Mechanochemical interconversion between discrete complexes and coordination networks - formal hydration/dehydration by LAG

Using a small planetary ball mill, liquid-assisted grinding (LAG) of metal salts or oxides (ZnO, CdO, CdCO3, Cu(OAc)(2)center dot H2O, Co(OAc)(2)center dot 4H(2)O, Mn(OAc)(2)center dot 4H(2)O, Ni(OAc)(2)center dot 4H(2)O, FeSO4 center dot 7H(2)O) with two equivalents of isonicotinic acid (HINA) and small amounts of water ( up to 5.6 molar equivalents) gave discrete aquo complexes trans-[M(INA)(2)(OH2)(4)] (M = Zn, Cd, Cu, Fe, Co, Ni, Mn) efficiently within 30 min. For M = Zn, Cd and Cu these complexes readily undergo reversible formal dehydration to the extended network structures [M(INA)(2)] (M = Zn, Cu) or [Cd(INA)(2)(OH2)]center dot DMF by further LAG with non-aqueous liquids such as methanol or DMF. Overall, the mechanochemical dehydrations are more effective than heating or immersion in bulk solvents. The work demonstrates a two-step mechanochemical synthesis of coordination networks via discrete aquo complexes which may be preferable to single step reactions or grinding-annealing procedures in some cases. For example, the two step method was the only way to prepare [Cd(INA)(2)(OH2)]center dot DMF mechanochemically and the porous network Cu(INA)(2) could not be obtained from the aquo complex by heating.

Active Controllers or Wealthy Rentiers?: Large Shareholders in Victorian Public Companies

This article addresses the issue of whether large shareholders in Victorian public companies were active in the control of companies or were simply wealthy rentiers. Using ownership records for 890 firm-years, we examine the control rights, socio-occupational background, and wealth of large shareholders. We find that many large shareholders had limited voting rights and neither they nor family members were directors. This implies that the majority of public companies in the second half of the nineteenth century cannot be characterized as family companies and that large shareholders are better viewed as wealthy gentlemen capitalists rather than entrepreneurs.

Energy Saving - Another Perspective for Parameter Optimization of P and PI Controllers

Modern control methods like optimal control and model predictive control (MPC) provide a framework for simultaneous regulation of the tracking performance and limiting the control energy, thus have been widely deployed in industrial applications. Yet, due to its simplicity and robustness, the conventional P (Proportional) and PI (Proportional–Integral) control are still the most common methods used in many engineering systems, such as electric power systems, automotive, and Heating, Ventilation and Air Conditioning (HVAC) for buildings, where energy efficiency and energy saving are the critical issues to be addressed. Yet, little has been done so far to explore the effect of its parameter tuning on both the system performance and control energy consumption, and how these two objectives are correlated within the P and PI control framework. In this paper, the P and PI controllers are designed with a simultaneous consideration of these two aspects. Two case studies are investigated in detail, including the control of Voltage Source Converters (VSCs) for transmitting offshore wind power to onshore AC grid through High Voltage DC links, and the control of HVAC systems. Results reveal that there exists a better trade-off between the tracking performance and the control energy through a proper choice of the P and PI controller parameters.

On Infinite Time Performance of Nonlinear Model Predictive Controllers

This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal performance limits of closed-loop model predictive control systems. Based on the Principle of Optimality, an upper and a lower bound of the ratio between the total performance and the finite horizon optimal cost are obtained explicitly expressed by the optimization horizon. The results also illustrate, from viewpoint of performance, how model predictive controllers approaches to infinite optimal controllers as the optimization horizon increases.

Design and deployment of secure, robust, and resilient SDN Controllers

The scale of the Software-Defined Network (SDN) Controller design problem has become apparent with the expansion of SDN deployments. Initial SDN deployments were small-scale, single controller environments for research and usecase testing. Today, enterprise deployments requiring multiple controllers are gathering momentum e.g. Google’s backbone network, Microsoft’s public cloud, and NTT’s edge gateway. Third-party applications are also becoming available e.g. HP SDN App Store. The increase in components and interfaces for the evolved SDN implementation increases the security challenges of the SDN controller design. In this work, the requirements of a secure, robust, and resilient SDN controller are identified, stateof-the-art open-source SDN controllers are analyzed with respect to the security of their design, and recommendations for security improvements are provided. This contribution highlights the gap between the potential security solutions for SDN controllers and the actual security level of current controller designs.

Automatic tuning of PID controllers using a neuro-genetic system

Neural networks and genetic algorithms have been in the past successfully applied, separately, to controller turning problems. In this paper we propose to combine its joint use, by exploiting the nonlinear mapping capabilites of neural networks to model objective functions, and to use them to supply their values to a genetic algorithm which performs on-line minimization.

Neural network assisted autotuning of industrial PID controllers

A recent servey (1) has reported that the majority of industrial loops are controlled by PID-type controllers and many of the PID controllers in operation are poorly tuned. poor PID tuning is due to the lack of a simple and practical tuning method for avarage users, and due to the tedious procedurs involved in the tuning and retuning of PID controllers.

Optimal controllers with complex order derivatives

This paper studies the optimization of complex-order algorithms for the discrete-time control of linear and nonlinear systems. The fundamentals of fractional systems and genetic algorithms are introduced. Based on these concepts, complexorder control schemes and their implementation are evaluated in the perspective of evolutionary optimization. The results demonstrate not only that complex-order derivatives constitute a valuable alternative for deriving control algorithms, but also the feasibility of the adopted optimization strategy.

Application of fractional controllers for quad rotor

This paper studies the application of fractional algorithms in the control of a quad-rotor rotorcraft. The development of a flight simulator provide the evaluation of the controller algorithm. Several basic maneuvers are investigated, namely the elevation and the position control.

Optimal tuning of fractional controllers using genetic algorithms

This study addresses the optimization of fractional algorithms for the discrete-time control of linear and non-linear systems. The paper starts by analyzing the fundamentals of fractional control systems and genetic algorithms. In a second phase the paper evaluates the problem in an optimization perspective. The results demonstrate the feasibility of the evolutionary strategy and the adaptability to distinct types of systems.

Automatic Analysis and Synthesis of Controllers for Dynamical Systems Based On P

I present a novel design methodology for the synthesis of automatic controllers, together with a computational environment---the Control Engineer's Workbench---integrating a suite of programs that automatically analyze and design controllers for high-performance, global control of nonlinear systems. This work demonstrates that difficult control synthesis tasks can be automated, using programs that actively exploit and efficiently represent knowledge of nonlinear dynamics and phase space and effectively use the representation to guide and perform the control design. The Control Engineer's Workbench combines powerful numerical and symbolic computations with artificial intelligence reasoning techniques. As a demonstration, the Workbench automatically designed a high-quality maglev controller that outperforms a previous linear design by a factor of 20.