Control Engineering Practice最新文献

Safe and efficient operations of thermal power plants become increasingly important, especially in compensating for electricity supply fluctuations in renewable energy. This paper proposes a method to build an operating zone model for safety and efficiency monitoring of power generation units in thermal power plants. The operating zone is a high-dimensional geometric space formed by all steady-state operating points in safe conditions of the coal flow rate, steam valve position, steam pressure and active power. Those operating points are obtained based on allowable variation ranges of process variables and mechanistic models describing the relationships among process variables. The main technical challenge is how to measure uncertainties of mechanistic models estimated from historical data disturbed by noticeable measurement noise. Bayesian theory and goodness-of-fit tests are exploited to tackle this challenge by yielding probability density functions of model parameters. A performance index is defined based on the operating zone model to assess the safety and efficiency of power generation units. Industrial case studies at a large-scale thermal power plant are provided to illustrate the effectiveness of the proposed method.

Despite the diverse potential applications of networked rotating platforms (NRP), the problem of robust cooperative control of NRP has been rarely investigated. In this paper, a robust synchronized control solution with an explicit parameter-tuning mechanism on comprehensive performance is proposed for NRP. The main features of our solution are two-fold: (a) the historical nominal commands (HNC) of neighbors are used to actively enhance the system cohesiveness performance, (b) an uncertainty and disturbance estimator (UDE) is incorporated into the controller to actively reject disturbances. The idea behind the design is to force the actual error dynamics of each controlled platform to approximate an ideal model equation. As a direct advantage of this design, the performance regulation is reduced to the tuning of the parameters of ideal model and the parameters determining the approximation accuracy. A parameter condition is derived under which the system stability is robust to the actively introduced delay. The relationship between the ultimate bounds of tracking errors and the parameter of UDE is characterized using an inequality. An experimental platform is constructed to verify performance of the controller using several Quanser AEROs and laser pointers. Simulation and experimental results have demonstrated: (a) the effectiveness of the stability condition; (b) the convenience and efficiency in regulating the system performance. Using the proposed controller, the projected points maneuver as an organic whole with excellent cohesiveness and robustness.

The horizontal hydraulic flight motion simulator (HHFMS) is preferred in the situations of large power and high dynamic for the hardware-in-the-loop simulation of the aerospace field. However, multiple modeling uncertainties prevent HHFMS from receiving accurate simulation of aircraft attitude. Particularly, due to suffering from serious and complex disturbance, pitch frame of HHFMS is more difficult to achieve high-precision control compared with yaw frame and roll frame. Aiming at this issue, through defining the disturbance only related to states of pitch frame itself as internal disturbance and the one also related to states of other frames as external disturbance, in view of theory and application, internal and external disturbances rejection control (IEDRC) was investigated for pitch frame. In the proposed control method, by means of backstepping, nonlinear disturbance observers (NDOs) were designed to handle internal disturbance from parametric uncertainties involving hydraulic flow, as well as friction torque and gravity torque when yaw frame is at zero position and load is replaced. Additionally, robust integral of the sign of the error (RISE) was assigned to suppress external disturbance composed of coupling torque, as well as the variations of inertia torque and gravity torque caused by position change of yaw frame. Here, RISE can assist NDO to overcome its limitation in dealing with the time-varying disturbance, and NDO can alleviate chattering risk resulted from the sign function in RISE. The two complement each other. As a result, via rejecting internal and external disturbances caused by hydraulic parameters, load replacement and simultaneous movement of three frames, IEDRC can not only guarantee an excellent control performance, but also avoid the huge workload spent on the acquisition of model parameters. Finally, comparative experiments including a few Cases were conducted, which demonstrates the overall performance of the developed control method.

The ongoing advancement of industry and technology has led industrial control systems to evolve towards integration. System structures become increasingly complex, which renders them increasingly vulnerable to external attacks. Due to their clandestine and destructive character, covert attacks pose a significant threat to the secure operation of nuclear power unit control systems. In order to optimize the performance of control systems for nuclear power units, it is important to study the damage process caused by covert attacks on these systems. Facing the problem of obtaining high-precision estimation models of attack targets for covert attacks, this paper proposes a model estimation method based on long and short-term memory (LSTM) neural network and symbiotic organisms search (SOS) algorithm, which takes the feedback controller output and input signals of the attacking target as the dataset of the LSTM neural network, and optimizes the network parameters of the LSTM neural network using SOS algorithm to improve the accuracy of the model, and designs the covert attacker by obtaining the estimation model of the attacked area through training. The root mean square error of the estimation model for the primary loop of the nuclear power unit has been verified by comparative experiments to be reduced by at least 93.59%, 96.52%, and 91.11%, respectively, compared with the other methods. Loop experiment results concerning the covert attack for the primary loop of nuclear power unit illustrate that this attack method successfully meets the predefined objectives while maintaining high levels of stealthiness.

This paper addresses the challenge of fault propagation in industrial facilities, where a fault in one process variable can lead to cascading faults in other variables. As a result, the propagation of alarms corresponding to these faulty variables occurs, leading operators to potentially receive an excessive number of alarm notifications that could significantly impact their decision-making capabilities. To address this issue, a systematic method is proposed to investigate potential fault propagation paths to provide decision support in response to alarm notifications, in order to minimize industrial process failures. The contributions of this paper are twofold. Firstly, it involves enhanced dependency analysis that captures dependent alarms and identifies both weak and strong dependencies among alarm variables using historical alarm and event (A&E) logs that are generated by industrial control systems. Secondly, it offers comprehensive visualization of fault propagation in response to single and multiple alarms, including extracting crucial timing information, identifying the shortest, longest, and critical paths, and determining effective operator actions. The proposed method is designed to enhance process operation and provide essential decision support for industrial operators. The effectiveness of the proposed approach is validated through a case study using real industrial data.

This work presents the design and the corresponding stability analysis of a robust backstepping controller for robot manipulators driven by brushless DC motors. The overall stability of the mechanical and electrical subsystems is validated via Lyapunov based arguments. The proposed methodology achieves global practical tracking (i.e., globally uniformly ultimate boundedness) of the desired joint level trajectories despite the presence of uncertainties associated with the dynamical parameters of the mechanical and the electrical actuation system. Experimental studies performed on an in house built 2 link robotic device actuated via brushless DC motors are presented to illustrate the performance and feasibility of the proposed method.

Solar-grade silicon production is a critical component in the solar energy sector, with fluidized-bed reactors (FBRs) emerging as a promising alternative offering superior energy efficiency and operational advantages over conventional technologies. However, the operational complexity of FBR systems poses significant challenges to effectively controlling their operation at optimal conditions. This study introduces a predictive modeling framework for silicon production in fluidized bed reactors to characterize both the particle size distribution of the product and powder loss. Two different flow regime modeling approaches are explored to describe the silane pyrolysis reaction and illustrate how the deposition rate affects particle growth and powder loss. A discrete population balance equation is employed to estimate the particle size distribution as a function of the deposition rate. Subsequently, a robust nonlinear model predictive control (RNMPC) approach is utilized to regulate the system at the desired operating conditions, stabilize the product particle size distribution, and minimize powder loss. Detailed open-loop and closed-loop simulation studies demonstrate the successful integration of RNMPC and the proposed predictive modeling approach.

Soft sensors, as a significant paradigm for industrial intelligence, are extensively utilized in large-scale industrial integration systems to estimate the pivotal quality variables. For deep neural network-based soft sensors, redundancy in input variables and network structure has emerged as one of the most important challenges. In this article, a sparse regularized soft sensor based on the gated recurrent unit (GRU) and self-interpretation dual nonnegative garrote is proposed. Initially, a proficiently trained GRU network is established as the pre-trained model, followed by the design of a set of self-interpretation factors based on the mean influence value of different input variables. Secondly, the contraction coefficients of the nonnegative garrote are sequentially incorporated into the GRU input and hidden layer weight matrices. Meanwhile, the self-interpretation factors are introduced into the constraints of the nonnegative garrote algorithm to guide it to adaptively adjust the applied penalty strength based on the relative importance of different input variables. The strategy integrates variable selection with the model training process to sparsify the network structure and provide self-interpretable variable selection results. Finally, the performance of the developed approach is verified through a practical application in power plant desulfurization systems. The case studies demonstrate that the developed approach for soft sensor modeling outperforms other existing methods and shows promising application prospects. In addition, the validity of the self-interpretable variable selection results is verified via the known mechanism analysis and expert experience.

Remaining useful life (RUL) prediction is critical to formulating appropriate maintenance strategies for machinery health management and is playing a vital role in the field of predictive maintenance. Limited by the time-varying operational conditions, traditional RUL prediction models trained on some run-to-failure (RTF) datasets are unlikely to be generalized to a new degradation process. To enhance the generalizability, recent studies have focused on the development of deep domain adaptation methods for RUL prediction, which mainly align the global temporal features across the source and target domains, resulting in imprecise predictions under time-varying operational conditions. In addition, existing RUL prediction methods are lacking in clear physical significance and interpretability. To address the above-mentioned issues, an operational condition attention (OCA) subnetwork is constructed to eliminate the entanglement between the time-varying operational conditions and monitoring data. Adversarial-based domain adaptation (ABDA) and distance-based domain adaptation (DBDA) methods were utilized respectively to reduce the distribution discrepancy of the temporal features. In this way, two novel domain adaption methods were proposed for RUL prediction with time-varying operational conditions. The comprehensive experiments were conducted on aero-engines to validate the proposed methods. Owing to the explicit modeling of the influence mechanism between the operational conditions and monitoring data, the proposed methods exhibit improved performance as well as higher prediction accuracy than traditional deep domain adaption methods while being certainly interpretable.

The orbital game theory is a fundamental technology for the cleanup of space debris to improve the safety of useful spacecraft in future, thus, this work develops a decision-making method by reinforcement learning technology to implement the pursuit-evasion game in elliptical orbits. The linearized Tschauner-Hempel equation describes the spacecraft's motion and the problem is formulated by game theory. Subsequently, an impulsive maneuvering model in a complete three-dimensional elliptical orbit is established. Then an algorithm based on deep deterministic policy gradient is designed to solve the optimal strategy for the pursuit-evasion game. For the successful decision of the pursuer, an extensive reward function is designed and improved considering the shortest time, optimal fuel, and collision avoidance. Finally, numerical simulations of a pursuit-evasion mission are performed to demonstrate the effectiveness and superiority of the proposed decision-making algorithm. The game success rate of the algorithm against targets with different maneuvering abilities is verified, which implies that the algorithm can be applied in extended scenarios.