Control Engineering Practice最新文献

Interconnected structures are commonly found in process networks. In this paper, an output consensus framework is proposed for a class of continuous interconnected linear heterogeneous systems subject to disturbances and constraints. The distributed output consensus control strategy is developed by combining integral sliding mode control with model predictive control. The integral sliding mode control is designed to eliminate a class of matched disturbances. The model predictive control plays two main roles: On the one hand, it drives the system states to track the steady state values so as to achieve output consensus; on the other hand, it helps to deal with interconnections and constraints existing in systems. In the meantime, a distributed iterative algorithm is designed to acquire the system steady states. A simulation example and an experiment relating to control of systems of interconnected CSTRs are presented to validate the effectiveness and superiority of the proposed method.

Restrictions arising from the limited training data and privacy preservation make large-scale lithium-ion battery degradation trajectory prediction challenging. In this study, a novel heterogeneous federated transfer learning with knowledge distillation approach is proposed for lithium-ion battery lifetime prediction with scarce training data and privacy concerns. The approach enables each device in large-scale decentralized system to not only own its private data, but also a unique network designed based on its resource constraints. Specifically, the central server first designs its unique network according to the resource constraints of each device, and trains the network on publicly available data with entire degradation cycles, thus avoiding the high cost of collecting abundant degradation cycles. Then, the trained model is transferred to each device for collaborative training, in which the knowledge of heterogeneous models extracted by knowledge distillation is used for communication between the isolated devices, rather than the parameters in conventional federated learning. Extensive real-world datasets are leveraged to verify the effectiveness of the proposed approach. The comparison results demonstrate that the proposed method outperforms seven benchmarks. An ablation study indicates that the approach can achieve satisfactory battery residual life prediction while preserving privacy.

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.