Control Engineering Practice最新文献

The creation of low-order dynamic models for complex industrial systems is complicated by disturbances and limited sensor accuracy. This work presents a system identification procedure that uses machine learning methods and process knowledge to robustly identify a low-order closed-loop model of a municipal solid waste (MSW) grate incineration plant. These types of plants are known for their strong disturbances coming from fuel composition variations. Using Bayesian Optimization, the algorithm both ranks and selects inputs from the available sensor data and chooses the model structure from a broad grey-box model class. This results in accurate low-order models that respect the known physics of the process. Multiple flue gas composition measurements are used as inputs to provide information on the fuel composition. The method is applied and validated using data of an industrial MSW incineration plant and compared against four established methods, of which the resulting models either show unphysical dynamic behaviour or have lower performance than the proposed method. Also on a numerical benchmark, the proposed method outperforms the alternative methods. The obtained MSW incinerator models give excellent predictions and confidence intervals for the steam capacity and intermediate quantities such as supply air flow and flue gas temperature. The identified continuous-time models are fully given, and their step-response dynamics are discussed. The models can be used to develop model-based coordinated unit control schemes for grate incineration plants. The presented method shows great potential for low-order grey-box identification of systems with partial knowledge of the model structure.

This research addresses the challenge of insufficient control margin caused by the coupling of multiple constraints in the cooperative precise reentry guidance of hypersonic vehicles. Drawing inspiration from the concept of spatiotemporal decoupling control, a rapid guidance strategy is developed to ensure precise handling of all constraints, including attack time, attack angle, and trajectory constraints. Initially, during the early phase of gliding flight, the adjustment of the heading angle is conceptualized as a single variable root-solving problem, in relation to the entrance width of the lateral azimuth error corridor. Subsequently, a lateral azimuth error corridor with adaptively narrowing entrance width, coupled with a Transformer network-based bank angle predictor, is incorporated to achieve precise fine-tuning of the heading angle under the soft constraint of velocity. In the later phase of gliding flight, the design of a cooperative guidance law under complex multiple constraints is transformed into a nonlinear rapid optimization problem of control commands. An enhanced beluga whale optimization suited to this guidance task is proposed. Finally, numerical simulations are carried out to validate the effectiveness of the proposed strategy under both nominal and uncertain conditions.

Consider a multi-agent system that must find an unknown number of static targets at unknown locations as quickly as possible. To estimate the number and positions of targets from noisy and sometimes missing measurements, we use a customized particle-based probability hypothesis density filter. Novel methods are introduced that select waypoints for the agents in a decoupled manner from taking measurements, which allows optimizing over waypoints arbitrarily far in the environment while taking as many measurements as necessary along the way. Optimization involves control cost, target refinement, and exploration of the environment. Measurements are taken either periodically, or only when they are expected to improve target detection, in an event-triggered manner. All this is done in 2D and 3D environments, for a single agent as well as for multiple homogeneous or heterogeneous agents, leading to a comprehensive framework for (Multi-Agent) Active target Search with Intermittent measurements – (MA)ASI. In simulations and real-life experiments involving a Parrot Mambo drone and a TurtleBot3 ground robot, the novel framework works better than baselines including lawnmowers, mutual-information-based methods, active search methods, and our earlier exploration-based techniques.

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.