Active disturbance rejection control (ADRC) is widely applied in the motion control and the industrial process control for its merits of the uncertainty rejection, efficacy and ease of use. However, most process control models exist time-delay, which greatly deteriorates the closed-loop control performance of ADRC. Moreover, the controller gain tuning of ADRC is complex, laborious and time-consuming. Considering the above two difficulties, the key innovation of the paper is that aiming at the practical wet electrostatic precipitator (WESP) system with time-delay, a simple yet robust TD-ADRC gain tuning method is developed, which is mainly constructed by artificial time-delay block, observer and feedback control loop. The proposed gain tuning method is based on the bandwidth-parameterization technique, further using the scaling skill to simplify the gain tuning rule and stability analysis. Additionally, the exhaustive robustness metrics of closed-loop system are analyzed, which ensures that the gain tuning method maintains the controlled time-delay system with qualified robustness and well control performance. Alternatively, another robust stable region of TD-ADRC controller parameters are explored through the distinctively rigorous stability proof. Finally, plenty of simulations of WESP based on the practical operation data validate the effectiveness of the proposed method. Note to Practitioners — This paper was focused on gain tuning problem of the time-delay active disturbance rejection control (TD-ADRC) method. The controlled system is the first order plus time-delay (FOPTD) wet electrostatic precipitator (WESP) system. Thus, the novelties can be viewed from perspectives of method research and the combination of method and WESP model. From the perspective of the method research, time-delay in the process model is the obstacle to the most ADRC design or other kinds of controller design, and it always brings out lots of complexities to the gain tuning. In order to deal with the aforementioned problems, proposed TD-ADRC controller is designed for the purpose of simpler controller structure compared to the existing TD-ADRC method. Consequently, the simple gain tuning rule of proposed TD-ADRC method is explored by the exhaustive robustness analysis, which is under the pillar of the rigorous stability proof. Therefore, the engineer can avoid the laborious and time-consuming gain tuning procedure at the field and have the comprehensive hold to the robustness metric of time-delay system. From another perspective, proposed method can robustly regulate the WESP output in the presence of time-delay and disturbances in simulation, whose model is based on the practical data from the step response test. By the way, the proposed TD-ADRC is universal for higher order system which has the potential to apply in other system. It is promising that the proposed method could apply to the coal-fired station in the mid-term. In the future, more detailed researches about the versatile of the proposed method for the higher order system should be taken in an effort.

It is interesting and challenging to develop advanced controller for industrial boiler-turbine units due to their genuine nonlinearities, serious couplings among state variables, physical constraints imposed on control inputs, and in particular, various types of unknown uncertainties/disturbances. In this paper, a nonlinear disturbance rejection control method is investigated in a composite design manner for an oil-fired drum-type boiler-turbine unit. A baseline exponentially stable feedback controller is first designed, and then a high-order sliding mode observer is utilized to estimate and thus to compensate unknown lumped disturbances. It is shown that the obtained results for the boiler-turbine unit can be extended to a wide class of nonlinear systems. More interestingly, the finite-time stability of the closed-loop systems can be rendered in several cases. Finally, some numerical simulation scenarios are conducted on the boiler-turbine unit to demonstrate the claimed control performance.

Flue gas desulfurization in thermal power plants is critical to the environmental and human health. Aiming for field application, this article presents a new predictor-and-observer-based active disturbance rejection control (ADRC) scheme for Wet Flue Gas Desulfurization (WFGD) process which is subject to long time delay and unknown disturbances. First, the working principles and control problems of WFGD system are illustrated, and the WFGD process model is identified based on the field experimental data. Then, a predictor-based ADRC approach is carefully devised to address its control issues. Specifically, a filtered smith predictor (FSP) is designed to obtain stable delay-free prediction output, and a reduced-order model-assisted extended state observer (MESO) is devised to estimate the “total disturbance” with less phase lag. Based on the equivalent control structure, its stability, including the perfect delay matching and delay mismatch cases, is theoretically investigated using the zero exclusion criterion. A simple “separation tuning procedure” is also provided, where the ADRC parameters are tuned directly based on the delay-free process, while the FSP filter is specified to achieve desired robustness. Finally, the validity of the proposed method is demonstrated by comparison simulations and field tests in a 1000MW thermal power unit. Note to Practitioners —This article focuses on sulfur dioxide (SO $_2$ ) control and proposes a new predictor-based ADRC approach for the WFGD process subject to long time delay and unknown disturbances. Taking the WFGD system as an example, it systematically describes the whole process of modeling, control system design and simulation & experimental verification, which can provide an intuitive reference for practitioners. The zero exclusion criterion is used to analyze the closed-loop stability and determine the stable delay range for a given predictor-based ADRC control system. A detailed tuning procedure is also provided, which can simplify the time-consuming tuning process and help engineers grasp the performance and robustness of the control system. It is evident that the proposed method can be extended to a general class of industrial time-delay systems, which facilitates the practitioner’s practice in general single-input-single-output time-delay system control. The field test results demonstrate the superiority of the proposed method and show promise for practical application in other systems.

This paper proposes a nonlinear model predictive control (NMPC) strategy based on a local model network (LMN) and a heuristic optimization method to solve the control problem for a nonlinear boiler–turbine unit. First, the LMN model of the boiler–turbine unit is identified by using a data-driven modeling method and converted into a time-varying global predictor. Then, the nonlinear constrained optimization problem for the predictive control is solved online by a specially designed immune genetic algorithm (IGA), which calculates the optimal control law at each sampling instant. By introducing an adaptive terminal cost in the objective function and utilizing local fictitious controllers to improve the initial population of IGA, the proposed NMPC can guarantee the system stability while the computational complexity is reduced since a shorter prediction horizon can be adopted. The effectiveness of the proposed NMPC is validated by simulations on a 500 MW coal-fired boiler–turbine unit.

The control of the boiler-turbine unit is important for its sustainable and robust operation in power plants, which faces great challenges due to the control unit’s serious nonlinearity, unmeasurable states, variable constraints, and unknown time-varying lumped disturbances. To address the above issues, this paper proposes a receding Galerkin optimal controller with a high-order sliding mode disturbance observer in a composite scheme, in which a high-order sliding mode disturbance observer is first employed to estimate the lumped disturbances based on a deviation form of the mathematical model of the boiler-turbine unit. Subsequently, under the hypothesis of state constraint, a receding Galerkin optimal controller is designed to compensate the lumped disturbances by embedding their estimates into the mathematically based predictive model at each sampling time instant. With the help of an interpolation polynomial, Gauss integration, and nonlinear solvers, an optimal control law is then obtained based on a Galerkin optimization algorithm. Consequently, disturbance rejection, target tracking, and constraint handling performance of a controlled closed-loop system are improved. Some simulation cases are conducted on a mathematical boiler-turbine unit model to demonstrate the effectiveness of the proposed method, which is supported by the quantitative result analysis, such as tracking and disturbance rejection performance indexes.