Dynamics analysis and integrated design of real-time control systems

Real-time control systems are widely deployed in many applications. Theory and practice for the design and deployment of real-time control systems have evolved significantly. From the design perspective, control strategy development has been the focus of the research in the control community. In order to develop good control strategies, process modelling and analysis have been investigated for decades, and stability analysis and model-based control have been heavily studied in the literature. From the implementation perspective, real-time control systems require timeliness and predictable timing behaviour in addition to logical correctness, and a real-time control system may behave very differently with different software implementations of the control strategies on a digital controller, which typically has limited computing resources. Most current research activities on software implementations concentrate on various scheduling methodologies to ensure the schedulability of multiple control tasks in constrained environments. Recently, more and more real-time control systems are implemented over data networks, leading to increasing interest worldwide in the design and implementation of networked control systems (NCS). Major research activities in NCS include control-oriented and scheduling-oriented investigations. In spite of significant progress in the research and development of real-time control systems, major difficulties exist in the state of the art. A key issue is the lack of integrated design for control development and its software implementation. For control design, the model-based control technique, the current focus of control research, does not work when a good process model is not available or is too complicated for control design. For control implementation on digital controllers running multiple tasks, the system schedulability is essential but is not enough; the ultimate objective of satisfactory quality-of-control (QoC) performance has not been addressed directly. For networked control, the majority of the control-oriented investigations are based on two unrealistic assumptions about the network induced delay. The scheduling-oriented research focuses on schedulability and does not directly link to the overall QoC of the system. General solutions with direct QoC consideration from the network perspective to the challenging problems of network delay and packet dropout in NCS have not been found in the literature. This thesis addresses the design and implementation of real-time control systems with regard to dynamics analysis and integrated design. Three related areas have been investigated, namely control development for controllers, control implementation and scheduling on controllers, and real-time control in networked environments. Seven research problems are identified from these areas for investigation in this thesis, and accordingly seven major contributions have been claimed. Timing behaviour, quality of control, and integrated design for real-time control systems are highlighted throughout this thesis. In control design, a model-free control technique, pattern predictive control, is developed for complex reactive distillation processes. Alleviating the requirement of accurate process models, the developed control technique integrates pattern recognition, fuzzy logic, non-linear transformation, and predictive control into a unified framework to solve complex problems. Characterising the QoC indirectly with control latency and jitter, scheduling strategies for multiple control tasks are proposed to minimise the latency and/or jitter. Also, a hierarchical, QoC driven, and event-triggering feedback scheduling architecture is developed with plug-ins of either the earliest-deadline-first or fixed priority scheduling. Linking to the QoC directly, the architecture minimises the use of computing resources without sacrifice of the system QoC. It considers the control requirements, but does not rely on the control design. For real-time NCS, the dynamics of the network delay are analysed first, and the nonuniform distribution and multi-fractal nature of the delay are revealed. These results do not support two fundamental assumptions used in existing NCS literature. Then, considering the control requirements, solutions are provided to the challenging NCS problems from the network perspective. To compensate for the network delay, a real-time queuing protocol is developed to smooth out the time-varying delay and thus to achieve more predictable behaviour of packet transmissions. For control packet dropout, simple yet effective compensators are proposed. Finally, combining the queuing protocol, the packet loss compensation, the configuration of the worst-case communication delay, and the control design, an integrated design framework is developed for real-time NCS. With this framework, the network delay is limited to within a single control period, leading to simplified system analysis and improved QoC

Design and development of a hybrid control system for flexible manufacturing : a thesis presented in partial fulfilment of the requirements for the degree of Master of Technology in Manufacturing and Industrial Technology at Massey University

Irregular Pagination MisnumberedFlexible Manufacturing Systems (FMS) appeared upon the manufacturing scene in the early 1970s, installations presently number in the thousands. However, many current installations in fact lack flexibility, do not operate in real-time and are prohibitively expensive. Therefore there are obvious benefits to be gained from making improvements to existing flexible manufacturing systems. Research conducted for this thesis focused on two major areas. The implementation of the FMS control system on a SCADA package and the development of an auction based scheduling system. This entailed the development of a hybrid control model composed of three distinct layers; factory, cell and intelligent entity. Key portions of both the factory and cell controllers were then implemented so as to create a minimal system. This has been completed to the point where the auction algorithm has been implemented and tested in an appropriate framework. In achieving the goals mentioned above a number of novel design concepts have been utilised. There are two which are most important, these are the use of low cost modules for the construction of a flexible co-operative manufacturing system, and the ability of this system to operate in a physically distributed area via a Local Area Network. Meaning it is inherently adaptable and resistant to failure. These novel design concepts were ingrained throughout the entire three layered control model. It is felt that this research has succeeded in demonstrating the possibility of implementing a FMS control system on a low cost SCADA package using low cost software and computing elements. The ability of the distributed, auction-based approach to operate successfully within this system, has also been demonstrated through simulation

Nonlinear model predictive control strategy based on soft computing approaches and real time implementation on a coupled-tank system

In order to effectively implement a good model based control strategy, the combination of different linear models working at various operating regions are mostly utilised since a single model that can operate in that fashion is always a difficult task to develop. This work presents the use of soft computing approaches such as evolutional algorithm called simulated annealing (SA), a genetic algorithm (GA) and an artificial neural network (ANN) to design both a robust single nonlinear dynamic ANN model derived from an experimental data driven system identification approach and a nonlinear model predictive control (NMPC) strategy. SA is employed to give an initial weight for the training of the ANN model structure while a gradient descent based Levenberg–Marquardt Algorithm (LMA) approach is used to optimise the ANN weights. The designed NMPC strategy is optimised using a stochastic GA optimisation method and is tested first in simulation and then implemented in real time practical experiment on a highly nonlinear single input single output (SISO) coupled tank system (CTS). An excellent control performance is reported over the conventional proportional-integral-derivative (PID) controller and results show the effectiveness of the approach under disturbances. The nonlinear neural network model proved very reliable in different operating regions. The SISO system can be upgraded to multi-input multi-output (MIMO) system while the whole NMPC approach can easily be adapted to other industrial processes

Neuromodulation Based Control of Autonomous Robots on a Cloud Computing Platform

In recent years, the advancement of neurobiologically plausible models and computer networking has resulted in new ways of implementing control systems on robotic platforms. The work presents a control approach based on vertebrate neuromodulation and its implementation on autonomous robots in the open-source, open-access environment of robot operating system (ROS). A spiking neural network (SNN) is used to model the neuromodulatory function for generating context based behavioral responses of the robots to sensory input signals. The neural network incorporates three types of neurons- cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for rewards- and curiosity-seeking, and serotonergic (5-HT) neurons for risk aversion behaviors. This model depicts neuron activity that is biologically realistic but computationally efficient to allow for large-scale simulation of thousands of neurons. The model is implemented using graphics processing units (GPUs) for parallel computing in real-time using the ROS environment. The model is implemented to study the risk-taking, risk-aversive, and distracted behaviors of the neuromodulated robots in single- and multi-robot configurations. The entire process is implemented in a cloud computing environment using ROS where the robots communicate wirelessly with the computing nodes through the on-board laptops. However, unlike the traditional neural networks, the neuromodulatory models do not need any pre-training. Instead, the robots learn from the sensory inputs and follow the behavioral facets of living organisms. The details of algorithm development, the experimental setup and implementation results under different conditions, in both single- and multi-robot configurations, are presented along with a discussion on the scope of further work

Optimal Operation of Power Distribution Feeders with Smart Loads

Distribution systems have been going through significant changes in recent years, moving away from traditional systems with low-level control toward smart grids with high-level control, with improved technologies in communications, monitoring, computation, and real-time control. In the context of smart grids, Demand Response (DR) programs have been introduced so that customers are able to control and alter their energy consumption in consideration with distribution system operators, with benefits accruing to both customers and Local Distribution Companies (LDCs). This thesis focuses on the integration of DR with the intelligent operation of distribution system feeders. Thus, it proposes a mathematical model of an unbalanced three-phase distribution system power flow, including different kinds of loads and other components of distribution systems. In this context, an unbalanced three-phase Distribution Optimal Power Flow (DOPF) model is proposed, which includes the models of lines, transformers, voltage-based loads, smart loads, Load Tap Changers (LTCs), and Switched Capacitors (SCs), together with their respective operating limits, to determine the optimal switching decisions for LTCs, SCs, and control signals for smart loads, in particular, Energy Hub Management System loads and Peaksaver PLUS loads. Hence, Neural-Network-based models of controllable smart loads, which are integrated into the DOPF model are proposed, developed, and tested. Since the DOPF model has different discrete variables such as LTCs and SCs, the model is a Mixed-Integer Non-Linear Programming (MINLP) problem, which presents a considerable computational challenge. In order to solve this MINLP problem without approximations and ad-hoc heuristics, a Genetic Algorithm (GA) is used to determine the optimal control decisions of controllable feeder elements and loads. Since the number of control variables in a realistic distribution system is large, solving the DOPF for real-time applications using GA is computationally expensive. Hence, a decentralized system with parallel computing nodes based on a Smart Grid Communication Middleware (SGCM) system is proposed. Using a "MapReduce" model, the SGCM system executes the DOPF model, communicates between the master and the worker computing nodes, and sends/receives data amongst different parts of the parallel computing system. When large number of nodes are involved, the SGCM system has a fast performance, is reliable, and is able to handle different fault tolerance levels with the available computing resources. The proposed approaches are tested and validated on a practical feeder with the objective of minimizing energy losses and/or energy drawn from the substation. The results demonstrate the feasibility of the developed techniques for real-time distribution feeder control, highlighting the advantages of integration of smart loads in the operation of distribution systems by LDCs

Context-aware collaborative storage and programming for mobile users

Since people generate and access most digital content from mobile devices, novel innovative mobile apps and services are possible. Most people are interested in sharing this content with communities defined by friendship, similar interests, or geography in exchange for valuable services from these innovative apps. At the same time, they want to own and control their content. Collaborative mobile computing is an ideal choice for this situation. However, due to the distributed nature of this computing environment and the limited resources on mobile devices, maintaining content availability and storage fairness as well as providing efficient programming frameworks are challenging. This dissertation explores several techniques to improve these shortcomings of collaborative mobile computing platforms. First, it proposes a medley of three techniques into one system, MobiStore, that offers content availability in mobile peer-to-peer networks: topology maintenance with robust connectivity, structural reorientation based on the current state of the network, and gossip-based hierarchical updates. Experimental results showed that MobiStore outperforms a state-of-the-art comparison system in terms of content availability and resource usage fairness. Next, the dissertation explores the usage of social relationship properties (i.e., network centrality) to improve the fairness of resource allocation for collaborative computing in peer-to-peer online social networks. The challenge is how to provide fairness in content replication for P2P-OSN, given that the peers in these networks exchange information only with one-hop neighbors. The proposed solution provides fairness by selecting the peers to replicate content based on their potential to introduce the storage skewness, which is determined from their structural properties in the network. The proposed solution, Philia, achieves higher content availability and storage fairness than several comparison systems. The dissertation concludes with a high-level distributed programming model, which efficiently uses computing resources on a cloud-assisted, collaborative mobile computing platform. This platform pairs mobile devices with virtual machines (VMs) in the cloud for increased execution performance and availability. On such a platform, two important challenges arise: first, pairing the two computing entities into a seamless computation, communication, and storage unit; and second, using the computing resources in a cost-effective way. This dissertation proposes Moitree, a distributed programming model and middleware that translates high-level programming constructs into events and provides the illusion of a single computing entity over the mobile-VM pairs. From programmers’ viewpoint, the Moitree API models user collaborations into dynamic groups formed over location, time, or social hierarchies. Experimental results from a prototype implementation show that Moitree is scalable, suitable for real-time apps, and can improve the performance of collaborating apps regarding latency and energy consumption

A Black-box Approach for Containerized Microservice Monitoring in Fog Computing

The goal of the Internet of Things (IoT) is to convert the physical world into a smart space in which physical objects, called things, are equipped with computing and communication capabilities. Those things can connect with anything, anyone at any time, any space via any network or service. The predominant Internet of Things (IoT) system model today is cloud centric. This model introduces latencies into the application execution, as data travels first upstream for processing and secondly the results, i.e., control commands, travel downstream to the devices. In contrast with the cloud-model, the cloud-fog-based model pushes computing capability to the edge of the network, which is closer to the data sources. This enables lower latency and a faster response time. The end-device can directly receive the service from the fog node instead of sending all the data to the central cloud server. In addition, with the application of microservice containerization technology, fog nodes can quickly set up various environments for heterogeneous services. Compared with cloud computing, fog computing needs to consider users’ mobility and geographic location. The application scenarios that fog computing is more dynamic and flexible. Therefore, fog computing requires real-time data monitoring and service management. In this thesis, we will explore how to deploy fog computing resources, what data is needed in the deployment process, and how to implement data monitoring

Soft computing based controllers for automotive air conditioning system with variable speed compressor

The inefficient On/Off control for the compressor operation has long been regarded as the major factor contributing to energy loss and poor cabin temperature control of an automotive air conditioning (AAC) system. In this study, two soft computing based controllers, namely the proportional-integral-derivative (PID) based controllers tuned using differential evolution (DE) algorithm and an adaptive neural network based model predictive controller (A-NNMPC), are proposed to be used in the regulation of cabin temperature through proper compressor speed modulation. The implementation of the control schemes in conjunction with DE and neural network aims to improve the AAC performance in terms of reference tracking and power efficiency in comparison to the conventional On/Off operation. An AAC experimental rig equipped with variable speed compressor has been developed for the implementation of the proposed controllers. The dynamics of the AAC system is modelled using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Based on the plant model, the PID gains are offline optimized using the DE algorithm. Experimental results show that the DE tuned PID based controller gives better tracking performance than the Ziegler-Nichols tuning method. For A-NNMPC, the identified NARX model is incorporated as a predictive model in the control system. It is trained in real time throughout the control process and therefore able to adaptively capture the time varying dynamics of the AAC system. Consequently, optimal performance can be achieved even when the operating point is drifted away from the nominal condition. Finally, the comparative assessment indicates clearly that A-NNMPC outperforms its counterparts, followed by DE tuned PID based controller and the On/Off controller. Both proposed control schemes achieve up to 47% power saving over the On/Off operation, indicating that the proposed control schemes can be potential alternatives to replace the On/Off operation in an AAC system