Control Engineering Practice最新文献

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.

To enhance vehicle stability and safety, the four-wheel independent steer-by-wire (4WISBW) system has garnered significant attention. However, the characteristics of corner modules, including model parameters uncertainty and disturbance torque, directly contribute to the deterioration of dynamic response in tracking control. And the differences in the characteristics leading to reduced synchronization performance in the 4WISBW system and hindering effective coordination. To enhance the synchronization and tracking control performance of the 4WISBW system, a novel control strategy, coupled with the fictitious master-generalized mean deviation coupling structure (FMGMDCS), is proposed. Firstly, the corner module dynamic model and the vehicle dynamic model are established. Subsequently, the impact of the differences in the characteristics on the system's tracking and synchronization control is analyzed. Next, the FMGMDCS and the angle synchronization controller based on a new reaching law sliding mode control (NRLSMC) are proposed to compensate for synchronization errors in the 4WISBW system caused by the differences in the characteristics of corner modules. Finally, a radial basis function neural network fast terminal sliding mode control (RBF-FTSMC) steering angle tracking controller is designed to enhance the tracking performance of corner modules. Simulation and experimental results indicate that the proposed control strategy can effectively solve the synchronization problem of the 4WISBW system and improve the system's tracking performance.

The efficient antiskid braking control of aircraft is achieved by accurately tracking the optimal slip ratio. However, aircraft antiskid braking systems are subject to many parametric uncertainties and uncertain disturbances, and the limited sensor signals make it more difficult to design a high-performance antiskid braking system controller. To address this issue, an observer-based adaptive robust aircraft antiskid braking system controller with disturbance compensation is proposed to enhance the tracking performance and disturbance rejection of aircraft antiskid braking system. The proposed controller effectively integrates parameter identification, adaptive control, and extended state observer using the backstepping method. Parametric uncertainties and fast time-varying brake torque conversion coefficient are handled by adaptive law and least squares parameter identification method, respectively. After that, the remaining parametric uncertainties, parameter identification errors, and uncertain disturbances are observed integrally by constructing extended state observer and compensated in a feedforward way. Another feature of the designed controller is that the dynamics of the hydraulic system are considered, and the disturbances of the hydraulic system are also observed and compensated with extended state observer, thus further improving tracking accuracy. Since the burden of extended state observer is greatly reduced by adaptive law and parameter identification, the proposed controller can effectively avoid high-gain feedback while theoretically guaranteeing that the tracking error is bounded in the presence of time-variant uncertainties. The effectiveness of the proposed controller is proved by several sets of simulation tests and brake testing platform experiments.

Formation generation with connectivity maintenance under efficiency restrictions for a group of autonomous vehicles is a challenging problem. By planning trajectories offline, the vehicles can follow optimized paths, resulting in improved efficiency in terms of time, energy, and resource utilization. This paper introduces a coherent approach that leverages evolutionary computing, notably a differential evolutionary algorithm, along with B-spline parametrizations, to effectively coordinate multiple indoor nanodrones. Off-line trajectories for both the leader and followers are designed to enforce multiple constraints (i.e., position, velocity, angles, thrust, angular velocity, waypoint passing, obstacle avoidance). The proposed approach accommodates intricate maneuvers such as formation switching and obstacle avoidance, facilitated by a knot refinement procedure that minimizes conservatism in constraint enforcement. The theoretical results are validated in both simulation and experiments.

The nonlinear external disturbances and unmodeled dynamics characteristics have crucial impacts on trajectory tracking control accuracy of a four-wheel mobile robot (FWMR) under complicated working conditions. In this work, an adaptive trajectory tracking controller is designed for the FWMR to achieve the prescribed-prediction performance. On the basis of establishing the FWMR’s dynamics equations, an enhanced prescribed performance function (EPPF) is constructed to restrain the tracking errors of the FWMR within a certain range without requiring the exact initial conditions, thus guaranteeing the transient performance of the control system. Then, an optimal-predictive control (OPC) approach is presented to fulfill the asymptotic stability of the tracking errors of the FWMR. Specifically, the radial basis function neural network (RBFNN) incorporating a minimum parameter learning approach that are implanted into the expected controller is designed to attenuate the nonlinear external disturbances and the unmodeled dynamics of the FWMR. Lastly, comparative simulation investigations are carried out to illustrate the superiority of the proposed EPPF-OPC controller, and moreover, the comparative experiments are further performed to validate the practical effectiveness of the EPPF-OPC controller based on a self-established robot operating system (ROS) test platform of the FWMR.