The model-based control of a boiler-turbine system (BTS) is a formidable task due to coupling in state variables, nonlinearities and constraints on the control inputs. In this paper, a model-based, multi-variable dynamic sliding mode control (DSMC) is designed for the nonlinear BTS model to maintain the drum pressure, electric power and water level at the desired levels. In DSMC, an implicit sliding manifold is designed for the water level due to its complexity and explicit dependence on the control inputs. For this purpose, an auxiliary function is computed, and the sliding mode is enforced in such a way that the system fluid density tracks the auxiliary function, and subsequently the water level tracks the desired trajectory. Owing to its complexity, the time derivatives of the auxiliary function are computed using the uniform robust exact differentiator (URED). An adaptive Kalman filter (AKF) is designed for the estimation of the unmeasurable state i.e., system fluid density. The design of AKF is based on the quasi-linear model of the BTS. Furthermore, a detailed stability analysis is carried out to ensure the boundedness of the closed-loop system. The simulation results depict that the designed control scheme exhibits the desired tracking performance in the presence of external disturbances, nonlinearities, constraints on the inputs, and measurement and process noises.

由于状态变量、非线性和控制输入约束的耦合，锅炉-涡轮系统 (BTS) 的基于模型的控制是一项艰巨的任务。在本文中，针对非线性 BTS 模型设计了一种基于模型的多变量动态滑模控制 (DSMC)，以将滚筒压力、电力和水位保持在所需水平。在 DSMC 中，由于其复杂性和对控制输入的显式依赖性，为水位设计了一个隐式滑动歧管。为此，计算辅助函数，并以系统流体密度跟踪辅助函数，随后水位跟踪所需轨迹的方式强制执行滑模。由于其复杂性，辅助函数的时间导数是使用统一鲁棒精确微分器 (URED) 计算的。自适应卡尔曼滤波器 (AKF) 被设计用于估计不可测量状态，即系统流体密度。AKF的设计基于BTS的准线性模型。此外，还进行了详细的稳定性分析，以确保闭环系统的有界性。仿真结果表明，设计的控制方案在存在外部干扰、非线性、输入约束以及测量和过程噪声的情况下表现出所需的跟踪性能。进行了详细的稳定性分析，以确保闭环系统的有界性。仿真结果表明，设计的控制方案在存在外部干扰、非线性、输入约束以及测量和过程噪声的情况下表现出所需的跟踪性能。进行了详细的稳定性分析，以确保闭环系统的有界性。仿真结果表明，设计的控制方案在存在外部干扰、非线性、输入约束以及测量和过程噪声的情况下表现出所需的跟踪性能。