Data-driven dual-rate cascade control and application to pitch angle control of UAV

A data-driven design method for a cascade control system is proposed. The cascade control system consists of inner and outer loops, where the control interval of the outer loop is an integer multiple of the inner loop; hence, the system is a dual-rate system. In the proposed method, controllers in the inner and outer loops are designed based on one-shot data. In such a dual-rate cascade system, since the controllers are designed using different data-rate signals, the lifting technique is applied to align the dual-rate data. To show its effectiveness, the proposed method is compared with a conventional single-rate cascade control method, and numerical simulations and experiments are presented to examine servo and regulation performance.     

Survey of actual viewing conditions at home and appropriate luminance of LCD‐TV screens

Abstract— To understand actual viewing conditions at home is important for TV design. And the preferred luminance level of LCD TVs under actual viewing conditions is also important in order to obtain both good picture quality and low power consumption. The actual viewing conditions of households and the preferred luminance levels was investigated. In a field test of 83 households, the display luminance, screen illuminance, and viewing locations were measured on site. In laboratory experiments, young and elderly subjects adjusted the luminance of an LCD‐TV screen to their preferred levels under different screen illuminance levels, angular screen sizes, and average luminance levels (ALL) of the images. As a result, two equations, which represent the preferred luminance level of LCD‐TV screens corresponding to different viewing conditions for young and elderly subjects were obtained. When the ALL of the images was 25% and the screen illuminance and angular screen size were set at 100 lx and 20°, respectively, the preferred luminance was 1 60 cd/m² for the young subjects and 248 cd/m² for the elderly subjects. By using the setting of the preferred luminance of an LCD TV under actual viewing conditions, it is possible to conserve energy consumption.     

A new learning method using prior information of neural networks

In this paper, we present a new learning method using prior information for three-layer neural networks. Usually when neural networks are used for identification of systems, all of their weights are trained independently, without considering interrelated weights values. Thus, the training results are usually not good. The reason for this in that each parameter has its influence on others during learning. To overcome this problem, we first give an exact mathematical equation that describes the relation between weight values given a set of data conveying prior information. The we present a new learning method that trains part of the weights and calculates the others using these exact mathematical equations. This method often a priori keeps the given mathematical structure exactly the same during learning; in other words, training is done so that the network follows a predetermined trajectory. Numerical computer simulation results are provided to support this approach. This work was presented, in part, at the Fourth International Symposium on Artificial Life and Robotics, Oita, Japan, January 19–22, 1999.     

Iterative Path Integral Approach to Nonlinear Stochastic Optimal Control Under Compound Poisson Noise

Nonlinear stochastic optimal control theory has played an important role in many fields. In this theory, uncertainties of dynamics have usually been represented by Brownian motion, which is Gaussian white noise. However, there are many stochastic phenomena whose probability density has a long tail, which suggests the necessity to study the effect of non‐Gaussianity. This paper employs Lévy processes, which cause outliers with a significantly higher probability than Brownian motion, to describe such uncertainties. In general, the optimal control law is obtained by solving the Hamilton–Jacobi–Bellman equation. This paper shows that the path‐integral approach combined with the policy iteration method is efficiently applicable to solve the Hamilton–Jacobi–Bellman equation in the Lévy problem setting. Finally, numerical simulations illustrate the usefulness of this method.     

Optimal operation of cogeneration system coordinated by aggregator for providing the demand and supply regulation capacity

Recent growth of renewable energy generations with natural variability, such as photovoltaic generation and wind turbine generation, would make the demand and supply control in a whole power system more difficult, and therefore, alternatives for demand and supply regulation resources would be required. The authors focus on cogeneration system (CGS) as one of regulation resources. In order to procure adequate volume of regulation capability, an aggregator coordinates a number of CGSs efficiently and flexibly considering the wide variety of electricity/thermal demands of CGS owners. This paper proposes a novel optimal operation strategy of CGS coordinated by the aggregator considering the energy balance and operation cost of individual CGS owner. This paper also demonstrates the availability of CGSs for regulation capability by numerical case studies in which the actual consumption profile is employed.     

Development of Selective TGR5 Ligands Based on the 5,6,7,8-Tetrahydro-5,5,8,8-tetramethylnaphthalene Skeleton

TGR5, a G-protein-coupled receptor (GPCR), plays an important role in several physiological functions. TGR5 activation through bile acids induces an increase in energy expenditure. Therefore, synthetic TGR5 ligands could be useful for the treatment of obesity or dyslipidemia. In this study, we designed and synthesized a set of TGR5 ligands with a 5,6,7,8-tetrahydro-5,5,8,8-tetramethylnaphthalene (TMN) skeleton, and evaluated their TGR5 agonistic activity. We also investigated the selectivity of the synthesized compounds for TGR5 relative to the farnesoid X receptor (FXR) and retinoic acid receptor (RAR). Our results show that compound 4 b [N-(2-chlorophenyl)-5,6,7,8-tetrahydro-5,5,8,8-tetramethyl-2-naphthalenecarboxamide] exhibited potent TGR5 agonist activity with an IC₅₀ value of 8.4 nM without significant cytotoxicity. In addition, compound 4 b showed only slight agonistic activity toward FXR and RAR at 1 μM treatment. These data indicate that compound 4 b is a selective TGR5 agonist, and could be a promising therapeutic agent for dyslipidemia.     

A Quasi-ARMAX approach to modelling of non-linear systems

This paper proposes a class of quasi-ARMAX models for non-linear systems. Similar to ordinary non-linear ARMAX models, the quasi-ARMAX models are flexible black-box models, but they have various linearity properties similar to those of linear ARMAX models. A modelling scheme is introduced to construct models consisting of two parts: a macro-part and a kernel-part. By using Taylor expansion and other mathematical transformation techniques, it is first constructed as a class of quasi-ARMAX interfaces (macro-parts) that have various linearity properties but contain some complicated coefficients. MIMO neurofuzzy models (kernel-parts) are then introduced to represent the complicated coefficients. It is shown that the proposed quasi-ARMAX models have both good approximation ability and some easy-to-use properties. The proposed models have been successfully applied to prediction, fault detection and adaptive control of non-linear systems.