Wireless Communication in Process Control Loop: Requirements Analysis, Industry Practices and Experimental Evaluation

Wireless communication is already used in process automation for process monitoring. The next stage of implementation of wireless technology in industrial applications is for process control. The need for wireless networked control systems has evolved because of the necessity for extensibility, mobility, modularity, fast deployment, and reduced installation and maintenance cost. These benefits are only applicable given that the wireless network of choice can meet the strict requirements of process control applications, such as latency. In this regard, this paper is an effort towards identifying current industry practices related to implementing process control over a wireless link and evaluates the suitability of ISA100.11a network for use in process control through experiments

Implementation of Model Based Networked Predictive Control System

Networked control systems are made up of several computer nodes communicating over a communication channel, cooperating to control a plant. The stability of the plant depends on the end to end delay from sensor to the actuator. Although computational delays within the computer nodes can be made bounded, delays through the communication network are generally unpredictable. A method which aims to protect the stability of the plant under communication delays and data loss, Model Based Predictive Networked Control System (MBPNCS), has previously been proposed by the authors. This paper aims to demonstrate the implementation of this type of networked control system on a non-real-time communication network; Ethernet. In this paper, we first briefly describe the MBPNCS method, then discuss the implementation, detailing the properties of the operating system, communications and hardware, and later give the results on the performance of the Model Based Predictive Networked Control System implementation controlling a DC motor. This work was supported in part by the Scientific and Technological Re search Council of Turkey, project code 106E155

Autonomous Vehicle Coordination with Wireless Sensor and Actuator Networks

A coordinated team of mobile wireless sensor and actuator nodes can bring numerous benefits for various applications in the field of cooperative surveillance, mapping unknown areas, disaster management, automated highway and space exploration. This article explores the idea of mobile nodes using vehicles on wheels, augmented with wireless, sensing, and control capabilities. One of the vehicles acts as a leader, being remotely driven by the user, the others represent the followers. Each vehicle has a low-power wireless sensor node attached, featuring a 3D accelerometer and a magnetic compass. Speed and orientation are computed in real time using inertial navigation techniques. The leader periodically transmits these measures to the followers, which implement a lightweight fuzzy logic controller for imitating the leader's movement pattern. We report in detail on all development phases, covering design, simulation, controller tuning, inertial sensor evaluation, calibration, scheduling, fixed-point computation, debugging, benchmarking, field experiments, and lessons learned

Body randomization reduces the sim-to-real gap for compliant quadruped locomotion

Designing controllers for compliant, underactuated robots is challenging and usually requires a learning procedure. Learning robotic control in simulated environments can speed up the process whilst lowering risk of physical damage. Since perfect simulations are unfeasible, several techniques are used to improve transfer to the real world. Here, we investigate the impact of randomizing body parameters during learning of CPG controllers in simulation. The controllers are evaluated on our physical quadruped robot. We find that body randomization in simulation increases chances of finding gaits that function well on the real robot

Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications

Cross-Layer Adaptive Feedback Scheduling of Wireless Control Systems

There is a trend towards using wireless technologies in networked control systems. However, the adverse properties of the radio channels make it difficult to design and implement control systems in wireless environments. To attack the uncertainty in available communication resources in wireless control systems closed over WLAN, a cross-layer adaptive feedback scheduling (CLAFS) scheme is developed, which takes advantage of the co-design of control and wireless communications. By exploiting cross-layer design, CLAFS adjusts the sampling periods of control systems at the application layer based on information about deadline miss ratio and transmission rate from the physical layer. Within the framework of feedback scheduling, the control performance is maximized through controlling the deadline miss ratio. Key design parameters of the feedback scheduler are adapted to dynamic changes in the channel condition. An event-driven invocation mechanism for the feedback scheduler is also developed. Simulation results show that the proposed approach is efficient in dealing with channel capacity variations and noise interference, thus providing an enabling technology for control over WLAN.Comment: 17 pages, 12 figures; Open Access at http://www.mdpi.org/sensors/papers/s8074265.pd