This paper presents an attempt to integrate attention and navigation skills in 3D embodied agents (virtual humanoids). The neural model presented has been divided in two main phases. Firstly the environment-categoriza...
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This paper presents an attempt to integrate attention and navigation skills in 3D embodied agents (virtual humanoids). The neural model presented has been divided in two main phases. Firstly the environment-categorization phase, where an online pattern recognition and categorization of the agent current sensory input data is carried out by an adaptive resonance driven self organizing neural network, which will finally simulate the agent's short term memory (STM). Secondly, the model must also learn how and when to map its current STM state into the navigation and attention motor layers of the 3D agent. We also review the world modelling and the agent vision system, and finally we present the first results extracted from two of the subsystems which conforms the complete neural model, such as, the environment categorization subsystem and the base navigation neural model.
This paper addresses the problem of implementing predictive controllers for supervisory level controlsystems. In this configuration the manipulated variables calculated by the Predictive controller are used as comman...
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This paper addresses the problem of implementing predictive controllers for supervisory level controlsystems. In this configuration the manipulated variables calculated by the Predictive controller are used as command signals for the Distributed controlsystems, which provide references to the operator-tuned local PID controllers that act on the physical system. This structure introduces the problem of loosing of performance if the inner-loop controllers are re-tuned. The paper discusses the solution to this problem based on the use of a two-degrees-of-freedom structure in the inner loop, that separates open and closed-loop properties. Both design guidelines and robustness issues are discussed.
In this paper we extend earlier results on Invariance control to the special class of nonlinear control input affine systems with relative degree two. A switching strategy for control parameters and an easy to calcula...
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In this paper we address the problem of vehicle rollover avoidance control by applying the novel control method of Invariance control which allows a nonlinear and non smooth controller design together with a formal pr...
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In this paper we extend earlier results on Invariance control to the special class of nonlinear control input affine systems with relative degree two. A switching strategy for control parameters and an easy to calcula...
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In this paper we extend earlier results on Invariance control to the special class of nonlinear control input affine systems with relative degree two. A switching strategy for control parameters and an easy to calculate invariance region together form an intelligent switching Invariance controller, achieving asymptotic stability for the controlled dynamics and positive invariance of a prescribed bounded state space region. Experiments with an underactuated robot show the applicability of the proposed theory.
In this paper we address the problem of vehicle rollover avoidance control by applying the novel control method of Invariance control which allows a nonlinear and non smooth controller design together with a formal pr...
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In this paper we address the problem of vehicle rollover avoidance control by applying the novel control method of Invariance control which allows a nonlinear and non smooth controller design together with a formal proof of stability. The presented strategy leaves a maximum degree of freedom for steering to the driver. In addition a time-discrete rollover avoidance controller suitable for implementation is derived, which is both, robust and real-time computable. We validate the proposed approach by hardware-in-the-loop simulations of the nonlinear vehicle with a human driver in the loop, using a force feedback steering device.
This paper addresses the problem of making a given state space region positively invariant while guaranteeing global exponential stability for a class of systems with reduced relative degree in normal form where the c...
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This paper addresses the problem of making a given state space region positively invariant while guaranteeing global exponential stability for a class of systems with reduced relative degree in normal form where the control variable appears in the internal dynamics. The linear subsystem is globally exponentially stabilized by a dissipativity approach. This allows the freedom to switch one control parameter at arbitrary times which is used to control a state space region positively invariant. A design method for the resulting invariance controller and the state space region is presented. The presented theory is evaluated by simulations of a peaking system.
This paper introduces the architecture of a virtual machine designed to play Grafcet models. The architecture proposed is highly flexible and can be used to implement different types of Grafcet players. In addition, m...
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This paper introduces the architecture of a virtual machine designed to play Grafcet models. The architecture proposed is highly flexible and can be used to implement different types of Grafcet players. In addition, many of the variable aspects considered when playing a Grafcet can be completely redefined, even changed dynamically. An object-oriented static meta-model for the Grafcet language is also introduced.
This paper introduces an object oriented static meta-model for Sequential Function Charts (SFC). This meta-model is being implemented as part of a visual programming tool for the development of distributed control sof...
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This paper introduces an object oriented static meta-model for Sequential Function Charts (SFC). This meta-model is being implemented as part of a visual programming tool for the development of distributed control software. It could be used as well to allow object oriented SFC tools to interchange SFC models.
Maximum likelihood estimation of single-input single-output linear time-invariant dynamic models requires that the model innovation (the nonmeasurable white noise source that is assumed to be the source of the randomn...
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Maximum likelihood estimation of single-input single-output linear time-invariant dynamic models requires that the model innovation (the nonmeasurable white noise source that is assumed to be the source of the randomness of the system) can be computed from the observed data. For many model structures, the prediction error and the model innovation coincide and the prediction error can be used in maximum likelihood estimation. However, when the model dynamics and the noise model have unstable poles which are not shared or when the noise dynamics have unstable zeros this is not the case. One such example is an unstable output error model. In this contribution we show that in this situation the model innovation can be computed by noncausal filtering. Different implementations of the model innovation filter are also studied.
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