CAN Fieldbus Communication in the CSP-based CT Library

In closed-loop control systems several realworld entities are simultaneously communicated to through a multitude of spatially distributed sensors and actuators. This intrinsic parallelism and complexity motivates implementing control software in the form of concurrent processes deployed on distributed hardware architectures. A CSP based occam-like architecture seems to be the most convenient for such a purpose. Many, often conflicting, requirements make design and implementation of distributed real-time control systems an extremely difficult task. The scope of this paper is limited to achieving safe and real-time communication over a CAN fieldbus for an\ud existing CSP-based framework

A development framework for artificial intelligence based distributed operations support systems

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself

An Advance Distributed Control Design for Wide-Area Power System Stability

The development of control of a power system that supply electricity is a major concern in the world. Some trends have led to power systems becoming overstated including the rapid growth in the demand for electrical power, the increasing penetration of the system from renewable energy, and uncertainties in power schedules and transfers. To deal with these challenges, power control has to overcome several structural hurdles, a major one of which is dealing with the high dimensionality of the system. Dimensionality reduction of the controller structure produces effective control signals with reduced computational load. In most of the existing studies, the topology of the control and communication structure is known prior to synthesis, and the design of distributed control is performed subject to this particular structure. However, in this thesis we present an advanced model of design for distributed control in which the control systems and their communication structure are designed simultaneously. In such cases, a structure optimization problem is solved involving the incorporation of communication constraints that will punish any communication complexity in the interconnection and thus will be topology dependent. This structure optimization problem can be formulated in the context of Linear Matrix Inequalities and l1-minimization. Interconnected power systems typically show multiple dominant inter-area low-frequency oscillations which lead to widespread blackouts. In this thesis, the specific goal of stability control is to suppress these inter-area oscillations. Simulation results on large-scale power system are presented to show how an optimal structure of distributed control would be designed. Then, this structure is compared with fixed control structures, a completely decentralized control structure and a centralized control structure

A fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled building automation systems

Internet of Things (IoT) technologies are increasingly implemented in buildings as the cost-effective smart sensing infrastructure of building automation systems (BASs). They are also dispersed computing resources for novel distributed optimal control approaches. However, wireless communication networks are critical to fulfill these tasks with the performance influenced by inherent uncertainties in networks, e.g., unpredictable occurrence of link failures. Centralized and hierarchical distributed approaches are vulnerable against link failure, while the robustness of fully distributed approaches depends on the algorithms adopted. This study therefore proposes a fully distributed robust optimal control approach for air-conditioning systems considering uncertainties of communication link in IoT-enabled BASs. The distributed algorithm is adopted that agents know their out-neighbors only. Agents directly coordinate with the connected neighbors for global optimization. Tests are conducted to test and validate the proposed approach by comparing with existing approaches, i.e., the centralized, the hierarchical distributed and the fully distributed approaches. Results show that different approaches are vulnerable against to uncertainties of communication link to different extents. The proposed approach always guarantees the optimal control performance under normal conditions and conditions with link failures, verifying its high robustness. It also has low computation complexity and high optimization efficiency, thus applicable on IoT-enabled BASs