Fast and Accurate FSA System Using ELBERT: An Efficient and Lightweight BERT

With the development of deep learning and Transformer-based pre-trained models like BERT, the accuracy of many NLP tasks has been dramatically improved. However, the large number of parameters and computations also pose challenges for their deployment. For instance, using BERT can improve the predictions in the financial sentiment analysis (FSA) task but slow it down, where speed and accuracy are equally important in terms of profits. To address these issues, we first propose an efficient and lightweight BERT (ELBERT) along with a novel confidence-window-based (CWB) early exit mechanism. Based on ELBERT, an innovative method to accelerate text processing on the GPU platform is developed, solving the difficult problem of making the early exit mechanism work more effectively with a large input batch size. Afterward, a fast and high-accuracy FSA system is built. Experimental results show that the proposed CWB early exit mechanism achieves significantly higher accuracy than existing early exit methods on BERT under the same computation cost. By using this acceleration method, our FSA system can boost the processing speed by nearly 40 times to over 1000 texts per second with sufficient accuracy, which is nearly twice as fast as FastBERT, thus providing a more powerful text processing capability for modern trading systems.

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