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In the context of widespread global information sharing, information security and privacy protection have become focal points. Steganographic systems enhance information security by embedding confidential information into public carriers; however, existing generative text steganography methods face challenges in handling the long-tail distribution of candidate word pools, which impacts the imperceptibility of steganographic information. This paper proposes a quality control theory for steganographic text generation based on information entropy constraints, exploring the relationship between the imperceptibility of steganographic texts and information entropy. By controlling the information entropy of the candidate word pool within a specific range, we optimize the imperceptibility of the steganographic text. We establish upper and lower bounds for information entropy and introduce an adaptive truncation method to balance semantic coherence and lexical diversity. Experimental results demonstrate that reasonably controlling the candidate pool size and information entropy thresholds significantly enhances the quality and detection resistance of steganographic texts, showcasing broad application potential in the field of natural language processing.

This paper addresses the safe stabilization problem, focusing on controlling the system state to the origin while avoiding entry into unsafe state sets. The current methods for solving this issue rely on smooth Lyapunov and barrier functions, which do not always ensure the existence of an effective controller even when such smooth functions are created. To tackle this challenge, we introduce the concept of a generalized (nonsmooth) Lyapunov barrier function (GenLBF), which guarantees the existence of a safe and stable controller. We outline a systematic approach for constructing a GenLBF, including a technique for efficiently calculating the upper generalized derivative of the GenLBF. Using the constructed GenLBF, we propose a method for certifying safe stabilization of autonomous systems and design a piecewise continuous feedback control to achieve safe stabilization of non-autonomous systems. A general controller refinement strategy is further proposed to help the state trajectory escape from undesired local points occurring in systems with special physical structure. A thorough theoretical analysis demonstrates the effectiveness of our method in addressing the safe stabilization problem for systems with single or multiple bounded unsafe state sets. Extensive simulations of linear and nonlinear systems further illustrate the efficacy of the proposed method and its superiority over the smooth control Lyapunov barrier function method., Comment: 19 pages, 14 figures

Greenhouse climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth., Comment: 12 pages, 6 figures, code available at https://github.com/SamuelMallick/mpcrl-greenhouse, submitted to Computers and Electronics in Agriculture

This paper introduces a new design method for data-driven control of nonlinear systems with partially unknown dynamics and unknown bounded disturbance. Since it is not possible to achieve exact nonlinearity cancellation in the presence of unknown disturbance, this paper adapts the idea of sliding mode control (SMC) to ensure system stability and robustness without assuming that the nonlinearity goes to zero faster than the state as in the existing methods. The SMC consists of a data-dependent robust controller ensuring the system state trajectory reach and remain on the sliding surface and a nominal controller solved from a data-dependent semidefinite program (SDP) ensuring robust stability of the state trajectory on the sliding surface. Numerical simulation results demonstrate effectiveness of the proposed data-driven SMC and its superior in terms of robust stability over the existing data-driven control that also uses approximate nonlinearity cancellation., Comment: Submitted to IEEE CDC 2024

Greenhouse is an important protected horticulture system for feeding the world with enough fresh food. However, to maintain an ideal growing climate in a greenhouse requires resources and operational costs. In order to achieve economical and sustainable crop growth, efficient climate control of greenhouse production becomes essential. Model Predictive Control (MPC) is the most commonly used approach in the scientific literature for greenhouse climate control. However, with the developments of sensing and computing techniques, reinforcement learning (RL) is getting increasing attention recently. With each control method having its own way to state the control problem, define control goals, and seek for optimal control actions, MPC and RL are representatives of model-based and learning-based control approaches, respectively. Although researchers have applied certain forms of MPC and RL to control the greenhouse climate, very few effort has been allocated to analyze connections, differences, pros and cons between MPC and RL either from a mathematical or performance perspective. Therefore, this paper will 1) propose MPC and RL approaches for greenhouse climate control in an unified framework; 2) analyze connections and differences between MPC and RL from a mathematical perspective; 3) compare performance of MPC and RL in a simulation study and afterwards present and interpret comparative results into insights for the application of the different control approaches in different scenarios.

In urban drainage systems (UDS), a proven method for reducing the combined sewer overflow (CSO) pollution is real-time control (RTC) based on model predictive control (MPC). MPC methodologies for RTC of UDSs in the literature rely on the computation of the optimal control strategies based on deterministic rain forecast. However, in reality, uncertainties exist in rainfall forecasts which affect severely accuracy of computing the optimal control strategies. Under this context, this work aims to focus on the uncertainty associated with the rainfall forecasting and its effects. One option is to use stochastic information about the rain events in the controller; in the case of using MPC methods, the class called stochastic MPC is available, including several approaches such as the chance-constrained MPC method. In this study, we apply stochastic MPC to the UDS using the chance-constrained method. Moreover, we also compare the operational behavior of both the classical MPC with perfect forecast and the chance-constrained MPC based on different stochastic scenarios of the rain forecast. The application and comparison have been based on simulations using a SWMM model of the Astlingen urban drainage benchmark network., Comment: A preprint of a submitted article to the Journal Control Engineering Practice