This paper presents an application of a model checking framework to robotic search, particularly search problems known as pursuit-evasion that assume a smart, fast and evading target. Within the framework we can model...
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This paper presents a modelling design framework and results for human participants undergoing mental stress. A mental stressful scenario is achieved while participants perform operations in a simulated control proces...
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In this paper we consider constrained 0 sparse optimization problems, that is, constrained problems with the objective function composed of a smooth part and an 0 regular-ization term. We analyze a penalty decompositi...
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This paper is concerned with the state estimation problem for a class of nonlinear discrete-time complex networks subject to false data injection attacks. By utilizing Bernoulli random binary distributed white sequenc...
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ISBN:
(纸本)9781467374439
This paper is concerned with the state estimation problem for a class of nonlinear discrete-time complex networks subject to false data injection attacks. By utilizing Bernoulli random binary distributed white sequences, the false data injection attack model is established to describe the characteristics of false data injection attacks applying to the complex networks under consideration. An estimator is designed to guarantee the ultimate boundedness of the estimation error in mean square. By employing stochastic analysis approach, suf cient conditions are derived for the existence of the desired estimators whose gains are parameterized by minimizing an upper bound of the output variance of the estimation errors. Finally, a numerical example is given to illustrate the effectiveness of the results.
This paper lays down the foundations of developing a reconfigurable control system within the Robot Operating System (ROS) for autonomous robots. The essential components of robots are programmed under a ROS system. A...
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In this paper, we propose a self-triggered formulation of Model Predictive control for continuous-time nonlinear networked controlsystems. Our control method derives not only when to execute control tasks but also pr...
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ISBN:
(纸本)9781479917730
In this paper, we propose a self-triggered formulation of Model Predictive control for continuous-time nonlinear networked controlsystems. Our control method derives not only when to execute control tasks but also provides the way to discretize the optimal control trajectory so as to alleviate the communication burden as much as possible. Stability analysis under the sample-and-hold implementation is also given in detail, which guarantees that the state converges to a terminal region where the local linear state feedback can stabilize the system. A simulation example verifies our proposed framework.
The paper proposes a simple Takagi-Sugeno PI-fuzzy controller for an anaerobic digestion process. A fifth order nonlinear model of the anaerobic digestion process is first presented. A second order linear model of the...
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The paper proposes a simple Takagi-Sugeno PI-fuzzy controller for an anaerobic digestion process. A fifth order nonlinear model of the anaerobic digestion process is first presented. A second order linear model of the process is next proposed, and the parameters of the linear model are obtained from an optimization problem solved by a Gravitational Search Algorithm. A PI controller is designed for the linear process model using a frequency domain approach in terms of imposing the phase margin. The Takagi-Sugeno PI-fuzzy controller is designed on the basis of the linear PI controller. The Takagi-Sugeno PI-fuzzy controller is validated by simulation using the nonlinear process model in the fuzzy control system.
Hidden Markov Models (HMMs) and associated Markov modulated time series are widely used for estimation and control in e.g. robotics, econometrics and bioinformatics. In this paper, we modify and extend a recently prop...
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ISBN:
(纸本)9781479978878
Hidden Markov Models (HMMs) and associated Markov modulated time series are widely used for estimation and control in e.g. robotics, econometrics and bioinformatics. In this paper, we modify and extend a recently proposed approach in the machine learning literature that uses the method of moments and a Non-Negative Matrix Factorization (NNMF) to estimate the parameters of an HMM. In general, the method aims to solve a constrained non-convex optimization problem. In this paper, it is shown that if the observation probabilities of the HMM are known, then estimating the transition probabilities reduces to a convex optimization problem. Three recursive algorithms are proposed for estimating the transition probabilities of the underlying Markov chain, one of which employs a generalization of the Pythagorean trigonometric identity to recast the problem into a non-constrained optimization problem. Numerical examples are presented to illustrate how these algorithms can track slowly time-varying transition probabilities.
In this paper,we focus on energy management of distributed generators(DGs) and energy storage system(ESS) in microgrids(MG) considering uncertainties in renewable energy and load *** MG energy management problem is fo...
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ISBN:
(纸本)9781479919819
In this paper,we focus on energy management of distributed generators(DGs) and energy storage system(ESS) in microgrids(MG) considering uncertainties in renewable energy and load *** MG energy management problem is formulated as a two-stage stochastic programming model based on optimization ***,the optimization model is decomposed into a mixed integer quadratic programming problem by using discrete stochastic scenarios to approximate the continuous random variables.A Scenarios generation approach based on time-homogeneous Markov chain model is proposed to generate simulated time-series of renewable energy generation and load ***,the proposed stochastic programming model is tested in a typical LV network and solved by Matlab optimization *** simulation results show that the proposed stochastic programming model has a better performance to obtain robust scheduling solutions and lower the operating cost compared to the deterministic optimization modeling methods.
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