Hasil untuk "Control engineering systems. Automatic machinery (General)"

Yandong Ma, Deli Liu, Kun Ma et al.

Jaime Palomo, Rafael Romero, María Victoria Cuevas et al.

Este trabajo aborda la importancia de optimizar el uso del agua en la agricultura, dado el aumento de la escasez hídrica y la necesidad de estrategias sostenibles como el riego deficitario controlado. Propone un sistema automatizado basado en aprendizaje automático para estimar los niveles de estrés hídrico en olivares de la variedad 'Arbequina'. Este método utiliza mediciones de presión de turgencia foliar y datos meteorológicos recolectados entre 2014 y 2019, y emplea técnicas como el análisis de componentes principales y el análisis discriminante lineal para clasificar niveles de estrés hídrico. El sistema busca facilitar la integración de estas herramientas en estrategias comerciales de riego, superando las limitaciones de métodos invasivos o dependientes de hardware costoso.

Victor Geadah, Juncal Arbelaiz, Harrison Ritz et al.

We consider the joint problem of system identification and inverse optimal control for discrete-time stochastic Linear Quadratic Regulators. We analyze finite and infinite time horizons in a partially observed setting, where the state is observed noisily. To recover closed-loop system parameters, we develop inference methods based on probabilistic state-space model (SSM) techniques. First, we show that the system parameters exhibit non-identifiability in the infinite-horizon from closed-loop measurements, and we provide exact and numerical methods to disentangle the parameters. Second, to improve parameter identifiability, we show that we can further enhance recovery by either (1) incorporating additional partial measurements of the control signals or (2) moving to the finite-horizon setting. We further illustrate the performance of our methodology through numerical examples.

G. Sudha, C. Tharini

In Wireless Sensor Networks (WSN), transmitting an uncompressed image consumes more energy than a compressed image, and it is, therefore, the prime requirement to establish energy-aware compression methods to extend the life of the sensor node, and ultimately the network as a whole. This work suggests an image compression algorithm with a low degree of complexity for WSNs in structural health monitoring applications. This algorithm represents a pruning approach to a Discrete Cosine Transform approximation transform in which the transformation matrix is modified to reduce the series of steps and the compression ratio achieved is better compared to the actual image which makes it easy for the data storage. Because of the reduction in the number of data bits, it enhances the lifetime of the network by reducing the number of node failures caused by resource scarcity. The implementation is tested by capturing real-time images of concrete walls in buildings using Raspberry Pi3B + WSN gateway fitted with camera modules. The scheme is also investigated in terms of a variety of parameters like Peak Signal to Noise Ratio, Mean Square Error, Structural SIMilarity Index and Compression Ratio. This technique achieves the best arbitration of energy consumption and image quality.

Shiqing Wei, Prashanth Krishnamurthy, Farshad Khorrami

Designing control inputs that satisfy safety requirements is crucial in safety-critical nonlinear control, and this task becomes particularly challenging when full-state measurements are unavailable. In this work, we address the problem of synthesizing safe and stable control for control-affine systems via output feedback (using an observer) while reducing the estimation error of the observer. To achieve this, we adapt control Lyapunov function (CLF) and control barrier function (CBF) techniques to the output feedback setting. Building upon the existing CLF-CBF-QP (Quadratic Program) and CBF-QP frameworks, we formulate two confidence-aware optimization problems and establish the Lipschitz continuity of the obtained solutions. To validate our approach, we conduct simulation studies on two illustrative examples. The simulation studies indicate both improvements in the observer's estimation accuracy and the fulfillment of safety and control requirements.

Jan C. Schulze, Alexander Mitsos

We use Koopman theory for data-driven model reduction of nonlinear dynamical systems with controls. We propose generic model structures combining delay-coordinate encoding of measurements and full-state decoding to integrate reduced Koopman modeling and state estimation. We present a deep-learning approach to train the proposed models. A case study demonstrates that our approach provides accurate control models and enables real-time capable nonlinear model predictive control of a high-purity cryogenic distillation column.

Xinxin Zhang, S. Hassan HosseinNia

Reset controllers have demonstrated their effectiveness in enhancing performance in precision motion systems. To further exploiting the potential of reset controllers, this study introduces a parallel-partial reset control structure. Frequency response analysis is effective for the design and fine-tuning of controllers in industries. However, conducting frequency response analysis for reset control systems poses challenges due to their nonlinearities. We develop frequency response analysis methods for both the open-loop and closed-loop parallel-partial reset systems. Simulation results validate the accuracy of the analysis methods, showcasing precision enhancements exceeding 100% compared to the traditional describing function method. Furthermore, we design a parallel-partial reset controller within the Proportional-Integral-Derivative (PID) control structure for a mass-spring-damper system. The frequency response analysis of the designed system indicates that, while maintaining the same bandwidth and phase margin of the first-order harmonics, the new system exhibits lower magnitudes of higher-order harmonics, compared to the traditional reset system. Moreover, simulation results demonstrate that the new system achieves lower overshoot and quicker settling time compared to both the traditional reset and linear systems.

Rihard Karba, Juš Kocijan, Tadej Bajd et al.

Hafidh Djouadi, Kamel Ouari, Youcef Belkhier et al.

Abstract Permanent magnet synchronous motors (PMSM) have become prevalent in industry and play an essential role in managing industrial processes, automation systems, and renewable energy sources due to their superior efficiency, torque, and power density. However, because it operates like a non‐linear system with quick dynamics, variable parameters during operation, and unknown disturbances, PMSM presents challenges for machine control. Non‐linear controls are required to account for the non‐linearities of the permanent magnet synchronous machine. Recently, predictive control techniques for non‐linear multi‐variable systems have gained popularity. In this work, a novel approach to robust non‐linear generalized predictive control (RNGPC) has been developed for PMSM, with the aim of tracking the reference speed while maintaining minimum reactive power, robustness to external disturbances, and parameter uncertainties. A new finite horizon cost function is integrated, with an integral action introduced in the control law. The main advantage of this technique is that it does not require the measurement and observation of external disturbance as well as parametric uncertainties. The control strategy method has been tested in the MATLAB/Simulink environment with various operating conditions. The results showed good robustness against parameter changes and ensured fast convergence.

Fei Han, Jianhua Liu, Jiahui Li et al.

This paper studies the distributed [Formula: see text]-consensus control problem for a class of multi-rate multi-agent systems with fading measurements. A multi-rate sampling strategy is adopted to be more in line with actual need and the channels between each agent and its sensor always fade non-identically. The multi-rate system is transformed into a single-rate system via the lifting technique. For the purpose of reducing the transmission burden, a dynamic event-triggered mechanism is utilized to determine whether the agent's information is allowed to transmit to its neighbours. This paper aims to design an observer-based event-triggered controller for each agent to achieve the [Formula: see text]-consensus control performance constraint. With the help of the Lyapuonv stability theory, sufficient conditions are obtained that can ensure the desired control performance for the resulting closed-loop systems, and then the desired gain matrices are calculated by solving the linear matrix inequality. Finally, a numerical simulation example is given to demonstrate the effectiveness of the distributed event-triggered consensus control scheme.

ZHUO Haijun, XIAO Xin, HU Zhenfan et al.

To deal with the obstacles in the automatic tamping system of D09-32 type tamping vehicles, such as the complicated braking distance prediction algorithm of satellite car and the challenges in engineering implementation, this paper proposes a braking distance prediction method based on BP neural network. By leveraging the data of real-time speed and position of the satellite car, the braking distance of the satellite car at the current speed and position is predicted by a BP neural network model representing the relationship between car's braking speed and position and its corresponding braking distance. The key model of this prediction method was simulated using Matlab software and the results were compared with the sample values. The comparison results show that the proposed braking distance prediction method based on BP neural network can satisfactorily predict the satellite car's braking distance. Compared with the currently prevailing linear fitting prediction method, the prediction error of this method is reduced by 48.4% on average, which can effectively improve the precision of automatic tamping, thus raising applicability and robustness of the tamping vehicles.

Houssine Zine, El Mehdi Lotfi, Delfim F. M. Torres et al.

Using the recent weighted generalized fractional order operators of Hattaf, a general fractional optimal control problem without constraints on the values of the control functions is formulated and a corresponding (weak) version of Pontryagin's maximum principle is proved. As corollaries, necessary optimality conditions for Caputo-Fabrizio, Atangana-Baleanu and weighted Atangana-Baleanu fractional dynamic optimization problems are trivially obtained. As an application, the weighted generalized fractional problem of the calculus of variations is investigated and a new more general fractional Euler-Lagrange equation is given.

Patrick Flüs, Olaf Stursberg

This paper introduces a method to control a class of jump Markov linear systems with uncertain initialization of the continuous state and affected by disturbances. Both types of uncertainties are modeled as stochastic processes with arbitrarily chosen probability distributions, for which however, the expected values and (co-)variances are known. The paper elaborates on the control task of steering the uncertain system into a target set by use of continuous controls, while chance constraints have to be satisfied for all possible state sequences of the Markov chain. The proposed approach uses a stochastic model predictive control approach on moving finite-time horizons with tailored constraints to achieve the control goal with prescribed confidence. Key steps of the procedure are (i) to over-approximate probabilistic reachable sets by use of the Chebyshev inequality, and (ii) to embed a tightened version of the original constraints into the optimization problem, in order to obtain a control strategy satisfying the specifications. Convergence of the probabilistic reachable sets is attained by suitable bounding of the state covariance matrices for arbitrary Markov chain sequences. The paper presents the main steps of the solution approach, discusses its properties, and illustrates the principle for a numeric example.

Tatyana S. Katermina, Maksim V. Sliva

Chenchu Zhang, Linhan Zhao, Renfei Chen et al.

Soft robotics has been widely adopted in numerous applications of soft grippers, which utilize compliance to achieve superior grasping performances with excellent simplicity, adaptability, and robustness. The critical concerns for soft grippers are insufficient grasping ability and the limitation of functions. Herein, a multibionic soft gripper with multiscale microstructures is demonstrated, featuring light weight (12 mg), versatility, and endurance to heavy dust. The multibionic gripper mimics the grasping structure of eagle claws, the friction‐increasing structure of gecko feet, and the shining structural color on butterfly wings. External laser stimuli allow the grasping of various objects and precise measurement of the target sizes. The soft actuator realizes a systematic bionic function rather than a single bionic function, providing new possibilities for miniaturization, environmental adaptation, and multifunctionality of grasping actuators.

Cong Fang, Hua-Yao Li, Long Li et al.

An electronic nose (e‐nose) mimics the mammalian olfactory system in identifying odors and expands human olfaction boundaries by tracing toxins and explosives. However, existing feature‐based odor recognition algorithms rely on domain‐specific expertise, which may limit the performance due to information loss during the feature extraction process. Inspired by human olfaction, a smart electronic nose enabled by an all‐feature olfactory algorithm (AFOA) is proposed, whereby all features in a gas sensing cycle of semiconductor gas sensors, including the response, equilibrium, and recovery processes are utilized. Specifically, our method combines 1D convolutional and recurrent neural networks with channel and temporal attention modules to fully utilize complementary global and dynamic information. It is further demonstrated that a novel data augmentation method can transform the raw data into a suitable representation for feature extraction. Results show that the e‐nose simply comprising of six semiconductor gas sensors achieves superior performances to state‐of‐the‐art methods on the Chinese liquor data. Ablation studies reveal the contribution of each sensor in odor recognition. Therefore, a deep‐learning‐enabled codesign of sensor arrays and recognition algorithms can reduce the heavy demand for a huge amount of highly specialized gas sensors and provide interpretable insights into odor recognition dynamics in an iterative way.

Kanghong Shi, Nastaran Nikooienejad, Ian R. Petersen et al.

In this paper, we show that a hybrid integrator-gain system (HIGS) is a nonlinear negative imaginary (NNI) system. We prove that the positive feedback interconnection of a linear negative imaginary (NI) system and a HIGS is asymptotically stable. We apply the HIGS to a MEMS nanopositioner, as an example of a linear NI system, in a single-input single-output framework. We analyze the stability and the performance of the closed-loop interconnection in both time and frequency domains through simulations and demonstrate the applicability of HIGS as an NNI controller to a linear NI system.

Do Thu Ha, Nguyen Xuan Hoang, Nguyen Viet Hoang et al.

Industrial Control Systems (ICSs) are becoming more and more important in managing the operation of many important systems in smart manufacturing, such as power stations, water supply systems, and manufacturing sites. While massive digital data can be a driving force for system performance, data security has raised serious concerns. Anomaly detection, therefore, is essential for preventing network security intrusions and system attacks. Many AI-based anomaly detection methods have been proposed and achieved high detection performance, however, are still a "black box" that is hard to be interpreted. In this study, we suggest using Explainable Artificial Intelligence to enhance the perspective and reliable results of an LSTM-based Autoencoder-OCSVM learning model for anomaly detection in ICS. We demonstrate the performance of our proposed method based on a well-known SCADA dataset.

Filipe A. S. Rocha, G. Garcia, Raphael F. S. Pereira et al.

Qi Lian, Yu Qi, Gang Pan et al.

Objective. Brain-computer interface (BCI) has demonstrated its effectiveness in epilepsy treatment and control. In a BCI-aided epilepsy treatment system, therapic electrical stimulus is delivered in response to the prediction of upcoming seizure onsets, therefore timely and accurate seizure prediction algorithm plays an important role. However, unlike typical signatures such as slow or sharp waves in ictal periods, the signal patterns in preictal periods are usually subtle, and highly individual-dependent. How to extract effective and robust preictal features is still a challenging problem. Approach. Most recently, graph convolutional neural network (GCNN) has demonstrated the strength in the electroencephalogram (EEG) and intracranial electroencephalogram (iEEG) signal modeling, due to its advantages in describing complex relationships among different EEG/iEEG regions. However, current GCNN models are not suitable for seizure prediction. The effectiveness of GCNNs highly relies on prior graphs that describe the underlying relationships in EEG regions. However, due to the complex mechanism of seizure evolution, the underlying relationship in the preictal period can be diverse in different patients, making it almost impossible to build a proper prior graph in general. To deal with this problem, we propose a novel approach to automatically learn a patient-specific graph in a data-driven way, which is called the joint graph structure and representation learning network (JGRN). JGRN constructs a global-local graph convolutional neural network which jointly learns the graph structures and connection weights in a task-related learning process in iEEG signals, thus the learned graph and feature representations can be optimized toward the objective of seizure prediction. Main results. Experimental results show that our JGRN outperforms CNN and GCNN models remarkably, and the improvement is more obvious when preictal features are subtle. Significance. The proposed approach promises to achieve robust seizure prediction performance and to have the potential to be extended to general problems in brain-computer interfaces.