Control Engineering Practice最新文献

Process monitoring through automated fault detection and diagnosis (FDD) plays a crucial role in maintaining a productive and reliable chemical process system. Developments in AI and machine learning have boosted FDD model performances especially with deep learning methods. However, these neural network models are considered black-boxes where the reasoning behind a diagnosis is unclear, preventing industrial adoption. Therefore, in this study, an interpretable neural network model is proposed for FDD in chemical processes. This framework detects and diagnoses faults based on the propagation paths of different faults which are embedded into the architecture through graph convolutional networks. A mechanism for interpreting the node activations which represent process variables is developed for decision verification. The proposed method is evaluated on the benchmark Tennessee Eastman Process where it achieves a 93.56% accuracy on selected faults.

This paper proposes an innovative next-generation wheel loader derived from a production series electric wheel loader by John Deere. The proposed wheel loader features a series hybrid electric powertrain with an energy storage system and an electrically-boosted turbocharged diesel engine. A detailed system design inclusive of powertrain configuration and control development including a novel heuristics-based vehicle power management is presented. A simulation model was created to evaluate the potential fuel savings of the proposed wheel loader, revealing a fuel saving potential of over 10% when compared to the baseline configuration. Subsequent in-field testing of an actual demo wheel loader verified its ability to achieve over 10% fuel savings, thereby confirming the simulation outcomes, demonstrating the promise of the proposed hybrid powertrain, and validating the efficacy of the control system developed in this research.

Disturbance observer-based control is widely employed in control systems. It demonstrates the ability to estimate intricate disturbances for subsequent measurement, thereby enabling their compensation in the output of existing controllers. Nevertheless, disturbance observer-based control shows limited robustness when utilizing a fixed nominal model. Simultaneously, the fixed time constant of the Q-filter prevents the securement of filtering performance across various flight speed areas. This study aims to enhance robustness in both open- and closed-loop disturbance observer-based control by carefully selecting the Q-filter time constant through a comprehensive transfer function analysis. The transfer function characterizing the quadrotor unmanned aerial vehicle is derived from highly reliable dynamic modeling obtained through wind-tunnel testing, with the nominal model computed by the flight speed. We propose variable disturbance observer-based control by using a nominal model according to flight speed and selecting the Q-filter of disturbance observer-based control by considering the characteristics of the dynamic model, disturbance, and sensor noise. Numerical simulations and flight tests are conducted to compare the controller’s performance, focusing specifically on attitude tracking during a round-trip flight with a slung load.

Exact and trusty prediction of pollutant emissions is pivotal for optimal combustion control in biomass cogeneration systems, which possess multiple variables, high-volume data streams, and dynamic characteristics. Aiming at the multivariate dynamic systems, this paper extends a classical fast relevance vector regression (FRVR) algorithm into a multivariate form to accomplish synchronous multi-pollutant prediction. Meanwhile, a flexible and effective online training strategy is proposed to solve the problems of low accuracy of multi-step prediction and lack of dynamic updating capability. First, the given dataset is divided utilizing the k-means clustering method to enhance the clustering of similar features and expedite the prediction process. Then, the classical FRVR algorithm is extended into a multiple-output form, enabling the simultaneous prediction of multiple pollutant emissions. Moreover, the incremental learning method is introduced into the proposed multivariate FRVR model to improve its dynamic performance and online learning ability. Finally, the proposed method’s effectiveness is verified through a biomass cogeneration systems case. Experimental findings fully illustrate that the proposed method provides the lower RMSE and MAE while runtime decreases by 50% and $R^2$ reaches 96%. The proposed method significantly outperforms others, showing excellent potential in the pollutant prediction field.

This article introduces an adaptive control for high-rise building systems afflicted by input backlash. In this approach, the input backlash is deconstructed into an expected control and a bounded but unmeasurable nonlinear errors, and an adaptive term is employed to address the nonlinear error, resulting in the inclusion of a sign function term in the controller, which leads to the chattering phenomenon. In order to avoid it and enhance control effects, adaptive inverse dynamics of backlash are introduced, and a new adaptive inverse control strategy is formulated to achieve vibration control in high-rise buildings. Under the proposed control strategy, a suitable Lyapunov function is chosen for stability analysis, affirming that the system is uniformly ultimately bounded. Finally, results from both numerical simulations and experiments substantiate the efficacy of the proposed scheme.

A differential drive catamaran unmanned surface vehicle tracking control in presence of external disturbances and uncertainties is addressed. A super-twisting control considering a single adaptive gain is adopted, where the number of tuning parameters of this control gain is reduced compared with standard adaptive super-twisting approaches. Furthermore, the gain exhibits smooth dynamics compared with controls using discontinuous functions signals. An analysis of the closed loop stability is provided using a Lyapunov approach. The proposed adaptive super-twisting approach is designed to drive the vessel along time-varying trajectories in presence of perturbations, ensuring robustness, and practical finite-time convergence. Numerical simulations, and real-time experimental tests using a prototype, illustrate the control performance under payload uncertainty and external perturbations. For further demonstration, a comparison with some of the existing versions of the super-twisting control included one with variable gain is provided.

Iterative learning control (ILC) and repetitive control (RC) can lead to high performance by attenuating repeating disturbances perfectly, yet these approaches may amplify non-repeating disturbances. The aim of this paper is to achieve both perfect, fast attenuation of repeating disturbances and limited amplification of non-repeating disturbances. This is achieved by including a deadzone nonlinearity in the learning filter, which distinguishes disturbances based on their different amplitudes to apply different learning gains. Convergence conditions for nonlinear ILC and RC are developed, which are used in combination with system measurements in a comprehensive design procedure. Experimental implementation demonstrates fast learning and small errors.

Used water treatment plays a pivotal role in advancing environmental sustainability. Economic model predictive control holds the promise of enhancing the overall operational performance of the water treatment facilities. In this study, we propose a data-driven economic predictive control approach within the Koopman modeling framework. First, we propose a deep learning-enabled input–output Koopman modeling approach, which predicts the overall economic operational cost of the wastewater treatment process based on input data and available output measurements that are directly linked to the operational costs. Subsequently, by leveraging this learned input–output Koopman model, a convex economic predictive control scheme is developed. The resulting predictive control problem can be efficiently solved by leveraging quadratic programming solvers, and complex non-convex optimization problems are bypassed. The proposed method is applied to a benchmark wastewater treatment process. The proposed method significantly improves the overall economic operational performance of the water treatment process. Additionally, the computational efficiency of the proposed method is significantly enhanced as compared to benchmark control solutions.

This paper introduces an innovative control strategy for a dual active bridges DC–DC converter employed to regulate voltage levels between two feeders in a DC microgrid. The controller is specifically designed to regulate the output voltage at a predetermined reference value and to ensure a zero mean value for both primary and secondary currents of the high-frequency transformer, regardless of any imbalance in one of the active bridges. This is particularly important in the face of potential imbalances in one of the active bridges resulting from construction disparities in converters or variations in conduction resistances in power transistors. Given the nonlinear nature of the converter, the controller is designed using feedback linearization. The output is selected with a relative degree equal to the system order, resulting in a stable controller exhibiting good dynamic performance under scenarios such as reference changes, linear load fluctuations, and the integration of constant power loads. The performance of the proposed control strategy is validated through both simulation and experimental results.

In this paper, a novel hierarchical fusion based on maximum correntropy decentralized information filtering and covariance intersection has been proposed for clustered WSNs. First, the maximum correntropy extended information filtering is derived in a decentralized manner, which combines the advantages of the insensitivity to the impulsive noise from maximum correntropy with the stability of the decentralized algorithms. Then, the fusion center with an MCC-based covariance intersection algorithm is designed to fuse the local estimation from different clusters and retain the estimation consistency. Moreover, both simulations and physical experiments in the mobile target tracking application prove that the proposed method is capable of achieving a more precise estimation than the local estimators and the decentralized extended information filtering against impulsive noise and random link failures.