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LPV modeling of type 1 diabetes mellitus

LPV modeling of type 1 diabetes mellitus

引用

8th International symposium of Hungarian Researchers on computational intelligence and Informatics, CINTI 2007

作者： Kovács, Levente Kulcsár, Balázs Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Magyar tudósok krt. 2 H-1117 Budapest Hungary Department of Transport Automation Faculty of Transportation Engineering Budapest University of Technology and Economics Hungary

The paper investigates the possibility of modeling the highly nonlinear and very complex Sorensen model, [1], of Type 1 diabetic patients using the polytopic LPV modeling possibility. It is illustrated that the LPV model is working correctly only in the considered polytope region, but inside it is approximating well the nonlinear model.

关键词： Diabetes mellitus Glucose-insulin control LPV control Polytope region

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Optimal control for a variable-speed wind turbine

Optimal control for a variable-speed wind turbine

引用

6th ieee International symposium on Applied computational intelligence and Informatics, SACI 2011

作者： Koch-Ciobotaru, Cosmin Boraci, Radu Prostean, Octavian Budisan, Nicolae Politehnica University Department of Automation and Applied Informatics Timisoara Romania

ISBN: (纸本)9781424491094

The paper presents a control strategy for a wind energy conversion system (WECS) which tracks the maximum power point of the wind turbine. The topology of the system consists of a permanent magnet synchronous generator (PMSG), a diode bridge rectifier, a hybrid dc-dc converter which lowers the dc voltage, a supercapacitor and a battery charger. This strategy uses the measured wind speed and imposes the optimal rotation speed of the wind turbine. The control method is simulated in Matlab/ Simulink and the results are presented and discussed. © 2011 ieee.

关键词： Variable speed drives

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Uncertainty modeling and control design of variable-geometry suspension

Uncertainty modeling and control design of variable-geometry...

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12th ieee International symposium on computational intelligence and Informatics, CINTI 2011

作者： Németh, Balázs Gáspár, Péter Systems and Control Laboratory Computer and Automation Research Institute Hungary

ISBN: (纸本)9781457700453

The paper proposes the modeling and control design of a variable-geometry suspension system. The goal of the modification of wheel camber is to track road trajectory and enhance the lateral stability of the vehicle. The purpose of the system is to assist the driver to perform various maneuvers by influencing the camber angles of the rear wheels. First a tyre model and a dynamical model of the suspension are described. Second, the uncertainties of the system, which are considered at the control design of a variable-geometry suspension are formalized. The control design is based on the robust H∞/μ method, which meets the performance specifications and guarantees robustness against model uncertainties. © 2011 ieee.

关键词： Uncertainty analysis

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Modeling and analyzing technical systems as complex networks: Detecting inverse response

Modeling and analyzing technical systems as complex networks...

引用

ieee symposium on computational intelligence in control and automation, cica

作者： Andreas Geiger Andreas Kroll Measurement and Control Department University of Kassel Kassel Germany

”Complex networks” is the term for a research area where complex systems are modeled by a graph to analyze their structural behavior. They are mostly used in the areas of social sciences, biology and physics. For example, complex networks are a proper method to describe and analyze the non-trivial characteristics depending on the interconnection in a society or between human organs. In the context of computational intelligence, this paper introduces an idea to transfer the methods of the area of complex networks to technical systems, and, fur-thermore, enhance them to permit analyzing dynamic behavior. As a basis for the method transfer a transfer-function-based graph is presented which allows modeling technical systems in the same way as complex networks. The potential to detect dynamical behavior, in addition to structural behavior, is demonstrated by a new algorithm that detects inverse response in interconnected systems based on methods of complex networks. The introduced algorithm provides a qualitative answer if inverse response behavior is possible between a pair of input and output of a system. Finally, two case studies are used to demonstrate the algorithm.

关键词： Complex networks Transfer functions Approximation methods Mathematical model Equations Analytical models Time factors

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Fixed-order H∞ loop-shaping synthesis: A time domain approach

Fixed-order H∞ loop-shaping synthesis: A time domain approa...

引用

ieee symposium on computational intelligence in control and automation, cica

作者： Philippe Feyel Gilles Duc Guillaume Sandou Optronics & Defense division Sagem Defense & Security Safran Group Massy France Control Department SUPELEC Systems Sciences Gif-sur-Yvette France

This work deals with a new approach of loop-shaping H ∞ synthesis based on the Particle Swarm Optimization (PSO) algorithm. Indeed, such stochastic algorithms are interesting to solve problems based on complex industrial specifications and so seem to be particularly well suited to optimal robust controller synthesis. In this work we investigate the optimal weight tuning along a fixed order controller computation without any structural assumption on the searched filters except of course their order. The absence of any structural assumption is important to avoid affecting the quality of the solution towards a complex specification and allows us to reduce the synthesis problem to a simple time domain one with static scalings in place of frequency weights. Using a version of PSO well adapted for high dimensional problems, we succeed in computing a fixed-order controller for complex industrial specifications toward a generic non constraint fitness with quite reasonable computing time.

关键词： Optimization Tuning Robustness Stability criteria Time-domain analysis Transfer functions computational intelligence

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Annealing robust nonlinear adaptive inverse control with FNNBSVR for magnetic bearing systems

Annealing robust nonlinear adaptive inverse control with FNN...

引用

2003 ieee International symposium on computational intelligence in Robotics and automation, CIRA 2003

作者： Jeng, Jin-Tsong Chuang, Chen-Chia Lee, Y.C. Department of Computer Science and Information Engineering National Huwei Institute of Technology P.O. BOX 6-058 Yunlm County632 Taiwan Department of Electronic Engineering Hwa-Hsia College of Technology and Commerce 11 1 Hwa-Shin Street Chung-Ho City235 Taiwan Department of Electrical Engineering National Huwei Institute of Technology Yunlin County632 Taiwan

ISBN: (纸本)0780378660

In this paper, a new design procedure of nonlinear adaptive inverse control with the fuzzy neural networks based on support vector regression (FNNBSVR) and annealing robust learning algorithm (ARLA) is proposed for the magnetic bearing systems. The FNNBSVR is used to overcome initial structure problem and long training time in the nonlinear adaptive inverse control. Besides, the ARLA is proposed to overcome the outlier in the training procedure. It turns out that the proposed method can use less training time to get the FNNBSVR plant, inverse FNNBSVR plant and fuzzy neural network (FNN) controller to overcome the outlier in noise. Finally, this proposed method is applied to control magnetic bearing system. The experimental results show that the proposed method provides a greater flexibility and better performance in controlling magnetic bearing systems. © 2003 ieee.

关键词： Fuzzy neural networks

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Local Online support vector regression for learning control

Local Online support vector regression for learning control

引用

International symposium on computational intelligence in Robotics and automation

作者： Choi, Younggeun Cheong, Shin-Young Schweighofer, Nicolas Univ So Calif Dept Comp Sci Los Angeles CA 90089 USA Univ So Calif Dept Elect Engn Los Angeles CA 90089 USA Univ So Calif Dept Comp Sci Biokinesiol Dept Los Angeles CA 90089 USA

ISBN: (纸本)9781424407897

Support vector regression (SVR) is a class of machine learning technique that has been successfully applied to low-level learning control in robotics. Because of the large amount of computation required by SVR, however, most studies have used a batch mode. Although a recently developed online form of SVR shows faster learning performance than batch SNIP, the amount of computation required by online SVR prevent its use in real-time robot learning control, which requires short sampling time. Here, we present a novel method, Local online SVR for Learning control, or LoSVR, that extends online SVR with a windowing method. We demonstrate the performance of LoSVR in learning the inverse dynamics of both a simulated two-joint robot and a real one-link robot arm. Our results show that, in both cases, LoSVR can learn the inverse dynamics on-line faster and with a better accuracy than batch SVR.

关键词： Support vector machines

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Neural inverse modeling and control of a base-excited inverted pendulum

Neural inverse modeling and control of a base-excited invert...

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ieee International symposium on computational intelligence in Robotics and automation

作者： Wu, Q Sepehri, N Univ Manitoba Dept Mech & Ind Engn Winnipeg MB R3T 5V6 Canada

ISBN: (纸本)0780372034

In this paper multilayer neural networks are used to control the balancing of a base-excited inverted pendulum. The pendulum has two degrees of rotational freedom and the base-point moves freely in the three-dimensional space. The goal is to apply control torques to keep the pendulum in a prescribed orientation in spite of disturbing base-point movement. The inclusion of the base-point motion leads to a non-autonomous dynamic system with time-varying parametric excitation, which makes the design of the controller challenging. A control algorithm is proposed that utilizes a set of neural networks to compensate for the effect of system's nonlinearities. These networks are updated on-line. Furthermore, since the pendulum's base-point movement is considered unmeasurable, a novel neural inverse model is employed to estimate it from measurable variables. The performance of the proposed neural controller has been compared with the performance of the recently developed control law on the same problem. It is shown that the proposed neural controller produces fast, yet well maintained damped responses with reasonable control torques and without a knowledge of the model or model parameters. Additionally, the developed controller does not require measurement of the base-point accelerations, which are difficult to obtain in practice. The work presented here benefits practical problems such as the study of stable locomotion of human upper body and bipedal locomotion.

关键词： nonlinear control systems control torques multilayer neural networks 2-DOF pendulum 3D space base-excited inverted pendulum neural inverse control neural inverse modeling inverse problems multilayer perceptrons neurocontrollers pendulums

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An application to robot manipulator joint control by using constrained PID based PSO 12

An application to robot manipulator joint control by using c...

引用

12th ieee International symposium on Applied computational intelligence and Informatics, SACI 2018

作者： Copot, Cosmin Muresan, Cristina Macthi, Thoa Ionescu, Clara Department of Electromechanics University of Antwerp Op3Mech Groenenborgerlaan 171 Antwerp2020 Belgium Department of Automation Technical University of Cluj-Napoca Memorandumului street No. 28 Cluj Napoca Romania EEDT Group Member of Flanders Make Ghent University Technologiepark 914 Ghent9052 Belgium School of Mechanical Engineering Hanoi University of Science and Technology Dai Co Viet 1 Viet Nam

ISBN: (纸本)9781538646403

This paper introduces a new methodology for PID control tuning. The proposed method is based on particle swarm optimization. In oder to deal with the limitation of the real process and to generate feasible solutions for industrial applications, constrained control effort is introduced into the objective formulation function. For validation, the proposed algorithm has been tested on a manipulator robot. The tuning of the PID controller is formulated as an optimization problem with the aim of optimizing two or more objective functions while satisfying several inequality constraints. By using this approach, the developed algorithm will facilitate the convergence to an optimal controller and thus to an increased performance. The experimental results indicate that the new approach is suitable for control of complex system. © 2018 ieee.

关键词： Three term control systems

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Optimization of model predictive control by means of sequential parameter optimization

Optimization of model predictive control by means of sequent...

引用

ieee symposium on computational intelligence in control and automation, cica

作者： A. Davtyan S. Hoffmann R. Scheuring Institute of Automation & Industrial IT Cologne University of Applied Sciences Germany

A methodology is developed for automatically tuning the main parameters of model predictive control (MPC) such as prediction horizon, control horizon and control interval. The tuning of parameters is done by means of sequential parameter optimization. In the process of optimization one of the major issues is the choice of an objective function. Several types of objective functions are tested in order to choose the one which solves the MPC tuning problem most adequate. In addition, different scenarios are analyzed if an exact model of the true plant does not exist.

关键词： Optimization Predictive models Mathematical model Algorithm design and analysis Predictive control Transfer functions Prediction algorithms

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