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

Jan Wilch, Birgit Vogel-Heuser, Florian Sax et al.

In automated production, resilience describes a system’s capacity to absorb disturbances by reconfiguring itself, thus retaining its Overall Equipment Effectiveness at least partially. This includes online behavior reconfiguration to automatically recover from or prevent faults, collectively called fault handling. Promising research exists for fault handling in automated Production Systems. In process engineering, fault diagnosis and automatic parameter adaptions are already industrially available. However, handling faults in discrete manufacturing requires a series of distinct operations, which cannot be achieved by parameter changes alone. Further, core requirements must remain fulfilled by automatic fault handling approaches, including real-time control and extra-functional requirements like changing operation modes, monitoring interlocks, and an alarming and communication system. This article proposes a concept for reconfigurable IEC 61131–3 software for automatic fault handling, validated by a public reference implementation for a demonstrator, an industrial production system, and a modified industrial test rig. Eight experiments were successfully conducted, showcasing four use cases of the concept: The prevention of faults by avoiding anomalous components, the recovery from a fault state to automatic operation, the definition of previously undefined state variables, and the monitoring of global interlocks to trigger a controlled stop. All mentioned extra-functional requirements are fulfilled. Note to Practitioners—Identification, reporting, diagnosis, and recovery of faults in automated production incur substantial effort. Project-specific code is required for diagnosis, and the recovery and re-initialization are often performed manually. To our knowledge, automatic recovery approaches from scientific literature are not widely used in discrete manufacturing. Reasons may include a frequent disregard of extra-functional requirements mentioned above. Further, some approaches are incompatible with IEC 61131–3 or industry-typical software modularization. This article proposes a PLC software concept that aims to be compatible with real-world challenges and solutions. The functional software is vertically modularized from organizational hardware-level code. The horizontal modularization separates devices or equipment groups. Support for multiple changing operation modes including two types of controlled stop (run to completion or abort), alarming, data exchange, and global interlocks are incorporated. A prototypical IEC 61131–3 implementation is publicly available that separates a reusable generic part from hardware-specific and project-specific code. The resulting control code is highly reusable, such that all modes (derived from PackML), including dynamic reconfigurations, are composed from the same software modules. Note that we do not expect the concept to be well-adoptable in continuous processes, as elaborated in the Preliminaries section.

M. Saravanan, G. K. Rajini

N. Zorchenko, T. G. Tyupina, M. E. Parshutin

C. De Persis, R. Postoyan, P. Tesi

We present a data-based approach to design event-triggered state-feedback controllers for unknown continuous-time linear systems affected by disturbances. By an event, we mean state measurements transmission from the sensors to the controller over a digital network. By exploiting a sufficiently rich finite set of noisy state measurements and inputs collected off-line, we first design a data-driven state-feedback controller to ensure an input-to-state stability property for the closed-loop system ignoring the network. We then take into account sampling induced by the network and we present robust data-driven triggering strategies to (approximately) preserve this stability property. The approach is general in the sense that it allows deriving data-based versions of various popular triggering rules of the literature. In all cases, the designed transmission policies ensure the existence of a (global) strictly positive minimum interevent time thereby excluding Zeno phenomenon despite disturbances. These results can be viewed as a step towards plug-and-play control for networked control systems, i.e., mechanisms that automatically learn to control and to communicate over a network.

Kehan Long, Jorge Cortes, Nikolay Atanasov

This article presents novel methods for synthesizing distributionally robust stabilizing neural controllers and certificates for control systems under model uncertainty. A key challenge in designing controllers with stability guarantees for uncertain systems is the accurate determination of and adaptation to shifts in model parametric uncertainty during online deployment. We tackle this with a novel distributionally robust formulation of the Lyapunov derivative chance constraint ensuring a monotonic decrease of the Lyapunov certificate. To avoid the computational complexity involved in dealing with the space of probability measures, we identify a sufficient condition in the form of deterministic convex constraints that ensures the Lyapunov derivative constraint is satisfied. We integrate this condition into a loss function for training a neural network-based controller and show that, for the resulting closed-loop system, the global asymptotic stability of its equilibrium can be certified with high confidence, even with Out-of-Distribution (OoD) model uncertainties. To demonstrate the efficacy and efficiency of the proposed methodology, we compare it with an uncertainty-agnostic baseline approach and several reinforcement learning approaches in two control problems in simulation. Open-source implementations of the examples are available at <uri>https://github.com/KehanLong/DR_Stabilizing_Policy</uri>.

Xiao Qi, Lingyao Lei, Changhui Yu et al.

Abstract The penetration of offshore wind farms (OWFs) in city‐close power systems is rapidly increasing. System inertia will be further reduced. Active frequency support of wind power is essential to solve the load frequency control (LFC) problem. Here, the dynamic virtual inertia control (VIC) method is employed to enhance frequency stability within the permitted operating states of OWFs. An adaptive distributed model predictive control (DMPC) method is proposed and applied to an interconnected power system. The dynamic VIC‐based LFC model is derived and used to construct the predictive model of DMPC. To expand the adaptation of the analytical linearized model of OWFs in different operating points, the adaptive law is further designed to dynamically adjust the parameters of DMPC. The simulation results demonstrate the effectiveness of the proposed control method. The frequency fluctuations can be well‐restrained under different disturbances.

William D. Chicaiza, Javier Gómez, Adolfo J. Sánchez et al.

Una de las tecnologías mas atractivas y actualmente en auge que se esta desarrollando rápidamente es el Gemelo Digital (Digital Twin en inglés, DT). Es bien conocido como un habilitador para la Industria 4.0. Tanto en la comunidad científica como industrial, el concepto, la tecnología y las aplicaciones asociadas al mismo, van generando controversia. Sigue habiendo una gran variedad de definiciones del mismo concepto. Aparentemente no parece haber una comprensión común de este término en la literatura. Se utiliza de forma diferente en diferentes disciplinas. La simulación basada en modelos ha sido, desde hace mucho tiempo, una herramienta común para el diseño en una fase inicial de planificación, pero no durante el tiempo de trabajo del sistema ya diseñado. En este trabajo se pretende abarcar una revisión histórica de este concepto. Mientras que muchas revisiones bibliográficas existentes se centran principalmente en la industria de manufactura, este artículo hará un enfoque en aplicaciones de los gemelos digitales en el campo de la Automática.

D. Abramovitch

This article offers insights on teaching practical improvements to control methods to engineers already practicing in the field. Virtually all these engineers have taken an introductory control class (perhaps many years ago) and have some experience with circuitry, programming, and actual control implementations. We argue that the lessons that have the most impact on these engineers are those that tie theoretical insight to methods that they can adapt almost immediately to make their jobs easier. We discuss both the environment of typical practicing engineers and some of the lessons that can immediately make them more effective. An earlier version of this article was presented at the 2019 International Federation of Automatic Control (IFAC) Advances in Control Engineering Conference [1]. This expanded version draws heavily on discussions at the author’s Practical Methods for Real World Control Systems workshops and the companion book [2].

Zeyu Yang, Liang He, H. Yu et al.

Semantic attacks have incurred increasing threats to Industrial Control Systems (ICSs), which manipulate targeted system modules by identifying the physical semantics of variables in Programmable Logic Controllers (PLCs) programs, i.e., the sensing/actuating modules represented by the variables. This is usually (and inefficiently) achieved via manual examination of system documents and long-term observation of system behavior. In this paper, we design ARES, a method that Automatically Reverse Engineers the Semantics of variables in PLC programs without requiring any domain knowledge. ARES is built on the fact that the Supervisory Control And Data Acquisition (SCADA) system monitors the behavior of PLC using a fixed mapping between the variables of program code and data log, and the data log variables are marked with physical semantics. By identifying the mapping between PLC code and SCADA data (i.e., the code-data mapping), ARES reverse engineers the physical semantics of program variables. ARES also sheds light on the preferred practices in implementing control rules that improve the resistance of PLC programs to semantic attacks. We have experimentally evaluated ARES and the recommended implementation practices on two ICS platforms.

Wankun Sirichotiyakul, A. Satici

Since its introduction in the late 1980s, passivity-based control (PBC) has proven to be successful in controlling many robotic systems. The connection between stability and passivity theory is the most attractive feature of controllers designed using this methodology. However, the need to solve nonlinear partial differential equations (PDE) in closed-form has been a major challenge in applying PBC to general robotic systems. Here, we introduce a systematic approach to design controllers for a class of underactuated mechanical systems based on interconnection and damping assignment. Exploiting the universal approximation capability of neural networks, we formulate a data-driven optimisation problem that discovers solutions to the required PDEs automatically. Our approach does not destroy the passivity structure, preserving the inherent stability properties. We demonstrate the efficacy of our framework on two benchmark problems: the inertia wheel pendulum and the ball and beam system.

Hong Jianwang, Ricardo A. Ramirez-Mendoza

This paper shows our new contributions on data driven model predictive control, such as persistent excitation, optimal state feedback controller, output predictor and stability. After reviewing the definition of persistent excitation and its important property, the idea of data driven is introduced in model predictive control to construct our considered data driven model predictive control, whose state information and output variable are generated by measured data online. Variation tool is applied to obtain the optimal controller or predictive controller through our own derivation. Furthermore, for the cost function in data driven model predictive control, its preliminary stability is analysed by using the linear matrix inequality and one single optimal state feedback controller is given. To bridge the gap between our derived results and other control strategies, output predictor is constructed from the point of data driven idea, i.e. using some collected input–output data from one experiment to establish the output predictor at any later time instant. Finally, one simulation example is given to prove the efficiency of our derived results.

Min Xiao, Jing Liang, Li Ji et al.

Quad-rotor unmanned aerial vehicles (UAV) are prone to external interference during aerial photography of farmland environments. For example, they are affected by external airflow and load, resulting in route deviation and irregular image overlap, which seriously affects image quality. An aerial trajectory tracking controller is designed for this aerial photography process. To ensure that a drone can fly according to the established route during the aerial photography process and meet the requirements of large-scale topographic map stereo mapping for the flight control accuracy of the drone platform, the system was divided into a full-drive subsystem and an underactuated subsystem. The full-drive subsystem uses a fast terminal sliding mode controller to ensure that the variable ( z , ψ ) reaches the desired value. The under-actuated subsystem adopts the second-order sliding mode control was used to achieve effective position and attitude tracking of variables ( x , y , ϕ , θ ). The flight controllers are derived by using Lyapunov theory. Finally, with the aerial trajectory of a farmland taken as an example, the flight path control of the UAV is simulated. Simulation results show that the designed control system can be applied to the aerial photography process of the UAV and has strong anti-system parameter perturbation, robustness and good trajectory tracking.

Shuqiao Geng, Changan Zhu, Yi Jin et al.

A gaze control system for tracking Quasi-1D high-speed moving object is proposed, it can keep the object in the centre of the image within a certain range. Initially, the system structure is designed, and the tracking range of the system is expanded using a single saccade mirror. Then the model between the deflection angle of the saccade mirror and the pixel displacement is established. Finally, a frame-difference method based on image cropping is proposed to rapidly extract the moving object in the complex dynamic background. It feeds back the object position to the saccade mirror control system. The system adjusts the deflection angle of the saccade mirror in real time. Experimental results show that the system can satisfy the requirements of gaze control for tracking Quasi-1D high-speed moving object.

Patryk Grelewicz, T. T. Khuat, J. Czeczot et al.

In this article, a novel machine learning (ML)-derived control performance assessment (CPA) classification system is proposed. It is dedicated for a wide class of PID-based control industrial loops with processes exhibiting dynamical properties close to second order plus delay time (SOPDT). The proposed concept is very general and easy to configure to distinguish between acceptable and poor closed-loop performance. This approach allows for determining the best (but also robust and practically achievable) closed-loop performance based on very popular and intuitive closed-loop quality factors. Training set can be automatically derived off-line using a number of different, diverse control performance indices (CPIs) used as discriminative features of the assessed control system. The proposed extended set of CPIs is discussed with comprehensive performance assessment of different ML-based classification methods and practical application of the suggested solution. As a result, a general-purpose CPA system is derived that can be immediately applied in practice without any preliminary or additional learning stage during normal closed-loop operation. It is verified by practical application to assess the control system for a laboratory heat exchange and distribution setup.

Huanfeng Liu, Zhen Gao, Lan Cao et al.

Many important engineering systems can be classified as Euler–Lagrange (EL) systems. In this work we develop a new Proportional‐Integral‐Derivative (PID) ‐based tracking control solution for uncertain EL systems subject to actuation failures and saturation. Two set of control algorithms are developed using robust adaptive and neuroadaptive methods, which are shown to exhibit several salient features: (1) the control schemes are of PID form, hence is characterized with simplicity in structure and intuition in concept; (2) the PID gains in the scheme are automatically updated by analytic algorithms with no need for manual tuning, rendering the control scheme more user‐friendly; and (3) the developed control algorithms are robust against nonvanishing disturbances, adaptive to unknown virtual parameter and immune to partial actuation effectiveness faults.

Song Han, Chenchen Fan, Xinbin Li et al.

This study deals with the task assignment problem of heterogeneous unmanned aerial vehicle (UAV) system with the limited resources and task priority constraints. The optimization model which comprehensively considers the resource consumption, task completion effect, and workload balance is formulated. Then, a concept of fuzzy elite degree is proposed to optimize and balance the transmission of good genes and the variation strength of population during the operations of algorithm. Based on the concept, we propose the fuzzy elite strategy genetic algorithm (FESGA) to efficiently solve the complex task assignment problem. In the proposed algorithm, two unlock methods are presented to solve the deadlock problem in the random optimization process; a sudden threat countermeasure (STC) mechanism is presented to help the algorithm quickly respond to the change of task environment caused by sudden threats. The simulation results demonstrate the superiority of the proposed algorithm. Meanwhile, the effectiveness and feasibility of the algorithm in workload balance and task priority constraints are verified.

Yufei Zhang, Hongwei Wang, Xiang Zhai et al.

Effective and efficient provision and reuse of knowledge across a knowledge network in the global value chain still faces two challenges, namely the interpretability of model and the efficacy of construction. This research aims to address these challenges by proposing a novel representation model for design knowledge. First, a knowledge representation model based on the integration of the cognitive process theory and the Requirement-Function-Behavior-Structure model (C-RFBS model) is proposed to incorporate key elements from the cognitive process of designers to capture the rationale of deliberation and the context of decision-making, which the knowledge records created become more interpretable. Second, knowledge graph is employed to improve the productivity of knowledge records creation, storage and exploration. On this basis, we describe the creation of knowledge records using the C-RFBS model as well as the computational framework and methods for storing knowledge using knowledge graph. The proposed model and methods are implemented in a knowledge retrieval system on which we have conducted a fork design case study to evaluate and demonstrate the models and methods. As shown in the evaluation, the proposed model can effectively support knowledge elicitation and achieved improved performance in terms of knowledge retrieval through incorporating knowledge graph.

Mohammad Sadraey

Diogo M. F. Izidio, Antonyus P. A. Ferreira, H. R. Medeiros et al.