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

M. Groover

Teruki Kato, Ryotaro Shima, Kenji Kashima

This paper presents a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and operational constraints. By jointly optimizing control inputs and risk allocation under general (possibly non-Gaussian) uncertainties, the proposed method guarantees probabilistic constraint satisfaction while ensuring strict convexity, leading to uniqueness and continuity of the optimal solution. The formulation is further extended to nonlinear model-based control using exactly linearizable models identified through machine learning. The effectiveness of the proposed approach is demonstrated through model predictive control applied to a hybrid powertrain system.

Xuan Li, Yonglin Tian, Peijun Ye et al.

Foundation models are used to train a broad system of general data to build adaptations to new bottlenecks. Typically, they contain hundreds of billions of hyperparameters that have been trained with hundreds of gigabytes of data. However, this type of black-box vulnerability places foundation models at risk of data poisoning attacks that are designed to pass on misinformation or purposely introduce machine bias. Moreover, ordinary researchers have not been able to completely participate due to the rise in deployment standards. This study introduces the theoretical framework of scenarios engineering (SE) for building accessible and reliable foundation models in metaverse, namely, “SE-enabled foundation models in metaverse.” Particularly, the research framework comprises a six-layer architecture (infrastructure layer, operation layer, knowledge layer, intelligence layer, management layer, and interaction layer), which can provide controllability, trustworthiness, and interactivity for the foundation models in metaverse. This creates closed-loop, virtual–real, and human–machine environments that provides the best indices and goals for the foundation models, which allows us to fully validate and calibrate the corresponding models. Then, examples of use cases from the automotive industry are listed to provide transparency on the possible use and benefits of our approach. Finally, the open research topics of related frameworks are discussed.

Na Li, Lijun Chen, Changhong Zhao et al.

Takumi Iwata, Shun-ichi Azuma, Masaaki Nagahara

This article proposes a data-driven design method of structured sparse feedback controllers. In situations where the system model is unavailable but the measurement data are available, we address the problem of finding a feedback controller which minimizes the number of nonzero rows in the feedback gain such that the closed-loop system has stability with the guaranteed rate of convergence. It is shown that this problem can be reduced to a nonconvex optimization problem with a constraint described by a linear matrix inequality. Moreover, its convex relaxation problem is also considered. To demonstrate the effectiveness of our approach, a numerical example is provided. Furthermore, as an application of the above results, we develop a data-driven design method of block-structured sparse feedback controllers.

SD Zhou, XM Shi, QX Yu et al.

Regarding the design optimization of the heat exchanger, the traditional method of changing the size of the macroscopic structure can no longer meet the increasing demand for heat exchange performance. The superhydrophobic surface can significantly reduce the flow resistance, which is expected to be a new method for the optimal design of heat exchanger. However, when the superhydrophobic effect is applied to a specific type of heat exchanger, its flow and heat transfer performance is not clear. In this paper, the flow and heat transfer model of the traversing superhydrophobic tube bundle is studied. Combined with the FLUENT 18.0 software, the slip boundary conditions of the superhydrophobic surface are deduced, and the flow and heat transfer characteristics of the superhydrophobic tube bundle surface and the wake are numerically analyzed, the influence mechanisms of slip effect on flow and heat transfer characteristics were obtained. Overall, as Ls increases, the comprehensive Nu, also known as heat transfer performance, on the cylindrical surface gradually increases, while the comprehensive Cf, also known as frictional resistance, on the cylindrical surface gradually decreases, indicating that superhydrophobic performance is expected to achieve heat transfer enhancement. The above conclusions can provide a theoretical basis for the optimal design of high-efficiency compact heat exchanger, and have important engineering application significance.

Kangkang Shi, Dongsheng Li, Dongsen Hu et al.

The lateral plates are usually used in the field of structural design of acoustic metamaterials (AMs), which can realize the control of AMs on sound waves. Presently, researches on the application of AMs with lateral plates mainly focus on the regulation of sound waves in air media, and rarely involve the research on their underwater acoustic properties. Therefore, a composite acoustic structure is designed by inserting regularly distributed lateral plates into the viscoelastic rubber, and then, the AMs with multiple coupling substructure (AMs-MCS) can be obtained through combining the local resonance structure and functional gradient structure. Based on underwater acoustic calculation model for the functional gradient acoustic structure established by grade finite element method (G-FEM), the underwater sound absorption characteristics of the AMs-MCS are studied, and the influence of each substructure on the acoustic performance of the AMs-MCS is explored. Numerical results indicate inserting gradient-distributed multiple lateral plates inside the homogeneous acoustic structure can improve the sound absorption performance of the acoustic structure in the mid-and high-frequency ranges and the sound absorption frequency band of the acoustic structure can be effectively broadened. Moreover, the sound absorption coefficient of the AMs-MCS is greater than 0.8 at 500Hz-10 kHz, and the average sound absorption coefficient reaches 0.893, thus achieving low-frequency and broadband sound absorption performance.

András Sasfi, Ivan Markovsky, Alberto Padoan et al.

We propose a modeling framework for stochastic systems, termed Gaussian behaviors, that describes finite-length trajectories of a system as a Gaussian process. The proposed model naturally quantifies the uncertainty in the trajectories, yet it is simple enough to allow for tractable formulations. We relate the proposed model to existing descriptions of dynamical systems including deterministic and stochastic behaviors, and linear time-invariant (LTI) state-space models with Gaussian noise. Gaussian behaviors can be estimated directly from observed data as the empirical sample covariance. The distribution of future outputs conditioned on inputs and past outputs provides a predictive model that can be incorporated in predictive control frameworks. We show that subspace predictive control is a certainty-equivalence control formulation with the estimated Gaussian behavior. Furthermore, the regularized data-enabled predictive control (DeePC) method is shown to be a distributionally optimistic formulation that optimistically accounts for uncertainty in the Gaussian behavior. To mitigate the excessive optimism of DeePC, we propose a novel distributionally robust control formulation, and provide a convex reformulation allowing for efficient implementation.

Filiberto Fele, José M. Maestre, Eduardo F. Camacho

The recent major developments in information technologies have opened interesting possibilities for the effective management of multi-agent systems. In many cases, the important role of central control nodes can now be undertaken by several controllers in a distributed topology that suits better the structure of the system. This opens as well the possibility to promote cooperation between control agents in competitive environments, establishing links between controllers in order to adapt the exchange of critical information to the degree of subsystems' interactions. In this paper a bottom-up approach to coalitional control is presented, where the structure of each agent's model predictive controller is adapted to the time-variant coupling conditions, promoting the formation of coalitions - clusters of control agents where communication is essential to ensure the cooperation - whenever it can bring benefit to the overall system performance.

Hao Wu, Yulong Liu, Jiankang Yang

Francesca Stabile, Walter Lucia, Amr Youssef et al.

The proliferation of cloud computing technologies has paved the way for deploying networked encrypted control systems, offering high performance, remote accessibility and privacy. However, in scenarios where the control algorithms run on third-party cloud service providers, the control logic might be changed by a malicious agent on the cloud. Consequently, it is imperative to verify the correctness of the control signals received from the cloud. Traditional verification methods, like zero-knowledge proof techniques, are computationally demanding in both proof generation and verification, may require several rounds of interactions between the prover and verifier and, consequently, are inapplicable in realtime control system applications. In this paper, we present a novel computationally inexpensive verifiable computing solution inspired by the probabilistic cut-and-choose approach. The proposed scheme allows the plant's actuator to validate the computations accomplished by the encrypted cloud-based networked controller without compromising the control scheme's performance. We showcase the effectiveness and real-time applicability of the proposed verifiable computation scheme using a remotely controlled Khepera IV differential-drive robot.

Samuel Folorunsho, Maggie Ni, William Norris

This paper presents the development of a comprehensive dynamics and stabilizing control architecture for Tethered Unmanned Aerial Vehicle (TUAV) systems. The proposed architecture integrates both onboard and ground-based controllers, employing nonlinear backstepping control techniques to achieve asymptotic stability of the TUAV's equilibrium. The onboard controllers are responsible for the position and attitude control of the TUAV, while the ground controllers regulate the winder mechanism to maintain the desired tether length, ensuring it retains its catenary form. Simulation results demonstrate the ability of the TUAV system to accurately track linear and circular trajectories, ensuring robust performance under various operational scenarios. The code and movies demonstrating the performance of the system can be found at https://github.com/sof-danny/TUAV\_system\_control.

P. Zhou, Shuai Zhang, L. Wen et al.

The automatic control of blast furnace (BF) ironmaking process has always been an important yet arduous task in metallurgic engineering and automation. In this article, a novel Kalman filter-based robust model-free adaptive predictive control (MFAPC) method is proposed for the direct data-driven control of molten iron quality in BF ironmaking. First, a compact-form dynamic linearization-based extended MFAPC method for multivariable molten iron quality control is proposed by generalizing the existing single-variable MFAPC method to multivariable systems. Based on it, a Kalman filter-based robust MFAPC is further proposed considering the problems of data loss and measurement noise in quality detection. Specifically, the robust mechanism in the robust MFAPC combines a novel dynamic linearization method with a concept termed Pseudo-Jacobian matrix to predict the missing data during data loss. After that, a Kalman filter is constructed based on a prediction model to filter the measurement noise. The stability of the proposed control method is analyzed, and various data experiments using actual industrial data are performed to verify the effectiveness of the proposed methods. Note to Practitioners—The extremely complicated dynamics of blast furnace ironmaking process make the model-based controllers difficult to realize in practice. In this article, a novel robust model-free adaptive predictive control method is proposed for direct data-driven control of multivariate molten iron quality in the ironmaking process. This method directly uses the process input and output data to design the multivariable quality controller online by the compact-form dynamic linearization technology and the internal multilayer prediction mechanism, thus avoids the drawback of model-based controllers in troublesome process modeling. Moreover, the proposed method can effectively avoid the influence of data loss and measurement noise on the controller performance with the designed Kalman filter-based robust mechanism. The superiority and practicability of the proposed method are verified using various experiments against actual industrial data.

LI Jinglan, WANG Zhiwei, YIN Yuan

The GK1C diesel locomotives used in steel production base adopt manual driving. To reduce labor intensity of the drivers, and flexibly adjust the running intervals and the number of on-track locomotives, the corresponding autonomous driving vehicle system of the intelligent transportation system emerges. To satisfy the laboratory verification requirements of the intelligent transportation system, enable its autonomous driving vehicle system to monitor train operation more accurately, and solve the problem that closed-loop verification testing cannot be conducted in laboratories due to the lack of locomotives, a locomotive simulation model is designed to simulate closed-loop control of various locomotive functions such as startup, operation, hibernation and fault injection function, based on the verification & testing requirements, and referring to the technical parameters and actual operating conditions of GK1C diesel locomotives. Laboratory application results show that the verification and testing of GK1C diesel locomotive startup, hibernation, traction, braking, fault injection and other functions can be completed through the simulation model, which can greatly improve the efficiency of indoor testing, shorten debugging time on site, and lay a foundation for the promotion and use of intelligent transportation systems in the steel transportation industry.

Pablo Krupa, Daniel Limon, Alberto Bemporad et al.

Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations. This article presents an extension of the HMPC formulation to track periodic harmonic/sinusoidal references and discusses its use for tracking arbitrary trajectories. The proposed formulation inherits the benefits of its predecessor, namely its good performance and large domain of attraction when using small prediction horizons, and that the complexity of its optimization problem does not depend on the period of the reference. We show closed-loop results discussing its performance and comparing it to other MPC formulations.

Rodrigo Aldana-López, Richard Seeber, Hernan Haimovich et al.

Recently, there has been a great deal of attention in a class of controllers based on time-varying gains, called prescribed-time controllers, that steer the system's state to the origin in the desired time, a priori set by the user, regardless of the initial condition. Furthermore, such a class of controllers has been shown to maintain a prescribed-time convergence in the presence of disturbances even if the disturbance bound is unknown. However, such properties require a time-varying gain that becomes singular at the terminal time, which limits its application to scenarios under quantization or measurement noise. This chapter presents a methodology to design a broader class of controllers, called predefined-time controllers, with a prescribed convergence-time bound. Our approach allows designing robust predefined-time controllers based on time-varying gains while maintaining uniformly bounded time-varying gains. We analyze the condition for uniform Lyapunov stability under the proposed time-varying controllers.

Lukas Brunke, Siqi Zhou, Mingxuan Che et al.

Providing safety guarantees for learning-based controllers is important for real-world applications. One approach to realizing safety for arbitrary control policies is safety filtering. If necessary, the filter modifies control inputs to ensure that the trajectories of a closed-loop system stay within a given state constraint set for all future time, referred to as the set being positive invariant or the system being safe. Under the assumption of fully known dynamics, safety can be certified using control barrier functions (CBFs). However, the dynamics model is often either unknown or only partially known in practice. Learning-based methods have been proposed to approximate the CBF condition for unknown or uncertain systems from data; however, these techniques do not account for input constraints and, as a result, may not yield a valid CBF condition to render the safe set invariant. In this work, we study conditions that guarantee control invariance of the system under input constraints and propose an optimization problem to reduce the conservativeness of CBF-based safety filters. Building on these theoretical insights, we further develop a probabilistic learning approach that allows us to build a safety filter that guarantees safety for uncertain, input-constrained systems with high probability. We demonstrate the efficacy of our proposed approach in simulation and real-world experiments on a quadrotor and show that we can achieve safe closed-loop behavior for a learned system while satisfying state and input constraints.

H. Fadhlillah, Kevin Feichtinger, Kristof Meixner et al.

Cyber-Physical Production Systems (CPPSs) are complex systems comprised of software and hardware interacting with each other and the environment. In industry, over time, a plethora of CPPSs are developed to satisfy varying customer requirements and changing technologies. Managing variability is challenging, especially in multidisciplinary environments like in CPPS engineering. For instance, when supporting the automatic derivation and configuration of control software, one needs to understand variability from not only a software perspective, but also a mechatronic, electrical, process, and business perspective. It is unrealistic to use a single model or even one type of model across these perspectives. In this paper, we describe a Multidisciplinary Delta-Oriented Variability Management approach for CPPSs that we are currently developing. Our approach aims to express CPPS variability in different disciplines using heterogeneous variability models, relating models via cross-discipline constraints, and automatically generating control software based on variability models. We implemented a prototype of our approach by realizing delta-oriented variability modeling for IEC 61499-based distributed control software and a configuration tool to enact the configuration options from multiple variability models. We performed a feasibility study of our approach using two systems of different size and complexity. We conclude that, despite current limitations, our approach can successfully and automatically generate control software based on related multidisciplinary variability models. We think that our approach is a good starting point to manage CPPS variability in practice.

Z. Fang

In recent years, with the deep development of applied computer technology, test and inspection technology, technical economics and other disciplines related to its engineering, the research on new algorithms for automation equipment has made great progress in recent ten years. Accompanied by various Internet application technology gradually rise up research and development and large-scale application of the product, based on real-time position and the real-time exchange of data collection service relationship in consistently use way, more and more related areas of science and technology have begun to pay attention to its development focus gradually in agriculture data itself, the precision agriculture it is undoubtedly that such a biological information Technology development is a hot field in the direction of science and technology. With the detection function of real-time sensor and the application technology of biological data information mining, people have been able to control digital agriculture precision farming more accurately, timely and effectively, and achieve large-scale production and benefit maximization. In this paper the main research content is by using computer technology such as data mining methods to start integrating of the analysis on farmland environment geographic information system in a large number of agricultural machinery tillage operation dynamic information, for a variety of information related to farmland farming status indicators for scientific and accurate analysis calculation, but also to the agricultural farming in quality level to conduct a comprehensive monitoring and evaluation, to make all kinds of agricultural machinery The operator can adjust the state of the farming environment reasonably as soon as possible so as to greatly reduce the loss and waste of the value of the national agricultural resources caused by the abnormal farming environment in the field..

ZHANG Huiyuan, SUN Mulan, CHEN Hao

Nylon bush is an essential part of dropper in high speed railway contact line equipment, its missing would have high change of causing the carrier cable burnt, which is a high risk to railway security. However, because of small size of nylon bush, complex background and little of negative sample, missing nylon bush detection is difficult. Therefore, this paper uses the method of "detection first and then classification" and presents a proposal to detect missing nylon bushes based on the combination of YOLOv4 and AlexNet. The model detects droppers by YOLOv4 first, then the location coordinate of nylon bush is calculated by the location of dropper. After that, AlexNet model would discriminate whether the nylon bushes are missing in the locations. Besides,some data enhancement methods are applied in this model to solve the problem of sample imbalance. Low detection rate of small target could be avoided in this method,which also meets real-time requirements by using simple classification network to replace the complex operator. The model have relatively high accuracy and low false alarm rate, which can be used in 3C pantograph-OCS intelligent detection system to detect the missing of nylon bushes in real-time.