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Physica D: Nonlinear Phenomena, 2020
Abstract There is an abundance of instances when the state of a complex network needs to be altered to a pre-specified target state, one potential example being the need to alter a disease-induced tissue to a healthy state. Unfortunately, in many such cases, accurate models of the underlying network are unavailable.
Jason Shulman +2 more
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Abstract There is an abundance of instances when the state of a complex network needs to be altered to a pre-specified target state, one potential example being the need to alter a disease-induced tissue to a healthy state. Unfortunately, in many such cases, accurate models of the underlying network are unavailable.
Jason Shulman +2 more
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Event-Triggered Model-Free Adaptive Control
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021This paper investigates an event-triggered model-free adaptive control for nonaffined nonlinear systems under a data-driven design framework. By introducing a compact form dynamic linearization (CFDL) scheme, a linear data model of the nonlinear nonaffine system is derived.
Na Lin 0002, Ronghu Chi, Biao Huang 0001
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Iterative Feedback Tuning of Model-Free Controllers
2021 25th International Conference on System Theory, Control and Computing (ICSTCC), 2021Iterative Feedback Tuning (IFT) and Model Free Control (MFC) are strategies from the Data Driven Control (DDC) class. The first is a controller tuning method and the second is a control algorithm, both based only on the data collected from the process. In this paper, a new strategy that improves the MFC control law performance with the IFT algorithm is
Andrei Baciu +2 more
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A model-free decentralized control for robot manipulators
Proceedings of International Conference on Robotics and Automation, 2002A model-free robust/adaptive decentralized controller for nonlinear uncertain robot manipulators is presented. The proposed controller consists of a PD controller, a robust/adaptive controller and dynamic compensator. The dynamic compensator is online up-dated based on the Taylor expansion in terms of the historical information of the individual local ...
Lilong Cai, Xiaoqi Tang
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A model free approach to controlling blood glucose
2003 European Control Conference (ECC), 2003We present a problem of controlling type 1 diabetes mellitus — a situation where plant is complex and dynamic, the measurements are sparse, and the data display erratic fluctuating behaviour. These characteristics make it very difficult to derive a model of the plant.
Lucia Santoso, Iven M. Y. Mareels
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Model‐free control of a seeded batch crystallizer
The Canadian Journal of Chemical Engineering, 2017AbstractAs the use of a batch crystallization process in several industrial applications is extensive, finding an effective control strategy is important to improve the product quality, which is typically characterized by a unimodal and narrow crystal size distribution (CSD) with a large mean crystal size.
Afsi, Nawel +3 more
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Model free analysis and tuning of PID controller
2013 9th Asian Control Conference (ASCC), 2013In this paper, a new PID parameter tuning method is proposed. First, extensive analysis of the PID frequency properties is conducted. Based on the analysis results, the concept of characteristic frequency of the PID controller is proposed, which builds a relationship between the PID parameters and the oscillation characteristics of the closed loop ...
Zhiqiang Zhu +4 more
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Design and Control of a Neonatal Incubator Using Model-Free Control
2021 29th Mediterranean Conference on Control and Automation (MED), 2021Prematurely newborn infants are unable to regulate their own body temperature. Therefore, these infants are placed in incubators where temperature is maintained at suitable levels. Because stability of temperature in the incubator is essential to the survival and recovery of the infant, a robust control system needs to be used in order to maintain it ...
Ali Ismail +3 more
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Model-free learning of wire winding control
2013 9th Asian Control Conference (ASCC), 2013In this paper we introduce a reinforcement learning approach to optimize the wire profile generated by an automated wire winding machine. The wire winder spools wire onto large bobbins, while trying to maintain an even wire profile across the bobbin. Uneven profiles that contain bumps or gaps (i.e.
Abdel Rodríguez +3 more
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An Improved Model Free Adaptive Control Algorithm
2009 Fifth International Conference on Natural Computation, 2009Generally the application of traditional adaptive control algorithm relies on the mathematic model of system. But mathematic models of some dynamic systems are difficult to establish. According to this actual problem and the existing structure of algorithm, an improved Model Free Adaptive control algorithm based on neural network is put forward in this
Aidong Xu +3 more
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