This paper considers the modeling and compensator design for Self-ServoWriting (SSW) in disk drives. An Iterative Learning Control (ILC) based scheme is established to deal with radial error propagation and improve the quality of written tracks. In the proposed scheme, a feedback controller for track following is first designed to achieve good disturbance attenuation. Then, an ILC structure is applied to generate an external signal, which is injected into the feedback loop in order to compensate for the written-in errors in the previous track while the next track is written. As a result, the error propagation can be contained. The learning controller is synthesized by solving Linear Matrix Inequality (LMI) equations to ensure the stability and monotonic convergence of the control algorithm. Simulation results show the effectiveness of the proposed scheme on the error containment which results in good quality written tracks.
Wind energy is the most used renewable energy worldwide second only to hydropower. However, the stochastic nature of wind speed makes it harder for wind farms to manage the future power production and maintenance schedules efficiently. Many wind speed prediction models exist that focus on advance neural networks and/or preprocessing techniques to improve the accuracy. Since most of these models require a large amount of historic wind data and are validated using the data split method, the application to real-world scenarios cannot be determined. In this paper, we present a multi-step univariate prediction model for wind speed data inspired by the residual U-net architecture of the convolutional neural network (CNN). We propose a residual dilated causal convolutional neural network (Res-DCCNN) with nonlinear attention for multi-step-ahead wind speed forecasting. Our model can outperform long-term short-term memory networks (LSTM), gated recurrent units (GRU), and Res-DCCNN using sliding window validation techniques for 50-step-ahead wind speed prediction. We tested the performance of the proposed model on six real-world wind speed datasets with different probability distributions to confirm its effectiveness, and using several error metrics, we demonstrated that our proposed model was robust, precise, and applicable to real-world cases.
This paper considers the modeling and compensator design for Self-ServoWriting (SSW) in disk drives. An Iterative Learning Control (ILC) based scheme is established to deal with radial error propagation and improve the quality of written tracks. In the proposed scheme, a feedback controller for track following is first designed to achieve good disturbance attenuation. Then, an ILC structure is applied to generate an external signal, which is injected into the feedback loop in order to compensate for the written-in errors in the previous track while the next track is written. As a result, the error propagation can be contained. The learning controller is synthesized by solving Linear Matrix Inequality (LMI) equations to ensure the stability and monotonic convergence of the control algorithm. Simulation results show the effectiveness of the proposed scheme on the error containment which results in good quality written tracks.
Increased concerns over global warming and air pollution has pushed governments to consider renewable energy as an alternative to meet the required energy demands of countries. Many government policies are deployed in Taiwan to promote solar and wind energy to cope with air pollution and self-dependency for energy generation. However, the residential sector contribution is not significant despite higher feed-in tariff rates set by government. This study analyzes wind and solar power availability of four different locations of southern Taiwan, based on the Köppen–Geiger climate classification system. The solar–wind hybrid system (SWHS) considered in this study consists of multi-crystalline photovoltaic (PV) modules, vertical wind turbines, inverters and batteries. Global reanalysis weather data and a climate-based electricity load profile at a 1-h resolution was used for the simulation. A general framework for multi-objective optimization using this simulation technique is proposed for solar–wind hybrid system, considering the feed-in tariff regulations, environmental regulations and installation area constraints of Taiwan. The hourly load profile is selected using a climate classification system. A decomposition-based differential evolutionary algorithm is used for finding the optimal Pareto set of two economic objectives and one environmental objective with maximum installation area and maximum PV capacity constraints. Two types of buildings are chosen for analysis at four climate locations. Analysis of Pareto sets revealed that the photovoltaic modules are economic options for a grid-connected mode at all four locations, whereas solar–wind hybrid systems are more environmentally friendly. A method of finding the fitness index for the Pareto front sets and a balanced strategy for choosing the optimal configuration is proposed. The proposed balanced strategy provides savings to users—up to 49% for urban residential buildings and up to 32% for rural residential buildings with respect to buildings without a hybrid energy system (HES)—while keeping carbon dioxide (CO2) emissions lower than 50% for the total project lifecycle time of 20 years. The case study reveals that for all four locations and two building types an HES system comprising a 15 kW photovoltaic system and a small capacity battery bank provides the optimal balance between economic and environmental objectives.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.