Deep learning frameworks to learn prediction and simulation focused control system models

Deep learning (DL) methods have brought world-shattering breakthroughs, especially in computer vision and classification problems. Yet, the design and deployment of DL methods in time series prediction and nonlinear system identification applications still need more progress. In this paper, we present DL frameworks that are developed to provide novel approaches as solutions to the aforementioned engineering problems. The proposed DL frameworks leverage the advantages of autoencoders and long-short term memory network, which are known being data compression and recurrent structures, respectively, to design Deep Neural Networks (DNN) for modeling time series and nonlinear systems with high performance. We provide recommendations on how deep AEs and LSTMs should be utilized to end up with efficient Prediction-focused (Pf) and Simulation-focused (Sf) DNNs for time series and system identification problems. We present systematic learning methods for the DL frameworks that allow straightforward learning of Pf-DNN and Sf-DNN models in detail. To demonstrate the efficiency of the developed DNNs, we present various comparative results conducted on the benchmark and real-world datasets in comparison with their conventional, shallow, and deep neural network counterparts. The results clearly show that the deployment of the proposed DL frameworks results with DNNs that have high accuracy, even with a low dimensional feature vector.

     

Interval type-2 fuzzy inverse controller design in nonlinear IMC structure

In the recent years it has been demonstrated that type-2 fuzzy logic systems are more effective in modeling and control of complex nonlinear systems compared to type-1 fuzzy logic systems. An inverse controller based on type-2 fuzzy model can be proposed since inverse model controllers provide an efficient way to control nonlinear processes. Even though various fuzzy inversion methods have been devised for type-1 fuzzy logic systems up to now, there does not exist any method for type-2 fuzzy logic systems. In this study, a systematic method has been proposed to form the inverse of the interval type-2 Takagi-Sugeno fuzzy model based on a pure analytical method. The calculation of inverse model is done based on simple manipulations of the antecedent and consequence parts of the fuzzy model. Moreover, the type-2 fuzzy model and its inverse as the primary controller are embedded into a nonlinear internal model control structure to provide an effective and robust control performance. Finally, the proposed control scheme has been implemented on an experimental pH neutralization process where the beneficial sides are shown clearly.     

A fuzzy assessment framework to select among transportation investment projects in Turkey

Selecting the best transportation investment project (TIP) is often a difficult task, since many social, environmental and economic criteria have to be considered simultaneously. Evaluating a set of different projects, especially the best set of alternatives, portfolios, is even more complex. Pursuing the goal of selecting the best TIP portfolio, we propose a fuzzy assessment method to aid the selection process of a multi-criterion project by utilizing the concept of entropy and interval normalization procedure in a fuzzy analytic hierarchy process (F-AHP). Then, regarding this informative phase, we propose a fuzzy linear programming model to select the best TIP portfolio under uncertain cost pressure. A real case study is conducted to illustrate the efficiency of the proposed method.     

Fuzzy logic control of vehicle suspensions with dry friction nonlinearity

We design and investigate the performance of fuzzy logic-controlled (FLC) active suspensions on a nonlinear vehicle model with four degrees of freedom, without causing any degeneration in suspension working limits. Force actuators were mounted parallel to the suspensions. In this new approach, linear combinations of the vertical velocities of the suspension ends and accelerations of the points of connection of the suspension to the body have been used as input variables. The study clearly demonstrates the effectiveness of the fuzzy logic controller for active suspension systems. Suspension working space degeneration is the most important problem in various applications. Decreasing the amplitudes of vehicle body vibrations improves ride comfort. Body bounce and pitch motion of the vehicle are presented both in time domain when travelling over a ramp-step road profile and in frequency domain. The results are compared with those of uncontrolled systems. At the end of this study, the performance and the advantage of the suggested approach and the improvement in ride comfort are discussed.     

Porous polyurethane film fabricated via the breath figure approach for sustained drug release

The breath figure (BF) method is an effective process for fabricating porous polymeric films. In this study, we fabricated porous polymer films from thermoplastic polyurethane (PU) through static BF with CHCl₃ as a solvent under 55–80% relative humidity. The porous PU films were prepared within various pore structures and sizes, which were adjustable, depending on the fabrication conditions. The humidity and exposure time were examined as variable parameters affecting the surface morphology, wettability, and cytotoxicity. Atorvastatin calcium, a hyperlipidemic agent, was loaded into the porous films during the casting process, and the drug-loading and drug-releasing behaviors of the porous PU membranes were evaluated. Approximately 60–80% of the drug was released in 14 days. The films exhibited sustained drug-release performances because of the hydrophobicity and nonbiodegradable nature of PU for perivascular drug administration. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47658.     

Coupling Between Magnetic Exchange and Charge Activation in Cu‐Doped LaFeO3

Material research on perovskite‐type oxides (ABO₃) has been driven by the recognition of their unique properties primarily attributed to the presence of oxygen octahedron (BO₆). Since 2003, the discovery of strong coupling in TbMnO₃ and BiFeO₃ has stimulated new interests in understanding the relationship between magnetic and electric properties in perovskites. In this article, we report our recent work on the magnetic superexchange interaction and charge formation in copper‐doped LaFeO₃ using high‐temperature neutron diffraction and thermoelectric measurements. In situ neutron diffraction measurements show a loss of antiferromagnetic ordering above 450°C. With an increase in Cu content, the (Fe, Cu)‐O bond length decreases and the (Fe, Cu)–O–(Fe, Cu) bond angle increases, which leads to an enhancement of the Fe–O–Fe superexchange interaction. Thermoelectric and electrical measurements show that the formation of electron holes in Cu‐doped LaFeO₃ is a thermally activated process with two distinct regions with a transition temperature near 450°C, in congruence with the magnetic measurements. Our work show that Cu is in 3+ state in La(Fe,Cu)O₃ at room temperature, resulting in the maximum superexchange interaction between Fe³⁺ ions.