Detection of Oscillations in Process Control Loops From Visual Image Space Using Deep Convolutional Networks

Oscillation detection has been a hot research topic in industries due to the high incidence of oscillation loops and their negative impact on plant profitability.Although numerous automatic detection techniques have been proposed,most of them can only address part of the practical difficulties.An oscillation is heuristically defined as a visually apparent periodic variation.However,manual visual inspection is labor-intensive and prone to missed detection.Convolutional neural networks(CNNs),inspired by animal visual systems,have been raised with powerful feature extraction capabilities.In this work,an exploration of the typical CNN models for visual oscillation detection is performed.Specifically,we tested MobileNet-V1,ShuffleNet-V2,Efficient Net-B0,and GhostNet models,and found that such a visual framework is well-suited for oscillation detection.The feasibility and validity of this framework are verified utilizing extensive numerical and industrial cases.Compared with state-of-theart oscillation detectors,the suggested framework is more straightforward and more robust to noise and mean-nonstationarity.In addition,this framework generalizes well and is capable of handling features that are not present in the training data,such as multiple oscillations and outliers.

An approximation for the boundary optimal control problem of a heat equation defined in a variable domain

In this paper, we consider a numerical approximation for the boundary optimal control problem with the control constraint governed by a heat equation defined in a variable domain. For this variable domain problem, the boundary of the domain is moving and the shape of theboundary is defined by a known time-dependent function. By making use of the Galerkin finite element method, we first project the original optimal control problem into a semi-discrete optimal control problem governed by a system of ordinary differential equations. Then, based on the aforementioned semi-discrete problem, we apply the control parameterization method to obtain an optimal parameter selection problem governed by a lumped parameter system, which can be solved as a nonlinear optimization problem by a Sequential Quadratic Programming （SQP） algorithm. The numerical simulation is given to illustrate the effectiveness of our numerical approximation for the variable domain problem with the finite element method and the control parameterization method.

Augmented Industrial Data-Driven Modeling Under the Curse of Dimensionality

The curse of dimensionality refers to the problem o increased sparsity and computational complexity when dealing with high-dimensional data.In recent years,the types and vari ables of industrial data have increased significantly,making data driven models more challenging to develop.To address this prob lem,data augmentation technology has been introduced as an effective tool to solve the sparsity problem of high-dimensiona industrial data.This paper systematically explores and discusses the necessity,feasibility,and effectiveness of augmented indus trial data-driven modeling in the context of the curse of dimen sionality and virtual big data.Then,the process of data augmen tation modeling is analyzed,and the concept of data boosting augmentation is proposed.The data boosting augmentation involves designing the reliability weight and actual-virtual weigh functions,and developing a double weighted partial least squares model to optimize the three stages of data generation,data fusion and modeling.This approach significantly improves the inter pretability,effectiveness,and practicality of data augmentation in the industrial modeling.Finally,the proposed method is verified using practical examples of fault diagnosis systems and virtua measurement systems in the industry.The results demonstrate the effectiveness of the proposed approach in improving the accu racy and robustness of data-driven models,making them more suitable for real-world industrial applications.

A feedback control method for the stabilization of a nonlinear diffusion system on a graph

In this paper, we consider the internal stabilization problems of FitzHugh-Nagumo （FHN） systems on the locally finite connected weighted graphs, which describe the process of signal transmission across axons in neurobiology. We will establish the proper condition on the weighted Dirichlet-Laplace operator on a graph such that the nonlinear FHN system can be stabilized exponentially and globally only using internal actuation over a sub-domain with a linear feedback form.

Computing Open-Loop Optimal Control of the q-Profile in Ramp-Up Tokamak Plasmas Using the Minimal-Surface Theory

The q-profile control problem in the ramp-up phase of plasma discharges is consid- ered in this work. The magnetic diffusion partial differential equation （PDE） models the dynamics of the poloidal magnetic flux profile, which is used in this work to formulate a PDE-constrained op-timization problem under a quasi-static assumption. The minimum surface theory and constrained numeric optimization are then applied to achieve suboptimal solutions. Since the transient dy- namics is pre-given by the minimum surface theory, then this method can dramatically accelerate the solution process. In order to be robust under external uncertainties in real implementations, PID （proportional-integral-derivative） controllers are used to force the actuators to follow the computational input trajectories. It has the potential to implement in real-time for long time discharges by combining this method with the magnetic equilibrium update.

Detecting the One-Shot Dummy Attack on the Power Industrial Control Processes With an Unsupervised Data-Driven Approach

Dear Editor,Dummy attack(DA), a deep stealthy but impactful data integrity attack on power industrial control processes, is recently recognized as hiding the corrupted measurements in normal measurements. In this letter, targeting a more practical case, we aim to detect the oneshot DA, with the purpose of revealing the DA once it is launched.Specifically, we first formulate an optimization problem to generate one-shot DAs. Then, an unsupervised data-driven approach based on a modified local outlier factor(MLOF) is proposed to detect them.

A Survey of Model Predictive Control Methods for Traffic Signal Control

Enhancing traffic efficiency and alleviating(even circumventing) traffic congestion with advanced traffic signal control(TSC) strategies are always the main issues to be addressed in urban transportation systems. Since model predictive control(MPC) has a lot of advantages in modeling complex dynamic systems, it has been widely studied in traffic signal control over the past 20 years. There is a need for an in-depth understanding of MPC-based TSC methods for traffic networks. Therefore, this paper presents the motivation of using MPC for TSC and how MPC-based TSC approaches are implemented to manage and control the dynamics of traffic flows both in urban road networks and freeway networks. Meanwhile, typical performance evaluation metrics, solution methods, examples of simulations,and applications related to MPC-based TSC approaches are reported. More importantly, this paper summarizes the recent developments and the research trends in coordination and control of traffic networks with MPC-based TSC approaches. Remaining challenges and open issues are discussed towards the end of this paper to discover potential future research directions.

Distributed Control of Multiple-Bus Microgrid With Paralleled Distributed Generators

A microgrid is hard to control due to its reduced inertia and increased uncertainties. To overcome the challenges of microgrid control, advanced controllers need to be developed.In this paper, a distributed, two-level, communication-economic control scheme is presented for multiple-bus microgrids with each bus having multiple distributed generators(DGs) connected in parallel. The control objective of the upper level is to calculate the voltage references for one-bus subsystems. The objectives of the lower control level are to make the subsystems' bus voltages track the voltage references and to enhance load current sharing accuracy among the local DGs. Firstly, a distributed consensusbased power sharing algorithm is introduced to determine the power generations of the subsystems. Secondly, a discrete-time droop equation is used to adjust subsystem frequencies for voltage reference calculations. Finally, a Lyapunov-based decentralized control algorithm is designed for bus voltage regulation and proportional load current sharing. Extensive simulation studies with microgrid models of different levels of detail are performed to demonstrate the merits of the proposed control scheme.

Adaptive Decentralized Output-Constrained Control of Single-Bus DC Microgrids

A single-bus DC microgrid can represent a wide range of applications. Control objectives of such systems include high-performance bus voltage regulation and proper load sharing among multiple distributed generators(DGs) under various operating conditions. This paper presents a novel decentralized control algorithm that can guarantee both the transient voltage control performance and realize the predefined load sharing percentages. First, the output-constrained control problem is transformed into an equivalent unconstrained one. Second, a two-step backstepping control algorithm is designed based on the transformed model for bus-voltage regulation. Since the overall control effort can be split proportionally and calculated with locally-measurable signals, decentralized load sharing can be realized. The control design requires neither accurate parameters of the output filters nor load measurement. The stability of the transformed systems under the proposed control algorithm can indirectly guarantee the transient bus voltage performance of the original system. Additionally, the high-performance control design is robust, flexible, and reliable. Switch-level simulations under both normal and fault operating conditions demonstrate the effectiveness of the proposed algorithm.

Impact Analysis of MTD on the Frequency Stability in Smart Grid

Dear Editor, In order to accommodate the effects of false data injection attacks(FDIAs), the moving target defense(MTD) strategy is recently proposed to enhance the security of the smart grid by perturbing branch susceptances. However, most pioneer work only focus on the defending performance of MTD in terms of detecting FDIAs and the impact of MTD on the static factors such as the power and economic losses.

Distributed Adaptive Fault-Tolerant Output Regulation of Heterogeneous Multi-Agent Systems With Coupling Uncertainties and Actuator Faults

In this paper, we consider the distributed adaptive fault-tolerant output regulation problem for heterogeneous multiagent systems with matched system uncertainties and mismatched coupling uncertainties among subsystems under the influence of actuator faults. First, distributed finite-time observers are proposed for all subsystems to observe the state of the exosystem. Then, a novel fault-tolerant controller is designed to compensate for the influence of matched system uncertainties and actuator faults. By using the linear matrix inequality technique, a sufficient condition is provided to guarantee the solvability of the considered problem in the presence of mismatched coupling uncertainties. Moreover, it is shown that the system in closed-loop with the developed controller can achieve output regulation by using the Lyapunov stability theory and cyclic-small-gain theory.Finally, a numerical example is given to illustrate the effectiveness of the obtained result.

Minimax entropy-based co-training for fault diagnosis of blast furnace

Due to the problems of few fault samples and large data fluctuations in the blast furnace(BF)ironmaking process,some transfer learning-based fault diagnosis methods are proposed.The vast majority of such methods perform distribution adaptation by reducing the distance between data distributions and applying a classifier to generate pseudo-labels for self-training.However,since the training data is dominated by labeled source domain data,such classifiers tend to be weak classifiers in the target domain.In addition,the features generated after domain adaptation are likely to be at the decision boundary,resulting in a loss of classification performance.Hence,we propose a novel method called minimax entropy-based co-training(MMEC)that adversarially optimizes a transferable fault diagnosis model for the BF.The structure of MMEC includes a dual-view feature extractor,followed by two classifiers that compute the feature's cosine similarity to representative vector of each class.Knowledge transfer is achieved by alternately increasing and decreasing the entropy of unlabeled target samples with the classifier and the feature extractor,respectively.Transfer BF fault diagnosis experiments show that our method improves accuracy by about 5%over state-of-the-art methods.

Intelligent Ironmaking Optimization Service on a Cloud Computing Platform by Digital Twin

The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes.To improve the operational levels of the process industries,we propose a multi-objective optimization framework based on cloud services and a cloud distribution system.Real-time data from manufacturing procedures are first temporarily stored in a local database,and then transferred to the relational database in the cloud.Next,a distribution system with elastic compute power is set up for the optimization framework.Finally,a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process.With the application of this optimization service in a cloud factory,iron production was found to increase by 83.91 t∙d^(-1),the coke ratio decreased 13.50 kg∙t^(-1),and the silicon content decreased by an average of 0.047%.

Collision and Deadlock Avoidance in Multi-Robot Systems Based on Glued Nodes

Dear editor,In this letter,we would like to discuss a method to avoid collisions and deadlocks in multi-robot systems based on a new concept of glued nodes.In terms of collision and deadlock avoidance,many methods are based on zone control which has two disadvantages.First,unless all nodes are collision-free,the roadmap must be divided into disjoint zones,which increases the difficulty of applying the methods.Moreover,each zone should be able to accommodate a robot,which leads to imprecision and waste of space.This letter proposes the concept of glued nodes,which can dynamically determine the mutual influence between nodes based on the real-time sizes and paths of the robots.Based on the glued nodes,this letter proposes a collision and deadlock avoidance algorithm,which can be applied to multi-robot systems with variable-sized robots and roadmaps with any structure.The experimental results indicate that the method proposed in this letter is effective and efficient.

Boosting multi-features with prior knowledge for mini unmanned helicopter landmark detection

Without sufficient real training data,the data driven classification algorithms based on boosting method cannot solely be utilized to applications such as the mini unmanned helicopter landmark image detection.In this paper,we propose an approach which uses a boosting algorithm with the prior knowledge for the mini unmanned helicopter landmark image detection.The stage forward stagewise additive model of boosting is analyzed,and the approach how to combine it with the prior knowledge model is presented.The approach is then applied to landmark image detection,where the multi-features are boosted to solve a series of problems,such as rotation,noises affected,etc.Results of real flight experiments demonstrate that for small training examples the boosted learning system using prior knowledge is dramatically better than the one driven by data only.

Guest Editorial for Special Issue on Cyber-Physical Systems

Ⅰ.Introduction CYBER-PHYSICAL system is a system of collaborating computational elements to control physical entities.The coordination and the tight link between computational,virtual and physical resources in cyber-physical system will have a pervasive effect on our everyday life.The development of cyber-physical system will create new opportunities for the introduction of services that will enhance the quality of life

Distributed event-triggered adaptive control for second-order nonlinear uncertain multi-agent systems

In this paper,the event-triggered consensus control problem for nonlinear uncertain multi-agent systems subject to unknown parameters and external disturbances is considered.The dynamics of subsystems are second-order with similar structures,and the nodes are connected by undirected graphs.The event-triggered mechanisms are not only utilized in the transmission of information from the controllers to the actuators,and from the sensors to the controllers within each agent,but also in the communication between agents.Based on the adaptive backstepping method,extra estimators are introduced to handle the unknown parameters,and the measurement errors that occur during the event-triggered communication are well handled by designing compensating terms for the control signals.The presented distributed event-triggered adaptive control laws can guarantee the boundness of the consensus tracking errors and the Zeno behavior is avoided.Meanwhile,the update frequency of the controllers and the load of communication burden are vastly reduced.The obtained control protocol is further applied to a multi-input multi-output second-order nonlinear multi-agent system,and the simulation results show the effectiveness and advantages of our proposed method.

Variable Universe Fuzzy Control for Battery Equalization

In order to avoid the overcharge and overdischarge damages, and to improve the lifetime of the lithium-ion batteries, it is essential to keep the cell voltages in a battery pack at the same level,i.e., battery equalization. Based on the bi-directional modified Cuk converter, variable universe fuzzy controllers are proposed to adaptively maintain equalizing currents between cells of a serially connected battery pack in varying conditions. The inputs to the fuzzy controller are the voltage differences and the average voltages of adjacent cell pairs. A large voltage difference requires large equalizing current while adjacent cells both with low/high voltages can only stand small discharge/charge currents. Compared with the conventional fuzzy control method, the proposed method differs in that the universe can shrink or expand as the effects of the input changes. This is important as the input may change in a small range. Simulation results demonstrate that the proposed variable universe fuzzy control method has fast equalization speed and good adaptiveness for varying conditions.

A proposal on centralised and distributed optimisation via proportional–integral–derivative controllers(PID)control perspective

Motivated by the excellent performance of proportional–integral–derivative controllers(PIDs)in the field of control,the authors injected the philosophy of PID into optimi-sation and introduced two types of novel PID optimisers from a continuous-time view,which benefit from the idea that discrete-time optimisation algorithm can be modelled as a continuous dynamical system/controlled system.For centralised optimisation,the au-thors discuss the idea of the first-order PID optimiser and the second-order accelerated PID optimiser.Furthermore,this framework is extended into distributed optimisation settings,and a distributed PID optimiser is proposed.Finally,some numerical examples are given to verify our ideas.

Technical report:PID design of second-order non-linear uncertain systems with fractional order operations

This study considers a control problem related to the regulation of fractional-order systems controlled by fractional order proportional-integral-derivative controllers(PI^(λ)D^(μ)).The stability result of PIλDμ-based control systems is provided,such that the closed-loop systems can accomplish global stabilisation under some suitable conditions related to the system uncertainties.Finally,a simulation is demonstrated to verify the results.