His scientific interests lie mostly in Control theory, Genetic algorithm, Fuzzy logic, Control engineering and Positioning system. In the subject of general Control theory, his work in Machine control, Control theory and Adaptive algorithm is often linked to Laser and Hysteresis, thereby combining diverse domains of study. His Genetic algorithm research includes themes of Evolutionary algorithm, Search algorithm, Heuristics and Operations research.
His work on Fuzzy control system and Soft computing as part of general Fuzzy logic study is frequently connected to Schema, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them. The Control engineering study which covers Linear motor that intersects with Stability, Adaptive control and Lyapunov function. In Control system, David Naso works on issues like Multi-agent system, which are connected to Mathematical optimization.
His primary areas of investigation include Control theory, Actuator, Control engineering, Control theory and Mathematical optimization. His research in the fields of Adaptive control, Nonlinear system, Control system and PID controller overlaps with other disciplines such as Positioning system. His Control engineering research is multidisciplinary, relying on both Control, Linear motor and Robustness.
His Control theory research incorporates themes from Automatic control, Optimal control, Linear system and Fuzzy logic. In most of his Mathematical optimization studies, his work intersects topics such as Economic dispatch. David Naso has included themes like Scheduling and Heuristics in his Genetic algorithm study.
David Naso focuses on Actuator, Control theory, Nonlinear system, Control engineering and Shape-memory alloy. His studies in Actuator integrate themes in fields like Mechanical engineering, Elastomer and Electronic engineering. David Naso merges Control theory with Positioning system in his study.
His research investigates the connection between Nonlinear system and topics such as Bistability that intersect with problems in Design choice. His Control engineering research is multidisciplinary, incorporating elements of Discretization, Dielectric elastomer actuator and Energy management. As a part of the same scientific study, David Naso usually deals with the Control theory, concentrating on Haptic technology and frequently concerns with Stability and Algorithm design.
His primary areas of study are Actuator, Control theory, Nonlinear system, Positioning system and Control engineering. His biological study spans a wide range of topics, including Capacitance, Biasing and Stress. His Capacitance study also includes
His study of Robust control is a part of Control theory. His Control engineering study focuses on Control theory in particular. His work deals with themes such as Mechanical engineering, Linear motor, Realization and Sensitivity, which intersect with Dielectric elastomer actuator.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Distributed Consensus-Based Economic Dispatch With Transmission Losses
Giulio Binetti;Ali Davoudi;Frank L. Lewis;David Naso.
IEEE Transactions on Power Systems (2014)
Genetic algorithms for supply-chain scheduling: A case study in the distribution of ready-mixed concrete
David Naso;Michele Surico;Biagio Turchiano;Uzay Kaymak.
European Journal of Operational Research (2007)
Compact Differential Evolution
E Mininno;F Neri;F Cupertino;D Naso.
IEEE Transactions on Evolutionary Computation (2011)
A Distributed Auction-Based Algorithm for the Nonconvex Economic Dispatch Problem
Giulio Binetti;Ali Davoudi;David Naso;Biagio Turchiano.
IEEE Transactions on Industrial Informatics (2014)
Real-Valued Compact Genetic Algorithms for Embedded Microcontroller Optimization
E. Mininno;F. Cupertino;D. Naso.
IEEE Transactions on Evolutionary Computation (2008)
Modeling, Identification, and Control of a Dielectric Electro-Active Polymer Positioning System
Gianluca Rizzello;David Naso;Alexander York;Stefan Seelecke.
IEEE Transactions on Control Systems and Technology (2015)
Fuzzy control of a mobile robot
F. Cupertino;V. Giordano;D. Naso;L. Delfine.
IEEE Robotics & Automation Magazine (2006)
Closed loop control of dielectric elastomer actuators based on self-sensing displacement feedback
G Rizzello;G Rizzello;D Naso;A York;S Seelecke.
Smart Materials and Structures (2016)
Sliding-Mode Control With Double Boundary Layer for Robust Compensation of Payload Mass and Friction in Linear Motors
F. Cupertino;D. Naso;E. Mininno;B. Turchiano.
IEEE Transactions on Industry Applications (2009)
A precise positioning actuator based on feedback-controlled magnetic shape memory alloys
Leonardo Riccardi;David Naso;Hartmut Janocha;Biagio Turchiano.
Profile was last updated on December 6th, 2021.
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