This article presents a modified active-disturbance-rejection control (ADRC) combined with a sliding mode control (SMC) regarding the tracking control problems for plants with unmatched uncertainty. The proposed modified active-disturbance-rejection control with sliding mode (ADRC-SM) employs a reduced-order extended state observer (ESO) for estimating various uncertainties of system in time, including unmatched and matched uncertainties. Meanwhile, a novel reaching law of SMC was designed by using the cycloid function as the main controller of ADRC, which ensures the robustness of the uncertain system. Due to the reduced-order ESO tracking and compensating for various uncertainties in the system as a total disturbance, the upper bound of the disturbance in the SMC is relaxed. The gain coefficient of the reaching law only needs to be designed to be larger than the limit of the lumped disturbance; thus, the chattering problem is greatly reduced. The designed new reaching law of the cycloid function shortens the time for the system state’s convergence to the sliding mode’s surface. The cycloid function replaces the switching function in the traditional reaching law, making the actual control input continuous and shortening the approach time. Compared with traditional ADRC-SM, the use of multiple ESOs or intelligent algorithms to approximate plant parameters can be avoided, the design is simplified, its robustness is enhanced, computational costs are reduced, and the convergence time is reduced. The controlled object with unmatched uncertainty is transformed into a system with matched uncertainty using state-space transformation, which reduces the complexity of the controller’s design. In addition, the stability analysis of the closed-loop system is carried out based on the Lyapunov method. Simulations and experiments verify that the modified ADRC-SM has the merits of fast response, small overshoot, small steady-state error, strong anti-interference competence, and high control accuracy.

The paper studies the control problem for nonlinear uncertain systems with multiple channel uncertainties, and presents the manifold of a class of second-order systems including unmatched and matched disturbances, which is mainly found in flexible components. The system model is first transformed into a Brunovsky form, where the multiple uncertainties in the system are lumped as one term-equivalent total effects. A backstepping united with a reduced-order ESO control design is proposed, where the intermediate variables need not be measurable, and only the system output is required for the control implementation. Finally, the proposed control structure is equivalent to PID, and its working mechanism is revealed. The use of multiple ESO or iterative learners can be avoided to match plant parameters, leading to reduced computational costs, simpler parameter tuning, and improved convergence as compared to traditional control methods. Finally, the simulation and experimental results illustrate the effectiveness of the proposed method.

The positioning results of terrain matching in flat terrain areas will significantly deteriorate due to the influence of terrain nonlinearity and multibeam measurement noise. To tackle this problem, this study presents the Pulse-Coupled Neural Network (PCNN), which has been effectively utilized for image denoising. The interconnection of surface terrain data nodes is achieved through PCNN ignition, which serves to alleviate the reduction in terrain similarity caused by measurement error. This enables the efficient selection of terrain data, ensuring that points with high measurement accuracy are preserved for terrain matching and positioning operations. The simulation results illustrate that the suggested methodology effectively removes terrain data points with low measurement accuracy, thereby improving the performance of terrain matching and positioning.

Existing traditional expansion state observers exhibit good tracking performance for constant and low-frequency disturbances. However, their ability to track non-constant disturbances such as ramp and high-frequency harmonics is inadequate. This paper proposes an extended state observer design method based on the internal model principle. This method achieves precise tracking of non-constant disturbances in the system, effectively addressing the issue of disturbance estimation errors in conventional expansion state observers. When applied to control systems, this approach significantly mitigates or suppresses system vibrations caused by non-constant disturbances, thereby enhancing control accuracy. Furthermore, it demonstrates the stability of the controlled system and the active disturbance rejection controller parameters over a wide range of variations. Simulation results indicate that the ADRC controller based on the proposed observer in this paper offers notable advantages, including high tracking accuracy, strong disturbance rejection capability, and good stability, leading to commendable control performance.

The intermittent and volatile nature of wind power generation necessitates thermal power units to provide deep-peak shaving and fast frequency regulation services within the same grid. However, the current proportional integral differential (PID) control performance in the process control foundation of thermal power units falls short of meeting these requirements. Despite the lack of comprehensive research on engineering fastest control (EFC), this article aims to address this gap by studying and analyzing the mechanism and physical defects of the fast tracking filter (FTF), the output tracking input characteristics of EFTF reconstructed by FTF engineering, and the control performance of the constructed EFC. Through mathematical calculation analysis, simulation experiments, and real-world engineering practice, it is concluded that EFC surpasses the limitations of PID control performance and effectively enhances feedback control performance. As a result, it is deemed suitable for process control in thermal power units.