The problem of networking supervision and traffic rate control in multi-service network systems is studied by using concepts of Nash equilibrium and Stackelberg strategy under assumption of two-level system architectu...
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The problem of networking supervision and traffic rate control in multi-service network systems is studied by using concepts of Nash equilibrium and Stackelberg strategy under assumption of two-level system architecture. Models of users, network provider and overall system in game-theoretic setting are presented. Linear Stackelberg incentive strategies are derived for both elastic and non-elastic traffic, and verified using Kelly's example. Outlines of the two-level integrated control and supervision architecture, and of the proposed fuzzy-Perti-net supervisor are given.
A neuro controller for high precision manoeuvring of underwater vehicles require special attention to a number of factors including thruster and vehicle’s nonlinearities, couplings which exist between various degrees...
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A neuro controller for high precision manoeuvring of underwater vehicles require special attention to a number of factors including thruster and vehicle’s nonlinearities, couplings which exist between various degrees of freedom as well as effects of the sea currents. The neuro control system for underwater vehicle maneouvring described here is based on the conventional controller supported with the so called adaptive neural network.
Maintaining the technical currency becomes one of the challenging tasks facing industrial professionals. There is a strong need to extend the traditional topics such as feedback control, with topics concerning conditi...
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Maintaining the technical currency becomes one of the challenging tasks facing industrial professionals. There is a strong need to extend the traditional topics such as feedback control, with topics concerning condition monitoring and automatic fault diagnosis. A framework for disseminating basic underlying knowledge through the life-cycle paradigm is presented. The paper addresses the design requirements and practical guidelines with experimental validation on laboratory test processes. The approach should help the practitioners to better understand the potentials of model-based fault diagnosis in industrial practice.
The problem of detecting sensor faults in the presence of modelling errors in linear discrete time systems is addressed. In order to avoid false alarms due to such errors, a statistical test is proposed in which the e...
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The problem of detecting sensor faults in the presence of modelling errors in linear discrete time systems is addressed. In order to avoid false alarms due to such errors, a statistical test is proposed in which the effects of missmodelling are properly taken into account by using stochastic embedding technique. The effectiveness of the resulting (cautious) decision rule is demonstrated on a simulated DC motor-generator process.
This paper deals with sensitivity optimization of detection filters. The robust detection filter design problem as a H ∞ filtering problem is considered. The objective is to provide the smallest scaled L 2 gain of th...
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This paper deals with sensitivity optimization of detection filters. The robust detection filter design problem as a H ∞ filtering problem is considered. The objective is to provide the smallest scaled L 2 gain of the unknown input of the system that is guaranteed to be less than a prespecified level, i.e. , to produce a filter with optimal disturbance suppression capability in such a way that sufficient sensitivity to failure modes is still maintained. The effect of two different input scaling approaches to the optimization process is investigated. It is shown how to obtain bounds on the scaled L 2 gain by transforming the standard H ∞ filtering problem into a convex feasibility problem, specifically, a structured, linear matrix inequality (LMI). Numerical examples demonstrating the effect of the scaled optimization with respect to conventional H ∞ filtering is presented.
This paper investigates real-time scheduling algorithms on upcoming multithreaded processors. As evaluation testbed we introduce a multithreaded processor kernel which is specifically designed as core processor of a m...
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This paper investigates real-time scheduling algorithms on upcoming multithreaded processors. As evaluation testbed we introduce a multithreaded processor kernel which is specifically designed as core processor of a micro-controller or system-on-a-chip. Handling of external real-time events is performed through multithreading. Real-time threads are used as interrupt service threads (ISTs) instead of interrupt service routines (ISRs). Our proposed micro-controller supports multiple ISTs with zero-cycle context switching overhead. We investigate the behavior of fixed priority preemptive, earliest deadline first, least laxity first and guaranteed percentage scheduling with respect to multithreaded processors. Our finding is that the strategies GP and LLF result in a good blending of instructions of different threads thus enabling a multithreaded processor to utilize latencies best. Assuming a zero-cycle context switch LLF performs best, however implementation cost context, are prohibitive.
This paper presents a formulation for a class of robust fault detection problems, and its corresponding solution approach. In order to provide insight into the robust algorithm, one needs to consider the past and unde...
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This paper presents a formulation for a class of robust fault detection problems, and its corresponding solution approach. In order to provide insight into the robust algorithm, one needs to consider the past and understand its historical origins in optimal fault detection, where it is assumed that both plant and noise models are accurate. The deleterious effect of plant model uncertainties on the performance of the optimal tests is illustrated, thus motivating the idea of robust fault detection, which makes use of robust game theoretic ( H ∞ ) or risk sensitive estimators. These estimators provide test residuals or statistics that are insensitive to uncertainties, but still sensitive to failure modes. The paper concludes by motivating and suggesting new ideas for future research.
Combining position and force control is highly desirable in many robot applications. Many years of extensive research has resulted in a number of promising control approaches, but often too much engineering effort is ...
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Combining position and force control is highly desirable in many robot applications. Many years of extensive research has resulted in a number of promising control approaches, but often too much engineering effort is required in order to evaluate a low level (high performance) control scheme on a real manipulator. The new control system we have obtained has two main advantages: first it provides a total open control architecture, and second it is the simplicity and the interactivity of the platform developed. Using this new control architecture, any force and motion control strategy can be programmed and implemented in a graphical and easy way.
Decomposing dynamical systems in terms of orthogonal expansions enables the modelling/approximation of a system with a finite length expansion. By flexibly tuning the basis functions to underlying system characteristi...
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Decomposing dynamical systems in terms of orthogonal expansions enables the modelling/approximation of a system with a finite length expansion. By flexibly tuning the basis functions to underlying system characteristics, the rate of convergence of these expansions can be drastically increased, leading to highly accurate models (small bias) being represented by few parameters (small variance). Additionally algorithmic and numerical aspects are favourable. A recently developed general theory for basis construction will be presented, that is a generalization of the classical Laguerre theory. The basis functions are applied in problems of identification, approximation, realization, uncertainty modelling, and adaptive filtering, particularly exploiting the property that basis function models are linearly parametrized. Besides powerful algorithms, they also provide useful analysis tools for understanding the underlying identification/approximation algorithms.
Robotic soccer is an interesting test bench for the field of self-organizing and cooperating multi-agent systems. This paper deals with learning of two basic low-level behaviors that will enable the robotic player to ...
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Robotic soccer is an interesting test bench for the field of self-organizing and cooperating multi-agent systems. This paper deals with learning of two basic low-level behaviors that will enable the robotic player to participate further in higher-level collaborative and adversarial learning situations. First, a ball interception and obstacle avoidance behavior is learned. Then the acquired skills are incorporated into a next higher-level multi-agent learning scenario, namely the shooting ball behavior. The proposed control scheme for these behaviors consists of a trajectory generator with a layered structure, which supplies data to a trajectory-tracking controller.
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