Process Control Architecture Research Articles

A Fractional-Order On-Line Self Optimizing Control Framework and a Benchmark Control System Accelerated Using Fractional-Order Stochasticity

This paper presents a design and evaluation of a fractional-order self optimizing control (FOSOC) architecture for process control. It is based on a real-time derivative-free optimization layer that adjusts the parameters of a discrete-time fractional-order proportional integral (FOPI) controller according to an economic cost function. A simulation benchmark is designed to assess the performance of the FOSOC controller based on a first order plus dead time system. Similarly, an acceleration mechanism is proposed for the fractional-order self optimizing control framework employing fractional-order Gaussian noise with long-range dependence given by the Hurst exponent. The obtained results show that the FOSOC controller can improve the system closed-loop response under different operating conditions and reduce the convergence time of the real-time derivative-free optimization algorithm by using fractional-order stochasticity.

SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control

We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.

MPC-Based Process Control of Deep Drawing: An Industry 4.0 Case Study in Automotive

Deep drawing is a metalworking procedure aimed at getting a cold metal sheet plastically deformed in accordance with a pre-defined mould. Although this procedure is well-established in industry, it is still susceptible to several issues affecting the quality of the stamped metal products. In order to reduce defects of workpieces, process control approaches can be performed. Typically, process control employs simple proportional-integral-derivative (PID) regulators that steer the blank holder force (BHF) based on the error on the punch force. However, a single PID can only control single-input single-output systems and cannot handle constraints on the process variables. Differently from the state of the art, in this paper we propose a process control architecture based on Model Predictive Control (MPC), which considers a multi-variable system model. In particular, we represent the deep drawing process with a single-input multiple-output Hammerstein-Wiener model that relates the BHF with the draw-in of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> different critical points around the die. This allows the avoidance of workpiece defects that are due to the abnormal sliding of the metal sheet during the forming phase. The effectiveness of the proposed process controller is shown on a real case study in a digital twin framework, where the performance achieved by the MPC-based system is analyzed in detail and compared against the results obtained through an ad-hoc defined multiple PID-based control architecture. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work is motivated by the emerging need for the effective implementation of the zero-defect manufacturing paradigm in the Industry 4.0 framework. Especially in the deep drawing process, various quality issues in stamped parts can lead to significant product waste and manufacturing inefficiencies. This turns into considerable economic losses for companies, particularly in the automotive sector, where deep drawing is one of the most used cold sheet metal forming techniques. In most applications, only sample inspections are performed on batches of finished-product, with subsequent losses of time and resources. For the sake of improving the workpiece quality, innovative strategies for real-time process control represent a viable and promising solution. In this context, the proposed MPC-based process control approach allows the correct shaping of the metal sheet that is getting deformed during the forming stroke, thanks to the draw-in monitoring at various locations around the die. The draw-in is indeed one of the most effective forming variables to control in order to provide a correct BHF during the forming stroke. A useful and easy-to-implement non-linear metal sheet deep drawing process model is provided by this paper to perform an innovative process control strategy. A comprehensive methodology is applied in detail to an automotive case study, ranging from process modeling (model identification and validation based on experimental data acquisition) to MPC implementation (controller tuning and testing and software-in-the-loop system validation). The presented method can be easily implemented on any real deep drawing press, providing the multivariable constrained process with a suitable control system able to make the stamped parts well formed.

Cloud-edge orchestration-based bi-level autonomous process control for mass individualization of rapid printed circuit boards prototyping services

Rapid printed circuit boards (PCB) prototyping refers to the pilot production of PCB before mass production for verifying the circuit design. The rapid PCB prototyping service providers are pursuing the mass individualization paradigm. Yet, these providers suffer from low efficiency and high cost in the large-scale small-batched mixed pilot production of rapid PCB prototyping. To improve the production efficiency in mass individualization of rapid PCB prototyping, this paper proposed a cloud-edge orchestration-based bi-level autonomous process control (CEO-BAPC) framework. A blockchained smart contracts-driven multi-agent system is established at the edge to realize efficient tasks coordination under disturbances. Based on the operation events data and control decisions collected at the edge, a customized deep learning-driven prediction model is established at the cloud for supporting the rescheduling decisions. The blockchained smart contracts (e.g., Stackelberg game solution) at the edge proactively decentralize short-term fine-grained individualized task execution among manufacturing units/machines and make the results available on upper-level deep learning model at the cloud for supporting holistic rescheduling decisions. The bi-level autonomous process control architecture avoids the inconsistency between holistic control and local execution under frequent random disturbances and thereby realizes efficient manufacturing of mass individualization. Through a simulated case based on data collected from a rapid PCB prototyping service provider in China, the feasibility and efficiency of the proposed CEO-BAPC framework are verified.

Open Process Automation: A standards-based, open, secure, interoperable process control architecture

Open Process Automation is an industry standards initiative whose objective is to bring open architecture technology and business practices into the industrial control system industry. More than 100 organizations (operating companies, hardware and software suppliers, system integrators) are working in a managed Forum of The Open Group to define the Open Process Automation Standard (O-PASTM). Three versions of the O-PAS standard have been published. The paper provides an overview of the standard. The paper also describes results from two O-PAS prototyping projects by ExxonMobil and Lockheed Martin – a laboratory proof-of-concept and a hydrocarbon process-controlling prototype system. Finally, the paper relates Open Process Automation and two complementary initiatives by NAMUR, i.e., NAMUR Open Architecture (NOA) and Module Type Package (MTP).

ASM-ROBOT: A Cyber-Physical Home Automation Controller with Memristive Reconfigurable State Machine

In the next 5 to 10 years, digital Artificial Intelligence with Machine Circuit Learning Algorithms (MCLA) will become the mainstream in complex automated robots. Its power concerns, ethical perspectives, including the issues of digital sensing, actuation, mobility, efficient process-computation, and wireless communication will require advanced neuromorphic process variable controls. Existing home automated robots lack memristic associative memory. This work presents Cyber-Physical Home Automation System (CPHAS) using Memristive Reconfigurable Algorithmic State Machine (MRASM) chart. A process control architecture that supports Concurrent Wireless Data Streams and Power-Transfer (CWDSPT) is developed. Unlike legacy systems with power-splitting (PS) and time-switching (TS) controls, the MRASM-ROBOT explores granular wireless signal controls through unmodulated high-power continuous wave (CW). This transmits infinite process variables using Orthogonal Space-Time Block Code (OSTBC) for interference reduction. The CWDSPT transmitter and receiver circuit design for signal processing are implemented with complexity noise-error reduction during telemetry data decoding. Received signals are error-buffered while gathering control variables' status. Transceiver Memristive neuromorphic circuits are introduced for computational acceleration in the design. Hardware circuit design is tested for system reliability considering the derived schematic models for all process variables. Under small range space diversity, the system demonstrated significant memory stabilization at the synchronous iteration of the synaptic circuitry.

Methodology for Highly Efficient Pump System Management

Maintaining pump operation in the best efficiency zone (BEZ) is one of the most important tasks of the control system. The main management tasks include detecting work outside the BEZ, preventing actions that may move the work point to undesirable areas, and regulating pumping parameters to keep work within the recommended limits. Thanks to the process control architecture of pump systems that use PLCs and programmable frequency converters, new algorithms can be implemented on these devices. This study provides suggestions for using sensor data, internal frequency converter parameters, or pump performance data from performance graphs for new control algorithms. For this purpose, the methodology of predictive control and performance monitoring were described. Pump operation outside BEZ and related phenomena were considered. The proposed algorithm for high-efficiency pump control in certain speed ranges were elaborated. Simulation of test runs of a pumping system relied on computer simulation tools modeling and software for VSD selection. Experimental tests were also introduced on a laboratory installation that simulates a pumping system.

Evaluation of process control architectures for agile manufacturing systems

Current manufacturing systems are faced with a rapidly changing environment caused by customization, which leads to smaller lot sizes. The answer to this challenging situation are agile production systems, which are able to react to new tasks very quickly. One major aspect for realizing such systems is a flexible control architecture for orchestration of machine tending tasks. The aim of this paper is to compare different approaches currently developed and applied in research and industry, e.g. centralized cloud control or head control by a master device. For evaluation, suitable requirements are determined with respect to current limitations and implementation effort.

Мягкие измерения и вычисления при эволюционном управлении непрерывным многостадийным производством

A mechanism for efficient management of the evolution of multi-stage production based on the use of soft measurements and calculations is considered. To reduce the sensitivity to noise interference and delays along dynamic channels between the input and output of continuous multistage production, a diagnostic system based on the analysis of the intervals of change in the values of diagnostic variables for a given time interval is proposed. The decision making system is based on the methods of the Dempster-Schafer theory. The process control architecture model is described as a multi-agent system in which agents interact with each other and with the center. Methods and tools for managing the evolution of multi-stage continuous processes are substantiated to improve efficiency and long-term sustainability throughout the entire life cycle by introducing innovative solutions.

Soft Measurement and Calculations in Evolutionary Control of Continuous Multistage

A mechanism for efficient management of the evolution of multi-stage production based on the use of soft measurements and calculations is considered. To reduce the sensitivity to noise interference and delays along dynamic channels between the input and output of continuous multistage production, a diagnostic system based on the analysis of the intervals of change in the values of diagnostic variables for a given time interval is proposed. The decision making system is based on the methods of the Dempster-Schafer theory. The process control architecture model is described as a multi-agent system in which agents interact with each other and with the center. Methods and tools for managing the evolution of multi-stage continuous processes are substantiated to improve efficiency and long-term sustainability throughout the entire life cycle by introducing innovative solutions.

Performance evaluation of redundant OPC UA architecture for process control

Classical control applications with a centralized logic and distributed input/output system are being replaced by dynamic environments of cooperating components. Thus, the OPC (Object Linking and Embedding for Process Control) UA (Unified Architecture) is becoming more popular, because the OPC Data Access substandard is not well suited for distributed systems. Moreover, in many production systems, redundant data servers are preferred, for financial and legal reasons. Providing performance evaluation gives an estimate of the time required (and data samples lost) to switch to a backup data source for redundant OPC UA architecture, depending on the failure detection method, number of variables and redundancy mode.

Comparison of wireless sensor network and radio frequency identification for the process control of distributed industrial systems

In today’s demanding market, flexibility and reconfigurability play an important role in manufacturing industries. This can be achieved by employing distributed control system such as International Electrotechnical Committee 61499 function block, which is a new standard adopted for industrial-process measurement and control system. On the other hand, wireless systems have been adopted recently in manufacturing systems as a replacement for conventional wired systems. This includes the wireless sensor network and radio frequency identification, which are emerging technologies adopted recently in a wide range of applications, especially in industry sectors. This article presents and compares the application of wireless sensor network and radio frequency identification in the process control of industrial system on the basis of International Electrotechnical Committee 61499 standard. Two process control architectures are considered for a case of an assembly line in order to compare and measure performance by employing Petri net as a mathematical modelling tool.

Development of remote control and monitoring of web-based distributed OPC system

This paper focuses on implementing remote control and monitoring of a web-based distributed OLE for Process Control (OPC) system. Remote monitoring and control of OPC-based systems realized at N different local control points on the Internet are achieved by using a distributed OPC (DOPC) architecture. In this proposed architecture, every local control center can control and monitor every other control point. DOPC architecture is developed by using an OPC standard created for the automation industry. This OPC was developed as an industrial standard using Microsoft's Object Linking and Embedding (OLE) technology. OPC enables different control devices to communicate with each other by exchanging data. This proposed architecture permits different OPC-based process control architectures from a wide range of fields to communicate with each other. Thus, OPC-based local control systems located in different places can communicate with each other on the Internet without using bus architecture. The basic functions of the proposed system were implemented using the Delphi software package. Local control architectures developed at N points can communicate with each other and a remote control point on a dynamic web page constructed using Active Server Pages (ASP). Consequently, the proposed DOPC architecture allows the user to control and monitor local systems from any location with internet access, and to implement data exchange between N OPC points.

BEACON SYNCHRONIZATION FOR GTS COLLISION AVOIDANCE IN AN IEEE802.15.4 MESHED NETWORK

Industrial process control architectures are generally composed of nodes organized in a cluster-tree. Today, wired communications between nodes enable guaranteeing the constraint respect attached to determinism. Innovations in wireless technology allow using these new technologies instead of wired systems. IEEE 802.15.4 standard meet industrial local network needs, but it does not propose any mechanisms to avoid beacon and GTS (Guaranteed Time Slot) collisions in meshed network. This communication proposes a new synchronization method for beacons and GTSs in meshed networks using IEEE 802.15.4.

Flexible Process Control Structures in Multi-Product and Redundant-Routing Plants

The paper describes a process control architecture, which is suitable to use as a framework by implementing flexible control applications. Based on the modular software concept of function blocks, first of all a forward driven information exchange between several control units is introduced. Using this new communication principle, the so called ‘hierarchical task concept’ is presented. This process control model guarantees, that the process-oriented recipe control of batch processes and the rigid structure of a plant-oriented aggregation of control units fit together.

Control and Management of Wastewater Treatment Plants

A key issue in the successful implementation of the computer control systems for the Wastewater Treatment Plants (WWTPs) is the choice of control architecture. In recent years, attempts to use the conventional process control architectures lead to disappointing results. This is due to the rather different nature of wastewater control problems. As a consequence the role of Distributed Control System (DeS) is reduced to a platform for data collection and the control loops are often disabled. Hence, the existing control systems are rarely operated effectively over a long period of time. The barrier to the successful implementation of the control system is not the control software or hardware, but rather the problem of designing control systems that are integrated with the plant operation and management and have a high degree of local autonomy, flexibility and reliability. within the framework of a distributed control system, a multilayered control architecture is proposed in this paper which meets these requirements.

Temporal management using relative time in knowledge-based process control

In this paper, a knowledge-based approach is proposed for the management of temporal information in process control. A common-sense theory of temporal constraints over processes/events, allowing relative temporal knowledge, is employed here as the temporal basis for the system. This theory supports duration reasoning and consistency checking, and accepts relative temporal knowledge which is in a form normally used by human operators. An architecture for process control is proposed which centres on an historical database consisting of events and processes, together with the qualitative temporal relationships between their occurrences. The dynamics of the system is expressed by means of three types of rule: database updating rules, process control rules, and data deletion rules. An example is provided in the form of a life scheduler, to illustrate the database and the rule sets. The example demonstrates the transitions of the database over time, and identifies the procedure in terms of a state transition model for the application. The dividing instant problem for logical inference is discussed with reference to this process control example, and it is shown how the temporal theory employed can be used to deal with the problem.

Neural network architecture for process control based on the RTRL algorithm

AbstractNeural‐network‐based control schemes are generally designed by replacing standard elements of the classic control schemes by feedforward neural networks. The introduction of discrete time recurrent networks, which are inherently dynamic systems, into those schemes can simplify the design of neural controllers. The concept of applying recurrent networks in indirect adaptive control schemes is described. A combined network cluster consisting of the control network and the model network is constructed to allow the use of the real‐time recurrent learning algorithm. To demonstrate the feasibility of the method two simulation examples are presented.

A control-to-target architecture for process control

In this paper, we present an architecture for a run-to-run supervisory process control system that allows the engineer to tailor the form of control for specific processes. The architecture supports different degrees of control, from model-based control to statistical process control to diagnosis. The architecture is compatible with different techniques for model optimization, data acquisition and analysis, and model adjustment and feedback. A primary feature of this architecture is that engineers can define processes In terms of their desired effects, and use process models that transform those effects into machine settings. We have used object technology as the basis for our design and implementation of the architecture. Object-oriented modeling provides the flexibility required to support the varying degrees of control required in a large-scale manufacturing facility. In this paper, we define the components of the architecture, and describe in detail a process control system that was built with this architecture and used in the Computer Integrated manufacturing (CIM) system built for the Microelectronics Manufacturing Science and Technology (MMST) demonstration facility. Although the architecture was developed for the purpose of controlling semiconductor manufacturing processes, the principles behind the architecture may be applied to the control of any process. >

Process control via artificial neural networks and reinforcement learning

In the training of artificial neural networks, reinforcement learning substitutes a qualitative binary target of “success” or “failure” for the quantitative error criterion of supervised learning. By this method of learning control can be established for the special case of operation in which no objective function exists. If no model whatsoever exists of the dynamics of a chemical process, it still may be possible to train an artificial neural network to control the process much as a human being would learn by trial and error. We describe a network architecture for process control, and explain quantitatively how the weights on the connections in the network can be adjusted to yield the desired control action. An example of a nonlinear CSTR is used to illustrate the performance of the proposed net and compare it with that of a tuned PID controller. The net can be trained to meet almost any criteria selected for control as long as the criteria can be expressed in the form of inequality constraints, but requires extensive, and perhaps excessively long, training on a serial computer to do so.