Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hy...
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Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the properties of this nonparametric representation. The Bayesian formalism enables systematic reasoning about the uncertainty in the system dynamics. We present an approach to maximum likelihood identification of the parameters in GP-SSMs, while retaining the full nonparametric description of the dynamics. The method is based on a stochastic approximation version of the EM algorithm that employs recent developments in particle Markov chain Monte Carlo for efficient identification.
The paradigm of Petri nets offers a rich modeling power that has been successfully exploited in many different application domains as manufacturing, logistic, traffic, computer and biological domains. In order to deve...
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The paradigm of Petri nets offers a rich modeling power that has been successfully exploited in many different application domains as manufacturing, logistic, traffic, computer and biological domains. In order to develop and analyze Petri models in an efficient and systematic way, appropriate software tools are required. Among the existing software platforms for scientific software development, MATLAB places at the user's disposal a large set of toolboxes with an inutitive graphical interface. This paper describes some of the existing Petri net toolboxes that have been developed on MATLAB, and that cover the needs for the modeling and analysis of discrete, continuous and hybrid systems.
The problem of cyclic scheduling of multimodal concurrent processes (MCPs) is considered. Processes composed of sub-sequences of local cyclic processes (LCPs) are treated as multimodal processes, e.g. in case local pr...
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The visualization of large graphs in interactive applications, specifically on small devices, can make harder to understand and analyze the displayed information. We show as simple topological properties of the graph ...
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Low cost and high reliability are the main issues considered in power converter design for hybrid electric vehicle. In this paper, a hybrid power converter is proposed for the power flow control of the battery-fuel ce...
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Low cost and high reliability are the main issues considered in power converter design for hybrid electric vehicle. In this paper, a hybrid power converter is proposed for the power flow control of the battery-fuel cell hybrid electric vehicle. The converter consists of a unidirectional Γ Z-source converter and a bidirectional DC-DC converter. By controlling the voltage of the Z source capacitor and the modulation index of the inverter, the power flow between the fuel cell, battery and the load can be controlled flexibly. The different operation modes of the system and the power flow control strategies are analysed. To verify the validity of the proposed power converter topology and the control methods, system simulation model is developed in Matlab/Simulink environment. In addition, the simulation model is also implemented in the RT-LAB real-time simulator. The simulation results demonstrated the feasibility of the proposed methods.
Performance of constrained movements in multiple directions of a workspace simultaneously and in presence of uncertainty is a great challenge for robots. Achieving such tasks by employing control policies which are fu...
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ISBN:
(纸本)9781479947287
Performance of constrained movements in multiple directions of a workspace simultaneously and in presence of uncertainty is a great challenge for robots. Achieving such tasks by employing control policies which are fully determined a priori and do not take into account the system uncertainty can cause undesired stress on the robot end-effector or the environment and result in poor performance. Instead, a sophisticated control policy is required, which can adjust to the varying conditions of a task while taking into account the coupling of motion dynamics between different directions of movement. To this aim, in this paper, we propose a MIMO Extremum Seeking control (ESC)-Model Reference Adaptive control (MRAC) approach with the view of executing fine motion tasks in presence of uncertain task dynamics. ESC enhances robustness of the system to non-parametric uncertainties compared to single MRAC. The proposed approach ensures state tracking as well as optimization of a global state-dependent cost criterion in all directions of movement. We evaluate our approach in simulations and in a real-world robotic engraving task.
This paper is concerned with the design of positive observers for positive interval systems with time delay. For that, a solution based on LMI programming is provided to the problem of designing observers that ensure ...
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This paper is concerned with the design of positive observers for positive interval systems with time delay. For that, a solution based on LMI programming is provided to the problem of designing observers that ensure guaranteed bounds on the estimated states that are nonnegative whenever the initial conditions are nonnegative. Furthermore, it is revealed that an unstable positive system cannot be positively stabilized without taking into account the positivity of the dynamic errors. Then, the problem of positive stabilization by dynamic feedback controllers based observer is investigated, taking into account that the positivity of the error signals is necessary to stabilize a positive system and keep it positive at the same time. We derive then necessary and sufficient conditions for the existence of positive observers and show that the solvability can be decided via LMI (Linear Matrix Inequalities) programming techniques.
Sensory environments for healthcare are commonplace nowadays. A patient monitoring system in such an environment deals with sensor data capture, transmission and processing in order to provide on-the-spot support for ...
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ISBN:
(纸本)9781479947164
Sensory environments for healthcare are commonplace nowadays. A patient monitoring system in such an environment deals with sensor data capture, transmission and processing in order to provide on-the-spot support for monitoring the vulnerable and critical patient. A fault in such a system can be hazardous on the health of the patient. Therefore, such a system must be dependable and ensure reliability, fault-tolerance, safety and other critical aspects, in order to deploy it in real scenario. This paper encounters some of these issues and proposes a component platform to develop a flexible framework with specific support for fault tolerance and safe inter-component communication related QoS aspects. The platform adopts the Service Component Architecture (SCA) model and defines a Data Distribution Service (DDS) binding, which provides the fault tolerance and the required safety-ensuring techniques and measures, as defined in the IEC 61784-3-3 standard.
Selective catalytic reduction (SCR) system is a complex chemical process which is used to treat exhaust gas in many applications, e.g. diesel engines in automobiles. An SCR model was constructed by General Motors (GM)...
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The use of robots in people daily life and, accordingly, the requirement for a robot to behave in a socially acceptable way are getting more and more attention. However, although many progresses have been done in the ...
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